docs(docker): move k8s deployment docs to wiki, drop README_K8S.md

The Kubernetes deployment guide now lives only in the wiki
(docs.langbot.app -> Installation -> Kubernetes). Remove the in-repo
docker/README_K8S.md, repoint the README language variants and the
docker-compose / kubernetes.yaml header comments to the wiki, and keep
kubernetes.yaml self-describing via inline comments.

AGENTS.md

@@ -1,81 +1,134 @@
 # AGENTS.md
 
-This file is for guiding code agents (like Claude Code, GitHub Copilot, OpenAI Codex, etc.) to work in LangBot project.
+This file guides code agents (Claude Code, GitHub Copilot, OpenAI Codex, etc.) working in the LangBot project. `CLAUDE.md` is a symlink to this file.
 
 ## Project Overview
 
-LangBot is a open-source LLM native instant messaging bot development platform, aiming to provide an out-of-the-box IM robot development experience, with Agent, RAG, MCP and other LLM application functions, supporting global instant messaging platforms, and providing rich API interfaces, supporting custom development.
+LangBot is an open-source, LLM-native instant-messaging bot development platform. It aims to provide an out-of-the-box IM bot development experience with Agent, RAG, MCP and other LLM application capabilities, supporting mainstream global IM platforms and exposing rich APIs for custom development.
 
-LangBot has a comprehensive frontend, all operations can be performed through the frontend. The project splited into these major parts:
+LangBot has a comprehensive web frontend — almost every operation can be performed through it.
 
-- `./src/langbot`: The main python package of the project, below are the main modules in this package:
-    - `./pkg`: The core python package of the project backend.
-        - `./pkg/platform`: The platform module of the project, containing the logic of message platform adapters, bot managers, message session managers, etc.
-        - `./pkg/provider`: The provider module of the project, containing the logic of LLM providers, tool providers, etc.
-        - `./pkg/pipeline`: The pipeline module of the project, containing the logic of pipelines, stages, query pool, etc.
-        - `./pkg/api`: The api module of the project, containing the http api controllers and services.
-        - `./pkg/plugin`: LangBot bridge for connecting with plugin system.
-    - `./libs`: Some SDKs we previously developed for the project, such as `qq_official_api`, `wecom_api`, etc.
-    - `./templates`: Templates of config files, components, etc.
-    - `./web`: Frontend codebase, built with Next.js + **shadcn** + **Tailwind CSS**.
-    - `./docker`: docker-compose deployment files.
+- **Python**: `>=3.11,<4.0`, dependencies managed by `uv`. Package version is in `pyproject.toml`.
+- **Frontend**: `web/` is a **Vite + React Router 7 + shadcn/ui + Tailwind CSS** SPA, managed by `pnpm`. (Note: this is NOT Next.js — the `dev` script is `vite`.)
+- **Backend framework**: Quart (the async flavour of Flask). The HTTP API and the pre-built web UI are both served by the backend on `http://127.0.0.1:5300`.
 
-## Backend Development
+## Repository Layout
 
-We use `uv` to manage dependencies.
+```
+LangBot/
+├── main.py                     # Entrypoint shim -> langbot.__main__.main()
+├── pyproject.toml              # Python project + deps (uv), pins langbot-plugin==<x.y.z>
+├── src/langbot/
+│   ├── __main__.py             # Real entrypoint, CLI args (--standalone-runtime, --standalone-box, --debug)
+│   ├── pkg/                    # Core backend package
+│   │   ├── api/                # HTTP API controllers + services (Quart)
+│   │   ├── core/               # App bootstrap, stages, task manager
+│   │   ├── platform/           # IM platform adapters, bot managers, session managers
+│   │   ├── provider/           # LLM providers, requesters, tool providers
+│   │   ├── pipeline/           # Pipelines, stages, query pool
+│   │   ├── plugin/             # Bridge connecting LangBot to the plugin runtime (see below)
+│   │   ├── box/                # Code-sandbox subsystem (Docker / nsjail / E2B backends)
+│   │   ├── skill/              # Skill subsystem
+│   │   ├── rag/ , vector/      # RAG + vector store
+│   │   ├── command/            # Built-in commands
+│   │   ├── persistence/        # ORM models + Alembic migrations (SQLite & PostgreSQL)
+│   │   ├── storage/            # Object/file storage abstractions
+│   │   ├── config/, entity/, discover/, utils/, telemetry/, survey/
+│   ├── libs/                   # Vendored SDKs (qq_official_api, wecom_api, etc.)
+│   └── templates/              # Config/component templates (e.g. templates/config.yaml)
+├── web/                        # Frontend SPA (Vite + React Router 7 + shadcn + Tailwind)
+└── docker/                     # docker-compose deployment files
+```
+
+## Development Environment Setup
+
+Full guide lives in the wiki: **["开发配置" / Dev Config](https://docs.langbot.app/zh/develop/dev-config)**. Summary:
+
+### Backend
 
 ```bash
 pip install uv
-uv sync --dev
+uv sync --dev          # uv creates a .venv/ for you; point your editor's interpreter at it
+uv run main.py         # serves API + web UI on http://127.0.0.1:5300
 ```
 
-Start the backend and run the project in development mode.
+On first run the config file is generated at `data/config.yaml`. DB is SQLite by default (zero setup); PostgreSQL is supported. Migrations run automatically on startup.
 
-```bash
-uv run main.py
-```
+### Frontend
 
-Then you can access the project at `http://127.0.0.1:5300`.
-
-## Frontend Development
-
-We use `pnpm` to manage dependencies.
+Requires Node.js + [pnpm](https://pnpm.io/installation).
 
 ```bash
 cd web
-cp .env.example .env
+cp .env.example .env   # Windows: copy .env.example .env
 pnpm install
-pnpm dev
+pnpm dev               # http://127.0.0.1:3000  (npm install / npm run dev also work)
 ```
 
-Then you can access the project at `http://127.0.0.1:3000`.
+`pnpm dev` reads `VITE_API_BASE_URL` from `web/.env` so the dev frontend can reach the backend on port `5300`. In production the frontend is pre-built into static files served by the backend on the same origin.
 
-## Plugin System Architecture
+### Code formatting
 
-LangBot is composed of various internal components such as Large Language Model tools, commands, messaging platform adapters, LLM requesters, and more. To meet extensibility and flexibility requirements, we have implemented a production-grade plugin system.
+The repo runs lint + format checks in CI. Install the pre-commit hooks so the same checks run locally before each commit:
 
-Each plugin runs in an independent process, managed uniformly by the Plugin Runtime. It has two operating modes: `stdio` and `websocket`. When LangBot is started directly by users (not running in a container), it uses `stdio` mode, which is common for personal users or lightweight environments. When LangBot runs in a container, it uses `websocket` mode, designed specifically for production environments.
+```bash
+uv run pre-commit install
+```
 
-Plugin Runtime automatically starts each installed plugin and interacts through stdio. In plugin development scenarios, developers can use the lbp command-line tool to start plugins and connect to the running Runtime via WebSocket for debugging.
+## Plugin System
 
-> Plugin SDK, CLI, Runtime, and entities definitions shared between LangBot and plugins are contained in the [`langbot-plugin-sdk`](https://github.com/langbot-app/langbot-plugin-sdk) repository.
+LangBot's plugin system (Plugin SDK, CLI `lbp`, Plugin Runtime, and the shared entity/API definitions) lives in a **separate repository**: [`langbot-plugin-sdk`](https://github.com/langbot-app/langbot-plugin-sdk). LangBot depends on it via the pinned `langbot-plugin` package in `pyproject.toml`.
 
-## Some Development Tips and Standards
+### Architecture (what to know inside this repo)
 
-- LangBot is a global project, any comments in code should be in English, and user experience should be considered in all aspects.
-- Thus you should consider the i18n support in all aspects.
-- LangBot is widely adopted in both toC and toB scenarios, so you should consider the compatibility and security in all aspects.
-- If you were asked to make a commit, please follow the commit message format: 
-    - format: <type>(<scope>): <subject>
-    - type: must be a specific type, such as feat (new feature), fix (bug fix), docs (documentation), style (code style), refactor (refactoring), perf (performance optimization), etc.
-    - scope: the scope of the commit, such as the package name, the file name, the function name, the class name, the module name, etc.
-    - subject: the subject of the commit, such as the description of the commit, the reason for the commit, the impact of the commit, etc.
-- LangBot uses [Alembic](https://alembic.sqlalchemy.org/) to manage database migrations, supporting both SQLite and PostgreSQL. Migration files are located in `src/langbot/pkg/persistence/alembic/versions/`. If you changed the definition of database entities (ORM models), generate a new migration script by running `uv run python -m langbot.pkg.persistence.alembic_runner autogenerate "description of your change"` in the project root (requires `data/config.yaml` to exist). Review and edit the generated script before committing. Migrations are executed automatically on LangBot startup. For data migrations (e.g. modifying JSON field content), you need to manually add the migration code in the generated script.
+- Plugins run as independent processes managed by the **Plugin Runtime**. The Runtime supports two control transports: `stdio` and `websocket`.
+- When LangBot is started directly by a user (not in a container), it spawns and connects to the Runtime over **stdio** (lightweight/personal use).
+- When LangBot runs in a container, it connects to a standalone Runtime over **WebSocket** (production).
+- The bridge code lives in `src/langbot/pkg/plugin/` (`connector.py`, `handler.py`).
+- Relevant config (`data/config.yaml`): `plugin.runtime_ws_url` (e.g. `ws://langbot_plugin_runtime:5400/control/ws`). Start LangBot with `--standalone-runtime` to make it connect to an externally-launched Runtime over WebSocket instead of spawning one over stdio.
+
+### Debugging the Plugin Runtime / CLI / SDK
+
+This is documented in detail in the **SDK repo's `AGENTS.md`** and in the wiki page **["调试插件运行时、CLI、SDK" / Plugin Runtime](https://docs.langbot.app/zh/develop/plugin-runtime)**. The short version:
+
+- Clone `LangBot` and `langbot-plugin-sdk` as siblings under one parent dir so the editor resolves shared entities.
+- Start a standalone Runtime from the SDK repo: `uv run --no-sync lbp rt` (control port `5400`, debug port `5401`).
+- To make LangBot use a locally-modified SDK: from the SDK dir, with LangBot's `.venv` active, run `uv pip install .`, then launch LangBot with `uv run --no-sync main.py --standalone-runtime` (keep `--no-sync` so your local SDK isn't overwritten).
+
+### Debugging the Box (sandbox) runtime
+
+The Box subsystem (`src/langbot/pkg/box/`) is the code sandbox. It picks the first available backend among **Docker / nsjail / E2B**. The standalone Box runtime is launched via the SDK CLI: `lbp box`. Backend selection details, the `lbp box` flags, and the SDK-side architecture are documented in the SDK repo's `AGENTS.md`.
+
+Relevant config (`data/config.yaml`, `box:` section): `box.enabled` (master switch — disabling it also disables the native sandbox tools, skill add/edit, and stdio-mode MCP servers), `box.backend` (`'local'` = Docker/nsjail auto-pick, or `'docker'` / `'nsjail'` / `'e2b'`; also settable via `BOX__BACKEND`), and `box.runtime.endpoint` (external Box runtime base URL, e.g. `ws://127.0.0.1:5410`; empty = local auto-managed runtime). Like the plugin runtime, LangBot can connect to an externally-launched Box runtime by setting that endpoint and starting with `--standalone-box`.
+
+> A common false "No supported sandbox backend (Docker / nsjail / E2B) is available" comes from Docker being installed and running but the current user not being in the `docker` group → `docker info` gets `permission denied` on the socket. Fix: `sudo usermod -aG docker <user>` and restart the backend in a shell that has the new group.
+
+## Development Standards
+
+- LangBot is a global project: **all code comments and docstrings must be in English**, and every user-facing string must support **i18n** (`en_US` + `zh_Hans` at minimum, plus `ja_JP` where the repo already has it).
+- LangBot is adopted in both toC and toB scenarios — always consider compatibility and security.
+- **Commit message format**: `<type>(<scope>): <subject>`
+  - `type`: one of `feat`, `fix`, `docs`, `style`, `refactor`, `perf`, `test`, `chore`, etc.
+  - `scope`: the affected package/module/file/class.
+  - `subject`: concise description of the change.
+
+### Database migrations (Alembic)
+
+LangBot uses [Alembic](https://alembic.sqlalchemy.org/) for migrations, supporting both SQLite and PostgreSQL from a single set of scripts. Migration files live in `src/langbot/pkg/persistence/alembic/versions/`.
+
+If you change ORM model definitions, generate a migration:
+
+```bash
+# Run from the project root (requires data/config.yaml to exist)
+uv run python -m langbot.pkg.persistence.alembic_runner autogenerate "description of your change"
+```
+
+Review and edit the generated script before committing. Migrations execute automatically on startup. `autogenerate` detects schema changes (add/drop columns, tables, type changes) but **data migrations** (e.g. mutating JSON field contents) must be hand-written into the generated script. `env.py` sets `render_as_batch=True`, so SQLite's ALTER TABLE limits are handled automatically — no need to branch per database. More in the wiki ["开发配置"](https://docs.langbot.app/zh/develop/dev-config#数据库迁移).
 
 ## Some Principles
 
 - Keep it simple, stupid.
-- Entities should not be multiplied unnecessarily
+- Entities should not be multiplied unnecessarily.
 - 八荣八耻
 
     以瞎猜接口为耻，以认真查询为荣。
@@ -85,4 +138,4 @@ Plugin Runtime automatically starts each installed plugin and interacts through
     以跳过验证为耻，以主动测试为荣。
     以破坏架构为耻，以遵循规范为荣。
     以假装理解为耻，以诚实无知为荣。
-    以盲目修改为耻，以谨慎重构为荣。
+    以盲目修改为耻，以谨慎重构为荣。

Dockerfile

@@ -14,10 +14,22 @@ COPY . .
 
 COPY --from=node /app/web/dist ./web/dist
 
-RUN apt update \
-    && apt install gcc -y \
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends gcc ca-certificates curl gnupg \
+    # Install the Docker CLI (client only) so the optional langbot_box
+    # service can drive the mounted host Docker socket and create sandbox
+    # containers. The same image powers langbot / plugin_runtime / box; only
+    # box uses the client. Arch-aware via dpkg so multi-arch builds work.
+    && install -m 0755 -d /etc/apt/keyrings \
+    && curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc \
+    && chmod a+r /etc/apt/keyrings/docker.asc \
+    && echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian $(. /etc/os-release && echo \"$VERSION_CODENAME\") stable" > /etc/apt/sources.list.d/docker.list \
+    && apt-get update \
+    && apt-get install -y --no-install-recommends docker-ce-cli \
     && python -m pip install --no-cache-dir uv \
     && uv sync \
+    && apt-get purge -y --auto-remove curl gnupg \
+    && rm -rf /var/lib/apt/lists/* \
     && touch /.dockerenv
 
 CMD [ "uv", "run", "--no-sync", "main.py" ]

README.md

@@ -38,7 +38,7 @@ LangBot is an **open-source, production-grade platform** for building AI-powered
 
 ### Key Capabilities
 
-- **AI Conversations & Agents** — Multi-turn dialogues, tool calling, multi-modal support, streaming output. Built-in RAG (knowledge base) with deep integration to [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **AI Conversations & Agents** — Multi-turn dialogues, tool calling, multi-modal support, streaming output. Built-in RAG (knowledge base) with deep integration to [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Universal IM Platform Support** — One codebase for Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Production-Ready** — Access control, rate limiting, sensitive word filtering, comprehensive monitoring, and exception handling. Trusted by enterprises.
 - **Plugin Ecosystem** — Hundreds of plugins, event-driven architecture, component extensions, and [MCP protocol](https://modelcontextprotocol.io/) support.
@@ -78,7 +78,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**More options:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**More options:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

README_CN.md

@@ -13,7 +13,7 @@
 [English](README.md) / 简体中文 / [繁體中文](README_TW.md) / [日本語](README_JP.md) / [Español](README_ES.md) / [Français](README_FR.md) / [한국어](README_KO.md) / [Русский](README_RU.md) / [Tiếng Việt](README_VI.md)
 
 [![Discord](https://img.shields.io/discord/1335141740050649118?logo=discord&labelColor=%20%235462eb&logoColor=%20%23f5f5f5&color=%20%235462eb)](https://discord.gg/wdNEHETs87)
-[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-1030838208-blue)](https://qm.qq.com/q/DxZZcNxM1W)
+[![QQ Group](https://img.shields.io/badge/%E7%A4%BE%E5%8C%BAQQ%E7%BE%A4-1030838208-blue)](https://qm.qq.com/q/IrlV8QFacU)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/langbot-app/LangBot)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/langbot-app/LangBot)](https://github.com/langbot-app/LangBot/releases/latest)
 <img src="https://img.shields.io/badge/python-3.10 ~ 3.13 -blue.svg" alt="python">
@@ -38,7 +38,7 @@ LangBot 是一个**开源的生产级平台**，用于构建 AI 驱动的即时
 
 ### 核心能力
 
-- **AI 对话与 Agent** — 多轮对话、工具调用、多模态、流式输出。自带 RAG（知识库），深度集成 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **AI 对话与 Agent** — 多轮对话、工具调用、多模态、流式输出。自带 RAG（知识库），深度集成 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org)、[Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com)等 LLMOps 平台。
 - **全平台支持** — 一套代码，覆盖 QQ、微信、企业微信、飞书、钉钉、Discord、Telegram、Slack、LINE、KOOK 等平台。
 - **生产就绪** — 访问控制、限速、敏感词过滤、全面监控与异常处理，已被多家企业采用。
 - **插件生态** — 数百个插件，跨进程的事件驱动架构，组件扩展，适配 [MCP 协议](https://modelcontextprotocol.io/)。
@@ -78,7 +78,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手动部署](https://link.langbot.app/zh/docs/manual-deploy) · [宝塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手动部署](https://link.langbot.app/zh/docs/manual-deploy) · [宝塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/zh/deploy/langbot/kubernetes)
 
 ---
 

README_ES.md

@@ -37,7 +37,7 @@ LangBot es una **plataforma de código abierto y grado de producción** para con
 
 ### Capacidades Clave
 
-- **Conversaciones e Agentes IA** — Diálogos de múltiples turnos, llamadas a herramientas, soporte multimodal, salida en streaming. RAG (base de conocimientos) incorporado con integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Conversaciones e Agentes IA** — Diálogos de múltiples turnos, llamadas a herramientas, soporte multimodal, salida en streaming. RAG (base de conocimientos) incorporado con integración profunda con [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com).
 - **Soporte Universal de Plataformas de MI** — Un solo código base para Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Listo para Producción** — Control de acceso, limitación de velocidad, filtrado de palabras sensibles, monitoreo completo y manejo de excepciones. De confianza para empresas.
 - **Ecosistema de Plugins** — Cientos de plugins, arquitectura basada en eventos, extensiones de componentes y soporte del [protocolo MCP](https://modelcontextprotocol.io/).
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Más opciones:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**Más opciones:** [Docker](https://link.langbot.app/en/docs/docker) · [Manual](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

README_FR.md

@@ -37,7 +37,7 @@ LangBot est une **plateforme open-source de niveau production** pour créer des
 
 ### Capacités Clés
 
-- **Conversations IA & Agents** — Dialogues multi-tours, appels d'outils, support multimodal, sortie en streaming. RAG (base de connaissances) intégré avec intégration profonde de [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Conversations IA & Agents** — Dialogues multi-tours, appels d'outils, support multimodal, sortie en streaming. RAG (base de connaissances) intégré avec intégration profonde de [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Support Universel des Plateformes de MI** — Un seul code pour Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Prêt pour la Production** — Contrôle d'accès, limitation de débit, filtrage de mots sensibles, surveillance complète et gestion des exceptions. Approuvé par les entreprises.
 - **Écosystème de Plugins** — Des centaines de plugins, architecture événementielle, extensions de composants, et support du [protocole MCP](https://modelcontextprotocol.io/).
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Plus d'options :** [Docker](https://link.langbot.app/en/docs/docker) · [Manuel](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**Plus d'options :** [Docker](https://link.langbot.app/en/docs/docker) · [Manuel](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

README_JP.md

@@ -37,7 +37,7 @@ LangBot は、AI搭載のインスタントメッセージングボットを構
 
 ### 主な機能
 
-- **AI対話とエージェント** — マルチターン対話、ツール呼び出し、マルチモーダル対応、ストリーミング出力。RAG（ナレッジベース）を内蔵し、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) と深く統合。
+- **AI対話とエージェント** — マルチターン対話、ツール呼び出し、マルチモーダル対応、ストリーミング出力。RAG（ナレッジベース）を内蔵し、[Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org)、[Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com) と深く統合。
 - **ユニバーサルIMプラットフォーム対応** — 単一のコードベースで Discord、Telegram、Slack、LINE、QQ、WeChat、WeCom、Lark、DingTalk、KOOK に対応。
 - **本番環境対応** — アクセス制御、レート制限、センシティブワードフィルタリング、包括的な監視、例外処理を搭載。エンタープライズの信頼に応える品質。
 - **プラグインエコシステム** — 数百のプラグイン、イベント駆動アーキテクチャ、コンポーネント拡張、[MCPプロトコル](https://modelcontextprotocol.io/)対応。
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**その他:** [Docker](https://link.langbot.app/en/docs/docker) · [手動デプロイ](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**その他:** [Docker](https://link.langbot.app/en/docs/docker) · [手動デプロイ](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

README_KO.md

@@ -37,7 +37,7 @@ LangBot은 AI 기반 인스턴트 메시징 봇을 구축하기 위한 **오픈
 
 ### 핵심 기능
 
-- **AI 대화 및 에이전트** — 멀티턴 대화, 도구 호출, 멀티모달 지원, 스트리밍 출력. 내장 RAG(지식 베이스)와 [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org) 심층 통합.
+- **AI 대화 및 에이전트** — 멀티턴 대화, 도구 호출, 멀티모달 지원, 스트리밍 출력. 내장 RAG(지식 베이스)와 [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com) 심층 통합.
 - **유니버설 IM 플랫폼 지원** — 단일 코드베이스로 Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK 지원.
 - **프로덕션 레디** — 접근 제어, 속도 제한, 민감어 필터링, 종합 모니터링 및 예외 처리. 기업 환경에서 검증됨.
 - **플러그인 생태계** — 수백 개의 플러그인, 이벤트 기반 아키텍처, 컴포넌트 확장, [MCP 프로토콜](https://modelcontextprotocol.io/) 지원.
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**더 많은 옵션:** [Docker](https://link.langbot.app/en/docs/docker) · [수동 배포](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**더 많은 옵션:** [Docker](https://link.langbot.app/en/docs/docker) · [수동 배포](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

README_RU.md

@@ -37,7 +37,7 @@ LangBot — это **платформа с открытым исходным к
 
 ### Ключевые возможности
 
-- **ИИ-диалоги и агенты** — Многораундовые диалоги, вызов инструментов, мультимодальная поддержка, потоковый вывод. Встроенная реализация RAG (база знаний) с глубокой интеграцией в [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **ИИ-диалоги и агенты** — Многораундовые диалоги, вызов инструментов, мультимодальная поддержка, потоковый вывод. Встроенная реализация RAG (база знаний) с глубокой интеграцией в [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Универсальная поддержка IM-платформ** — Единая кодовая база для Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Готовность к продакшену** — Контроль доступа, ограничение скорости, фильтрация чувствительных слов, комплексный мониторинг и обработка исключений. Проверено в корпоративной среде.
 - **Экосистема плагинов** — Сотни плагинов, событийно-ориентированная архитектура, расширения компонентов и поддержка [протокола MCP](https://modelcontextprotocol.io/).
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Другие варианты:** [Docker](https://link.langbot.app/en/docs/docker) · [Ручная установка](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**Другие варианты:** [Docker](https://link.langbot.app/en/docs/docker) · [Ручная установка](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

README_TW.md

@@ -39,7 +39,7 @@ LangBot 是一個**開源的生產級平台**，用於建構 AI 驅動的即時
 
 ### 核心能力
 
-- **AI 對話與 Agent** — 多輪對話、工具調用、多模態、流式輸出。自帶 RAG（知識庫），深度整合 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org) 等 LLMOps 平台。
+- **AI 對話與 Agent** — 多輪對話、工具調用、多模態、流式輸出。自帶 RAG（知識庫），深度整合 [Dify](https://dify.ai)、[Coze](https://coze.com)、[n8n](https://n8n.io)、[Langflow](https://langflow.org)、 [Deerflow](https://deerflow.tech)、[Weknora](https://weknora.weixin.qq.com)等 LLMOps 平台。
 - **全平台支援** — 一套程式碼，覆蓋 QQ、微信、企業微信、飛書、釘釘、Discord、Telegram、Slack、LINE、KOOK 等平台。
 - **生產就緒** — 存取控制、限速、敏感詞過濾、全面監控與異常處理，已被多家企業採用。
 - **外掛生態** — 數百個外掛，事件驅動架構，組件擴展，適配 [MCP 協議](https://modelcontextprotocol.io/)。
@@ -79,7 +79,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/zh-CN/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手動部署](https://link.langbot.app/zh/docs/manual-deploy) · [寶塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**更多方式：** [Docker](https://link.langbot.app/zh/docs/docker) · [手動部署](https://link.langbot.app/zh/docs/manual-deploy) · [寶塔面板](https://link.langbot.app/zh/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/zh/deploy/langbot/kubernetes)
 
 ---
 

README_VI.md

@@ -37,7 +37,7 @@ LangBot là một **nền tảng mã nguồn mở, cấp sản xuất** để x
 
 ### Khả năng chính
 
-- **Hội thoại AI & Agent** — Đối thoại nhiều lượt, gọi công cụ, hỗ trợ đa phương thức, đầu ra streaming. RAG (cơ sở kiến thức) tích hợp sẵn với tích hợp sâu vào [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org).
+- **Hội thoại AI & Agent** — Đối thoại nhiều lượt, gọi công cụ, hỗ trợ đa phương thức, đầu ra streaming. RAG (cơ sở kiến thức) tích hợp sẵn với tích hợp sâu vào [Dify](https://dify.ai), [Coze](https://coze.com), [n8n](https://n8n.io), [Langflow](https://langflow.org), [Deerflow](https://deerflow.tech), [Weknora](https://weknora.weixin.qq.com).
 - **Hỗ trợ đa nền tảng IM** — Một mã nguồn cho Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
 - **Sẵn sàng cho sản xuất** — Kiểm soát truy cập, giới hạn tốc độ, lọc từ nhạy cảm, giám sát toàn diện và xử lý ngoại lệ. Được doanh nghiệp tin dùng.
 - **Hệ sinh thái Plugin** — Hàng trăm plugin, kiến trúc hướng sự kiện, mở rộng thành phần, và hỗ trợ [giao thức MCP](https://modelcontextprotocol.io/).
@@ -77,7 +77,7 @@ docker compose up -d
 [![Deploy on Zeabur](https://zeabur.com/button.svg)](https://zeabur.com/en-US/templates/ZKTBDH)
 [![Deploy on Railway](https://railway.com/button.svg)](https://railway.app/template/yRrAyL?referralCode=vogKPF)
 
-**Thêm tùy chọn:** [Docker](https://link.langbot.app/en/docs/docker) · [Thủ công](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](./docker/README_K8S.md)
+**Thêm tùy chọn:** [Docker](https://link.langbot.app/en/docs/docker) · [Thủ công](https://link.langbot.app/en/docs/manual-deploy) · [BTPanel](https://link.langbot.app/en/docs/bt-panel) · [Kubernetes](https://docs.langbot.app/en/deploy/langbot/kubernetes)
 
 ---
 

docker/README_K8S.md

@@ -1,629 +0,0 @@
-# LangBot Kubernetes 部署指南 / Kubernetes Deployment Guide
-
-[简体中文](#简体中文) | [English](#english)
-
----
-
-## 简体中文
-
-### 概述
-
-本指南提供了在 Kubernetes 集群中部署 LangBot 的完整步骤。Kubernetes 部署配置基于 `docker-compose.yaml`，适用于生产环境的容器化部署。
-
-### 前置要求
-
-- Kubernetes 集群（版本 1.19+）
-- `kubectl` 命令行工具已配置并可访问集群
-- 集群中有可用的存储类（StorageClass）用于持久化存储（可选但推荐）
-- 至少 2 vCPU 和 4GB RAM 的可用资源
-
-### 架构说明
-
-Kubernetes 部署包含以下组件：
-
-1. **langbot**: 主应用服务
-   - 提供 Web UI（端口 5300）
-   - 处理平台 webhook（端口 2280-2290）
-   - 数据持久化卷
-   
-2. **langbot-plugin-runtime**: 插件运行时服务
-   - WebSocket 通信（端口 5400）
-   - 插件数据持久化卷
-
-3. **持久化存储**:
-   - `langbot-data`: LangBot 主数据
-   - `langbot-plugins`: 插件文件
-   - `langbot-plugin-runtime-data`: 插件运行时数据
-
-### 快速开始
-
-#### 1. 下载部署文件
-
-```bash
-# 克隆仓库
-git clone https://github.com/langbot-app/LangBot
-cd LangBot/docker
-
-# 或直接下载 kubernetes.yaml
-wget https://raw.githubusercontent.com/langbot-app/LangBot/main/docker/kubernetes.yaml
-```
-
-#### 2. 部署到 Kubernetes
-
-```bash
-# 应用所有配置
-kubectl apply -f kubernetes.yaml
-
-# 检查部署状态
-kubectl get all -n langbot
-
-# 查看 Pod 日志
-kubectl logs -n langbot -l app=langbot -f
-```
-
-#### 3. 访问 LangBot
-
-默认情况下，LangBot 服务使用 ClusterIP 类型，只能在集群内部访问。您可以选择以下方式之一来访问：
-
-**选项 A: 端口转发（推荐用于测试）**
-
-```bash
-kubectl port-forward -n langbot svc/langbot 5300:5300
-```
-
-然后访问 http://localhost:5300
-
-**选项 B: NodePort（适用于开发环境）**
-
-编辑 `kubernetes.yaml`，取消注释 NodePort Service 部分，然后：
-
-```bash
-kubectl apply -f kubernetes.yaml
-# 获取节点 IP
-kubectl get nodes -o wide
-# 访问 http://<NODE_IP>:30300
-```
-
-**选项 C: LoadBalancer（适用于云环境）**
-
-编辑 `kubernetes.yaml`，取消注释 LoadBalancer Service 部分，然后：
-
-```bash
-kubectl apply -f kubernetes.yaml
-# 获取外部 IP
-kubectl get svc -n langbot langbot-loadbalancer
-# 访问 http://<EXTERNAL_IP>
-```
-
-**选项 D: Ingress（推荐用于生产环境）**
-
-确保集群中已安装 Ingress Controller（如 nginx-ingress），然后：
-
-1. 编辑 `kubernetes.yaml` 中的 Ingress 配置
-2. 修改域名为您的实际域名
-3. 应用配置：
-
-```bash
-kubectl apply -f kubernetes.yaml
-# 访问 http://langbot.yourdomain.com
-```
-
-### 配置说明
-
-#### 环境变量
-
-在 `ConfigMap` 中配置环境变量：
-
-```yaml
-apiVersion: v1
-kind: ConfigMap
-metadata:
-  name: langbot-config
-  namespace: langbot
-data:
-  TZ: "Asia/Shanghai"  # 修改为您的时区
-```
-
-#### 存储配置
-
-默认使用动态存储分配。如果您有特定的 StorageClass，请在 PVC 中指定：
-
-```yaml
-spec:
-  storageClassName: your-storage-class-name
-  accessModes:
-    - ReadWriteOnce
-  resources:
-    requests:
-      storage: 10Gi
-```
-
-#### 资源限制
-
-根据您的需求调整资源限制：
-
-```yaml
-resources:
-  requests:
-    memory: "1Gi"
-    cpu: "500m"
-  limits:
-    memory: "4Gi"
-    cpu: "2000m"
-```
-
-### 常用操作
-
-#### 查看日志
-
-```bash
-# 查看 LangBot 主服务日志
-kubectl logs -n langbot -l app=langbot -f
-
-# 查看插件运行时日志
-kubectl logs -n langbot -l app=langbot-plugin-runtime -f
-```
-
-#### 重启服务
-
-```bash
-# 重启 LangBot
-kubectl rollout restart deployment/langbot -n langbot
-
-# 重启插件运行时
-kubectl rollout restart deployment/langbot-plugin-runtime -n langbot
-```
-
-#### 更新镜像
-
-```bash
-# 更新到最新版本
-kubectl set image deployment/langbot -n langbot langbot=rockchin/langbot:latest
-kubectl set image deployment/langbot-plugin-runtime -n langbot langbot-plugin-runtime=rockchin/langbot:latest
-
-# 检查更新状态
-kubectl rollout status deployment/langbot -n langbot
-```
-
-#### 扩容（不推荐）
-
-注意：由于 LangBot 使用 ReadWriteOnce 的持久化存储，不支持多副本扩容。如需高可用，请考虑使用 ReadWriteMany 存储或其他架构方案。
-
-#### 备份数据
-
-```bash
-# 备份 PVC 数据
-kubectl exec -n langbot -it <langbot-pod-name> -- tar czf /tmp/backup.tar.gz /app/data
-kubectl cp langbot/<langbot-pod-name>:/tmp/backup.tar.gz ./backup.tar.gz
-```
-
-### 卸载
-
-```bash
-# 删除所有资源（保留 PVC）
-kubectl delete deployment,service,configmap -n langbot --all
-
-# 删除 PVC（会删除数据）
-kubectl delete pvc -n langbot --all
-
-# 删除命名空间
-kubectl delete namespace langbot
-```
-
-### 故障排查
-
-#### Pod 无法启动
-
-```bash
-# 查看 Pod 状态
-kubectl get pods -n langbot
-
-# 查看详细信息
-kubectl describe pod -n langbot <pod-name>
-
-# 查看事件
-kubectl get events -n langbot --sort-by='.lastTimestamp'
-```
-
-#### 存储问题
-
-```bash
-# 检查 PVC 状态
-kubectl get pvc -n langbot
-
-# 检查 PV
-kubectl get pv
-```
-
-#### 网络访问问题
-
-```bash
-# 检查 Service
-kubectl get svc -n langbot
-
-# 检查端口转发
-kubectl port-forward -n langbot svc/langbot 5300:5300
-```
-
-### 生产环境建议
-
-1. **使用特定版本标签**：避免使用 `latest` 标签，使用具体版本号如 `rockchin/langbot:v1.0.0`
-2. **配置资源限制**：根据实际负载调整 CPU 和内存限制
-3. **使用 Ingress + TLS**：配置 HTTPS 访问和证书管理
-4. **配置监控和告警**：集成 Prometheus、Grafana 等监控工具
-5. **定期备份**：配置自动备份策略保护数据
-6. **使用专用 StorageClass**：为生产环境配置高性能存储
-7. **配置亲和性规则**：确保 Pod 调度到合适的节点
-
-### 高级配置
-
-#### 使用 Secrets 管理敏感信息
-
-如果需要配置 API 密钥等敏感信息：
-
-```yaml
-apiVersion: v1
-kind: Secret
-metadata:
-  name: langbot-secrets
-  namespace: langbot
-type: Opaque
-data:
-  api_key: <base64-encoded-value>
-```
-
-然后在 Deployment 中引用：
-
-```yaml
-env:
-- name: API_KEY
-  valueFrom:
-    secretKeyRef:
-      name: langbot-secrets
-      key: api_key
-```
-
-#### 配置水平自动扩缩容（HPA）
-
-注意：需要确保使用 ReadWriteMany 存储类型
-
-```yaml
-apiVersion: autoscaling/v2
-kind: HorizontalPodAutoscaler
-metadata:
-  name: langbot-hpa
-  namespace: langbot
-spec:
-  scaleTargetRef:
-    apiVersion: apps/v1
-    kind: Deployment
-    name: langbot
-  minReplicas: 1
-  maxReplicas: 3
-  metrics:
-  - type: Resource
-    resource:
-      name: cpu
-      target:
-        type: Utilization
-        averageUtilization: 70
-```
-
-### 参考资源
-
-- [LangBot 官方文档](https://docs.langbot.app)
-- [Docker 部署文档](https://link.langbot.app/zh/docs/docker)
-- [Kubernetes 官方文档](https://kubernetes.io/docs/)
-
----
-
-## English
-
-### Overview
-
-This guide provides complete steps for deploying LangBot in a Kubernetes cluster. The Kubernetes deployment configuration is based on `docker-compose.yaml` and is suitable for production containerized deployments.
-
-### Prerequisites
-
-- Kubernetes cluster (version 1.19+)
-- `kubectl` command-line tool configured with cluster access
-- Available StorageClass in the cluster for persistent storage (optional but recommended)
-- At least 2 vCPU and 4GB RAM of available resources
-
-### Architecture
-
-The Kubernetes deployment includes the following components:
-
-1. **langbot**: Main application service
-   - Provides Web UI (port 5300)
-   - Handles platform webhooks (ports 2280-2290)
-   - Data persistence volume
-   
-2. **langbot-plugin-runtime**: Plugin runtime service
-   - WebSocket communication (port 5400)
-   - Plugin data persistence volume
-
-3. **Persistent Storage**:
-   - `langbot-data`: LangBot main data
-   - `langbot-plugins`: Plugin files
-   - `langbot-plugin-runtime-data`: Plugin runtime data
-
-### Quick Start
-
-#### 1. Download Deployment Files
-
-```bash
-# Clone repository
-git clone https://github.com/langbot-app/LangBot
-cd LangBot/docker
-
-# Or download kubernetes.yaml directly
-wget https://raw.githubusercontent.com/langbot-app/LangBot/main/docker/kubernetes.yaml
-```
-
-#### 2. Deploy to Kubernetes
-
-```bash
-# Apply all configurations
-kubectl apply -f kubernetes.yaml
-
-# Check deployment status
-kubectl get all -n langbot
-
-# View Pod logs
-kubectl logs -n langbot -l app=langbot -f
-```
-
-#### 3. Access LangBot
-
-By default, LangBot service uses ClusterIP type, accessible only within the cluster. Choose one of the following methods to access:
-
-**Option A: Port Forwarding (Recommended for testing)**
-
-```bash
-kubectl port-forward -n langbot svc/langbot 5300:5300
-```
-
-Then visit http://localhost:5300
-
-**Option B: NodePort (Suitable for development)**
-
-Edit `kubernetes.yaml`, uncomment the NodePort Service section, then:
-
-```bash
-kubectl apply -f kubernetes.yaml
-# Get node IP
-kubectl get nodes -o wide
-# Visit http://<NODE_IP>:30300
-```
-
-**Option C: LoadBalancer (Suitable for cloud environments)**
-
-Edit `kubernetes.yaml`, uncomment the LoadBalancer Service section, then:
-
-```bash
-kubectl apply -f kubernetes.yaml
-# Get external IP
-kubectl get svc -n langbot langbot-loadbalancer
-# Visit http://<EXTERNAL_IP>
-```
-
-**Option D: Ingress (Recommended for production)**
-
-Ensure an Ingress Controller (e.g., nginx-ingress) is installed in the cluster, then:
-
-1. Edit the Ingress configuration in `kubernetes.yaml`
-2. Change the domain to your actual domain
-3. Apply configuration:
-
-```bash
-kubectl apply -f kubernetes.yaml
-# Visit http://langbot.yourdomain.com
-```
-
-### Configuration
-
-#### Environment Variables
-
-Configure environment variables in ConfigMap:
-
-```yaml
-apiVersion: v1
-kind: ConfigMap
-metadata:
-  name: langbot-config
-  namespace: langbot
-data:
-  TZ: "Asia/Shanghai"  # Change to your timezone
-```
-
-#### Storage Configuration
-
-Uses dynamic storage provisioning by default. If you have a specific StorageClass, specify it in PVC:
-
-```yaml
-spec:
-  storageClassName: your-storage-class-name
-  accessModes:
-    - ReadWriteOnce
-  resources:
-    requests:
-      storage: 10Gi
-```
-
-#### Resource Limits
-
-Adjust resource limits based on your needs:
-
-```yaml
-resources:
-  requests:
-    memory: "1Gi"
-    cpu: "500m"
-  limits:
-    memory: "4Gi"
-    cpu: "2000m"
-```
-
-### Common Operations
-
-#### View Logs
-
-```bash
-# View LangBot main service logs
-kubectl logs -n langbot -l app=langbot -f
-
-# View plugin runtime logs
-kubectl logs -n langbot -l app=langbot-plugin-runtime -f
-```
-
-#### Restart Services
-
-```bash
-# Restart LangBot
-kubectl rollout restart deployment/langbot -n langbot
-
-# Restart plugin runtime
-kubectl rollout restart deployment/langbot-plugin-runtime -n langbot
-```
-
-#### Update Images
-
-```bash
-# Update to latest version
-kubectl set image deployment/langbot -n langbot langbot=rockchin/langbot:latest
-kubectl set image deployment/langbot-plugin-runtime -n langbot langbot-plugin-runtime=rockchin/langbot:latest
-
-# Check update status
-kubectl rollout status deployment/langbot -n langbot
-```
-
-#### Scaling (Not Recommended)
-
-Note: Due to LangBot using ReadWriteOnce persistent storage, multi-replica scaling is not supported. For high availability, consider using ReadWriteMany storage or alternative architectures.
-
-#### Backup Data
-
-```bash
-# Backup PVC data
-kubectl exec -n langbot -it <langbot-pod-name> -- tar czf /tmp/backup.tar.gz /app/data
-kubectl cp langbot/<langbot-pod-name>:/tmp/backup.tar.gz ./backup.tar.gz
-```
-
-### Uninstall
-
-```bash
-# Delete all resources (keep PVCs)
-kubectl delete deployment,service,configmap -n langbot --all
-
-# Delete PVCs (will delete data)
-kubectl delete pvc -n langbot --all
-
-# Delete namespace
-kubectl delete namespace langbot
-```
-
-### Troubleshooting
-
-#### Pods Not Starting
-
-```bash
-# Check Pod status
-kubectl get pods -n langbot
-
-# View detailed information
-kubectl describe pod -n langbot <pod-name>
-
-# View events
-kubectl get events -n langbot --sort-by='.lastTimestamp'
-```
-
-#### Storage Issues
-
-```bash
-# Check PVC status
-kubectl get pvc -n langbot
-
-# Check PV
-kubectl get pv
-```
-
-#### Network Access Issues
-
-```bash
-# Check Service
-kubectl get svc -n langbot
-
-# Test port forwarding
-kubectl port-forward -n langbot svc/langbot 5300:5300
-```
-
-### Production Recommendations
-
-1. **Use specific version tags**: Avoid using `latest` tag, use specific version like `rockchin/langbot:v1.0.0`
-2. **Configure resource limits**: Adjust CPU and memory limits based on actual load
-3. **Use Ingress + TLS**: Configure HTTPS access and certificate management
-4. **Configure monitoring and alerts**: Integrate monitoring tools like Prometheus, Grafana
-5. **Regular backups**: Configure automated backup strategy to protect data
-6. **Use dedicated StorageClass**: Configure high-performance storage for production
-7. **Configure affinity rules**: Ensure Pods are scheduled to appropriate nodes
-
-### Advanced Configuration
-
-#### Using Secrets for Sensitive Information
-
-If you need to configure sensitive information like API keys:
-
-```yaml
-apiVersion: v1
-kind: Secret
-metadata:
-  name: langbot-secrets
-  namespace: langbot
-type: Opaque
-data:
-  api_key: <base64-encoded-value>
-```
-
-Then reference in Deployment:
-
-```yaml
-env:
-- name: API_KEY
-  valueFrom:
-    secretKeyRef:
-      name: langbot-secrets
-      key: api_key
-```
-
-#### Configure Horizontal Pod Autoscaling (HPA)
-
-Note: Requires ReadWriteMany storage type
-
-```yaml
-apiVersion: autoscaling/v2
-kind: HorizontalPodAutoscaler
-metadata:
-  name: langbot-hpa
-  namespace: langbot
-spec:
-  scaleTargetRef:
-    apiVersion: apps/v1
-    kind: Deployment
-    name: langbot
-  minReplicas: 1
-  maxReplicas: 3
-  metrics:
-  - type: Resource
-    resource:
-      name: cpu
-      target:
-        type: Utilization
-        averageUtilization: 70
-```
-
-### References
-
-- [LangBot Official Documentation](https://docs.langbot.app)
-- [Docker Deployment Guide](https://link.langbot.app/zh/docs/docker)
-- [Kubernetes Official Documentation](https://kubernetes.io/docs/)

docker/docker-compose.yaml

@@ -1,5 +1,5 @@
 # Docker Compose configuration for LangBot
-# For Kubernetes deployment, see kubernetes.yaml and README_K8S.md
+# For Kubernetes deployment, see kubernetes.yaml and the deployment guide at https://docs.langbot.app
 version: "3"
 
 services:

docker/kubernetes.yaml

@@ -1,6 +1,8 @@
 # Kubernetes Deployment for LangBot
 # This file provides Kubernetes deployment manifests for LangBot based on docker-compose.yaml
-# 
+#
+# Full deployment guide (zh/en/ja): https://docs.langbot.app -> Installation -> Kubernetes
+#
 # Usage:
 #   kubectl apply -f kubernetes.yaml
 #
@@ -8,13 +10,15 @@
 #   - A Kubernetes cluster (1.19+)
 #   - kubectl configured to communicate with your cluster
 #   - (Optional) A StorageClass for dynamic volume provisioning
+#   - For the Box sandbox runtime: a node with a reachable Docker daemon
+#     (the box mounts the node's /var/run/docker.sock). See the deployment guide.
 #
 # Components:
 #   - Namespace: langbot
 #   - PersistentVolumeClaims for data persistence
-#   - Deployments for langbot and langbot_plugin_runtime
+#   - Deployments for langbot, langbot-plugin-runtime, and langbot-box (sandbox)
 #   - Services for network access
-#   - ConfigMap for timezone configuration
+#   - ConfigMap for timezone + runtime endpoints
 
 ---
 # Namespace
@@ -83,6 +87,11 @@ metadata:
 data:
   TZ: "Asia/Shanghai"
   PLUGIN__RUNTIME_WS_URL: "ws://langbot-plugin-runtime:5400/control/ws"
+  # Box sandbox runtime endpoint. LangBot connects to the Box runtime over
+  # WebSocket. The hostname MUST match the langbot-box Service name. Note the
+  # in-container default ("langbot_box") uses an underscore, which is an
+  # invalid Kubernetes DNS name — so the endpoint is always set explicitly here.
+  BOX__RUNTIME__ENDPOINT: "ws://langbot-box:5410"
 
 ---
 # Deployment for LangBot Plugin Runtime
@@ -169,6 +178,136 @@ spec:
     protocol: TCP
     name: runtime
 
+---
+# Deployment for LangBot Box (sandbox) runtime
+#
+# The Box runtime backs LangBot's sandbox tools (exec / read / write / edit /
+# glob / grep), the `activate` skill tool, skill add/edit, and stdio-mode MCP
+# servers. It is OPTIONAL: if you do not deploy it, set `BOX__ENABLED=false` on
+# the langbot Deployment (or `box.enabled: false` in config.yaml) so the
+# dashboard renders cleanly with sandbox features disabled.
+#
+# IMPORTANT — how the sandbox actually runs:
+#   The bundled image ships only the Docker CLI (no dockerd, no nsjail). The Box
+#   runtime therefore creates sandbox containers by talking to a Docker daemon
+#   over the mounted socket (`/var/run/docker.sock`). Because that daemon
+#   resolves bind-mount paths on the NODE filesystem, the Box workspace root
+#   must be the SAME absolute path inside the box container, inside every
+#   sandbox container it spawns, AND on the node. That is why this manifest uses
+#   a hostPath at a fixed absolute path (/app/data/box) and pins langbot + box
+#   to the same node via podAffinity. A normal PVC will NOT work for the box
+#   workspace, because the node's dockerd cannot see paths that exist only
+#   inside the pod's mount namespace.
+#
+# Security note: mounting the host Docker socket grants the Box runtime (and any
+# code executed in the sandbox) effective root on the node. Only deploy Box on
+# nodes you trust for this workload, ideally a dedicated node pool. For a
+# stronger isolation boundary, switch box.backend to 'e2b' (set E2B_API_KEY) and
+# drop the docker.sock mount + hostPath entirely.
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+  name: langbot-box
+  namespace: langbot
+  labels:
+    app: langbot-box
+spec:
+  replicas: 1
+  selector:
+    matchLabels:
+      app: langbot-box
+  template:
+    metadata:
+      labels:
+        app: langbot-box
+    spec:
+      # Pin to the same node as langbot so they share the hostPath box root.
+      affinity:
+        podAffinity:
+          requiredDuringSchedulingIgnoredDuringExecution:
+          - labelSelector:
+              matchLabels:
+                app: langbot
+            topologyKey: kubernetes.io/hostname
+      containers:
+      - name: langbot-box
+        image: rockchin/langbot:latest
+        imagePullPolicy: Always
+        # Launched through the same CLI entry point as the plugin runtime.
+        # No flag => WebSocket control transport (default), listening on 5410.
+        command: ["uv", "run", "--no-sync", "-m", "langbot_plugin.cli.__init__", "box"]
+        ports:
+        - containerPort: 5410
+          name: box-rpc
+          protocol: TCP
+        env:
+        - name: TZ
+          valueFrom:
+            configMapKeyRef:
+              name: langbot-config
+              key: TZ
+        # The Box runtime does NOT read box.local.* / BOX__* from its own env;
+        # it receives its configuration from LangBot via the INIT RPC action.
+        # Do not add BOX__* here — they would be silently ignored.
+        volumeMounts:
+        # Box workspace root — identical path on node, box, and sandbox
+        # containers (see the IMPORTANT note above).
+        - name: box-root
+          mountPath: /app/data/box
+        # Host Docker socket — the sandbox backend uses it to create containers.
+        - name: docker-sock
+          mountPath: /var/run/docker.sock
+        resources:
+          requests:
+            memory: "256Mi"
+            cpu: "100m"
+          limits:
+            memory: "1Gi"
+            cpu: "1000m"
+        livenessProbe:
+          tcpSocket:
+            port: 5410
+          initialDelaySeconds: 20
+          periodSeconds: 10
+          timeoutSeconds: 5
+          failureThreshold: 3
+        readinessProbe:
+          tcpSocket:
+            port: 5410
+          initialDelaySeconds: 10
+          periodSeconds: 5
+          timeoutSeconds: 3
+          failureThreshold: 3
+      volumes:
+      - name: box-root
+        hostPath:
+          path: /app/data/box
+          type: DirectoryOrCreate
+      - name: docker-sock
+        hostPath:
+          path: /var/run/docker.sock
+          type: Socket
+      restartPolicy: Always
+
+---
+# Service for LangBot Box runtime
+apiVersion: v1
+kind: Service
+metadata:
+  name: langbot-box
+  namespace: langbot
+  labels:
+    app: langbot-box
+spec:
+  type: ClusterIP
+  selector:
+    app: langbot-box
+  ports:
+  - port: 5410
+    targetPort: 5410
+    protocol: TCP
+    name: box-rpc
+
 ---
 # Deployment for LangBot
 apiVersion: apps/v1
@@ -213,11 +352,36 @@ spec:
             configMapKeyRef:
               name: langbot-config
               key: PLUGIN__RUNTIME_WS_URL
+        # Box (sandbox) runtime endpoint. Connects LangBot to the langbot-box
+        # Service over WebSocket. Remove this (and the langbot-box Deployment)
+        # and set BOX__ENABLED=false if you do not want the sandbox.
+        - name: BOX__RUNTIME__ENDPOINT
+          valueFrom:
+            configMapKeyRef:
+              name: langbot-config
+              key: BOX__RUNTIME__ENDPOINT
+        # box.local.* config — forwarded to the Box runtime via INIT RPC. The
+        # host_root MUST match the box-root hostPath mountPath below AND the box
+        # Deployment's box-root mountPath, so that skill package paths resolve
+        # identically on both sides and on the node's Docker daemon.
+        - name: BOX__LOCAL__HOST_ROOT
+          value: "/app/data/box"
+        - name: BOX__LOCAL__DEFAULT_WORKSPACE
+          value: "default"
+        - name: BOX__LOCAL__SKILLS_ROOT
+          value: "skills"
+        - name: BOX__LOCAL__ALLOWED_MOUNT_ROOTS
+          value: "/app/data/box"
         volumeMounts:
         - name: data
           mountPath: /app/data
         - name: plugins
           mountPath: /app/plugins
+        # Same node-level box root as the langbot-box Deployment. Mounted over
+        # the data PVC's /app/data/box subpath so both LangBot and the Box
+        # runtime (and the node's dockerd) agree on one absolute path.
+        - name: box-root
+          mountPath: /app/data/box
         resources:
           requests:
             memory: "1Gi"
@@ -250,6 +414,13 @@ spec:
       - name: plugins
         persistentVolumeClaim:
           claimName: langbot-plugins
+      # Node-level box workspace root, shared with the langbot-box Deployment.
+      # hostPath (not PVC) because the node's Docker daemon must see the same
+      # absolute path when bind-mounting workspaces into sandbox containers.
+      - name: box-root
+        hostPath:
+          path: /app/data/box
+          type: DirectoryOrCreate
       restartPolicy: Always
 
 ---

docs/agent-runner-pluginization/AGENT_CONTEXT_PROTOCOL.md

@@ -1,153 +0,0 @@
-# Agent-owned Context 协议设计
-
-本文档描述插件化 AgentRunner 场景下的上下文边界**设计理由**。结论先行：LangBot 不应成为最终 agentic context manager；它提供 context substrate，AgentRunner 或其背后的 runtime 自己决定如何管理历史、压缩、召回和 KV cache。
-
-> 涉及的数据结构（`AgentRunContext`、`ContextAccess`、`AgentRunAPIProxy` 等）唯一定义在 [PROTOCOL_V1.md](./PROTOCOL_V1.md)。本文只讲语义和约束，不重抄 schema。实现进度见 [PROGRESS.md](./PROGRESS.md)。
-
-## 1. 设计原则
-
-### 1.1 Agent 拥有上下文策略
-
-不同 runner 背后的 runtime 差异很大：
-
-- 官方 local-agent 可能依赖 LangBot 的模型、工具、知识库和存储。
-- Claude Code SDK / Codex 类 runtime 有自己的 session、transcript、tool loop 和上下文压缩。
-- Pi Agent SDK 或外部 agent 平台可能只需要当前事件和一个外部 conversation key。
-
-因此 LangBot 不应强行决定最终传给模型的历史窗口。Host 只提供：当前事件的完整结构化信息、稳定身份和会话引用、可授权读取的 history / event / artifact / state API、可投影给外部 harness 的 scoped context / MCP / skill / resource refs、payload hard cap 和权限 guardrail。
-
-### 1.2 Host 不定义通用历史窗口
-
-历史窗口策略不是 AgentRunner 协议或 Query entry adapter 的核心概念。Host 只提供 history pull API、cursor、hard cap 和权限边界；runner 自己决定是否读取、读取多少、如何截断和压缩。
-
-正确的问题不是"LangBot 每轮裁几轮历史给 agent"，而是：
-
-- 这类 runner 是否自管 context？
-- 事件到来时 host 应 inline 哪些最小信息？
-- agent 需要更多上下文时通过什么 API 拉取？
-- host 如何保证安全、可审计和可分页？
-
-### 1.3 Host 保存事实源，Agent 管理 working context
-
-三类数据要分开：
-
-- `EventLog`: Host 保存原始事件、工具调用、投递结果、错误和系统事件。
-- `Transcript`: Host 从 EventLog 投影出的对话视图，用于 UI、审计和按需历史读取。
-- `Working context`: Agent 本轮实际送进模型或 runtime 的上下文，由 AgentRunner 决定。
-
-LangBot 不提供 host-side inline history window。简单 runner 如果需要历史窗口，应在 runner 内部通过 Host history API 拉取并裁剪。
-
-## 2. Event 到来时传什么
-
-默认 `AgentRunContext`（PROTOCOL_V1 §5.2）应尽量小且稳定。默认规则：
-
-- Host MUST NOT inline full history by default.
-- Host SHOULD inline only current event / input and context handles.
-- Runner owns working-context assembly.
-- Runner MAY use Host history / event / artifact / state / storage API when authorized.
-- Official runners MUST consume Host infrastructure through the same public API as third-party runners.
-
-### 2.1 必须 inline 的内容
-
-当前 event 的类型/id/时间/source；当前输入文本和结构化内容；附件/文件/图片的 metadata 和 artifact ref；actor / subject / conversation / thread / bot / workspace；delivery 能力；已授权资源列表；context cursors 和可用 API 能力；Agent/runner config。这些是 agent 决定下一步所需的最低信息。
-
-### 2.2 默认不 inline 的内容
-
-完整历史消息、大文件全文、大工具结果、全量知识库内容、平台原始 payload 大对象、每轮重新生成的大段 summary。这些会破坏跨进程序列化成本、泄露范围、KV cache 稳定性，也会迫使 host 替 agent 做 context 策略。
-
-### 2.3 不提供 Host Inline History Window
-
-`AgentRunContext` 不包含 `bootstrap` 字段。Host 不下发历史窗口，也不通过 Pipeline 配置决定窗口大小。runner 若需要类似 `recent_tail` 的策略，应在自己的 manifest/config schema 中声明参数，并在 runner 内部通过 history API 读取、裁剪和压缩。Host 只负责权限、分页、hard cap 和事实源。
-
-## 3. ContextAccess 的作用
-
-`ContextAccess`（PROTOCOL_V1 §5.8）是 host 交给 agent 的上下文读取入口描述，告诉 agent：当前事件位于哪条 conversation / thread、若需要更多历史从哪个 cursor 开始拉、host inline 了什么没 inline 什么、当前 run 有哪些 context API 权限。
-
-## 4. Agent 如何获取更多上下文
-
-所有 API 都走 `AgentRunAPIProxy`（PROTOCOL_V1 §8），由 host 用 `run_id` 校验。
-
-### 4.1 History
-
-```python
-await api.history.page(conversation_id=ctx.context.conversation_id,
-                       before_cursor=ctx.context.latest_cursor,
-                       limit=50, direction="backward", include_artifacts=False)
-```
-
-返回：
-
-```python
-class HistoryPage(BaseModel):
-    items: list[TranscriptItem]
-    next_cursor: str | None
-    prev_cursor: str | None
-    has_more: bool
-```
-
-约束：`limit` 有 host hard cap；默认只能读当前 conversation / thread；跨会话读取需 manifest permission + binding policy；返回 artifact ref，不默认返回大文件内容。
-
-### 4.2 Search
-
-```python
-await api.history.search(query="用户之前提到的数据库连接信息",
-                         filters={"conversation_id": ..., "event_types": ["message.received"]},
-                         top_k=10)
-```
-
-Search 可先用数据库全文索引，后续接 embedding recall。它是 host 检索能力，不等于 agent 的长期记忆策略。
-
-### 4.3 Event / Artifact / State
-
-- Event API（`events.get` / `events.page`）用于读取非消息事件、工具事件、系统事件。Agent 不应把所有事件都当成 user/assistant message。
-- Artifact API（`artifacts.metadata` / `read_range` / `open_stream`）必须校验 artifact 所属 conversation / run / binding，校验 MIME / 大小 / 过期 / 权限，大文件按 range/stream 读取，工具大结果也应 artifact 化。
-- State API（`state.get` / `set`）是可选寄宿能力。自管 runtime 可以完全不用；依附 LangBot 的官方 runner 可以使用，例如 `external.session_id`、`summary.checkpoint`。
-
-### 4.4 大文件与工具协作
-
-大文件、多模态输入和工具产物不要内联进 prompt 或 tool result：message/content 里只放小文本和必要摘要；大文件、图片、音频、长工具输出返回 artifact ref（`artifact_id`、`mime_type`、`size`、`digest`、`summary`、`expires_at`、`permissions`）。工具之间传递大结果时传 artifact ref，不传完整 blob。Host 校验 artifact 是否属于当前 run / scope，默认不允许插件直接读任意本地路径；临时文件应有 TTL 和清理机制。
-
-### 4.5 External harness context projection
-
-Claude Code、Codex、Kimi Code 这类 runtime 通常已有自己的 session、工具 loop、MCP 加载、上下文压缩和工作目录。LangBot 不应把它们改造成"host prompt assembler"，而应提供可审计的事件和资源投影。推荐 projection 形态：
-
-- `agent-context.json`：结构化 JSON，包含 `run_id`、`event`、`actor`、`subject`、`input`、`delivery`、`resources`、`context`、`state`、`runtime`。
-- `LANGBOT_CONTEXT.md`：人类可读摘要。
-- `resources`：只包含本次 run 授权后的句柄，不暴露 Host 内部私有对象。
-- `skills`：已授权 skill 投影为目标 harness 可读目录（如 Claude Code 的 `.claude/skills/<name>/SKILL.md`）。
-- `MCP config`：scoped MCP 配置，runner adapter 转成目标 harness 的配置文件或 CLI 参数。
-- `state pointers`：外部 session id、working directory、checkpoint 等小型 JSON 状态通过 Host state API 保存。
-
-当前 Claude Code runner 使用 schema `langbot.agent_runner.external_harness_context.v1`（现状见 OFFICIAL_RUNNER_PLUGINS §7）。这类 projection 是"把 LangBot 事实源和授权资源交给 harness"，不是"由 LangBot 决定最终模型上下文"。
-
-## 5. Runner manifest 中的上下文声明
-
-`AgentRunnerContextPolicy`（PROTOCOL_V1 §4.5）声明 runner 的上下文能力：`supports_history_pull` / `supports_history_search` / `supports_artifact_pull` / `owns_compaction` / `wants_static_context_refs`。它表示 Host 只给当前事件和 context handles；runner 自己决定是否拉取历史、是否搜索、何时摘要、如何构造最终 prompt。
-
-## 6. KV cache 友好的上下文管理
-
-支持 Claude Code SDK、Codex、Pi Agent SDK 等 runtime 时，必须避免每轮由 LangBot 重组大块 prompt：
-
-- 稳定 session key：`workspace/bot/binding/runner/conversation/thread`。
-- 静态内容使用 `ref + version/hash`（`ctx.runtime.static_refs`）：system prompt、resource manifest、tool schema、platform policy。
-- 每轮只传 delta：当前 event、artifact refs、少量 runtime metadata。
-- 历史 append-only：不要每轮改写同一段 history 文本。
-- Summary checkpoint 稳定：只有压缩发生时产生新 checkpoint。
-- 大文件和工具结果 artifact 化。
-- Tool/context API schema 稳定，数据通过 API 拉取而非塞入 prompt。
-- 对自管 runtime，优先让它复用自身 session/cache，而不是强制 LangBot 每轮重放 transcript。
-- LiteLLM 接入后，模型窗口元信息应作为 resource/runtime metadata 暴露给 runner，由 runner 决定预算和压缩策略。
-
-稳定 session key 的用途是隔离外部 runtime 的 resume/cache/state，不是改变 PROTOCOL_V1 §13 定义的 Agent 复用和 dispatch 边界。只有当某个外部 harness 的同一 native session 不支持并发 turn 时，runner 或 future runtime control plane 才应按 external session key 做 turn-level 串行化。
-
-对长期运行的 external harness / daemon，推荐运行形态是 reader 与 writer 分离：一个 session reader 独占读取 stdout/SSE/native event stream，并把 native event 转成 `AgentRunResult` 或 task progress；用户输入只作为 turn write 进入该 session。当前一次性 CLI subprocess runner 可以继续在单次 `run(ctx)` 内同步收集 stdout，但后续改成长连接时不应让多个 request 同时读取同一 native stream。
-
-## 7. Host guardrail
-
-Agent 自管 context 不代表无限制访问。LangBot 仍必须控制：每次 run 的 active `run_id`、runner identity、当前 binding 的 resource policy、conversation / actor / subject scope、page size / artifact read size / API rate limit、跨会话读取权限、数据脱敏和敏感变量过滤、审计日志。Host 不负责"最佳上下文策略"，但负责"不越权、不爆内存、不不可审计"。
-
-## 8. 官方 runner 与业务编排边界
-
-官方 runner 插件可以把状态寄宿在 LangBot，但必须和第三方 runner 一样通过公开 Host API 消费。LangBot core 不内置官方 agent 的业务流程（prompt 组装、tool loop、RAG 编排、summary/compaction、"local-agent 专用"状态字段）。
-
-官方 local-agent 应作为"依附 LangBot 基础设施的复杂 runner 参考实现"：transcript/history 通过 `api.history` 读取，summary/checkpoint/外部 session id/用户偏好通过 `api.state` 或 `api.storage` 保存，图片/文件/工具大结果通过 `api.artifacts` 读取，模型/工具/知识库通过 `api.models` / `api.tools` / `api.knowledge` 调用。这样 LangBot 保持为通用 agent host，不变成内置 agent 框架。具体迁移要求见 [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md)。

docs/agent-runner-pluginization/AGENT_RUNNER_QA_GUIDE.md

@@ -1,252 +0,0 @@
-# Agent Runner QA 指南
-
-本文档是 agent-runner 插件化下一轮测试的唯一 QA 入口。它合并并取代旧的 Phase 1 验收矩阵与 2026-05-18 / 2026-05-29 两份本地 QA 报告。
-
-目标不是保留完整历史流水账，而是指导测试 agent 用最小但高价值的路径判断当前分支是否仍然健康。
-
-## 1. 测试边界
-
-当前主线验证的是 AgentRunner Protocol v1：
-
-```text
-event -> binding -> runner.run(ctx) -> result stream
-```
-
-本指南验证：
-
-- Host 能通过当前 Query entry adapter 进入 event-first `run(event, binding)` 主链路。
-- Runner 来自插件 registry，而不是旧内置 runner 分支。
-- `local-agent` 能消费 Host 模型、工具、知识库、history、state、artifact 等基础设施。
-- 外部 harness runner（Claude Code / Codex）能消费 event-first context，并把 session / working directory 等指针写回 host-owned state。
-- 错误、权限裁剪、无输出、timeout 等路径不会破坏主聊天流程。
-
-本指南不验证：
-
-- Runtime Control Plane v2。
-- EventGateway / EventRouter 完整落地。
-- 发布级 path isolation、secret filtering、MCP allowlist、资源配额和 workspace cleanup。
-- 所有外部服务 runner 的真实凭据联调。
-
-这些属于后续能力或发布门槛，分别见 [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md) 与 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
-
-## 2. 状态定义
-
-测试报告只使用以下状态：
-
-| 状态 | 含义 |
-| --- | --- |
-| PASS | 按步骤执行，用户可见行为和日志证据都满足通过条件。 |
-| FAIL | 环境可用，但行为不满足通过条件。 |
-| BLOCKED | 凭据、CLI、外部服务、测试数据或本地配置缺失导致无法执行。必须写清阻塞原因。 |
-| N/A | 当前 runner 或平台明确不支持该能力。必须引用 manifest、文档或配置说明。 |
-
-不能使用“看起来正常”“大概通过”“基本没问题”等模糊状态。
-
-## 3. 执行顺序
-
-推荐按以下顺序执行，前一层失败时不要继续扩大测试面：
-
-1. Host / SDK / runner 单测。
-2. WebUI 登录与 Pipeline Debug Chat 基础 smoke。
-3. `local-agent` 高价值场景。
-4. Claude Code / Codex 外部 harness smoke。
-5. 权限和错误路径补充检查。
-6. 汇总 PASS / FAIL / BLOCKED，并给出下一步建议。
-
-用户可见流程必须通过 WebUI 或真实消息平台验证。API / curl 只能作为诊断证据，不能单独让 UI case PASS。
-
-## 4. 必跑基线
-
-### 4.1 单测基线
-
-在 LangBot 仓库运行：
-
-```bash
-uv run --frozen pytest tests/unit_tests/agent
-```
-
-如果本次改动只触及默认配置或 API service，也至少补跑相关目标测试，例如：
-
-```bash
-uv run pytest tests/unit_tests/api/test_pipeline_service_defaults.py
-```
-
-通过条件：
-
-- agent 单测全 PASS，或失败项已确认与本次 agent-runner 路径无关。
-- 若失败来自 `context_builder`、`orchestrator`、`session_registry`、`resource_builder`、`plugin/handler.py` 的 run action 权限路径，不应进入 UI smoke。
-
-### 4.2 环境基线
-
-用 `langbot-skills` 做环境检查：
-
-```bash
-cd "$LANGBOT_SKILLS_REPO"
-bin/lbs env doctor
-bin/lbs case list
-```
-
-`LANGBOT_SKILLS_REPO` 指向当前工作区里的 `langbot-skills` 仓库。优先使用已有 case，而不是临时发明测试路径。
-
-推荐首批 case：
-
-- `webui-login-state`
-- `pipeline-debug-chat`
-- `local-agent-basic-debug-chat`
-- `local-agent-rag-debug-chat`（改动涉及 RAG / knowledge）
-- `local-agent-plugin-tool-call-debug-chat`（改动涉及 tool / resource policy）
-
-## 5. WebUI 主链路 Smoke
-
-### 5.1 Runner registry
-
-步骤：
-
-1. 打开 WebUI Pipeline 配置页。
-2. 查看 AI runner 下拉列表。
-3. 选择 `plugin:langbot/local-agent/default`。
-4. 保存并刷新页面。
-
-通过条件：
-
-- runner 选项来自插件 registry。
-- 保存后配置仍为 `ai.runner.id` + `ai.runner_config[id]`。
-- `runner_config` 表示 Agent/runner config，不表示插件实例状态。
-- 不读取或回写旧 `ai.runner.runner` 字段。
-- 不出现旧内置 runner stage 名（例如裸 `local-agent`）作为当前选中项或配置 surface。
-- 插件没有循环重启或 metadata 加载失败。
-
-### 5.2 主聊天路径
-
-步骤：
-
-1. 使用绑定 `plugin:langbot/local-agent/default` 的 Pipeline。
-2. 在 Debug Chat 发送确定性普通文本。
-3. 查看 WebUI 回复和后端日志。
-
-通过条件：
-
-- 用户可见回复正常。
-- 后端日志显示走 `AgentRunOrchestrator` / `RUN_AGENT`。
-- 不走旧内置 local-agent 主执行分支。
-- conversation transcript 写入用户消息和助手消息。
-
-## 6. `local-agent` 高价值测试
-
-只保留最能覆盖架构边界的场景。
-
-| ID | 场景 | 操作 | 通过条件 |
-| --- | --- | --- | --- |
-| LA-01 | 绑定 prompt | 配置 system prompt 后发送文本。 | runner 使用 `ctx.config.prompt`，不读取 `ctx.adapter.extra["prompt"]`；回复体现绑定 prompt。 |
-| LA-02 | history API | 连续两轮对话，第二轮引用第一轮 marker。 | runner 通过 Host history API 或自管上下文读取历史，不依赖 inline history window。 |
-| LA-03 | 流式 / 非流式 | 分别用支持流式和关闭流式的路径发送文本。 | 流式 UI 不重复、不空白；非流式只输出最终消息。 |
-| LA-04 | 工具调用 | 绑定测试工具，发送会触发工具的 prompt。 | `ctx.resources.tools` 只包含授权工具；工具调用 started/completed；最终回复包含工具结果。 |
-| LA-05 | RAG | 绑定测试知识库，发送命中文档的 prompt。 | `ctx.resources.knowledge_bases` 包含所选知识库；runner 通过授权 API 检索；回复使用检索内容。 |
-| LA-06 | 多模态 | 发送图片输入。 | `ctx.input.contents` 保留图片；支持视觉模型时正常处理，不支持时受控失败。 |
-| LA-07 | fallback / 错误 | 模拟 primary 模型失败或 runner 抛错。 | fallback 或 `run.failed` 行为受控；后续请求不受影响。 |
-| LA-08 | 无输出保护 | 测试 runner 完成但不产出消息。 | 不产生空白成功回复；按受控失败或明确缺陷处理。 |
-
-Rerank、remove-think、文件输入等场景只在本次改动直接涉及时补测，不作为每轮必跑项。
-
-## 7. 外部 Harness Runner Smoke
-
-这些测试用于验证 Claude Code / Codex 这类自管 runtime 能走同一条 Host 协议路径。若本机没有 CLI、登录态或代理配置，标记 BLOCKED，不要伪造 PASS。
-
-Smoke 前应优先保留一层轻量单测或 fixture 测试：provider-native output（Claude stream-json、Codex JSONL、外部 API SSE / JSON）必须能稳定转换成 `AgentRunResult`，未知 native event 只记录诊断，不导致解析器崩溃。WebUI smoke 证明真实链路可用，但不能替代转换层和错误映射测试。
-
-### 7.1 Claude Code runner
-
-步骤：
-
-1. 确认 `claude` CLI 在 LangBot runtime host 上可执行。
-2. 绑定 `plugin:langbot/claude-code-agent/default`。
-3. 使用保守权限模式和确定性 prompt。
-4. 在 Debug Chat 执行一次真实 smoke。
-5. 检查 context / skill / MCP projection 和 host-owned state。
-
-通过条件：
-
-- WebUI 可见回复包含预期 sentinel。
-- context JSON schema 为 `langbot.agent_runner.external_harness_context.v1` 或当前文档声明的等价 schema。
-- context 包含 event、input、delivery、resources、context、state。
-- 如启用 skills / MCP，投影路径和配置可被 Claude Code 读取。
-- `external.session_id` / `external.working_directory` 写入 host-owned state。
-- CLI missing、nonzero exit、timeout、empty output 都转成受控 `run.failed`。
-- resume 到同一 `external.session_id` 时，不并发写入同一 native session；全局锁边界符合 PROTOCOL_V1 §13。
-
-### 7.2 Codex runner
-
-步骤：
-
-1. 确认 `codex` CLI 在 LangBot runtime host 上可执行。
-2. 绑定 `plugin:langbot/codex-agent/default`。
-3. 如需要代理，使用 Agent/runner config 的 `environment-json` 显式传入。
-4. 在 Debug Chat 执行一次真实 smoke。
-5. 检查 JSONL 事件、last message、host-owned state。
-
-通过条件：
-
-- WebUI 可见回复包含预期 sentinel。
-- Codex JSONL 至少包含 thread/session 起始事件、agent message、turn completed。
-- `external.session_id` / `external.working_directory` 写入 host-owned state。
-- timeout/cancel 不遗留 orphan CLI 子进程。
-- CLI missing、nonzero exit、timeout、empty output 都转成受控 `run.failed`。
-- resume 到同一 `thread_id` / `external.session_id` 时，不并发写入同一 native session；全局锁边界符合 PROTOCOL_V1 §13。
-
-### 7.3 API 型外部 runner
-
-Dify、n8n、Coze、DashScope、Langflow、Tbox 等外部服务 runner 不作为每轮必跑项。只有在本次改动触及对应 runner 或凭据已经可用时执行 smoke。
-
-通过条件：
-
-- runner 可选，配置可保存。
-- 请求成功，或外部服务错误被清晰返回。
-- 外部服务凭据缺失时标记 BLOCKED，并记录缺失项。
-
-## 8. 权限与隔离补充
-
-以下优先用单测 / targeted fixture 覆盖，不要求每次通过 UI 人工构造恶意 runner。
-
-| 场景 | 推荐证据 |
-| --- | --- |
-| 未授权模型调用被拒绝 | `plugin/handler.py` run action 权限测试或目标单测。 |
-| 未授权工具调用被拒绝 | `ctx.resources.tools` 与 host action 拒绝日志。 |
-| 未授权知识库检索被拒绝 | `ctx.resources.knowledge_bases` 与 host action 拒绝日志。 |
-| run_id 结束后复用被拒绝 | session registry 注销测试。 |
-| 插件身份不匹配被拒绝 | `caller_plugin_identity` mismatch 测试。 |
-| 绑定插件身份的 run_id 省略 caller identity 被拒绝 | `_validate_run_authorization(..., caller_plugin_identity=None)` 返回错误。 |
-| storage/state scope 越权被拒绝 | state/storage proxy 单测。 |
-
-如果这些单测失败，不能用 WebUI 正常回复替代。
-
-## 9. 证据要求
-
-每轮测试报告至少记录：
-
-- LangBot commit、SDK commit、相关 runner 插件 commit。
-- Pipeline UUID/name、runner id、关键 runner config 摘要。
-- WebUI 截图或 Playwright 操作记录。
-- 后端日志中对应 query id / run id 的关键行。
-- `langbot-skills` case/report 路径。
-- 外部 harness runner 的 context 文件、session id、working directory、CLI 错误摘要。
-- FAIL/BLOCKED 的复现步骤和归属仓库建议。
-
-报告结论必须回答：
-
-- 是否建议继续进入下一阶段测试。
-- 是否存在主聊天路径阻塞。
-- 是否只是凭据 / 外部服务 / 本机 CLI 缺失导致 BLOCKED。
-- 是否需要进入 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md) 的发布级验收。
-
-## 10. 历史高价值记录
-
-历史报告已合并为本指南，不再保留单独文档。后续若需要追溯，优先查看 `langbot-skills/reports/` 下的原始执行报告。
-
-截至 2026-05-29，已有本地 smoke 证明：
-
-- `local-agent` 可以通过 Pipeline Debug Chat 走插件化 `AgentRunOrchestrator` 主链路。
-- Claude Code runner 可以通过同一条 `run(event, binding)` 路径执行。
-- Claude Code runner 可以读取 LangBot event-first context / skill / MCP 投影，并写回 `external.session_id` / `external.working_directory`。
-- Codex runner 可以通过同一条路径执行，并把 Codex `thread_id` 写回 host-owned state。
-
-这些记录只证明本地协议闭环可用，不代表发布级 security hardening 已完成。

docs/agent-runner-pluginization/EVENT_BASED_AGENT.md

@@ -1,92 +0,0 @@
-# Event Based Agent 预留设计
-
-> **future design note**，不是当前分支实现范围。EventGateway、EventRouter、Event subscription/notification 由其他分支实现；本分支只预留 event-first 入口和 envelope/binding models。实现进度见 [PROGRESS.md](./PROGRESS.md)。
->
-> 数据结构唯一定义在 [PROTOCOL_V1.md](./PROTOCOL_V1.md)（runner 可见）与 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)（Host 内部模型）；本文只讲 EBA 语义，不重抄 schema。
-> 与当前 runner 外化分支、后续 Agent Platform / Runtime Control Plane 的边界见 [EXTENSION_SCOPE_MATRIX.md](./EXTENSION_SCOPE_MATRIX.md)。
-
-本文描述未来 EBA 接入时，事件如何进入 LangBot、如何触发 AgentRunner，以及如何复用插件化 agent 基础设施。本阶段不实现完整 EventBus / EventRouter / Platform API，目标是把协议边界设计对，避免当前消息入口继续绑死 Pipeline 和用户文本消息。
-
-## 1. 设计目标
-
-- 消息、撤回、入群、好友申请、定时任务、API 调用都能抽象为 host event。
-- EventRouter 可以根据 event type、bot、workspace、conversation、actor、subject 解析 `AgentBinding`。
-- AgentRunner 通过同一套 orchestrator 被调用。
-- 非消息事件不伪造成用户文本消息。
-- 平台动作执行通过显式 capability / permission / result type 预留，不混入普通文本回复。
-
-## 2. 事件不是消息
-
-`message.received` 只是事件的一种。协议不应假设：一定有用户文本、一定有 conversation history、一定要返回一条聊天消息、actor 一定等于 sender、subject 一定等于当前消息。
-
-| event_type | actor | subject | input |
-| --- | --- | --- | --- |
-| `message.received` | 发消息的人 | 当前消息 | 文本、图片、文件等 |
-| `message.recalled` | 撤回操作者，未知时为系统 | 被撤回消息 | 通常为空 |
-| `group.member_joined` | 新成员或邀请人 | 群/成员关系 | 通常为空 |
-| `friend.request_received` | 申请人 | 好友申请 | 验证消息或申请理由 |
-| `schedule.triggered` | 系统 | 定时任务 | 任务 payload |
-| `api.invoked` | API caller | API request | request payload |
-
-## 3. 稳定事件名
-
-先保留的稳定事件名（作为插件协议的一部分保持稳定）：
-
-- `message.received`
-- `message.recalled`
-- `group.member_joined`
-- `friend.request_received`
-
-平台原始事件名只能进入 `ctx.event.source_event_type` / `raw_ref`，不能成为 `ctx.event.event_type` 的公共契约。
-
-## 4. Event Envelope 与 Binding
-
-- 入口事件用 `AgentEventEnvelope`（HOST_SDK §4.1）承载；顶层字段使用 LangBot 稳定协议名，平台原始事件名和原始 payload 放 `metadata` / `raw_ref`。
-- 触发关系用 `AgentBinding`（HOST_SDK §4.2）表达。EBA 阶段 binding 通过 `event_types`、`scope`、`filters` 决定哪些事件触发当前 bot / channel 绑定的 Agent。
-
-EBA dispatch 基数、Agent 复用和 fan-out 边界以 PROTOCOL_V1 §13 为准；本节只说明 future EventRouter 如何产出当前 v1 主线需要的 binding。
-
-Binding scope 示例：workspace 全局、bot 级、platform channel 级、conversation / group / thread 级、user / actor 级。旧 Pipeline 可迁移为 `message.received` 的临时 binding source，但目标持久配置应是 Agent，不是 Pipeline。
-
-Event Source 可包括：`platform_adapter`（飞书、QQ、微信、Telegram 等）、`webui`、`http_api`、`scheduler`、`system`。EventRouter 不应写死平台 adapter 的类名。
-
-## 5. EventRouter 调用链
-
-```text
-Platform Adapter / WebUI / API
-  -> Event Gateway normalize payload
-  -> EventLog append raw event
-  -> EventRouter resolve one effective AgentBinding
-  -> AgentRunOrchestrator.run(event, binding)
-  -> AgentRunContextBuilder.build(event, binding)
-  -> PluginRuntimeConnector.run_agent()
-  -> AgentRunResult stream
-  -> DeliveryController render / platform action
-```
-
-约束：必须复用现有 orchestrator，不能为 EBA 单独实现另一套 plugin runner 调用协议；非消息事件不能绕过 resource authorization；delivery 和 platform action 走统一权限模型；外部 harness runner 也通过同一套 envelope/binding/context/result 协议接入，不为 Claude Code / Codex / Kimi 单独发明队列协议。observer / fan-out / parallel arbitration 的额外语义仍按 PROTOCOL_V1 §13 处理。
-
-## 6. 平台动作执行
-
-EBA 后 `action.requested`（PROTOCOL_V1 §7.3，当前仅 telemetry 不执行）将用于请求 host 执行平台动作：
-
-```json
-{ "type": "action.requested",
-  "data": { "action": "friend.request.accept",
-            "target": {"platform": "wechat", "request_id": "..."},
-            "reason": "policy matched" } }
-```
-
-Host 必须校验：runner manifest 是否声明 `platform_api` capability、binding 是否授权该 action、actor / bot / workspace 是否允许、是否需要人工审批。EBA 还可能预留 `delivery.requested`（请求投递到某 surface）。
-
-Delivery 方面，event 不一定回复到当前聊天窗口：消息事件通常带 reply target；系统事件可能没有默认 reply target，需要 runner 返回 `action.requested` 或由 binding 的 delivery policy 决定投递位置（`DeliveryContext` 见 PROTOCOL_V1 §5.7）。
-
-## 7. 与 Context 协议的关系
-
-EBA 事件进入 AgentRunner 时仍遵循 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md)：inline 当前事件、大 payload 用 raw/artifact ref、不默认 inline 完整 history、agent 按需通过 API 拉取、Host 保留 EventLog 和权限 guardrail。非消息事件可以被投影进 Transcript，但不能强制伪装为 user message；AgentRunner 根据 event type 自己决定是否纳入模型上下文。
-
-## 8. 未来 EBA 完整落地需要
-
-EventGateway 完整实现、EventRouter 与 BindingResolver 集成、`AgentBinding` 持久模型和 UI、`DeliveryContext` 完整实现、platform action permission model 和执行器、真实平台事件接入。
-
-落地顺序：① 把当前 Pipeline 消息入口适配成 `message.received` event（已完成）→ ② 增加 `AgentBinding` 抽象，先由 current config 生成（已完成）→ ③ context builder 改为从 event + binding 构造（已完成）→ ④ 引入 EventLog / Transcript（已完成）→ ⑤ 增加非消息事件的协议测试，不接真实平台 → ⑥ 接入真实 EventRouter 和 platform action。

docs/agent-runner-pluginization/EXTENSION_SCOPE_MATRIX.md

@@ -1,51 +0,0 @@
-# AgentRunner 外化扩展边界矩阵
-
-本文用于回答一个问题：本分支只做 AgentRunner 外化时，哪些能力已经作为扩展底座完成，哪些只是为后续 EBA / Agent Platform / Runtime Control Plane 预留，后续分支接入时应该走哪个扩展点。
-
-结论：本分支不实现完整 Agent Platform，也不实现完整 EBA。它必须把 runner 外化的 Host / SDK 边界做干净，让后续分支只需要接入持久模型、事件路由或 runtime task，而不需要重写 `AgentRunner Protocol v1`。
-
-调度基数、Agent 复用、插件实例无状态、Pipeline adapter 和 fan-out 边界的单一事实源是 [PROTOCOL_V1.md](./PROTOCOL_V1.md) §13；本矩阵只说明后续能力应该接入哪个扩展点。
-
-## 1. 分支边界
-
-| 范围 | 本分支职责 | 不在本分支做 |
-| --- | --- | --- |
-| AgentRunner Protocol v1 | 定义 Host 调用 runner 的稳定合同：discovery、`AgentRunContext`、result stream、Host pull API、错误和权限边界。 | 不定义 Agent Platform 的产品数据库模型；不定义 runtime task queue。 |
-| Host runner 外化底座 | 提供 `AgentEventEnvelope`、`AgentBinding` 运行投影、`run(event, binding)`、resource authorization、run-scoped session、EventLog / Transcript / Artifact / State。 | 不实现 EventGateway、scheduler、integration provider、Agent 管控面 UI。 |
-| 当前 Pipeline 入口 | 通过 `QueryEntryAdapter` 把旧 Query / Pipeline config 投影成 event + binding，作为迁移期入口。 | 不继续把 Pipeline 当作长期 agent 配置中心。 |
-| 官方 runner 插件 | 作为协议消费者验证 local-agent / 外部 harness runner 能接入 Host 基础设施。 | 不让官方 runner 的内部实现反向决定 Host / SDK 协议形态。 |
-
-## 2. 扩展矩阵
-
-| 能力 | 当前分支状态 | 后续归属 | 后续接入方式 | 禁止事项 |
-| --- | --- | --- | --- | --- |
-| Product `Agent` | 已有运行期 `AgentConfig` / `AgentBinding` 投影；还没有正式持久化产品对象。 | Agent Platform / binding persistence UI。 | 持久 Agent 保存 runner id、runner config、resource/state/delivery policy；运行前投影为 `AgentBinding`。 | 不把持久 Agent schema 加进 SDK 协议；插件实例边界见 PROTOCOL_V1 §13。 |
-| Bot / channel 绑定 Agent | 已有单次运行前的 `AgentBinding` 解析投影；目标调度语义见 PROTOCOL_V1 §13。 | EBA / Agent Platform。 | EventRouter 根据 bot、channel、workspace、conversation、event type 解析有效 `AgentBinding`。 | 不在本矩阵重定义 fan-out / observer 语义；需要时按 §3 新增设计。 |
-| Agent session / run | 当前只有 `run_id` 和 active `AgentRunSessionRegistry`，用于权限校验和生命周期。 | Agent Platform / Runtime Control Plane。 | 如需要可新增持久 `AgentRun` / `AgentSession` / task 表，但执行仍回到 `run(event, binding)` 或 runtime-managed 等价入口。 | 不把持久 session 字段塞进 `AgentRunContext` 顶层；不要求所有 runner 长期持有 LangBot session。 |
-| EventLog / Transcript / Artifact | 已完成 Host-owned store 和 pull API；runner 不直接写 DB。 | 本分支持续维护底座；Agent Platform 可复用。 | 后续 EBA、scheduler、integration、runtime task 都写同一套 EventLog / Transcript / Artifact。 | 不让 runner / sandbox 直接访问 Host DB；不把大 payload 内联进 prompt。 |
-| Host-owned state / storage | 已有 state snapshot、`state.updated` 处理和 State API；storage 作为授权能力保留。 | 本分支持续维护底座；Runtime / Platform 可复用。 | 外部 session id、working directory、checkpoint 等小 JSON 用 state；大对象用 storage / artifact。 | 不把跨轮次状态存在插件实例内；不绕过 run-scoped authorization。 |
-| EventGateway / EventRouter | 只预留 event-first envelope 和 `run(event, binding)` 入口。 | EBA 分支。 | EventGateway 规范化平台/WebUI/API/scheduler 事件；EventRouter 解析一个 binding；调用现有 orchestrator。 | 不为 EBA 新增另一套 runner 调用协议；不把非消息事件伪装成 user message。 |
-| Scheduler / Automation | 不实现。文档中只把 `scheduler` 作为 future event source。 | EBA / Agent Platform。 | 定时任务触发 `schedule.triggered` host event，复用 EventGateway -> EventRouter -> `run(event, binding)`。 | 不直接调用某个 runner 插件；不绕过 EventLog / authorization。 |
-| Integration provider | 不实现。IM platform adapter 仍是当前平台接入系统。 | EBA / Agent Platform。 | OAuth/webhook/outbound provider 应先转成 canonical host event 或 platform action，再交给 AgentRunner。 | 不把 Linear/Slack/GitHub 等 provider 私有 payload 扩散到 runner 协议顶层。 |
-| Platform action / delivery | `action.requested` 已预留但当前仅 telemetry，不执行。`DeliveryContext` 只作为上下文/策略投影。 | EBA / platform action executor。 | 后续 executor 校验 runner capability、binding policy、actor/bot/workspace 权限和审批后执行。 | 不让 runner 直接调用平台 adapter 私有 API；不把平台动作伪装成文本回复副作用。 |
-| Runtime registry / worker / task queue | 不实现。当前 Claude Code / Codex 是本机 subprocess MVP path。 | Runtime Control Plane v2。 | Host 新增 runtime registry、heartbeat、task queue、daemon claim、progress/audit；runner 可选择 runtime-managed 执行模式。 | 不把 heartbeat/task/warm pool 放进 Protocol v1；不让管理插件拥有 runtime/task 事实源。 |
-| Warm pool / reconcile / diagnose | 不实现。 | Runtime Control Plane v2 / deployment layer。 | 作为 task/runtime 的运维能力，围绕 Host-owned runtime/task/audit 表实现。 | 不把 runtime 运维语义写进普通 runner 协议；不把 pod/task 细节泄漏给普通 runner。 |
-| Agent memory | 不实现通用长期记忆产品层；提供 history/state/storage/artifact 基础能力。 | Agent Platform 或具体 runner/plugin。 | 平台 memory 可通过 Host storage/state 或独立产品表实现，runner 通过授权 API 拉取。 | 不在 Host core 内置通用 agentic memory 策略；不默认把 memory 全量 inline 到 context。 |
-| External harness native session | 已支持 external session id / working directory state handoff 和 resource projection。 | 官方 runner 后续增强；Runtime Control Plane v2 可接管执行。 | 一次性 CLI runner 可继续走 `runner.run(ctx)`；长连接/daemon 模式按 external session key 串行 turn，reader 独占 native stream。 | 不把 Claude/Codex native wire 变成 LangBot 协议；全局锁边界见 PROTOCOL_V1 §13。 |
-
-## 3. 后续分支接入规则
-
-后续 EBA、Agent Platform 或 Runtime Control Plane 分支接入时，默认遵守以下规则：
-
-- 新入口只生产或解析 Host 内部模型：`AgentEventEnvelope`、持久 Agent 投影出的 `AgentBinding`、以及必要的 delivery/resource/state policy。
-- runner 调用仍走 `AgentRunOrchestrator.run(event, binding)`，除非 Runtime Control Plane 明确引入 runtime-managed 执行模式；即便如此，runner 可见合同仍应保持 Protocol v1。
-- Host-owned facts 继续写入 EventLog / Transcript / Artifact / State；产品层可以新增更高阶视图，但不能替代这些事实源。
-- 新能力如果需要持久化，优先加 Host-owned 表或 service；不要把事实源藏在插件 storage 或 runner subprocess 内。
-- 新 result type 可以按 Protocol v1 的演进规则增加；不能用入口 adapter 私有字段绕过 schema。
-- 任何 fan-out、observer agent、parallel arbitration、platform action execution 都必须单独定义 delivery、state conflict、approval 和 audit 语义。
-
-## 4. 与 LiteLLM Agent Platform 的关系
-
-这里的 LiteLLM Agent Platform 指面向 agent 产品层的实体拆分：`Agent` 描述可配置 agent，`Session` / `SessionMessage` 描述会话事实，`Automation` 描述自动触发，`IntegrationBinding` 描述外部集成连接，`Memory` 描述长期记忆，`WarmTask` 描述预热/后台任务。这些拆分对 LangBot 后续产品层有参考价值，但不能直接搬进本分支。
-
-LangBot 当前分支的对应目标是更底层的：把 IM/WebUI/API 等入口统一投影到 Host event，把 Agent / binding 配置统一投影到 runner binding，把 runner 能力统一收束到 Protocol v1。完整 Agent Platform 可以在这个底座之上构建，而不应反过来污染本分支的 runner 外化边界。

docs/agent-runner-pluginization/HOST_SDK_INFRASTRUCTURE.md

@@ -1,264 +0,0 @@
-# LangBot Host 与 SDK 基础设施设计
-
-本文档描述 LangBot 作为 agent host 的内部能力与分层架构，以及 Host 内部模型。
-
-- SDK ↔ Host 的协议数据结构（`AgentRunContext`、`AgentRunnerManifest`、`AgentRunResult`、`AgentRunAPIProxy` 等）的**唯一定义在** [PROTOCOL_V1.md](./PROTOCOL_V1.md)；本文只引用，不重抄。
-- 实现进度见 [PROGRESS.md](./PROGRESS.md)。
-- 本文定义的 Host 内部模型（`AgentEventEnvelope`、`AgentBinding`、`AgentRunnerDescriptor`）不属于 SDK 协议字段。
-
-## 1. 目标
-
-LangBot 要转为 agent host，而不是内置 runner 容器：
-
-- 接收 IM、WebUI、API 和未来 EventRouter 产生的事件。
-- 根据事件、bot、workspace、scope 解析应该调用的 Agent / agent binding。
-- 发现、校验和调用插件提供的 AgentRunner。
-- 为每次 run 提供受限资源、状态、存储、上下文引用和生命周期控制。
-- 接收 AgentRunner 返回的事件流，投递到 IM、WebUI 或其他 output surface。
-
-## 2. 非目标
-
-- 不把 Pipeline 当作长期架构中心。
-- 不要求所有 AgentRunner 依赖 LangBot 的上下文管理。
-- 不要求官方 local-agent 的旧行为反向塑造 host 协议。
-- 不在 host 中实现通用 agentic prompt assembler。
-- 不强制 runner 使用 LangBot state / storage；只提供可选、受控的寄宿能力。
-- 不实现 EventGateway：它是 future integration point，由外部 event branch 提供。本分支只定义 host-side envelope/binding models 和 `run(event, binding)` 入口。
-
-## 3. 分层架构
-
-```text
-IM / WebUI / API / EventRouter (future)
-        |
-        v
-Event Gateway (future - external event branch)
-        |
-        v
-AgentBindingResolver
-        |
-        v
-AgentRunOrchestrator
-        |-- AgentRunnerRegistry
-        |-- AgentResourceBuilder
-        |-- AgentContextBuilder
-        |-- AgentRunSessionRegistry
-        |-- PersistentStateStore / EventLogStore / TranscriptStore / ArtifactStore
-        v
-Plugin Runtime / AgentRunner
-        |
-        v
-AgentRunResult stream
-        |
-        v
-Delivery / Renderer / Platform API
-```
-
-目标产品模型、单绑定调度、Agent 复用、插件实例无状态和 fan-out 边界以 [PROTOCOL_V1.md](./PROTOCOL_V1.md) §13 为准。本文只说明 Host 如何把当前入口投影为内部模型。当前 Pipeline 只应接入在 Query entry adapter 位置：它可以继续产生 `message.received` 并投影出临时 `AgentConfig` / `AgentBinding`，但不应再拥有 runner 选择、上下文裁剪和业务 agent 执行的核心语义。EventGateway 由外部 event branch 实现。
-
-## 4. LangBot 侧能力
-
-### 4.1 Event Gateway（Future Integration Point）
-
-> EventGateway 由外部 event branch 实现，不在本分支范围。本分支只预留 event-first 入口和 envelope/binding models。
-
-Event Gateway 将把入口统一成 host event（IM 平台消息、WebUI debug chat、API 触发、后续非消息事件），输出稳定的 `AgentEventEnvelope`（Host 内部模型）：
-
-```python
-class AgentEventEnvelope(BaseModel):
-    event_id: str
-    event_type: str
-    event_time: int | None
-    source: str
-    bot_id: str | None
-    workspace_id: str | None
-    conversation_id: str | None
-    thread_id: str | None
-    actor: ActorRef | None
-    subject: SubjectRef | None
-    input: AgentInput          # 见 PROTOCOL_V1 §5.6
-    delivery: DeliveryContext  # 见 PROTOCOL_V1 §5.7
-    raw_ref: RawEventRef | None
-    metadata: dict[str, Any] = {}
-```
-
-`AgentEventEnvelope` 是 Host 内部入口模型；投影给 runner 的是 `ctx.event`（PROTOCOL_V1 §5.4）。原始平台 payload 存为 raw event 或 artifact ref，不扩散到 runner 协议顶层。
-
-**当前 adapter source**：`QueryEntryAdapter.query_to_event(query)` 从 Query 生成 `AgentEventEnvelope`。
-
-### 4.2 AgentConfig 与 AgentBinding
-
-`AgentConfig` 是迁移期的 Host 内部 Agent 配置投影（不暴露给 SDK）。当前 Query entry adapter 从 Pipeline config 投影出它；未来持久 Agent 也应先投影成这个运行期配置，再由 BindingResolver 结合事件和 scope 解析为 `AgentBinding`。
-
-```python
-class AgentConfig(BaseModel):
-    agent_id: str | None = None
-    runner_id: str
-    runner_config: dict[str, Any] = {}
-    resource_policy: ResourcePolicy = ResourcePolicy()
-    state_policy: StatePolicy = StatePolicy()
-    delivery_policy: DeliveryPolicy = DeliveryPolicy()
-    event_types: list[str] = ["message.received"]
-    enabled: bool = True
-    metadata: dict[str, Any] = {}
-```
-
-`AgentBinding` 是"什么事件调用哪个 AgentRunner、带什么 Agent 配置"的 Host 内部运行投影（不暴露给 SDK）。它是 EventRouter / 当前 QueryEntryAdapter 在一次运行前解析出的有效绑定。
-
-```python
-class AgentBinding(BaseModel):
-    binding_id: str
-    enabled: bool
-    scope: BindingScope
-    event_types: list[str]
-    filters: list[EventFilter] = []   # EBA 阶段使用，见 EVENT_BASED_AGENT
-    runner_id: str
-    runner_config: dict[str, Any]
-    resource_policy: ResourcePolicy
-    state_policy: StatePolicy
-    delivery_policy: DeliveryPolicy
-```
-
-BindingResolver 的基数、fan-out 和冲突处理约束见 PROTOCOL_V1 §13；本节只定义 Host 内部投影形态。
-
-**当前 adapter source**：`QueryEntryAdapter.config_to_agent_config(query, runner_id)`
-先把 current config 投影为迁移期 `AgentConfig`，再由
-`AgentBindingResolver.resolve_one(event, [agent_config])` 解析出唯一
-`AgentBinding`。Pipeline 当前只是迁移期 Agent config source（AI runner config
-→ runner_config、extension preference → resource_policy、output settings →
-delivery_policy），但新设计不再把这些字段命名为 Pipeline 专属概念。
-
-### 4.3 AgentRunnerRegistry
-
-Registry 收集 runner descriptor（来自插件 runtime、开发期本地插件）：
-
-```python
-class AgentRunnerDescriptor(BaseModel):
-    id: str
-    source: Literal["plugin"]
-    label: I18nObject
-    description: I18nObject | None = None
-    protocol_version: str = "1"
-    capabilities: AgentRunnerCapabilities    # 见 PROTOCOL_V1 §4.3
-    permissions: AgentRunnerPermissions      # 见 PROTOCOL_V1 §4.4
-    config_schema: list[DynamicFormItemSchema]
-    plugin: PluginRef | None = None
-```
-
-职责：调用 `plugin_connector.list_agent_runners()` 拉取 runner、校验 manifest（`kind == AgentRunner`、`metadata.name/label` 存在、`protocol_version` 兼容、`spec.*` 类型正确）、输出 descriptor、缓存 discovery 结果并提供 `refresh()`。单个插件 manifest 失败只记 warning，不影响其它 runner。`plugin:author/name/runner` 是稳定 id 格式；插件实例边界见 PROTOCOL_V1 §13。
-
-Host 内置 runner / adapter 不能作为 `AgentRunnerDescriptor.source` 绕过插件
-runtime、`run_id`、`ctx.resources` 和 `AgentRunAPIProxy` 权限链。若需要
-开发期调试 adapter，应放在 Host 内部测试入口，不进入可选 runner 列表。
-
-刷新触发点：插件安装/卸载/升级/重启后；Pipeline metadata 请求时发现缓存为空；可选 TTL（优先保证正确性）。
-
-### 4.4 AgentRunOrchestrator
-
-Orchestrator 是唯一运行入口：
-
-```text
-run(event, binding)
-  -> resolve runner descriptor
-  -> build resources
-  -> build context
-  -> register run session
-  -> call plugin runtime
-  -> normalize result stream
-  -> update state
-  -> unregister run session
-```
-
-它负责：`run_id` 生成和生命周期、timeout/deadline/cancellation、插件异常隔离、result schema 校验和大小限制、`state.updated` 处理、delivery backpressure 和 telemetry。
-
-典型 run 时序：
-
-```text
-QueryEntryAdapter / EventRouter
-  -> AgentRunOrchestrator.run(event, binding)
-  -> AgentRunnerRegistry.resolve(runner_id)
-  -> AgentResourceBuilder.freeze_snapshot(binding, event)
-  -> AgentRunSessionRegistry.register(run_id, runner_id, snapshot)
-  -> AgentContextBuilder.build(event, binding, snapshot)
-  -> PluginRuntimeConnector.run_agent(ctx)
-       -> AgentRunAPIProxy action
-          -> validate active run session + caller identity + snapshot
-          -> Host API / Store
-       <- AgentRunResult stream
-  -> apply state.updated to PersistentStateStore
-  -> write message.completed / artifact.created to Transcript / ArtifactStore
-  -> render delivery or raise RunnerExecutionError
-  -> AgentRunSessionRegistry.unregister(run_id)
-```
-
-`run_from_query()` 保留为 Query entry adapter 入口，但内部转换成 event + binding 后走统一 `run()`。约束：`ChatMessageHandler` 不解析 `plugin:*`、不实例化 wrapper、不知道 runner 组件细节；`PipelineService` 从 registry 读取 metadata，不直接访问插件 runtime；跨请求持久化状态必须走授权 storage / 外部服务。
-
-### 4.5 Resource Authorization（三层裁剪）
-
-LangBot 在每次 run 前生成 `ctx.resources`（PROTOCOL_V1 §6），来自三层约束：
-
-1. runner manifest 声明的 `permissions`（最大能力）。
-2. binding / resource policy 允许的资源范围。
-3. 当前 event / actor / bot / workspace 的实际权限。
-
-这次裁剪结果必须冻结为 run-scoped authorization snapshot，并由
-`AgentRunSessionRegistry` 按 `run_id` 保存。`ctx.resources` 是投影给 runner
-看的同一份授权结果；运行期每个 proxy action 只依据该 snapshot 校验 active
-run session、caller plugin identity、resource id、scope、payload size、rate
-limit 和 deadline。Handler 不应重新执行三层裁剪，否则 build-time 与 runtime
-授权逻辑会漂移。
-
-SDK 侧本地校验只用于开发体验，host 侧 run authorization snapshot 才是安全边界。
-
-资源裁剪应通用，不写死 local-agent。selector 与资源的映射示例：`model-fallback-selector` → primary/fallback LLM、`llm-model-selector` → LLM、`rerank-model-selector` → rerank 模型、`knowledge-base-multi-selector` → 知识库；新增 selector 时在 resource builder 中统一扩展。
-
-执行/文件/skill/MCP 等能力的接入方向：先由 Host 封装成普通 tool，再通过 `ctx.resources.tools` 进入 runner；runner 不应识别或硬编码执行环境 provider。
-
-### 4.6 State / Storage
-
-LangBot 可提供 host-owned state 让 runner 寄宿状态（conversation / actor / subject / runner / binding / workspace state），但**不是强制**。Host 只需提供：授权开关、scope key、get/set/list/delete API（见 PROTOCOL_V1 §8）、持久化 backend、审计和清理策略。外部 agent runtime 可维护自己的 session 和 memory。进程内 state store 只能作为过渡实现，不能作为正式生产语义。
-
-### 4.7 EventLog / Transcript / Artifact（事实源）
-
-- `EventLog`: durable append-only，保存原始事件、系统事件、工具调用、投递结果、错误。
-- `Transcript`: 从 EventLog 投影出的对话视图，用于 UI、审计和按需历史读取。
-- `ArtifactStore`: 保存大文件、多模态输入、工具大结果、平台附件。
-
-三类数据与 working context 的边界、读取约束见 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md)。AgentRunner 可读取这些能力，但不被迫使用 LangBot 作为唯一记忆系统。
-
-### 4.8 Prompt / Instruction Package（占位）
-
-当前 Query 入口不把 preprocessing 后的有效 prompt 放进 adapter metadata。目标形态是 Host 保存或生成一个 run-scoped instruction package，runner 通过 Host API 拉取：
-
-- Host 记录静态绑定 prompt、host hook / user plugin 产生的 instruction fragment、来源和审计信息。
-- `ctx.context.available_apis` 增加 `prompt_get` 能力位表示拉取是否可用。
-- Runner 拉取后仍由自己决定如何与 history、RAG、tool 结果、memory 和当前输入组装最终 prompt。
-- Host 不实现通用 agentic prompt assembler，也不把 Query entry adapter prompt 作为长期业务输入契约。
-
-### 4.9 External harness resource projection
-
-Claude Code、Codex、Kimi Code 等外部 harness runner 可能不直接调用 LangBot 的 model/tool loop，而是把 LangBot 事件和授权资源投影到自己的 harness 执行。Host 侧仍保持统一边界：Host 负责构造 event-first context、资源授权、state/storage、EventLog/Transcript/ArtifactStore 和审计；Host 或 binding policy 决定哪些 MCP server、skill、artifact、history/state 句柄可投影给 runner；runner plugin 把 scoped projection 转成目标 harness 可消费形式；外部 harness 负责自己的 native session、tool loop、压缩、权限模式和 resume。
-
-投影的具体形态（context 文件、skill 目录、MCP config、state pointers）见 AGENT_CONTEXT_PROTOCOL §4.5；Claude Code / Codex 当前实现见 OFFICIAL_RUNNER_PLUGINS §7。发布级隔离要求见 SECURITY_HARDENING。
-
-## 5. SDK 侧协议
-
-SDK 组件入口如下；所有数据结构定义见 PROTOCOL_V1。
-
-```python
-class AgentRunner(BaseComponent):
-    __kind__ = "AgentRunner"
-
-    @classmethod
-    def get_capabilities(cls) -> AgentRunnerCapabilities: ...   # PROTOCOL_V1 §4.3
-
-    @classmethod
-    def get_config_schema(cls) -> list[dict]: ...
-
-    async def run(self, ctx: AgentRunContext) -> AsyncGenerator[AgentRunResult, None]: ...
-    # ctx: PROTOCOL_V1 §5.2 ; AgentRunResult: PROTOCOL_V1 §7
-```
-
-- Manifest / capabilities / permissions / context policy：PROTOCOL_V1 §4。
-- `AgentRunContext`：PROTOCOL_V1 §5.2。`messages` / `bootstrap` 不是协议字段。
-- `AgentRunResult`：PROTOCOL_V1 §7。
-- `AgentRunAPIProxy`：PROTOCOL_V1 §8，是 runner 访问 host 能力的唯一入口，所有请求带 `run_id`。

docs/agent-runner-pluginization/OFFICIAL_RUNNER_PLUGINS.md

@@ -1,151 +0,0 @@
-# 官方 AgentRunner 插件迁移计划
-
-本文档描述内置 `RequestRunner` 迁出 LangBot 后，官方 runner 插件如何组织、迁移和验收。它是 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md) 和 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md) 的下游落地计划，不是 LangBot 宿主协议的设计前提。验收状态见 [PROGRESS.md](./PROGRESS.md)，QA 入口见 [AGENT_RUNNER_QA_GUIDE.md](./AGENT_RUNNER_QA_GUIDE.md)。
-
-官方 `local-agent` 可以外移，也可以重写。设计重点不是保留旧内置 runner 的内部结构，而是验证一个依附 LangBot host 基础设施的官方 agent 能否完整工作。同时，LangBot host 协议必须服务 Claude Code SDK、Codex、Pi Agent SDK、外部 Agent 平台等自管 context/runtime 的 runner，不能被官方插件的实现细节绑死。
-
-## 1. 仓库组织
-
-官方 runner 插件与 LangBot 主仓库、SDK 仓库以不同节奏迭代：LangBot 主仓库只维护宿主协议和调度，SDK 仓库维护 AgentRunner 组件和 runtime 协议，官方 runner 插件承载业务 runner 的具体实现和第三方平台适配。
-
-当前推荐"官方插件可独立发布，必要时共享 SDK helper"。开发期采用本地多目录布局：
-
-```text
-langbot-app/
-  langbot-local-agent/                # plugin:langbot/local-agent/default
-    manifest.yaml
-    components/agent_runner/default.{yaml,py}
-  langbot-agent-runner/               # 外部服务 runner 仓库
-    claude-code-agent/  codex-agent/  dify-agent/  n8n-agent/  ...
-```
-
-后续可聚合进 monorepo，也可继续独立发布——这个选择不影响协议设计。重复逻辑优先沉淀到 SDK 或明确的共享 helper 包，不要把宿主私有结构泄漏给插件。旧 `src/langbot/pkg/provider/runners/*` 只作为历史行为对齐基准；当前未发布分支不提供旧内置 runner 的运行时 fallback。
-
-## 2. 插件命名和 runner id
-
-| 旧 runner | 官方插件 | runner id |
-| --- | --- | --- |
-| `local-agent` | `langbot/local-agent` | `plugin:langbot/local-agent/default` |
-| `dify-service-api` | `langbot/dify-agent` | `plugin:langbot/dify-agent/default` |
-| `n8n-service-api` | `langbot/n8n-agent` | `plugin:langbot/n8n-agent/default` |
-| `coze-api` | `langbot/coze-agent` | `plugin:langbot/coze-agent/default` |
-| - | `langbot/claude-code-agent` | `plugin:langbot/claude-code-agent/default` |
-| - | `langbot/codex-agent` | `plugin:langbot/codex-agent/default` |
-| `dashscope-app-api` | `langbot/dashscope-agent` | `plugin:langbot/dashscope-agent/default` |
-| `langflow-api` | `langbot/langflow-agent` | `plugin:langbot/langflow-agent/default` |
-| `tbox-app-api` | `langbot/tbox-agent` | `plugin:langbot/tbox-agent/default` |
-
-每个插件可后续提供多个 runner，但迁移目标的默认 runner 统一叫 `default`。
-
-## 3. 迁移批次
-
-- **Batch 1（打通协议）**：`local-agent`（能力最完整基准）、`claude-code-agent` / `codex-agent`（外部 code-agent harness 边界）、`dify-agent`（传统 service API runner）。
-- **Batch 2（外部 workflow）**：`n8n-agent`、`langflow-agent`（webhook/workflow 输入输出、timeout、外部 conversation id）。
-- **Batch 3（平台 Agent API）**：`coze-agent`、`dashscope-agent`、`tbox-agent`（平台特有响应格式、引用资料、文件/图片输入）。
-
-## 4. 每个官方插件的组件要求
-
-每个插件至少包含一个 `AgentRunner` 组件，manifest 示例：
-
-```yaml
-apiVersion: langbot/v1
-kind: AgentRunner
-metadata:
-  name: default
-  label: { en_US: Dify Agent, zh_Hans: Dify Agent }
-  description:
-    en_US: Run a Dify application as a LangBot AgentRunner.
-    zh_Hans: 将 Dify 应用作为 LangBot AgentRunner 运行。
-spec:
-  protocol_version: "1"
-  config: []
-  capabilities:        # 字段语义见 PROTOCOL_V1 §4.3
-    streaming: true
-    event_context: true
-    stateful_session: true
-  permissions:         # 字段语义见 PROTOCOL_V1 §4.4
-    storage: ["plugin"]
-  context:             # 字段语义见 PROTOCOL_V1 §4.5
-    supports_history_pull: true
-    owns_compaction: true
-execution:
-  python: { path: ./main.py, attr: DefaultAgentRunner }
-```
-
-## 5. local-agent 插件方向
-
-`local-agent` 是官方插件中能力最完整的消费者，但不是宿主协议的设计中心。它需要证明：一个主要依附 LangBot host 能力的 agent runner 可以通过公开协议完成模型、工具、知识库、状态、history、artifact、上下文压缩和消息投递。
-
-迁移或重写需覆盖旧内置 runner 的用户可见能力：model primary/fallback 选择、prompt、knowledge-bases、rerank-model、rerank-top-k、function calling、streaming、multimodal input、conversation history、monitoring metadata。
-
-责任边界与 Host API 消费方式见 AGENT_CONTEXT_PROTOCOL §8。关键约束：
-
-- 从 `ctx.config` 读取静态绑定 `prompt`，**不**读取 `ctx.adapter.extra["prompt"]`；不消费 Query entry adapter 生成的历史窗口。
-- 通过 `AgentRunAPIProxy.history` 拉取 transcript，而不是依赖 host 每轮强塞历史窗口。
-- `ctx.input.contents` 保留图片/文件等多模态内容；RAG 只替换/插入文本部分，不丢图片/文件。
-- 不能绕过 `ctx.resources` 调用未授权模型、工具或知识库。
-- manifest 声明自管上下文能力（`context.supports_history_pull/search`、`owns_compaction` 等）。
-
-### 5.1 Native Execution / Skills 后续接入
-
-本阶段不把 sandbox/skills 做成 AgentRunner 协议字段。后续 sandbox/skills 分支合并后，命令执行、文件操作、skill、MCP managed process 应先由 Host 封装成 scoped tools，再通过 `ctx.resources.tools` 暴露给 runner。这让 local-agent 只消费授权后的 Host 基础设施，而不是直接持有宿主机执行能力。
-
-## 6. 外部 runner 插件要求
-
-外部平台 runner 迁移遵循：旧配置字段尽量保持同名便于 migration 复制；输出统一转换为 `AgentRunResult`；外部 API timeout 从 runner config 读取；平台 conversation id 存 plugin storage 或 context runtime state，不依赖 LangBot 内置 conversation uuid 私有结构；流式按平台能力声明，没有流式就只发 `message.completed`。
-
-### 6.1 Code-agent harness runner
-
-Claude Code、Codex、Kimi Code 这类 runner 不一定通过 LangBot 的模型/工具 loop 执行，可以依赖自己的 harness，但仍必须遵守 Host 边界：输入来自 `ctx.event` / `ctx.input`，不依赖 Pipeline 私有 `Query`；授权资源投影为 harness 可读的 context 文件、MCP 配置、skill 目录、环境变量或 CLI 参数（投影形态见 AGENT_CONTEXT_PROTOCOL §4.5）；外部 session id / workspace / checkpoint 写入 Host state 或 plugin storage；插件实例边界见 PROTOCOL_V1 §13；CLI / subprocess runner 必须处理 timeout、取消、空输出、非零退出和 stderr 映射；harness 的 permission mode / allow-deny / MCP 配置只是一层执行约束，Host 仍负责调用前的资源授权、路径策略、secret 过滤和审计（发布级要求见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)）。
-
-实现结构应把 provider-native output 解析与 LangBot result stream 组装分开：Claude stream-json、Codex JSONL、Kimi / OpenCode 事件等只在 runner adapter 内解析，输出统一归一为 `AgentRunResult`（`message.completed` / `message.delta`、`state.updated`、`artifact.created`、`run.completed` / `run.failed`）。未知 native event 不应导致 run 崩溃；应记录诊断 metadata 或 warning。新增 harness 时优先补 native fixture -> `AgentRunResult` 的转换测试，再接 WebUI smoke。
-
-并发约束应按外部 session 粒度表达，而不是按 Agent / runner id / 插件实例表达；Agent 复用和全局锁边界见 PROTOCOL_V1 §13。若 runner 使用 `external.session_id` / `thread_id` resume 到同一 native session，且该 harness 不支持并发 turn，runner 应按稳定 external session key 串行写入；一次性 subprocess runner 可以只在单次 `run(ctx)` 内处理，长连接/daemon runner 则应采用 reader 独占 native stream、turn writer 串行写入的结构。
-
-### 6.2 SDK-owned LangBot MCP bridge
-
-外部 harness 不能直接持有进程内的 `plugin_runtime_handler`，因此不能像 `local-agent` 一样直接调用 `AgentRunAPIProxy`。当前轻量方案是由 SDK 提供一层 per-run MCP bridge：
-
-- `AgentRunner.create_external_mcp_bridge(ctx)` 是 runner 父类入口。
-- Bridge 由 `AgentRunAPIProxy` 和 `AgentRunContext` 构造，生命周期只覆盖当前 run。
-- Bridge 暴露 SDK 中显式注解的 `AgentRunExternalTools`，而不是导出全部 SDK action；MCP tool schema 由注解和 Pydantic args model 生成。
-- stdio MCP proxy 只把外部 harness 的 MCP 调用转发回当前 run 的本地 bridge；run 结束后 bridge 关闭。
-
-第一批工具保持很小：当前事件快照、history page、knowledge retrieve、authorized tool call。新增工具必须先进入 SDK-owned annotated surface，再由 MCP adapter 自动投影。
-
-## 7. Claude Code / Codex runner 当前形态
-
-`claude-code-agent` 与 `codex-agent` 是最小可运行 MVP / dev path，用来证明外部 harness runner 可以接入同一套 AgentRunner 协议。本地 smoke 验收记录见 [PROGRESS.md](./PROGRESS.md) 与 [AGENT_RUNNER_QA_GUIDE.md](./AGENT_RUNNER_QA_GUIDE.md)。
-
-MVP 含义：已验证 event-first context、resource projection、result stream 和
-基础 resume state 可以跑通；不表示 Docker 生产部署、发布级执行隔离、
-workspace lifecycle、secret projection、团队级 audit 或 runtime sidecar 已完成。
-
-### 7.1 Claude Code runner
-
-- Runner ID：`plugin:langbot/claude-code-agent/default`，执行方式：本地 Claude Code CLI print mode（默认 `claude -p`）。
-- 默认输出 `message.completed` + `run.completed`；默认权限 `permission-mode=plan`、`max-turns=1`、`disallowedTools=AskUserQuestion`。
-- 投影：写入 `agent-context.json`（schema `langbot.agent_runner.external_harness_context.v1`）和 `LANGBOT_CONTEXT.md`；可把 `skills-json` 投影到 `.claude/skills/<name>/SKILL.md`；可把 `mcp-config-json` 写成每次 run 的 MCP config 经 `--mcp-config` / `--strict-mcp-config` 传入；可通过 `enable-langbot-mcp=true` 启用 SDK-owned per-run LangBot MCP bridge。
-- 状态：Claude Code 返回 `session_id` 时通过 `state.updated` 写回 `external.session_id`；工作目录优先用 config 的 `working-directory`，其次用 Host state 的 `external.working_directory`。
-
-### 7.2 Codex runner
-
-- Runner ID：`plugin:langbot/codex-agent/default`，执行方式：本地 Codex CLI，读取 LangBot event context。
-- Codex `thread_id` 写回 host-owned state；支持 SDK-owned per-run LangBot MCP bridge；需要代理的本地环境可通过 config 的 `environment-json` 显式传递非 secret 环境变量。
-
-### 7.3 当前限制
-
-不是发布级安全边界实现；默认只做本地 CLI 调用，不实现完整执行隔离或 workspace 生命周期；不实现 issue-centric 队列、复杂 workflow engine 或长期任务调度；Docker 环境只能访问容器内 CLI 和凭据；Codex 仅验证协议形态，不代表 Codex 发布级能力或 Kimi runner 已完成。runtime 管控面方向见 [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md)。
-
-## 8. 发布和安装策略
-
-最终 LangBot 安装/升级时需保证官方 runner 插件可用，可选方案：首次启动检测缺失并提示安装；打包发行版预装；migration 前检查插件存在性。当前分支未发布，因此不把历史配置兼容或旧内置 runner fallback 写入运行时协议面。建议顺序：开发阶段用本地路径插件 → 发布前支持 marketplace 安装 → 若发布升级需要迁移历史配置，再在 release gate 中实现一次性 migration 并要求官方插件已可用。
-
-## 9. 验收标准
-
-- 每个目标 runner 都有对应官方 AgentRunner 插件和稳定 runner id；当前配置只使用 `ai.runner.id` + `ai.runner_config[id]`。
-- LangBot 主聊天路径不再通过 `RequestRunner` 执行业务 runner。
-- 官方插件测试覆盖非流式、流式、错误、timeout、配置缺失。
-- `local-agent` 能完成模型 fallback、tool calling、知识库检索、多模态输入、静态绑定 prompt 消费、history API 拉取、rerank。
-- `claude-code-agent` 或同类 code-agent harness runner 能消费 event-first context、投影 scoped resources、保存 external session state，并通过 WebUI Debug Chat smoke。
-- `local-agent` 覆盖旧内置 runner 的用户可见核心能力；代码结构和运行路径不需要相同。

docs/agent-runner-pluginization/PROGRESS.md

@@ -1,163 +0,0 @@
-# Agent Runner 插件化实现进度
-
-本文档跟踪 Agent Runner 插件化的实现状态，便于快速了解当前进度。
-
-> 本文是 agent-runner 插件化**实现状态的唯一事实源**。协议规范见 [PROTOCOL_V1.md](./PROTOCOL_V1.md)，Host 架构见 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)。规范类文档不再各自维护"当前状态/✅"段落，状态一律以本文为准。
-> 本文记录最近一次已知实现 / 验收状态，但不替代对当前 checkout 的代码和 WebUI smoke 复核；复核步骤见 [AGENT_RUNNER_QA_GUIDE.md](./AGENT_RUNNER_QA_GUIDE.md)。
-
-## 总体进度
-
-**当前阶段**: Phase 3.6 已完成，Event-first 基础设施与外部 harness runner smoke 已完成；2026-06-04 已完成协议 / 文档漂移复核，当前未发布分支不保留 PoC 兼容 shim。
-
-| Phase | 描述 | 状态 |
-|-------|------|------|
-| Phase 0 | PoC 验证 | ✅ 完成 |
-| Phase 1 | 核心架构（Registry、Orchestrator、上下文模型） | ✅ 完成 |
-| Phase 2 | 权限、能力声明、资源注入 | ✅ 完成 |
-| Phase 3 | 内置 runner 迁移到插件 | ✅ 完成（7/7） |
-| Phase 3.5 | Event-first 基础设施 | ✅ 完成 |
-| Phase 3.6 | 外部 harness runner 协议 smoke | ✅ 完成（Claude Code MVP） |
-| Phase 4 | EBA 事件支持 | 🔲 未开始（已预留 event-first 入口，EventGateway 由其他分支实现） |
-
----
-
-## 详细状态
-
-### SDK 侧 (`langbot-plugin-sdk`)
-
-| 组件 | 状态 | 备注 |
-|------|------|------|
-| `AgentRunner` 组件 | ✅ | `api/definition/components/agent_runner/runner.py` |
-| `AgentRunContext` | ✅ | `api/entities/builtin/agent_runner/context.py` |
-| `AgentRunResult` | ✅ | `api/entities/builtin/agent_runner/result.py` |
-| `AgentRunnerCapabilities` | ✅ | `api/entities/builtin/agent_runner/capabilities.py` |
-| `AgentRunnerPermissions` | ✅ | `api/entities/builtin/agent_runner/permissions.py` |
-| EBA 事件模型 (Event/Actor/Subject) | ✅ | `api/entities/builtin/agent_runner/event.py` |
-| `LIST_AGENT_RUNNERS` action | ✅ | `runtime/io/handlers/control.py` |
-| `RUN_AGENT` action | ✅ | `runtime/io/handlers/control.py` |
-| `AgentRunAPIProxy` | ✅ | `api/proxies/agent_run_api.py` |
-| Pull API handlers (State/History/Event/Artifact) | ✅ | `runtime/io/handlers/plugin.py` |
-| `caller_plugin_identity` injection | ✅ | Pull API handlers inject caller identity |
-
-### LangBot 侧
-
-| 组件 | 状态 | 备注 |
-|------|------|------|
-| `AgentRunnerRegistry` | ✅ | `pkg/agent/runner/registry.py` |
-| `AgentRunOrchestrator` | ✅ | `pkg/agent/runner/orchestrator.py` - event-first `run(event, binding)` |
-| `AgentRunnerDescriptor` | ✅ | `pkg/agent/runner/descriptor.py` |
-| `AgentResourceBuilder` | ✅ | `pkg/agent/runner/resource_builder.py` |
-| `AgentRunContextBuilder` | ✅ | `pkg/agent/runner/context_builder.py` - event-first context |
-| `AgentResultNormalizer` | ✅ | `pkg/agent/runner/result_normalizer.py` |
-| `ConfigMigration` | ✅ | `pkg/agent/runner/config_migration.py` |
-| `QueryEntryAdapter` | ✅ | `pkg/agent/runner/query_entry_adapter.py` - Query → Event + Binding |
-| `run_from_query()` → `run(event, binding)` | ✅ | Pipeline 路径委托到 event-first path |
-| `ChatMessageHandler` 集成 | ✅ | 使用 orchestrator 替代 wrapper |
-| `PipelineService` 集成 | ✅ | 从 registry 获取 runner metadata |
-| Plugin connector | ✅ | `list_agent_runners()` / `run_agent()` |
-| `EventLogStore` | ✅ | `pkg/agent/runner/event_log_store.py` |
-| `TranscriptStore` | ✅ | `pkg/agent/runner/transcript_store.py` |
-| `ArtifactStore` | ✅ | `pkg/agent/runner/artifact_store.py` |
-| `PersistentStateStore` | ✅ | `pkg/agent/runner/persistent_state_store.py` |
-| History / Event pull APIs | ✅ | Orchestrator + APIProxy |
-| Artifact pull APIs | ✅ | Orchestrator + APIProxy |
-| State pull APIs | ✅ | Orchestrator + APIProxy |
-| `artifact.created` / `state.updated` handling | ✅ | Event-first handlers in orchestrator |
-| Pipeline path host capability coverage | ✅ | EventLog/Transcript/ArtifactStore/PersistentStateStore |
-| External harness state handoff | ✅ | `external.session_id` / `external.working_directory` 写入 PersistentStateStore |
-
-### 官方插件
-
-> 外部服务插件仓库：`langbot-agent-runner/`  
-> 本地 Local Agent 插件仓库：`langbot-local-agent/`
-
-| 插件 | 状态 | 备注 |
-|------|------|------|
-| `local-agent` | ✅ 已完成 | 核心功能：模型、工具、知识库、流式、会话 |
-| `dify-agent` | ✅ 已完成 | 支持 chat/agent/workflow 三种应用类型 |
-| `n8n-agent` | ✅ 已完成 | Webhook 调用，支持 basic/jwt/header 认证 |
-| `coze-agent` | ✅ 已完成 | 多模态输入，思维链处理 |
-| `claude-code-agent` | ✅ MVP smoke 通过 | 本地 Claude Code CLI；context / skill / MCP 投影；host-owned resume state |
-| `dashscope-agent` | ✅ 已完成 | 阿里云百炼，支持 agent/workflow 两种模式 |
-| `langflow-agent` | ✅ 已完成 | SSE 流式，tweaks 配置支持 |
-| `tbox-agent` | ✅ 已完成 | 蚂蚁百宝箱，多模态输入 |
-
-**注意**: LangBot 内置 runner（`pkg/provider/runners/`）已停用，文件顶部添加了 DEPRECATED 注释。
-
-### 本地验收
-
-| 日期 | 范围 | 状态 | 证据 |
-|------|------|------|------|
-| 2026-05-29 | `local-agent` Pipeline Debug Chat | ✅ PASS | `langbot-skills/reports/2026-05-29-17-59-00-462-08-00-pipeline-debug-chat.md` |
-| 2026-05-29 | `claude-code-agent` Pipeline Debug Chat | ✅ PASS | `langbot-skills/reports/2026-05-29-18-03-31-169-08-00-pipeline-debug-chat.md` |
-| 2026-05-29 | Claude Code context / skill / MCP projection | ✅ PASS | `langbot-skills/reports/claude-code-agent-resource-context-20260529.md` |
-| 2026-05-29 | Claude Code resume state | ✅ PASS | `langbot-skills/reports/claude-code-agent-real-workdir-20260529.md` |
-| 2026-05-29 | `codex-agent` Debug Chat + thread_id resume state | ✅ PASS | 见 [AGENT_RUNNER_QA_GUIDE.md](./AGENT_RUNNER_QA_GUIDE.md) §10 / `langbot-skills/reports/` |
-| 2026-06-04 | 协议 / 文档漂移复核 | ✅ PASS | SDK scaffold 与 Protocol v1 对齐；LangBot UI 旧 runner fallback 已移除；run-scoped API 身份校验已收紧。 |
-
----
-
-## 未完成但仍属本分支收尾
-
-以下项目属于本分支收尾工作：
-
-- [x] Smoke / manual validation — `local-agent`、Claude Code MVP、Codex MVP 已通过本地 WebUI smoke
-- [x] Docs final QA — 2026-06-04 已完成当前 Protocol v1 / scaffold / QA 指南漂移复核
-- [ ] Claude Code runner 文档、安装和 marketplace 发布准备
-
----
-
-## 非本分支范围
-
-以下能力由其他分支负责：
-
-| 能力 | 负责分支 | 备注 |
-|------|----------|------|
-| EventGateway implementation | event branch | 完整事件网关、事件路由、持久化管理 |
-| Event subscription / notification | event branch | 事件订阅、推送通知 |
-| BindingResolver persistence UI | 其他模块 | 绑定配置的持久化 UI |
-| Event router integration | event branch | 与 BindingResolver 集成 |
-| Scheduler / background event source | 其他模块 | 定时任务、后台事件源 |
-| Security release hardening | 后续 release gate | 路径隔离、权限边界、secret、MCP/skill 投影策略、资源配额、审计 |
-| Codex / Kimi runner 全量接入 | 后续 runner 插件工作 | Codex MVP 已打通；Codex 发布级能力、Kimi runner 和全量 hardening 仍不扩大到当前协议闭环 |
-| Issue-centric 产品模型 / 异步队列 / workflow engine | 后续产品架构 | 不属于当前 agent-runner plugin 协议闭环 |
-
----
-
-## 待办事项
-
-### 高优先级
-
-- [x] 工具详情 API — SDK `GET_TOOL_DETAIL` action、`AgentRunAPIProxy.get_tool_detail()` 与 Host 侧授权校验已接通
-- [x] Pipeline `run_from_query()` → `run(event, binding)` — 已完成
-- [x] EventLog / Transcript / ArtifactStore / PersistentStateStore — 已完成
-- [x] History / Event / Artifact / State pull APIs — 已完成
-- [x] `caller_plugin_identity` 验证路径 — 已完成；run-scoped session 绑定插件身份时，省略或不匹配 caller identity 都会被拒绝
-
-### 低优先级 / 未来
-
-- [ ] EBA 完整集成 — EventGateway、event subscription、event notification 由其他分支实现
-- [ ] 平台 API 动作执行 — `action.requested` 结果类型存在但未执行
-- [ ] 安全发布级 hardening — 作为生产默认启用前的 release gate，不阻塞当前协议闭环
-
----
-
-## 关键决策记录
-
-| 日期 | 决策 |
-|------|------|
-| 2026-05-10 | Phase 0 集成测试通过，SDK v1 协议验证成功 |
-| 2026-05-13 | Phase 3 完成：所有 7 个官方 runner 插件迁移完成 |
-| 2026-05-23 | Phase 3.5 完成：`run_from_query()` 委托到 event-first `run(event, binding)`，Pipeline path 获得 host capabilities |
-| 2026-05-29 | 本地 `local-agent` 与 `claude-code-agent` 通过 WebUI smoke；Claude Code runner 验证 external harness context 投影和 host-owned resume state |
-| 2026-06-04 | 未发布协议面收敛：移除旧 runner 字段 / 旧本地 runner 名 / PoC schema 兼容分支，SDK 文档和模板对齐当前 `AgentRunContext` |
-
----
-
-## 相关文档
-
-- [README.md](./README.md) — 总体设计与路由
-- [PROTOCOL_V1.md](./PROTOCOL_V1.md) — 协议规范（唯一 schema 事实源）
-- [AGENT_RUNNER_QA_GUIDE.md](./AGENT_RUNNER_QA_GUIDE.md) — Agent Runner QA 指南和下一轮测试入口
-- [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md) — 官方插件仓库计划
-- [SECURITY_HARDENING.md](./SECURITY_HARDENING.md) — 安全发布级 hardening 后续门槛

docs/agent-runner-pluginization/PROTOCOL_V1.md

@@ -1,675 +0,0 @@
-# LangBot AgentRunner Protocol v1
-
-本文档是 LangBot Host 与插件 SDK / Runtime / AgentRunner 之间协议合同的**唯一规范来源（single source of truth）**。
-
-- 本文件描述"稳定接口应是什么"，是 normative spec，不混入实现进度。实现状态见 [PROGRESS.md](./PROGRESS.md)。
-- 本文件之外的任何文档**不得重新定义这里的数据结构**，只能引用，例如"见 PROTOCOL_V1 §4.2"。
-- Host 内部模型（`AgentEventEnvelope`、`AgentBinding`、Descriptor、各 Store）不属于 SDK 协议，定义在 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)。
-
-## 1. 协议目标
-
-Protocol v1 只解决四件事：
-
-- LangBot 如何发现插件提供的 AgentRunner。
-- LangBot 如何把一次事件调用封装成 `AgentRunContext`。
-- AgentRunner 如何以事件流形式返回运行结果。
-- AgentRunner 如何通过受限 API 访问 LangBot host 能力。
-
-Protocol v1 **不定义**：
-
-- LangBot 内部如何持久化 `AgentBinding`（见 HOST_SDK）。
-- AgentRunner 内部如何组装 prompt、压缩历史、管理 memory（见 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md)）。
-- 官方 runner 的具体实现（见 [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md)）。
-- Pipeline 的长期配置模型。
-- 发布级安全 hardening 的完整实现（见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)）。
-
-## 2. 参与方
-
-| 名称 | 职责 |
-| --- | --- |
-| LangBot Host | 事件入口、绑定解析、权限、资源、存储、生命周期、结果投递。 |
-| Plugin Runtime | 加载插件，响应 Host 的 runner discovery 和 run 调用。 |
-| AgentRunner | 插件提供的 agent 执行组件。 |
-| AgentRunAPIProxy | AgentRunner 访问 Host 能力的受限 API。 |
-| AgentBinding | Host 内部的事件到 runner 绑定配置，不直接暴露给 SDK（见 HOST_SDK §4.2）。 |
-
-产品层的 `Agent` 替代旧 Pipeline 承载 agent 配置：bot / IM channel
-绑定一个 Agent，一个 Agent 可以被多个 bot / channel 复用。Host 内部的
-`AgentBinding` 是一次事件运行前解析出的有效绑定，只影响 Host 构造出的
-`ctx.config`、`ctx.resources`、`ctx.context` 和 `ctx.delivery`。SDK 不需要知道
-Agent / binding 的持久化形态。
-
-外部 harness runner（Claude Code、Codex、Kimi Code 等）也是 `AgentRunner`：它们消费 event-first `AgentRunContext`、返回 `AgentRunResult`，并通过 Host 授权的 state/storage/artifact API 保存跨轮次指针。它们内部可以继续使用自己的 session、tool loop、MCP、上下文压缩和权限模型。
-
-## 3. 版本协商
-
-- `AgentRunnerManifest.protocol_version` 声明 runner 实现的协议大版本，当前为 `"1"`。
-- `AgentRuntimeContext.protocol_version`（`ctx.runtime.protocol_version`）声明 Host 下发的协议大版本。
-- Host 发现 runner 时校验 `protocol_version` 兼容性；不兼容的 runner 不进入可用列表，只记 warning。
-- 字段级演进规则：新增可选字段不提升大版本；删除或改语义需要提升大版本。
-- 结果流演进：Host **必须忽略未知 result type 并记录 warning**（除非该 type 明确要求强校验）。新增 result type 不提升大版本。
-
-## 4. Discovery 协议
-
-### 4.1 LIST_AGENT_RUNNERS
-
-Host 调用 Plugin Runtime 获取当前插件暴露的 runner 列表，请求无额外 payload。返回：
-
-```python
-class ListAgentRunnersResponse(BaseModel):
-    runners: list[AgentRunnerManifest]
-```
-
-### 4.2 AgentRunnerManifest
-
-```python
-class AgentRunnerManifest(BaseModel):
-    id: str
-    name: str
-    label: I18nObject
-    description: I18nObject | None = None
-    protocol_version: str = "1"
-    capabilities: AgentRunnerCapabilities
-    permissions: AgentRunnerPermissions
-    context: AgentRunnerContextPolicy
-    config_schema: list[DynamicFormItemSchema] = []
-    metadata: dict[str, Any] = {}
-```
-
-- `id` 必须稳定，格式 `plugin:author/name/runner`。
-- `name` 是插件内 runner 名称，例如 `default`。
-- `config_schema` 只描述绑定配置表单，不代表插件实例状态。
-- `metadata` 只放展示、诊断、非稳定扩展信息。
-
-### 4.3 Capabilities
-
-```python
-class AgentRunnerCapabilities(BaseModel):
-    streaming: bool = False
-    tool_calling: bool = False
-    knowledge_retrieval: bool = False
-    multimodal_input: bool = False
-    skill_authoring: bool = False
-    event_context: bool = True
-    platform_api: bool = False
-    interrupt: bool = False
-    stateful_session: bool = False
-    self_managed_context: bool = True
-```
-
-语义：
-
-- `streaming`: runner 可以返回 `message.delta`。
-- `tool_calling`: runner 可能调用 Host tool API。
-- `knowledge_retrieval`: runner 可能调用 Host knowledge API。
-- `multimodal_input`: runner 可以处理非纯文本 input / artifact。
-- `skill_authoring`: runner 需要 Host 提供 skill facts 以及 skill authoring tools，例如 `activate` / `register_skill`。
-- `event_context`: runner 理解 event-first 输入。
-- `platform_api`: runner 可能请求平台动作。
-- `interrupt`: runner 支持取消或中断。
-- `stateful_session`: runner 可能维护跨 run 会话状态。
-- `self_managed_context`: runner 自己管理 working context，Host 不应默认 inline 历史。
-
-> Capabilities 字段全部是 `bool`。runner 是否寄宿 host-owned state **不在 capabilities 表达**，而通过 `permissions.storage` 声明（见 §4.4），避免出现非 bool 取值。
-
-### 4.4 Permissions
-
-```python
-class AgentRunnerPermissions(BaseModel):
-    models: list[Literal["invoke", "stream", "rerank"]] = []
-    tools: list[Literal["detail", "call"]] = []
-    knowledge_bases: list[Literal["list", "retrieve"]] = []
-    history: list[Literal["page", "search"]] = []
-    events: list[Literal["get", "page"]] = []
-    artifacts: list[Literal["metadata", "read"]] = []
-    storage: list[Literal["plugin", "workspace", "binding"]] = []
-    files: list[Literal["config", "knowledge"]] = []
-    platform_api: list[str] = []
-```
-
-Manifest permissions 是 runner 需要的**最大能力**。实际可用资源还要经过 Host binding policy 和当前 run scope 裁剪（三层裁剪见 HOST_SDK §4.5）。
-
-### 4.5 Context Policy
-
-```python
-class AgentRunnerContextPolicy(BaseModel):
-    supports_history_pull: bool = True
-    supports_history_search: bool = False
-    supports_artifact_pull: bool = True
-    owns_compaction: bool = True
-    wants_static_context_refs: bool = True
-```
-
-Host 不使用该声明给 runner inline 历史窗口。默认原则：
-
-- Host 不得默认 inline 全量历史。
-- Host 只 inline 当前 event / input 和 context handles。
-- Runner 拥有 working context assembly。
-- Runner 可在授权后通过 Host history / event / artifact / state API 拉取更多上下文。
-- 历史窗口策略不属于 Protocol v1 字段，也不属于 Host 通用语义。
-
-context 边界的设计理由见 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md)。
-
-## 5. Run 协议
-
-### 5.1 RUN_AGENT
-
-Host 调用 Runtime：
-
-```python
-class AgentRunRequest(BaseModel):
-    runner_id: str
-    runner_name: str
-    context: AgentRunContext
-```
-
-Runtime 返回 `AgentRunResult` 异步流。底层 transport 可继续用 `plugin_author` / `plugin_name` / `runner_name` 定位组件，但协议语义以 `runner_id` 和 `context` 为准。
-
-### 5.2 AgentRunContext
-
-这是 SDK 看到的**唯一权威 context 定义**。
-
-```python
-class AgentRunContext(BaseModel):
-    run_id: str
-    trigger: AgentTrigger
-    event: AgentEventContext
-    conversation: ConversationContext | None = None
-    actor: ActorContext | None = None
-    subject: SubjectContext | None = None
-    input: AgentInput
-    delivery: DeliveryContext
-    resources: AgentResources
-    context: ContextAccess
-    state: AgentRunState
-    runtime: AgentRuntimeContext
-    config: dict[str, Any] = {}
-    adapter: AdapterContext | None = None
-    metadata: dict[str, Any] = {}
-```
-
-核心约束：
-
-- `event` 是必选字段，Protocol v1 是 event-first。
-- `input` 表示当前事件的主输入，不等于历史消息。
-- `bootstrap` / `messages` **不是协议字段**；Host 不内联历史窗口。
-- `adapter` 只放入口 adapter 的非核心元数据，runner 不应依赖它做长期能力。
-- `config` 是 Agent/runner config，不是插件实例状态。
-
-### 5.3 AgentTrigger
-
-```python
-class AgentTrigger(BaseModel):
-    type: str
-    source: Literal["platform", "webui", "api", "scheduler", "system", "host_adapter"]
-    timestamp: int | None = None
-```
-
-`trigger.type` 应与 `event.event_type` 一致或更粗粒度。例如入口适配器触发消息时：
-
-```json
-{ "type": "message.received", "source": "host_adapter" }
-```
-
-### 5.4 AgentEventContext
-
-```python
-class AgentEventContext(BaseModel):
-    event_id: str
-    event_type: str
-    event_time: int | None = None
-    source: str
-    source_event_type: str | None = None
-    raw_ref: RawEventRef | None = None
-    data: dict[str, Any] = {}
-```
-
-- `event_type` 使用 LangBot 稳定协议名，例如 `message.received`。稳定事件名清单见 [EVENT_BASED_AGENT.md](./EVENT_BASED_AGENT.md)。
-- 平台原始事件名放入 `source_event_type`。
-- 大型原始 payload 必须放入 `raw_ref` 或 artifact，不应直接塞入 `data`。
-
-### 5.5 Conversation / Actor / Subject
-
-```python
-class ConversationContext(BaseModel):
-    conversation_id: str | None = None
-    thread_id: str | None = None
-    launcher_type: str | None = None
-    launcher_id: str | None = None
-    bot_id: str | None = None
-    workspace_id: str | None = None
-
-class ActorContext(BaseModel):
-    actor_type: str
-    actor_id: str | None = None
-    actor_name: str | None = None
-    metadata: dict[str, Any] = {}
-
-class SubjectContext(BaseModel):
-    subject_type: str
-    subject_id: str | None = None
-    data: dict[str, Any] = {}
-```
-
-示例：
-
-- 消息事件：actor 是发消息的人，subject 是当前消息。
-- 入群事件：actor 是新成员或邀请人，subject 是群/成员关系。
-- 定时事件：actor 可以是 system，subject 是 schedule。
-
-### 5.6 AgentInput
-
-```python
-class AgentInput(BaseModel):
-    text: str | None = None
-    contents: list[ContentElement] = []
-    attachments: list[ArtifactRef] = []
-    message_chain: dict[str, Any] | None = None
-```
-
-- 文本、多模态、附件都属于当前 event input。
-- 大文件、图片、音频、工具大结果应以 artifact ref 传递。
-- `message_chain` 是平台兼容字段，不应成为长期稳定依赖。
-
-### 5.7 DeliveryContext
-
-```python
-class DeliveryContext(BaseModel):
-    surface: str
-    reply_target: dict[str, Any] | None = None
-    supports_streaming: bool = False
-    supports_edit: bool = False
-    supports_reaction: bool = False
-    max_message_size: int | None = None
-    platform_capabilities: dict[str, Any] = {}
-```
-
-Runner 可参考 delivery 能力决定返回 `message.delta`、`message.completed` 或 `action.requested`。
-
-### 5.8 ContextAccess
-
-```python
-class ContextAccess(BaseModel):
-    conversation_id: str | None = None
-    thread_id: str | None = None
-    latest_cursor: str | None = None
-    event_seq: int | None = None
-    transcript_seq: int | None = None
-    has_history_before: bool = False
-    inline_policy: InlineContextPolicy
-    available_apis: ContextAPICapabilities
-
-class InlineContextPolicy(BaseModel):
-    mode: Literal["none", "current_event", "recent_tail", "summary_tail"]
-    delivered_count: int = 0
-    source_total_count: int | None = None
-    messages_complete: bool = False
-    reason: str | None = None
-
-class ContextAPICapabilities(BaseModel):
-    history_page: bool = False
-    history_search: bool = False
-    event_get: bool = False
-    event_page: bool = False
-    artifact_metadata: bool = False
-    artifact_read: bool = False
-    state: bool = False
-    storage: bool = False
-```
-
-`ContextAccess` 告诉 runner：Host inline 了什么、没 inline 什么、需要更多上下文时走哪些 API。它是 runner 按需读取上下文的入口说明，不是 Host 的业务上下文编排策略。
-
-### 5.9 AgentRuntimeContext
-
-```python
-class AgentRuntimeContext(BaseModel):
-    host: str = "langbot"
-    protocol_version: str = "1"
-    langbot_version: str | None = None
-    trace_id: str
-    deadline_at: float | None = None
-    locale: str | None = None
-    timezone: str | None = None
-    static_refs: dict[str, StaticContextRef] = {}
-    metadata: dict[str, Any] = {}
-```
-
-`static_refs` 用于 KV cache 友好的静态上下文引用（system policy、tool schema、resource manifest 的 hash/version）。理由见 AGENT_CONTEXT_PROTOCOL §6。
-
-### 5.10 AgentRunState
-
-```python
-class AgentRunState(BaseModel):
-    conversation: dict[str, Any] = {}
-    actor: dict[str, Any] = {}
-    subject: dict[str, Any] = {}
-    runner: dict[str, Any] = {}
-```
-
-State 是可选 host-owned snapshot。Runner 也可以完全自管状态。
-
-## 6. Resources
-
-```python
-class SkillResource(BaseModel):
-    skill_name: str
-    display_name: str | None = None
-    description: str | None = None
-
-class AgentResources(BaseModel):
-    models: list[ModelResource] = []
-    tools: list[ToolResource] = []
-    knowledge_bases: list[KnowledgeBaseResource] = []
-    skills: list[SkillResource] = []
-    files: list[FileResource] = []
-    storage: StorageResource = StorageResource()
-    platform_capabilities: dict[str, Any] = {}
-```
-
-`skills` 只包含本次 run 中 pipeline-visible 的 skill facts，例如 `skill_name`、`display_name` 和 `description`。Host 不把这些 facts 追加到 system prompt，也不把它们编排进工具描述；runner 可以自行决定是否放入 model prompt、转换成 MCP surface，或只在自己的策略层使用。
-
-资源列表是本次 run 的授权结果。History / Event / Artifact 访问通过 permissions、`ctx.context.available_apis` 和 Host 侧 run session 校验控制，不作为可枚举 resource list 暴露。Runner 只能通过 `AgentRunAPIProxy` 访问这些能力。
-
-## 7. Result Stream
-
-### 7.1 AgentRunResult envelope
-
-```python
-JSONValue = str | int | float | bool | None | list["JSONValue"] | dict[str, "JSONValue"]
-
-ResultType = Literal[
-    "message.delta",
-    "message.completed",
-    "tool.call.started",
-    "tool.call.completed",
-    "artifact.created",
-    "state.updated",
-    "action.requested",
-    "run.completed",
-    "run.failed",
-]
-
-class AgentRunResultBase(BaseModel):
-    run_id: str
-    sequence: int | None = None
-    timestamp: int | None = None
-    metadata: dict[str, Any] = {}
-```
-
-`AgentRunResult` 是以下 typed result 的 discriminated union。Host 必须按 `type` 校验对应 `data` 结构；未知 `type` 按 §3 版本演进规则忽略并记录 warning。
-
-### 7.2 稳定 result payloads
-
-```python
-class AssistantMessageChunk(BaseModel):
-    role: Literal["assistant"] = "assistant"
-    content: str | None = None
-    contents: list[ContentElement] = []
-    metadata: dict[str, Any] = {}
-
-class AssistantMessage(BaseModel):
-    role: Literal["assistant"] = "assistant"
-    content: str | None = None
-    contents: list[ContentElement] = []
-    artifacts: list[ArtifactRef] = []
-    metadata: dict[str, Any] = {}
-
-class MessageDeltaData(BaseModel):
-    chunk: AssistantMessageChunk
-
-class MessageCompletedData(BaseModel):
-    message: AssistantMessage
-
-class ToolCallStartedData(BaseModel):
-    tool_call_id: str
-    tool_name: str
-    parameters: dict[str, Any] = {}
-
-class ToolCallCompletedData(BaseModel):
-    tool_call_id: str
-    tool_name: str
-    result_preview: dict[str, Any] | None = None
-    error_code: str | None = None
-    error_message: str | None = None
-
-class ArtifactCreatedData(BaseModel):
-    artifact: ArtifactRef
-
-class StateUpdatedData(BaseModel):
-    scope: Literal["conversation", "actor", "subject", "runner", "binding", "workspace"]
-    key: str
-    value: JSONValue
-
-class ActionRequestedData(BaseModel):
-    action: str
-    target: dict[str, Any]
-    payload: dict[str, Any] = {}
-    idempotency_key: str | None = None
-    approval_hint: str | None = None
-
-class RunCompletedData(BaseModel):
-    finish_reason: str = "stop"
-    message: AssistantMessage | None = None
-    usage: dict[str, Any] = {}
-
-class RunFailedData(BaseModel):
-    code: str
-    message: str
-    retryable: bool = False
-    details: dict[str, Any] = {}
-
-class MessageDeltaResult(AgentRunResultBase):
-    type: Literal["message.delta"]
-    data: MessageDeltaData
-
-class MessageCompletedResult(AgentRunResultBase):
-    type: Literal["message.completed"]
-    data: MessageCompletedData
-
-class ToolCallStartedResult(AgentRunResultBase):
-    type: Literal["tool.call.started"]
-    data: ToolCallStartedData
-
-class ToolCallCompletedResult(AgentRunResultBase):
-    type: Literal["tool.call.completed"]
-    data: ToolCallCompletedData
-
-class ArtifactCreatedResult(AgentRunResultBase):
-    type: Literal["artifact.created"]
-    data: ArtifactCreatedData
-
-class StateUpdatedResult(AgentRunResultBase):
-    type: Literal["state.updated"]
-    data: StateUpdatedData
-
-class ActionRequestedResult(AgentRunResultBase):
-    type: Literal["action.requested"]
-    data: ActionRequestedData
-
-class RunCompletedResult(AgentRunResultBase):
-    type: Literal["run.completed"]
-    data: RunCompletedData
-
-class RunFailedResult(AgentRunResultBase):
-    type: Literal["run.failed"]
-    data: RunFailedData
-
-AgentRunResult = (
-    MessageDeltaResult
-    | MessageCompletedResult
-    | ToolCallStartedResult
-    | ToolCallCompletedResult
-    | ArtifactCreatedResult
-    | StateUpdatedResult
-    | ActionRequestedResult
-    | RunCompletedResult
-    | RunFailedResult
-)
-```
-
-### 7.3 稳定 result types
-
-| type | 说明 | 当前消费 |
-| --- | --- | --- |
-| `message.delta` | 流式消息片段。 | ✅ |
-| `message.completed` | 完整消息。 | ✅ |
-| `tool.call.started` | 工具调用开始的可观测事件。 | telemetry |
-| `tool.call.completed` | 工具调用完成的可观测事件。 | telemetry |
-| `artifact.created` | runner 生成 artifact。 | ✅ |
-| `state.updated` | runner 请求更新 host-owned state。 | ✅ |
-| `action.requested` | runner 请求 Host 执行平台动作。 | **reserved / 仅 telemetry，不执行** |
-| `run.completed` | run 正常结束。 | ✅ |
-| `run.failed` | run 失败。 | ✅ |
-
-`action.requested` 是为 EBA 和 platform API 预留的协议表面：当前阶段 Host 收到后只记 telemetry，**不执行**，runner 作者不应依赖其副作用。执行模型见 EVENT_BASED_AGENT §6。
-
-Host 必须校验 `state.updated` 的 scope、key、value 大小和 JSON 可序列化性。`action.requested` 如果请求未来会产生外部副作用，runner 必须提供稳定 `idempotency_key`；当前阶段 Host 仍只记录 telemetry。
-
-### 7.4 Stream delivery semantics
-
-- Host 按 Runtime stream 顺序消费 result。当前 v1 不定义跨连接 replay，也不承诺 at-least-once；从 Host 视角，收到的 result 最多应用一次。
-- `sequence` 是单个 `run_id` 内的结果序号。in-process / stdio 这类天然有序的在线 stream 可以省略；任何会缓冲、重放、跨进程队列或 runtime-managed task 的 transport 必须提供从 1 开始严格递增的 `sequence`。
-- Host 看到已提供 `sequence` 的 result 时，应按 `(run_id, sequence)` 做重复检测，并在缺号或乱序时记录 warning；除非 transport 明确声明 replay 语义，Host 不应自行等待缺失序号重排用户可见输出。
-- `run.failed.data.retryable` 只表示整次 run 理论上可由上层重试；Protocol v1 不自动重试 run，也不自动重试 proxy action。任何未来自动重试的 side-effecting action 必须依赖 `idempotency_key` 或等价 Host-owned 去重键。
-- History / Event / Transcript cursor 是 opaque token。runner 不得解析 cursor，也不得假设 cursor 在不同 API、conversation、thread 或 retention window 之间可比较；当前实现即使返回数字字符串，也只是实现细节。
-
-### 7.5 示例
-
-```json
-{ "type": "message.delta",     "data": { "chunk": { "role": "assistant", "content": "hel" } } }
-{ "type": "message.completed", "data": { "message": { "role": "assistant", "content": "hello" } } }
-{ "type": "state.updated",     "data": { "scope": "conversation", "key": "external.session_id", "value": "abc" } }
-{ "type": "action.requested",  "data": { "action": "message.edit", "target": {"message_id": "..."}, "payload": {"text": "..."}, "idempotency_key": "run_1:edit:msg_1" } }
-```
-
-## 8. AgentRunAPIProxy
-
-所有 proxy action 必须携带 `run_id`。Host 必须校验：active run session 存在、caller plugin identity 匹配、resource 在本次 `ctx.resources` 中授权、scope 不越界、payload size / rate limit / deadline 合法。
-
-```python
-# Model
-await api.models.invoke(model_id, messages, tools=None, extra_args=None)
-await api.models.stream(model_id, messages, tools=None, extra_args=None)
-await api.models.rerank(model_id, query, documents, top_k=None)
-
-# Tool
-await api.tools.get_detail(tool_name)
-await api.tools.call(tool_name, parameters)
-
-# Knowledge
-await api.knowledge.retrieve(kb_id, query_text, top_k=5, filters=None)
-
-# History（返回 Transcript projection，不返回原始平台 payload）
-await api.history.page(conversation_id=None, before_cursor=None, after_cursor=None,
-                       limit=50, direction="backward", include_artifacts=False)
-await api.history.search(query, filters=None, top_k=10)
-
-# Event（返回稳定 event envelope 或受限 raw ref，不默认返回大 payload）
-await api.events.get(event_id)
-await api.events.page(before_cursor=None, limit=50)
-
-# Artifact（必须支持大小限制、MIME 校验、过期时间和授权范围）
-await api.artifacts.metadata(artifact_id)
-await api.artifacts.read_range(artifact_id, offset=0, length=65536)
-await api.artifacts.open_stream(artifact_id)
-
-# State / Storage
-await api.state.get(scope, key);   await api.state.set(scope, key, value);   await api.state.delete(scope, key)
-await api.storage.get(area, key);  await api.storage.set(area, key, value)
-await api.storage.delete(area, key); await api.storage.list(area, prefix=None)
-
-# Platform（受限能力，默认不开放，需 manifest + binding policy + 用户审批同时允许）
-await api.platform.request_action(action, target, payload)
-```
-
-`state` 与 `storage` 的建议边界：`state` 放小型 JSON（conversation / actor / runner / binding），`storage` 放 blob 或较大数据（插件私有数据、workspace 数据、checkpoint）。
-
-返回数据结构（如 `HistoryPage`、artifact metadata）见 AGENT_CONTEXT_PROTOCOL §4。
-
-## 9. 错误模型
-
-```python
-class AgentAPIError(BaseModel):
-    code: str
-    message: str
-    retryable: bool = False
-    details: dict[str, Any] = {}
-```
-
-| code | 说明 |
-| --- | --- |
-| `unauthorized` | 未授权访问资源或 scope。 |
-| `not_found` | 资源不存在或对当前 runner 不可见。 |
-| `deadline_exceeded` | 超过 run deadline。 |
-| `payload_too_large` | 请求或响应过大。 |
-| `rate_limited` | Host 限流。 |
-| `invalid_argument` | 参数错误。 |
-| `runtime_error` | Host 或下游能力错误。 |
-
-Runner 失败使用 `run.failed`：
-
-```json
-{ "type": "run.failed", "data": { "code": "runner.error", "message": "failed to call external agent", "retryable": false } }
-```
-
-## 10. Timeout 与 Cancellation
-
-- Host 在 `ctx.runtime.deadline_at` 下发总 deadline；SDK proxy 必须用该 deadline 限制单次 action timeout。
-- Host 可以取消 active run；Runtime 应尽力中断 runner。
-- Runner 支持中断时应返回或触发 `run.failed`，code 为 `cancelled`。
-- Host 必须 unregister active run session。
-
-## 11. Security 与 Guardrail（协议层）
-
-Protocol v1 的安全边界在 Host：
-
-- Runner 不能直接访问未授权 model/tool/kb/history/artifact/storage。
-- SDK 本地校验只提升开发体验，不能替代 Host 校验。
-- 所有 resource id 对 runner 来说都是 opaque。
-- 默认只能访问当前 conversation / thread 的 history；跨会话、workspace 级访问必须额外授权。
-- 大 payload 必须 artifact 化。
-- Host 必须记录 run_id、runner_id、action、resource、scope、result。
-
-Host 不负责业务编排：不拼接全量历史、不替 runner 做 prompt assembly、不内置 agent memory / tool loop / 上下文压缩策略。这些由官方或第三方 AgentRunner 插件实现。
-
-对外部 harness runner，Host 在调用前完成 binding/resource policy 裁剪、路径策略、secret 过滤和审计；runner plugin 把授权后的 context/resource projection 适配为目标 harness 的形式；harness 的 native permission mode、allowed/disallowed tools 只是额外执行约束，不能替代 Host 授权。
-
-> 发布级路径隔离、MCP allowlist、secret redaction、配额、workspace 清理等**不属于** v1 协议闭环，是生产默认启用前的 release gate，见 [SECURITY_HARDENING.md](./SECURITY_HARDENING.md)。
-
-## 12. Pipeline Adapter 边界
-
-Pipeline 是当前入口 adapter，不是协议中心。目标产品模型中 Agent 会替代
-Pipeline 承载 runner config、resource policy 和 delivery policy；当前 Query
-entry adapter 只是迁移桥。它负责：
-
-- 从 `Query` 构造 `AgentEventContext` 和临时 `AgentBinding`（见 HOST_SDK §4.2）。
-- 从当前 Agent/runner config 构造 `ctx.config`。
-- 将 Query-only 字段放入 `ctx.adapter`，例如 filtered params 放 `ctx.adapter.extra["params"]`。
-
-约束：
-
-- adapter **不**定义历史窗口、prompt 组装或 agentic context 策略。
-- `ctx.adapter.extra` 只允许承载一次性、JSON-safe、入口相关的非核心元数据，例如 `params`；不得承载 `prompt`、history window、RAG 结果、tool schema 或授权资源。
-- 静态绑定 prompt 属于 `ctx.config.prompt`。preprocessing / hook 后的动态有效指令不通过 `ctx.adapter.extra` 主动推送；后续如需要保留这类能力，应通过 Host prompt/instruction pull API 暴露（占位见 HOST_SDK §4.8）。
-- 新 runner 不应长期依赖 `adapter`，应只依赖 event-first context 和 Host API。
-
-## 13. 已确认约束
-
-- v1 / EBA 主线是 `one event -> one AgentBinding -> one run_id -> one runner`。
-- 一个 bot / IM channel 在同一时间只绑定一个负责 agentic 处理的 Agent；一个 Agent 可以被多个 bot / channel 复用。
-- 如果配置层出现多个匹配 AgentBinding，BindingResolver 必须按明确规则选出一个或拒绝配置，不应默认 fan-out。
-- observer agent、多 runner fan-out、并行裁决、result 合并等能力需要单独设计 delivery、state、platform action 和 audit 语义，不属于当前 v1 契约。
-- `AgentRunnerDescriptor.source` 只允许 `plugin`；Host 内置 adapter 不能作为 runner source 绕过插件/runtime/proxy 权限链。
-- `ctx.resources` 与 proxy action 校验必须来自同一个 run authorization snapshot；runtime handler 不应重新执行资源裁剪。
-- v1 不要求 Agent、AgentRunner 插件实例或 runner id 全局串行。多个 bot / channel 可复用同一个 Agent；并发隔离依赖 `run_id`、binding、conversation / thread scope 和 Host authorization snapshot。
-- 对 `stateful_session` runner，若外部 runtime 不支持同一 session 并发 turn，串行化粒度应是稳定的 external session key（例如 workspace / bot / binding / runner / conversation / thread / external session id），不是 Agent 或插件实例全局锁。
-- 外部 harness runner 当前是 MVP / dev path，证明协议可接入，不代表发布级安全边界或 Docker 生产可用性完成。
-
-## 14. 开放问题
-
-- `AgentBinding` 是否需要进入 SDK 文档作为只读诊断信息，还是完全 Host 内部。
-- `TranscriptItem` 的最小字段集如何定义。
-- ArtifactStore 是否复用现有 BinaryStorage backend，还是引入独立实体。
-- State 与 Storage 的边界是否需要更强类型。
-- `platform_api` action 的审批模型如何表达。
-- Host 侧 scoped MCP / skill / workspace projection 是否需要从 runner config 上移为一等 resource projection API。

docs/agent-runner-pluginization/README.md

@@ -1,153 +0,0 @@
-# Agent Runner 插件化文档入口
-
-本文档是 agent-runner 插件化工作的路由页。具体设计拆到独立文档中维护，避免把 LangBot 宿主架构、SDK 协议、上下文管理、EBA 预留和官方 runner 迁移混在同一份 README 里。
-
-## 背景与问题
-
-旧 runner 路径主要围绕 Pipeline / Query 和 `pkg/provider/runners` 内置实现展开，扩展外部 agent runtime 时容易把 runner 选择、上下文裁剪、资源授权和消息投递绑在同一条聊天链路里。这个分支要把 LangBot 收敛成 Agent Host：Host 负责事件、绑定、授权、事实源和结果投递；AgentRunner 作为插件或外部 harness 消费统一协议并自主管理 prompt / history / memory。
-
-## 文档维护原则（单一事实源）
-
-- **协议数据结构（schema）唯一定义在 [PROTOCOL_V1.md](./PROTOCOL_V1.md)。** 其他文档不得重抄 schema，只能引用，例如"见 PROTOCOL_V1 §4.2"。
-- **实现状态唯一记录在 [PROGRESS.md](./PROGRESS.md)。** 规范类文档不维护"当前状态/✅"段落。
-- Host 内部模型（`AgentEventEnvelope`、`AgentBinding`、Descriptor、各 Store）定义在 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)，不属于 SDK 协议。
-- 其余专题文档只讲"为什么/边界/怎么用"，避免重复叙述。
-
-## 本分支目标
-
-**本分支目标：AgentRunner 外化 / 插件化基础设施**
-
-本分支只做 LangBot 作为 Agent Host 的基础能力建设，为后续用 `Agent`
-替代 Pipeline 承载 agent 配置打底：
-
-- LangBot 与 SDK 的稳定协议合同（Protocol v1）
-- Host-side `AgentEventEnvelope` / `AgentBinding` 模型
-- `run(event, binding)` event-first 入口
-- `QueryEntryAdapter`：Query → AgentEventEnvelope + AgentBinding
-- EventLog / Transcript / ArtifactStore / PersistentStateStore
-- History / Event / Artifact / State pull APIs
-- SDK runtime forwarding pull APIs + `caller_plugin_identity` 验证路径
-
-## 本分支不实现
-
-以下能力由其他分支负责，本分支只预留 integration point：
-
-- **EventGateway**：完整事件网关实现、事件路由、事件持久化管理
-- **Event subscription / Event notification**：事件订阅、推送通知
-- **BindingResolver persistence UI**：绑定配置的持久化 UI 和 event router 集成（如由其他模块负责）
-- **Scheduler / Background event source**：定时任务、后台事件源
-- **Runtime control plane v2**：runtime registry、heartbeat、task queue、daemon claim、progress/cancel 和 runtime audit
-
-EventGateway 在本文档中描述为 **future integration point**，由外部 event branch 提供。本分支只定义 host-side envelope/binding models 和 `run(event, binding)` orchestrator 入口。
-
-本分支与后续 EBA / Agent Platform / Runtime Control Plane 的扩展边界见 [EXTENSION_SCOPE_MATRIX.md](./EXTENSION_SCOPE_MATRIX.md)。
-
-## 目标产品模型
-
-未来产品层应把 `Agent` 理解为 Pipeline 的替代物：原先 bot 绑定 Pipeline，Pipeline 携带 agent/provider/RAG/tool 等配置；后续应改为 bot 或 IM channel 绑定一个 Agent，Agent 携带 runner id、runner config、resource/state/delivery policy 等 agent 配置。
-
-调度基数、Agent 复用、插件实例无状态、Pipeline adapter 和 fan-out 边界的规范来源是 [PROTOCOL_V1.md](./PROTOCOL_V1.md) §13；README 不复写这些约束。
-
-## 当前入口关系
-
-**当前 Pipeline 是入口 adapter，不再是 agent runner 设计核心。**
-
-主入口仍可由 Pipeline 触发，但内部已转换成 event-first path：`run_from_query()` 经 `QueryEntryAdapter` 把 `Query` 转换为 `AgentEventEnvelope` + `AgentBinding`，再委托到统一的 `run(event, binding, ...)`。Pipeline path 因此获得了 event-first host capabilities（EventLog / Transcript / ArtifactStore / PersistentStateStore 写入，History / Event / Artifact / State pull API 可用）。
-
-详细实现进度、已验收能力和未完成收尾见 [PROGRESS.md](./PROGRESS.md)。
-
-## 术语表
-
-| 术语 | 含义 |
-| --- | --- |
-| Protocol v1 | Host 调用 AgentRunner 的 runner 可见合同：discovery、`AgentRunContext`、result stream、Host pull API 和错误模型。 |
-| Agent | 目标产品层配置对象，保存 runner id、runner config 和资源/状态/投递策略；不等于插件实例。 |
-| AgentConfig | Host 内部迁移期配置投影，由当前 Pipeline config 或未来持久 Agent 生成。 |
-| AgentBinding / binding | Host 在一次事件运行前解析出的有效绑定，决定调用哪个 runner 以及带什么策略。 |
-| envelope | Host 内部事件封装，即 `AgentEventEnvelope`；runner 看到的是由它投影出的 `ctx.event`。 |
-| descriptor / manifest | runner discovery 的能力和配置描述；manifest 来自插件，descriptor 是 Host 校验后的注册表视图。 |
-| EBA | Event Based Agent，未来把消息、撤回、入群、定时任务等都统一成 host event 的接入方向。 |
-| harness runner | Claude Code、Codex 等已有自身 session / tool loop / MCP / 压缩机制的外部 runtime adapter。 |
-| projection | Host 把内部事实源、授权资源或配置裁剪成 runner / harness 可消费视图的过程。 |
-| `static_refs` | KV cache 友好的静态上下文引用，例如 system policy、tool schema、resource manifest 的 hash/version。 |
-| Runtime Control Plane | v2 Host 能力层，负责 runtime registry、heartbeat、task queue、progress/cancel 和 audit；不是 Protocol v1 主线。 |
-
-## 设计文档
-
-| 文档 | 关注点 |
-| --- | --- |
-| [PROTOCOL_V1.md](./PROTOCOL_V1.md) | **🔒 唯一 schema 事实源**。LangBot Host 与 SDK / Runtime / AgentRunner 的协议合同：版本协商、discovery、run context、result stream、proxy actions、错误和 adapter 边界。 |
-| [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md) | LangBot 宿主能力与分层架构、Host 内部模型（`AgentEventEnvelope` / `AgentBinding` / Descriptor / 各 Store）、runner 发现、绑定、资源授权、状态、存储、生命周期和调用链。 |
-| [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md) | Agent-owned context 方向：事件到来时 LangBot 传什么，agent 如何按需拉取更多历史 / artifact / state，以及如何支持 KV cache 友好的上下文管理。 |
-| [EXTENSION_SCOPE_MATRIX.md](./EXTENSION_SCOPE_MATRIX.md) | AgentRunner 外化与后续 EBA / Agent Platform / Runtime Control Plane 的扩展边界矩阵，说明哪些是本分支底座、哪些由后续分支接入。 |
-| [EVENT_BASED_AGENT.md](./EVENT_BASED_AGENT.md) | EBA 预留：事件模型、事件来源、触发绑定、非消息事件如何复用 AgentRunner 调度。**标注为 future design note**。 |
-| [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md) | Agent Platform v2 / runtime 管控面预留：Host 新增 runtime registry、heartbeat、task queue、daemon 执行和 audit；管理插件构建在这些 Host 能力之上。**标注为 future design note**。 |
-| [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md) | 官方 runner 插件迁移，包括 local-agent 和外部 runner。它是下游落地计划，不是 LangBot 基础能力设计的前置约束。 |
-| [AGENT_RUNNER_QA_GUIDE.md](./AGENT_RUNNER_QA_GUIDE.md) | Agent Runner QA 指南：保留最高价值测试路径，指导 agent 开展下一轮 WebUI / runner smoke 验证。 |
-| [SECURITY_HARDENING.md](./SECURITY_HARDENING.md) | 安全发布级 hardening 的后续发布门槛：路径隔离、权限边界、secret、资源配额、MCP / skill 投影和审计。 |
-| [PROGRESS.md](./PROGRESS.md) | **🔒 唯一状态事实源**。当前实现进度、已验收能力、未完成收尾和非本分支范围。 |
-
-## 工作拆分
-
-### 1. LangBot + SDK 基础设施
-
-目标是把 LangBot 从内置 runner 执行器变成 agent host：
-
-- LangBot 与 SDK 的稳定协议合同
-- runner manifest / descriptor / registry
-- Agent / binding 配置解析
-- run orchestration 和生命周期管理
-- resource authorization 与 `run_id` 级权限校验
-- host-owned state / storage / event log / transcript / artifact 能力
-- SDK `AgentRunner`、`AgentRunContext`、`AgentRunResult`、`AgentRunAPIProxy`
-
-协议合同详见 [PROTOCOL_V1.md](./PROTOCOL_V1.md)。
-
-详见 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)。
-
-### 2. Agent-owned context
-
-LangBot 不应成为最终 agentic context manager。它应提供事实源、默认上下文引用和按需读取 API；agent 或其背后的 runtime 负责历史剪裁、摘要、召回和 KV cache 策略。
-
-Host 不定义通用历史窗口字段或策略；runner 通过 Host pull API 按需拉取历史并自行管理 working context。
-
-详见 [AGENT_CONTEXT_PROTOCOL.md](./AGENT_CONTEXT_PROTOCOL.md)。
-
-### 3. Event Based Agent（Future）
-
-消息只是事件的一种。后续 `message.received`、`message.recalled`、`group.member_joined`、`friend.request_received` 等事件都应能通过统一事件 envelope 触发 AgentRunner。
-
-EBA dispatch 的基数和 fan-out 边界仍以 PROTOCOL_V1 §13 为准；本文档只列出本分支为 EBA 预留的入口点。
-
-**本分支不实现 EBA 完整能力，只预留：**
-- event-first envelope (`AgentEventEnvelope`)
-- AgentBinding model
-- `run(event, binding)` 入口
-- QueryEntryAdapter（当前 AgentEventEnvelope / AgentBinding 的 Query entry adapter source）
-
-详见 [EVENT_BASED_AGENT.md](./EVENT_BASED_AGENT.md)。
-
-### 4. 官方 runner 插件
-
-官方 `local-agent` 和外部 runner 迁移是下游工作。它们需要依附 LangBot 提供的宿主能力，但不应反过来决定宿主协议。
-
-`local-agent` 可以外移，也可以重写。验收重点是它能完整消费 LangBot 的模型、工具、知识库、存储、事件、history API 和 result stream，而不是保留旧内置 runner 的内部结构。
-
-详见 [OFFICIAL_RUNNER_PLUGINS.md](./OFFICIAL_RUNNER_PLUGINS.md)。
-
-### 5. Runtime Control Plane v2（Future）
-
-当前 AgentRunner v1 主线只负责 `event -> binding -> runner.run(ctx) -> result stream`。
-后续 Agent Platform v2 可以在 Host 侧新增 runtime registry、heartbeat、task queue、daemon claim、progress/cancel 和 runtime audit。
-
-在这些 Host 能力之上，可以构建独立 agent 管控面插件；插件负责 UI、策略和编排体验，runtime/task 的事实源仍由 Host 持有。
-
-详见 [RUNTIME_CONTROL_PLANE_V2.md](./RUNTIME_CONTROL_PLANE_V2.md)。
-
-## 约束事实源
-
-本分支已确认约束不在 README 重写：
-
-- Runner 可见协议、result stream 和调度边界见 [PROTOCOL_V1.md](./PROTOCOL_V1.md)。
-- Host 内部 `AgentConfig` / `AgentBinding` 投影见 [HOST_SDK_INFRASTRUCTURE.md](./HOST_SDK_INFRASTRUCTURE.md)。
-- 后续 EBA / Agent Platform / Runtime Control Plane 接入边界见 [EXTENSION_SCOPE_MATRIX.md](./EXTENSION_SCOPE_MATRIX.md)。

docs/agent-runner-pluginization/RUNTIME_CONTROL_PLANE_V2.md

@@ -1,228 +0,0 @@
-# Agent Runtime Control Plane V2
-
-本文档记录后续 Agent Platform / runtime 管控面的设计方向。它是当前讨论中的 **v2 文档**，但这里的 v2 指 Host capability layer / runtime control plane，不是 `AgentRunner Protocol v2`，也不属于当前 AgentRunner Protocol v1 插件化主线的交付范围。
-
-> **future design note**。协议数据结构见 [PROTOCOL_V1.md](./PROTOCOL_V1.md)，实现进度见 [PROGRESS.md](./PROGRESS.md)。本文只讲 v2 管控面方向，不重抄 schema。
-> 与当前 runner 外化分支、EBA 和 Agent Platform 的边界见 [EXTENSION_SCOPE_MATRIX.md](./EXTENSION_SCOPE_MATRIX.md)。
-
-## 1. 结论
-
-当前主线应继续收口 AgentRunner v1：
-
-```text
-message/event -> binding -> runner.run(ctx) -> result stream
-```
-
-Runtime Control Plane v2 在 Host 侧新增 runtime control plane：
-
-```text
-event -> task -> runtime selection -> daemon claim -> execute -> progress/audit/result
-```
-
-在 Runtime Control Plane v2 之上，可以构建独立的 agent 管控面插件。插件负责 UI、策略和编排体验；runtime、task、heartbeat、audit 的事实源必须属于 LangBot Host，而不是插件私有 storage。
-
-## 2. 不影响 v1 主线
-
-v2 不应改变 AgentRunner v1 的基本契约：
-
-- 现有 `local-agent`、Dify、n8n、Coze 等 runner 仍可按 v1 直接执行。
-- 当前 Claude Code / Codex MVP runner 可以继续作为本机 subprocess 开发路径。
-- Host v1 已有的 event-first context、resource authorization、history / event / artifact / state / storage pull APIs 继续保留。
-- Pipeline 仍只是当前入口 adapter，不参与 v2 runtime 管控面的设计中心。
-
-v2 只是在 Host 上新增一层可选能力。需要管控面的 runner 或管理插件可以声明使用它；不需要的 runner 不受影响。
-
-## 3. 当前 Host 能力与缺口
-
-当前 Host 已经具备 v2 的基础设施底座：
-
-- `AgentEventEnvelope` / `AgentBinding`
-- run-scoped resource authorization
-- EventLog / Transcript / ArtifactStore / PersistentStateStore
-- History / Event / Artifact / State / Storage pull APIs
-- AgentRunner result stream 和受控错误回流
-- Agent/runner config 与 host-owned state
-
-这些能力足够支持一次 `runner.run(ctx)` 内的安全执行，但不足以承担完整 runtime 管控面。
-
-v2 还需要 Host 新增：
-
-- runtime registry：runtime id、所属 workspace、所在机器、provider 能力、状态。
-- capability discovery：`claude` / `codex` / 其它 CLI 是否存在、版本、登录状态、执行隔离能力。
-- heartbeat / liveness：runtime 在线、忙闲、最后心跳、可用 slot。
-- task queue：enqueue、claim、start、progress、complete、fail、cancel。
-- workspace mapping：LangBot workspace / project 如何映射到 runtime 上的真实目录、仓库或挂载。
-- secret / env projection：按授权向 runtime 投影 token、代理、MCP 配置、技能和环境变量。
-- runtime audit：stdout、stderr、事件流、产物、失败原因、执行耗时、使用量。
-- control API / UI：选择 runtime、测试 runtime、查看状态、下线、取消任务、重试任务。
-
-## 4. 角色边界
-
-### 4.1 LangBot Host
-
-Host 是事实源和控制面内核：
-
-- 保存 runtime / task / heartbeat / audit 状态。
-- 做权限校验、资源裁剪、workspace 绑定和审计。
-- 决定任务是否可被某 runtime claim。
-- 将执行结果统一回写到 event / transcript / artifact / state。
-
-Host 不应内置具体 agent CLI 的复杂业务逻辑，也不应把某个官方 runner 的特殊行为提升为通用协议。
-
-### 4.2 Agent 管控面插件
-
-管理插件是 v2 control plane 的产品化管理层：
-
-- 展示 runtime、agent、task、进度、失败、审计。
-- 提供策略配置，例如默认 runtime、provider 偏好、并发限制、重试策略。
-- 触发 runtime 测试、任务取消、任务重试、手动分配。
-
-管理插件不应把 runtime/task 的事实源放进自己的 plugin storage。它应该调用 Host v2 API。
-
-### 4.3 Runtime daemon / worker
-
-Runtime daemon 负责真实执行：
-
-- 在所在机器上检测 CLI 和版本。
-- 管理工作目录、仓库、挂载、临时文件和进程。
-- 从 Host claim 任务，执行后上报 progress / complete / fail。
-- 将 stdout / stderr / artifacts / session id 回流 Host。
-
-Claude Code、Codex、OpenCode、Gemini CLI 等 provider 适配逻辑应主要落在 daemon / worker 或 provider adapter 中。
-
-## 5. 部署形态
-
-### 5.1 uv / local embedded
-
-用户用 `uv` 或源码直接启动 LangBot 时，LangBot 进程所在机器就是 runtime host。
-
-这种模式下可以直接检测用户主机上的 `claude`、`codex` 等 CLI，也可以直接 subprocess 执行。它适合个人开发和本地 smoke，但不应作为团队级管控面的唯一形态。
-
-### 5.2 Docker embedded
-
-用户用 Docker 启动 LangBot 时，runtime host 是容器，不是宿主机。
-
-因此：
-
-- 只能检测容器内的 `claude`、`codex`。
-- 只能使用容器内的 HOME、PATH、凭据和挂载目录。
-- 如果镜像未安装 CLI，或未挂载认证文件 / workspace，CLI runner 会不可用。
-
-Docker embedded 可以作为高级部署选项，但需要用户显式安装 CLI、挂载工作区和凭据。Host 不应假设 Docker 容器能自动访问宿主机 CLI。
-
-### 5.3 Sidecar daemon
-
-推荐的 v2 形态是 sidecar daemon：
-
-```text
-LangBot Host (Docker or server)
-  <-> Runtime daemon on user host / worker host
-        -> claude / codex / other CLI
-```
-
-这种模式下，LangBot 可以跑在 Docker 内，runtime daemon 跑在宿主机或独立 worker 机器上。daemon 负责检测本机 CLI、持有本机凭据和工作区访问能力。
-
-### 5.4 Remote runtime
-
-团队场景可以使用远端 runtime：
-
-- 开发机、构建机、云主机或专用 worker。
-- 多个 workspace 可绑定不同 runtime。
-- Host 只通过 registry / task queue / heartbeat / audit 进行管理。
-
-### 5.5 API-only agent
-
-Dify、n8n、Coze、DashScope 等 API 型 runner 不依赖本地 CLI。它们可以继续按 v1 直接执行，也可以在未来按需要接入 v2 task/audit。
-
-## 6. 与 Claude Code / Codex MVP runner 的关系
-
-当前 Claude Code / Codex runner 是 v1 runner：
-
-```text
-runner.run(ctx) -> subprocess("claude" / "codex")
-```
-
-它们适合验证 Host context 投影、state resume、result stream 和基础 CLI 调用，但有明确限制：
-
-- 命令只在 LangBot runtime host 上执行。
-- Docker 环境只能看到容器内 CLI。
-- 没有 runtime registry、heartbeat、task queue、cancel、workspace lifecycle。
-- 不提供发布级执行隔离、secret projection、团队级 audit。
-
-v2 不需要删除这些 runner。它们可以继续作为 dev / MVP 路径存在。未来若接入管控面，可以增加 runtime-managed 执行模式：
-
-```text
-runner binding -> Host task -> runtime daemon -> provider CLI -> Host result
-```
-
-## 7. 最小 v2 API 草案
-
-以下仅记录能力边界，不代表最终 API 命名。
-
-Runtime：
-
-- `runtime.register`
-- `runtime.heartbeat`
-- `runtime.list`
-- `runtime.get`
-- `runtime.disable`
-- `runtime.capabilities.report`
-- `runtime.capabilities.probe`
-
-Task：
-
-- `task.enqueue`
-- `task.claim`
-- `task.start`
-- `task.progress`
-- `task.complete`
-- `task.fail`
-- `task.cancel`
-- `task.retry`
-
-Workspace：
-
-- `runtime.workspace.bind`
-- `runtime.workspace.unbind`
-- `runtime.workspace.resolve`
-
-Audit / artifacts：
-
-- `task.log.append`
-- `task.artifact.create`
-- `task.events.page`
-
-这些 API 应由 Host 提供，并受 workspace、runtime、binding、actor 和 plugin identity 约束。
-
-## 8. 管控面插件可以构建的能力
-
-基于 v2 Host 能力，可以实现一个类似 Multica 的 agent 管控面插件。这里的“类似 Multica”只指产品形态：一个集中页面管理 agent profile、runtime 连接、任务队列、执行进度、失败诊断和审计视图；不是引入新的 runner 协议或把 runtime/task 事实源交给插件。
-
-- runtime 列表、在线状态、CLI 能力、版本、认证状态。
-- agent profile 与 runtime/provider 绑定。
-- 任务看板、任务详情、进度流、失败原因、重试和取消。
-- workspace 到 runtime 目录 / 仓库的映射管理。
-- provider capability 测试，例如 Claude Code / Codex 是否可执行。
-- 审计视图：输入、输出、工具、artifact、stdout/stderr、session id。
-- 策略配置：并发、队列、默认 runtime、fallback runtime、权限模式。
-
-该插件应该是 Host v2 的消费者，而不是 Host v2 的替代品。
-
-## 9. 设计原则
-
-- v1 先稳定，v2 可选叠加。
-- Host 保存事实源，插件提供管理体验。
-- Runtime daemon 执行具体 CLI 和本机资源访问。
-- Docker 不假设拥有宿主机 CLI；需要 sidecar 或显式挂载。
-- Pipeline 不进入 v2 控制面中心。
-- 直接 subprocess runner 可保留，但只作为 local/dev/MVP 路径。
-- 发布级能力必须经过 Host 权限、审计和资源边界。
-
-## 10. 待定问题
-
-- runtime daemon 与 Host 的认证模型：workspace token、device token、还是 scoped PAT。
-- task 与 AgentRunner binding 的映射关系：由 binding 直接 enqueue，还是由独立 task policy 决定。
-- runtime capability schema 的稳定字段：provider、version、login status、execution isolation、workspace access、slot。
-- secret projection 的边界：Host 存储、用户本机存储、或外部 secret manager。
-- Docker compose 是否提供官方 sidecar daemon 示例。
-- v2 UI 是核心前端的一部分，还是完全由管理插件提供。

docs/agent-runner-pluginization/SECURITY_HARDENING.md

@@ -1,74 +0,0 @@
-# Agent Runner Security Hardening
-
-本文档记录 agent-runner 插件化进入生产发布前需要补齐的安全与稳定加固项。
-
-## 状态
-
-**当前结论：暂不塞进本阶段 agent-runner plugin 协议闭环。**
-
-本阶段目标是验证 LangBot 可以通过统一的 `run(event, binding)` 协议接入 `local-agent` 与外部 harness runner（如 Claude Code runner），并能传递事件、上下文、资源句柄、状态和结果流。
-
-安全发布级 hardening 是后续 release gate，不应阻塞当前协议闭环，但必须作为进入生产默认启用前的验收条件。
-
-> **硬规则**：能执行代码 / 访问工作目录的外部 harness runner（Claude Code、Codex、Kimi Code 等）在本文 Release Gate Checklist 完成前，**不得在生产环境默认启用**。本地 smoke 通过不等于可生产默认开启。
-
-## 责任边界
-
-### LangBot Host 负责
-
-- 资源授权：决定某个 `run_id` / binding 可以访问哪些模型、RAG、MCP、skill、artifact、history、state。
-- 资源投影：只把授权后的资源句柄、配置片段或上下文文件传给 runner。
-- 路径策略：限制 workspace / context file / artifact 的允许路径和清理策略。
-- Secret 策略：过滤环境变量、配置、日志和 transcript 中的 secret。
-- 运行约束：配置超时、轮次、并发、配额、输出大小和取消路径。
-- 审计记录：记录事件、绑定、资源授权、runner 调用、外部 harness session id、关键错误和结果摘要。
-
-### Runner Plugin 负责
-
-- 遵守 LangBot 下发的 Agent/runner config、授权资源和运行约束。
-- 将 LangBot 资源投影成目标 runner 可消费的形式，例如 context 文件、MCP 配置、环境变量或 CLI 参数。
-- 遵守 PROTOCOL_V1 §13 的插件实例边界；需要跨轮次保存的外部 session id / working directory 等状态应写入 host-owned state。
-- 对外部进程做最小必要封装，包括命令参数构造、超时、取消、输出解析和错误映射。
-
-### 外部 Harness 负责
-
-Claude Code、Codex、Kimi Code 等外部 harness 可以继续使用自身的权限模型、工具 allow / deny 规则、MCP 加载策略、session/resume 机制和沙箱能力。
-
-但外部 harness 不是 LangBot 的唯一安全边界。LangBot 仍必须在调用前完成资源授权、路径限制、secret 过滤和审计记录。
-
-## 当前 MVP 可接受边界
-
-当前阶段可以接受以下前提：
-
-- 由可信管理员配置 runner binding。
-- 工作目录和 context 输出目录为显式配置或 host 生成路径。
-- 外部 runner 默认使用保守权限，例如 plan / no-write 模式或禁用高风险工具。
-- 通过 timeout、max turns、输出长度和进程取消降低失控风险。
-- 通过 host-owned state 保存 `external.session_id`、`external.working_directory` 等 resume 所需指针。
-
-这些前提足够做本地 E2E 与协议验收，不等同于生产发布完成。
-
-## Release Gate Checklist
-
-进入生产默认启用前，需要补齐：
-
-- Path isolation：workspace allowlist、路径规范化、防止 `..` 逃逸、context / artifact 清理。
-- Permission boundary：runner 能力声明、binding 级资源授权、run 级权限校验。
-- Secret handling：环境变量白名单、配置脱敏、日志和 transcript redaction。
-- MCP policy：MCP server allowlist、scoped token、tool allow / deny、危险工具审计。
-- Skill projection policy：skill 来源验证、只读投影、版本和摘要记录。
-- Process isolation：进程组管理、取消、超时、CPU / 内存 / 输出配额。
-- State lifecycle：session id、workspace、artifact 的过期、清理、迁移和审计。
-- Audit first-class：事件、资源授权、外部命令、session id、结果摘要可追踪。
-- UI / Admin control：管理员能看到 runner 权限、风险提示、资源绑定和禁用入口。
-- Test matrix：路径逃逸、secret 泄漏、权限拒绝、timeout、取消、MCP deny、resume、cleanup、audit 完整性。
-
-## 非当前范围
-
-以下内容不属于本阶段协议闭环：
-
-- 完整异步队列与 issue-centric 产品模型。
-- 复杂 workflow engine。
-- Codex / Kimi runner 全量接入。
-- EBA 分支完整迁移和联调。
-- 发布级安全 hardening 的完整实现。

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "langbot"
-version = "4.10.0-beta.2"
+version = "4.10.0"
 description = "Production-grade platform for building agentic IM bots"
 readme = "README.md"
 license-files = ["LICENSE"]
@@ -8,7 +8,7 @@ requires-python = ">=3.11,<4.0"
 dependencies = [
     "aiocqhttp>=1.4.4",
     "aiofiles>=24.1.0",
-    "aiohttp>=3.13.4",
+    "aiohttp>=3.14.0",
     "aioshutil>=1.5",
     "aiosqlite>=0.21.0",
     "anthropic>=0.51.0",
@@ -31,27 +31,27 @@ dependencies = [
     "psutil>=7.0.0",
     "pycryptodome>=3.22.0",
     "pydantic>2.0",
-    "pyjwt>=2.10.1",
+    "pyjwt>=2.12.0",
     "python-telegram-bot>=22.0",
     "pyyaml>=6.0.2",
     "qq-botpy-rc>=1.2.1.6",
     "qrcode>=7.4",
     "quart>=0.20.0",
     "quart-cors>=0.8.0",
-    "requests>=2.32.3",
+    "requests>=2.33.0",
     "slack-sdk>=3.35.0",
     "alembic>=1.15.0",
     "sqlalchemy[asyncio]>=2.0.40",
     "sqlmodel>=0.0.24",
     "telegramify-markdown>=0.5.1",
     "tiktoken>=0.9.0",
-    "urllib3>=2.4.0",
+    "urllib3>=2.7.0",
     "websockets>=15.0.1",
     "python-socks>=2.7.1", # dingtalk missing dependency
-    "pip>=25.1.1",
+    "pip>=26.1",
     "ruff>=0.11.9",
     "pre-commit>=4.2.0",
-    "uv>=0.11.6",
+    "uv>=0.11.15",
     "mypy>=1.16.0",
     "PyPDF2>=3.0.1",
     "python-docx>=1.1.0",
@@ -62,15 +62,15 @@ dependencies = [
     "ebooklib>=0.18",
     "html2text>=2024.2.26",
     "langchain>=0.2.0",
-    "langchain-core>=1.2.28",
-    "langsmith>=0.7.31",
-    "python-multipart>=0.0.26",
-    "Mako>=1.3.11",
+    "langchain-core>=1.3.3",
+    "langsmith>=0.8.0",
+    "python-multipart>=0.0.27",
+    "Mako>=1.3.12",
     "langchain-text-splitters>=1.1.2",
     "chromadb>=1.0.0,<2.0.0",
     "qdrant-client (>=1.15.1,<2.0.0)",
     "pyseekdb==1.1.0.post3",
-    "langbot-plugin==0.4.0",
+    "langbot-plugin==0.4.1",
     "asyncpg>=0.30.0",
     "line-bot-sdk>=3.19.0",
     "matrix-nio>=0.25.2",
@@ -105,9 +105,6 @@ classifiers = [
     "Topic :: Communications :: Chat",
 ]
 
-[tool.uv.sources]
-langbot-plugin = { path = "../langbot-plugin-sdk", editable = true }
-
 [project.urls]
 Homepage = "https://langbot.app"
 Documentation = "https://docs.langbot.app"

src/langbot/__init__.py

@@ -1,3 +1,3 @@
 """LangBot - Production-grade platform for building agentic IM bots"""
 
-__version__ = '4.10.0-beta.2'
+__version__ = '4.10.0'

src/langbot/libs/deerflow_api/__init__.py

@@ -0,0 +1,5 @@
+from .client import AsyncDeerFlowClient
+from .errors import DeerFlowAPIError
+from . import stream_utils
+
+__all__ = ['AsyncDeerFlowClient', 'DeerFlowAPIError', 'stream_utils']

src/langbot/libs/deerflow_api/client.py

@@ -0,0 +1,204 @@
+"""DeerFlow LangGraph HTTP API 客户端
+
+参考 astrbot 的 deerflow_api_client 实现，使用 httpx 适配 LangBot 风格。
+"""
+
+from __future__ import annotations
+
+import codecs
+import json
+import typing
+from collections.abc import AsyncGenerator
+
+import httpx
+
+from .errors import DeerFlowAPIError
+
+
+SSE_MAX_BUFFER_CHARS = 1_048_576
+
+
+def _normalize_sse_newlines(text: str) -> str:
+    """规范化 CRLF/CR 为 LF，确保 SSE 块分割稳定"""
+    return text.replace('\r\n', '\n').replace('\r', '\n')
+
+
+def _parse_sse_data_lines(data_lines: list[str]) -> typing.Any:
+    raw_data = '\n'.join(data_lines)
+    try:
+        return json.loads(raw_data)
+    except json.JSONDecodeError:
+        # 某些 LangGraph 兼容服务端会在单个 SSE 事件中用多个 data 行
+        # 发送多段 JSON 片段（例如 tuple payload）
+        parsed_lines: list[typing.Any] = []
+        can_parse_all = True
+        for line in data_lines:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                parsed_lines.append(json.loads(line))
+            except json.JSONDecodeError:
+                can_parse_all = False
+                break
+        if can_parse_all and parsed_lines:
+            return parsed_lines[0] if len(parsed_lines) == 1 else parsed_lines
+        return raw_data
+
+
+def _parse_sse_block(block: str) -> dict[str, typing.Any] | None:
+    if not block.strip():
+        return None
+
+    event_name = 'message'
+    data_lines: list[str] = []
+    for line in block.splitlines():
+        if line.startswith('event:'):
+            event_name = line[6:].strip()
+        elif line.startswith('data:'):
+            data_lines.append(line[5:].lstrip())
+
+    if not data_lines:
+        return None
+    return {'event': event_name, 'data': _parse_sse_data_lines(data_lines)}
+
+
+class AsyncDeerFlowClient:
+    """DeerFlow LangGraph HTTP API 客户端"""
+
+    api_base: str
+    headers: dict[str, str]
+
+    def __init__(
+        self,
+        api_base: str = 'http://127.0.0.1:2026',
+        api_key: str = '',
+        auth_header: str = '',
+    ) -> None:
+        self.api_base = api_base.rstrip('/')
+        self.headers: dict[str, str] = {}
+        if auth_header:
+            self.headers['Authorization'] = auth_header
+        elif api_key:
+            self.headers['Authorization'] = f'Bearer {api_key}'
+
+    async def create_thread(self, timeout: float = 20) -> dict[str, typing.Any]:
+        """创建一个新的 LangGraph thread
+
+        Returns:
+            包含 thread_id 等信息的字典
+        """
+        url = f'{self.api_base}/api/langgraph/threads'
+        payload = {'metadata': {}}
+
+        async with httpx.AsyncClient(
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            response = await client.post(
+                url,
+                headers=self.headers,
+                json=payload,
+            )
+            if response.status_code not in (200, 201):
+                raise DeerFlowAPIError(
+                    operation='create thread',
+                    status=response.status_code,
+                    body=response.text,
+                    url=url,
+                )
+            return response.json()
+
+    async def delete_thread(self, thread_id: str, timeout: float = 20) -> None:
+        """删除指定 thread"""
+        url = f'{self.api_base}/api/threads/{thread_id}'
+
+        async with httpx.AsyncClient(
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            response = await client.delete(url, headers=self.headers)
+            if response.status_code not in (200, 202, 204, 404):
+                raise DeerFlowAPIError(
+                    operation='delete thread',
+                    status=response.status_code,
+                    body=response.text,
+                    url=url,
+                    thread_id=thread_id,
+                )
+
+    async def stream_run(
+        self,
+        thread_id: str,
+        payload: dict[str, typing.Any],
+        timeout: float = 120,
+    ) -> AsyncGenerator[dict[str, typing.Any], None]:
+        """运行一次 LangGraph stream 请求，逐事件 yield
+
+        Yields:
+            事件字典 {'event': event_name, 'data': parsed_data}
+        """
+        url = f'{self.api_base}/api/langgraph/threads/{thread_id}/runs/stream'
+
+        # 流式请求使用单独的 read timeout 控制
+        stream_timeout = httpx.Timeout(
+            connect=min(timeout, 30),
+            read=timeout,
+            write=timeout,
+            pool=timeout,
+        )
+
+        async with httpx.AsyncClient(
+            trust_env=True,
+            timeout=stream_timeout,
+        ) as client:
+            async with client.stream(
+                'POST',
+                url,
+                headers={
+                    **self.headers,
+                    'Accept': 'text/event-stream',
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            ) as resp:
+                if resp.status_code != 200:
+                    body = await resp.aread()
+                    raise DeerFlowAPIError(
+                        operation='runs/stream request',
+                        status=resp.status_code,
+                        body=body.decode('utf-8', errors='replace'),
+                        url=url,
+                        thread_id=thread_id,
+                    )
+
+                decoder = codecs.getincrementaldecoder('utf-8')('replace')
+                buffer = ''
+
+                async for chunk in resp.aiter_bytes(8192):
+                    buffer += _normalize_sse_newlines(decoder.decode(chunk))
+
+                    while '\n\n' in buffer:
+                        block, buffer = buffer.split('\n\n', 1)
+                        parsed = _parse_sse_block(block)
+                        if parsed is not None:
+                            yield parsed
+
+                    if len(buffer) > SSE_MAX_BUFFER_CHARS:
+                        # 缓冲区过大，强制 flush
+                        parsed = _parse_sse_block(buffer)
+                        if parsed is not None:
+                            yield parsed
+                        buffer = ''
+
+                # flush 剩余内容
+                buffer += _normalize_sse_newlines(decoder.decode(b'', final=True))
+                while '\n\n' in buffer:
+                    block, buffer = buffer.split('\n\n', 1)
+                    parsed = _parse_sse_block(block)
+                    if parsed is not None:
+                        yield parsed
+                if buffer.strip():
+                    parsed = _parse_sse_block(buffer)
+                    if parsed is not None:
+                        yield parsed

src/langbot/libs/deerflow_api/errors.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+
+class DeerFlowAPIError(Exception):
+    """DeerFlow API 请求失败"""
+
+    def __init__(
+        self,
+        *,
+        operation: str = '',
+        status: int = 0,
+        body: str = '',
+        url: str = '',
+        thread_id: str | None = None,
+        message: str = '',
+    ) -> None:
+        self.operation = operation
+        self.status = status
+        self.body = body
+        self.url = url
+        self.thread_id = thread_id
+
+        if message:
+            super().__init__(message)
+            return
+
+        msg = f'DeerFlow {operation} failed: status={status}, url={url}, body={body}'
+        if thread_id is not None:
+            msg = f'DeerFlow {operation} failed: thread_id={thread_id}, status={status}, url={url}, body={body}'
+        super().__init__(msg)

src/langbot/libs/deerflow_api/stream_utils.py

@@ -0,0 +1,212 @@
+"""DeerFlow LangGraph 流式响应解析工具
+
+参考 astrbot 实现的 deerflow_stream_utils。
+"""
+
+from __future__ import annotations
+
+import typing
+from collections.abc import Iterable
+
+
+def extract_text(content: typing.Any) -> str:
+    """从消息 content 中提取纯文本"""
+    if isinstance(content, str):
+        return content
+    if isinstance(content, dict):
+        if isinstance(content.get('text'), str):
+            return content['text']
+        if 'content' in content:
+            return extract_text(content.get('content'))
+        if 'kwargs' in content and isinstance(content['kwargs'], dict):
+            return extract_text(content['kwargs'].get('content'))
+    if isinstance(content, list):
+        parts: list[str] = []
+        for item in content:
+            if isinstance(item, str):
+                parts.append(item)
+            elif isinstance(item, dict):
+                item_type = item.get('type')
+                if item_type == 'text' and isinstance(item.get('text'), str):
+                    parts.append(item['text'])
+                elif 'content' in item:
+                    parts.append(extract_text(item['content']))
+        return '\n'.join([p for p in parts if p]).strip()
+    return str(content) if content is not None else ''
+
+
+def extract_messages_from_values_data(data: typing.Any) -> list[typing.Any]:
+    """从 values 事件中提取 messages 列表"""
+    candidates: list[typing.Any] = []
+    if isinstance(data, dict):
+        candidates.append(data)
+        if isinstance(data.get('values'), dict):
+            candidates.append(data['values'])
+    elif isinstance(data, list):
+        candidates.extend([x for x in data if isinstance(x, dict)])
+
+    for item in candidates:
+        messages = item.get('messages')
+        if isinstance(messages, list):
+            return messages
+    return []
+
+
+def is_ai_message(message: dict[str, typing.Any]) -> bool:
+    """判断是否为 AI/assistant 消息"""
+    role = str(message.get('role', '')).lower()
+    if role in {'assistant', 'ai'}:
+        return True
+
+    msg_type = str(message.get('type', '')).lower()
+    if msg_type in {'ai', 'assistant', 'aimessage', 'aimessagechunk'}:
+        return True
+    if 'ai' in msg_type and all(token not in msg_type for token in ('human', 'tool', 'system')):
+        return True
+    return False
+
+
+def extract_latest_ai_text(messages: Iterable[typing.Any]) -> str:
+    """获取最近一条 AI 消息的文本内容"""
+    if isinstance(messages, (list, tuple)):
+        iterable = reversed(messages)
+    else:
+        iterable = reversed(list(messages))
+
+    for msg in iterable:
+        if not isinstance(msg, dict):
+            continue
+        if is_ai_message(msg):
+            text = extract_text(msg.get('content'))
+            if text:
+                return text
+    return ''
+
+
+def extract_latest_ai_message(messages: Iterable[typing.Any]) -> dict[str, typing.Any] | None:
+    """获取最近一条 AI 消息对象"""
+    if isinstance(messages, (list, tuple)):
+        iterable = reversed(messages)
+    else:
+        iterable = reversed(list(messages))
+
+    for msg in iterable:
+        if not isinstance(msg, dict):
+            continue
+        if is_ai_message(msg):
+            return msg
+    return None
+
+
+def is_clarification_tool_message(message: dict[str, typing.Any]) -> bool:
+    """判断是否为澄清问题工具消息"""
+    msg_type = str(message.get('type', '')).lower()
+    tool_name = str(message.get('name', '')).lower()
+    return msg_type == 'tool' and tool_name == 'ask_clarification'
+
+
+def extract_latest_clarification_text(messages: Iterable[typing.Any]) -> str:
+    """提取最近的澄清问题文本"""
+    if isinstance(messages, (list, tuple)):
+        iterable = reversed(messages)
+    else:
+        iterable = reversed(list(messages))
+
+    for msg in iterable:
+        if not isinstance(msg, dict):
+            continue
+        if is_clarification_tool_message(msg):
+            text = extract_text(msg.get('content'))
+            if text:
+                return text
+    return ''
+
+
+def get_message_id(message: typing.Any) -> str:
+    """提取消息 ID"""
+    if not isinstance(message, dict):
+        return ''
+    msg_id = message.get('id')
+    return msg_id if isinstance(msg_id, str) else ''
+
+
+def extract_event_message_obj(data: typing.Any) -> dict[str, typing.Any] | None:
+    """从事件 data 中提取消息对象"""
+    msg_obj = data
+    if isinstance(data, (list, tuple)) and data:
+        msg_obj = data[0]
+    if isinstance(msg_obj, dict) and isinstance(msg_obj.get('data'), dict):
+        msg_obj = msg_obj['data']
+    return msg_obj if isinstance(msg_obj, dict) else None
+
+
+def extract_ai_delta_from_event_data(data: typing.Any) -> str:
+    """从 messages-tuple 事件中提取 AI delta 文本"""
+    msg_obj = extract_event_message_obj(data)
+    if not msg_obj:
+        return ''
+    if is_ai_message(msg_obj):
+        return extract_text(msg_obj.get('content'))
+    return ''
+
+
+def extract_clarification_from_event_data(data: typing.Any) -> str:
+    """从事件中提取澄清问题"""
+    msg_obj = extract_event_message_obj(data)
+    if not msg_obj:
+        return ''
+    if is_clarification_tool_message(msg_obj):
+        return extract_text(msg_obj.get('content'))
+    return ''
+
+
+def _iter_custom_event_items(data: typing.Any) -> list[dict[str, typing.Any]]:
+    items: list[dict[str, typing.Any]] = []
+    if isinstance(data, dict):
+        return [data]
+    if isinstance(data, list):
+        for item in data:
+            if isinstance(item, dict):
+                items.append(item)
+            elif isinstance(item, (list, tuple)):
+                for nested in item:
+                    if isinstance(nested, dict):
+                        items.append(nested)
+    return items
+
+
+def extract_task_failures_from_custom_event(data: typing.Any) -> list[str]:
+    """从 custom 事件中提取子任务失败信息"""
+    failures: list[str] = []
+    for item in _iter_custom_event_items(data):
+        event_type = str(item.get('type', '')).lower()
+        if event_type not in {'task_failed', 'task_timed_out'}:
+            continue
+
+        task_id = str(item.get('task_id', '')).strip()
+        error_text = extract_text(item.get('error')).strip()
+        if task_id and error_text:
+            failures.append(f'{task_id}: {error_text}')
+        elif error_text:
+            failures.append(error_text)
+        elif task_id:
+            failures.append(f'{task_id}: unknown error')
+        else:
+            failures.append('unknown task failure')
+    return failures
+
+
+def build_task_failure_summary(failures: list[str]) -> str:
+    """构建任务失败摘要"""
+    if not failures:
+        return ''
+    deduped: list[str] = []
+    seen: set[str] = set()
+    for failure in failures:
+        if failure not in seen:
+            seen.add(failure)
+            deduped.append(failure)
+    if len(deduped) == 1:
+        return f'DeerFlow subtask failed: {deduped[0]}'
+    joined = '\n'.join([f'- {item}' for item in deduped[:5]])
+    return f'DeerFlow subtasks failed:\n{joined}'

src/langbot/libs/weknora_api/__init__.py

@@ -0,0 +1,4 @@
+from .client import AsyncWeKnoraClient
+from .errors import WeKnoraAPIError
+
+__all__ = ['AsyncWeKnoraClient', 'WeKnoraAPIError']

src/langbot/libs/weknora_api/client.py

@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import httpx
+import typing
+import json
+
+from .errors import WeKnoraAPIError
+
+
+class AsyncWeKnoraClient:
+    """WeKnora API 客户端"""
+
+    api_key: str
+    base_url: str
+
+    def __init__(
+        self,
+        api_key: str,
+        base_url: str = 'http://localhost:80/api/v1',
+    ) -> None:
+        self.api_key = api_key
+        self.base_url = base_url
+
+    async def create_session(
+        self,
+        title: str = '',
+        description: str = '',
+        timeout: float = 30.0,
+    ) -> str:
+        """创建会话，返回 session_id"""
+        async with httpx.AsyncClient(
+            base_url=self.base_url,
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            payload: dict[str, typing.Any] = {}
+            if title:
+                payload['title'] = title
+            if description:
+                payload['description'] = description
+
+            response = await client.post(
+                '/sessions',
+                headers={
+                    'X-API-Key': self.api_key,
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            )
+
+            if response.status_code not in (200, 201):
+                raise WeKnoraAPIError(f'{response.status_code} {response.text}')
+
+            data = response.json()
+            return data['data']['id']
+
+    async def agent_chat(
+        self,
+        session_id: str,
+        query: str,
+        user: str,
+        agent_id: str = '',
+        knowledge_base_ids: list[str] | None = None,
+        web_search_enabled: bool = False,
+        timeout: float = 120.0,
+    ) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
+        """
+        Agent 智能对话（SSE 流式）
+
+        响应事件类型:
+        - agent_query: Agent 开始处理
+        - thinking: 思考过程
+        - tool_call: 工具调用
+        - tool_result: 工具结果
+        - references: 知识库引用
+        - answer: 回答内容
+        - reflection: 反思
+        - session_title: 会话标题
+        - error: 错误
+        """
+        if knowledge_base_ids is None:
+            knowledge_base_ids = []
+
+        async with httpx.AsyncClient(
+            base_url=self.base_url,
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            payload: dict[str, typing.Any] = {
+                'query': query,
+                'agent_enabled': True,
+                'channel': 'im',
+            }
+            if agent_id:
+                payload['agent_id'] = agent_id
+            if knowledge_base_ids:
+                payload['knowledge_base_ids'] = knowledge_base_ids
+            if web_search_enabled:
+                payload['web_search_enabled'] = True
+
+            async with client.stream(
+                'POST',
+                f'/agent-chat/{session_id}',
+                headers={
+                    'X-API-Key': self.api_key,
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            ) as r:
+                async for chunk in r.aiter_lines():
+                    if r.status_code != 200:
+                        raise WeKnoraAPIError(f'{r.status_code} {chunk}')
+                    if chunk.strip() == '':
+                        continue
+                    if chunk.startswith('data:'):
+                        try:
+                            data = json.loads(chunk[5:].strip())
+                        except json.JSONDecodeError:
+                            continue
+                        yield data
+                        # 收到 error 事件后主动结束流，避免上层未 raise 时持续等待
+                        if data.get('response_type') == 'error':
+                            return
+
+    async def knowledge_chat(
+        self,
+        session_id: str,
+        query: str,
+        user: str,
+        agent_id: str = 'builtin-quick-answer',
+        knowledge_base_ids: list[str] | None = None,
+        timeout: float = 120.0,
+    ) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
+        """
+        知识库 RAG 问答（SSE 流式）
+
+        响应事件类型:
+        - references: 知识库引用
+        - answer: 回答内容
+        """
+        if knowledge_base_ids is None:
+            knowledge_base_ids = []
+
+        async with httpx.AsyncClient(
+            base_url=self.base_url,
+            trust_env=True,
+            timeout=timeout,
+        ) as client:
+            payload: dict[str, typing.Any] = {
+                'query': query,
+                'channel': 'im',
+            }
+            if agent_id:
+                payload['agent_id'] = agent_id
+            if knowledge_base_ids:
+                payload['knowledge_base_ids'] = knowledge_base_ids
+
+            async with client.stream(
+                'POST',
+                f'/knowledge-chat/{session_id}',
+                headers={
+                    'X-API-Key': self.api_key,
+                    'Content-Type': 'application/json',
+                },
+                json=payload,
+            ) as r:
+                async for chunk in r.aiter_lines():
+                    if r.status_code != 200:
+                        raise WeKnoraAPIError(f'{r.status_code} {chunk}')
+                    if chunk.strip() == '':
+                        continue
+                    if chunk.startswith('data:'):
+                        try:
+                            data = json.loads(chunk[5:].strip())
+                        except json.JSONDecodeError:
+                            continue
+                        yield data
+                        # 收到 error 事件后主动结束流，避免上层未 raise 时持续等待
+                        if data.get('response_type') == 'error':
+                            return

src/langbot/libs/weknora_api/errors.py

@@ -0,0 +1,6 @@
+class WeKnoraAPIError(Exception):
+    """WeKnora API 请求失败"""
+
+    def __init__(self, message: str = ''):
+        self.message = message
+        super().__init__(self.message)

src/langbot/pkg/agent/__init__.py

@@ -1,37 +0,0 @@
-"""Agent runner subsystem for LangBot."""
-from __future__ import annotations
-
-from .runner.descriptor import AgentRunnerDescriptor
-from .runner.id import parse_runner_id, format_runner_id, RunnerIdParts, is_plugin_runner_id
-from .runner.errors import (
-    AgentRunnerError,
-    RunnerNotFoundError,
-    RunnerNotAuthorizedError,
-    RunnerProtocolError,
-    RunnerExecutionError,
-)
-from .runner.registry import AgentRunnerRegistry
-from .runner.context_builder import AgentRunContextBuilder
-from .runner.resource_builder import AgentResourceBuilder
-from .runner.result_normalizer import AgentResultNormalizer
-from .runner.orchestrator import AgentRunOrchestrator
-from .runner.config_migration import ConfigMigration
-
-__all__ = [
-    'AgentRunnerDescriptor',
-    'parse_runner_id',
-    'format_runner_id',
-    'is_plugin_runner_id',
-    'RunnerIdParts',
-    'AgentRunnerError',
-    'RunnerNotFoundError',
-    'RunnerNotAuthorizedError',
-    'RunnerProtocolError',
-    'RunnerExecutionError',
-    'AgentRunnerRegistry',
-    'AgentRunContextBuilder',
-    'AgentResourceBuilder',
-    'AgentResultNormalizer',
-    'AgentRunOrchestrator',
-    'ConfigMigration',
-]

src/langbot/pkg/agent/runner/__init__.py

@@ -1,63 +0,0 @@
-"""Agent runner modules."""
-from __future__ import annotations
-
-from .descriptor import AgentRunnerDescriptor
-from .id import parse_runner_id, format_runner_id, RunnerIdParts
-from .errors import (
-    AgentRunnerError,
-    RunnerNotFoundError,
-    RunnerNotAuthorizedError,
-    RunnerProtocolError,
-    RunnerExecutionError,
-)
-from .registry import AgentRunnerRegistry
-from .context_builder import AgentRunContextBuilder
-from .resource_builder import AgentResourceBuilder
-from .result_normalizer import AgentResultNormalizer
-from .orchestrator import AgentRunOrchestrator
-from .config_migration import ConfigMigration
-from .default_config import AgentRunnerDefaultConfigService
-from .binding_resolver import AgentBindingResolver, AgentBindingResolutionError
-from .session_registry import (
-    AgentRunSessionRegistry,
-    AgentRunSession,
-    RunAuthorizationSnapshot,
-    get_session_registry,
-)
-from .events import (
-    MESSAGE_RECEIVED,
-    MESSAGE_RECALLED,
-    GROUP_MEMBER_JOINED,
-    FRIEND_REQUEST_RECEIVED,
-    RESERVED_EVENT_TYPES,
-)
-
-__all__ = [
-    'AgentRunnerDescriptor',
-    'parse_runner_id',
-    'format_runner_id',
-    'RunnerIdParts',
-    'AgentRunnerError',
-    'RunnerNotFoundError',
-    'RunnerNotAuthorizedError',
-    'RunnerProtocolError',
-    'RunnerExecutionError',
-    'AgentRunnerRegistry',
-    'AgentRunContextBuilder',
-    'AgentResourceBuilder',
-    'AgentResultNormalizer',
-    'AgentRunOrchestrator',
-    'ConfigMigration',
-    'AgentRunnerDefaultConfigService',
-    'AgentBindingResolver',
-    'AgentBindingResolutionError',
-    'AgentRunSessionRegistry',
-    'AgentRunSession',
-    'RunAuthorizationSnapshot',
-    'get_session_registry',
-    'MESSAGE_RECEIVED',
-    'MESSAGE_RECALLED',
-    'GROUP_MEMBER_JOINED',
-    'FRIEND_REQUEST_RECEIVED',
-    'RESERVED_EVENT_TYPES',
-]

src/langbot/pkg/agent/runner/artifact_store.py

@@ -1,430 +0,0 @@
-"""Artifact store for managing Host-owned artifacts."""
-from __future__ import annotations
-
-import json
-import datetime
-import typing
-import uuid
-import base64
-import os
-
-import sqlalchemy
-from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession
-from sqlalchemy.orm import sessionmaker
-
-from ...entity.persistence.artifact import AgentArtifact
-from ...entity.persistence.bstorage import BinaryStorage
-
-_FILE_ARTIFACT_METADATA_KEY = '_langbot_file_artifact'
-
-
-class ArtifactStore:
-    """Store for AgentArtifact records.
-
-    Handles artifact metadata registration and content retrieval.
-    Actual blob storage is delegated to BinaryStorage or external storage.
-
-    All methods are async and use the provided database engine.
-    """
-
-    engine: AsyncEngine
-
-    # Hard limits
-    MAX_INLINE_READ_BYTES = 1024 * 1024  # 1MB max for inline base64
-    MAX_RANGE_READ_BYTES = 10 * 1024 * 1024  # 10MB max for range reads
-
-    def __init__(self, engine: AsyncEngine):
-        self.engine = engine
-        self._session_factory = sessionmaker(
-            engine, class_=AsyncSession, expire_on_commit=False
-        )
-
-    async def register_file_artifact(
-        self,
-        *,
-        artifact_id: str | None,
-        host_path: str,
-        host_root: str,
-        artifact_type: str = 'file',
-        source: str = 'tool',
-        mime_type: str | None = None,
-        name: str | None = None,
-        size_bytes: int | None = None,
-        sha256: str | None = None,
-        conversation_id: str | None = None,
-        run_id: str | None = None,
-        runner_id: str | None = None,
-        bot_id: str | None = None,
-        workspace_id: str | None = None,
-        expires_at: datetime.datetime | None = None,
-        metadata: dict[str, typing.Any] | None = None,
-    ) -> str:
-        """Register a Host-owned artifact backed by a bounded local file path.
-
-        The public metadata intentionally excludes the real host path. Reads go
-        through read_artifact(), which revalidates the path against host_root.
-        """
-        real_path, real_root = self._validate_file_artifact_path(host_path, host_root)
-        if not os.path.isfile(real_path):
-            raise ValueError('file artifact path must point to a file')
-
-        public_metadata = dict(metadata or {})
-        public_metadata[_FILE_ARTIFACT_METADATA_KEY] = {
-            'path': real_path,
-            'root': real_root,
-        }
-
-        if size_bytes is None:
-            size_bytes = os.path.getsize(real_path)
-
-        return await self.register_artifact(
-            artifact_id=artifact_id,
-            artifact_type=artifact_type,
-            source=source,
-            storage_key=f'file:{uuid.uuid4().hex}',
-            storage_type='file',
-            mime_type=mime_type,
-            name=name or os.path.basename(real_path),
-            size_bytes=size_bytes,
-            sha256=sha256,
-            conversation_id=conversation_id,
-            run_id=run_id,
-            runner_id=runner_id,
-            bot_id=bot_id,
-            workspace_id=workspace_id,
-            expires_at=expires_at,
-            metadata=public_metadata,
-            content=None,
-        )
-
-    async def register_artifact(
-        self,
-        artifact_id: str | None,
-        artifact_type: str,
-        source: str,
-        storage_key: str | None = None,
-        storage_type: str = 'binary_storage',
-        mime_type: str | None = None,
-        name: str | None = None,
-        size_bytes: int | None = None,
-        sha256: str | None = None,
-        conversation_id: str | None = None,
-        run_id: str | None = None,
-        runner_id: str | None = None,
-        bot_id: str | None = None,
-        workspace_id: str | None = None,
-        expires_at: datetime.datetime | None = None,
-        metadata: dict[str, typing.Any] | None = None,
-        content: bytes | None = None,
-    ) -> str:
-        """Register a new artifact.
-
-        If content is provided and storage_key is None, stores content
-        in BinaryStorage automatically.
-
-        Args:
-            artifact_id: Unique artifact ID (generated if None)
-            artifact_type: Type of artifact (image, file, voice, tool_result, etc.)
-            source: Source of artifact (platform, runner, tool, system)
-            storage_key: Key in BinaryStorage or external reference
-            storage_type: Storage type (binary_storage, file, url)
-            mime_type: MIME type
-            name: Original file name
-            size_bytes: Size in bytes
-            sha256: SHA256 hash
-            conversation_id: Conversation ID
-            run_id: Run ID that created this
-            runner_id: Runner ID that created this
-            bot_id: Bot UUID
-            workspace_id: Workspace ID
-            expires_at: Expiration time
-            metadata: Additional metadata
-            content: Optional content to store in BinaryStorage
-
-        Returns:
-            The artifact_id
-        """
-        if artifact_id is None:
-            artifact_id = str(uuid.uuid4())
-
-        # If content provided, store in BinaryStorage
-        if content is not None and storage_key is None:
-            storage_key = f"artifact:{artifact_id}"
-            storage_type = 'binary_storage'
-            if size_bytes is None:
-                size_bytes = len(content)
-
-        async with self._session_factory() as session:
-            # Store content in BinaryStorage if provided
-            if content is not None:
-                binary_storage = BinaryStorage(
-                    unique_key=f'artifact:{artifact_id}',
-                    key=storage_key,
-                    owner_type='artifact',
-                    owner='host',
-                    value=content,
-                )
-                session.add(binary_storage)
-
-            # Store artifact metadata
-            artifact = AgentArtifact(
-                artifact_id=artifact_id,
-                artifact_type=artifact_type,
-                mime_type=mime_type,
-                name=name,
-                size_bytes=size_bytes,
-                sha256=sha256,
-                source=source,
-                storage_key=storage_key,
-                storage_type=storage_type,
-                conversation_id=conversation_id,
-                run_id=run_id,
-                runner_id=runner_id,
-                bot_id=bot_id,
-                workspace_id=workspace_id,
-                created_at=datetime.datetime.utcnow(),
-                expires_at=expires_at,
-                metadata_json=json.dumps(metadata) if metadata else None,
-            )
-            session.add(artifact)
-            await session.commit()
-
-        return artifact_id
-
-    async def get_metadata(
-        self,
-        artifact_id: str,
-    ) -> dict[str, typing.Any] | None:
-        """Get artifact metadata (public fields only, no internal storage info).
-
-        Args:
-            artifact_id: Artifact ID
-
-        Returns:
-            Artifact metadata dict compatible with SDK ArtifactMetadata, or None if not found
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(AgentArtifact).where(
-                    AgentArtifact.artifact_id == artifact_id
-                )
-            )
-            row = result.scalars().first()
-            if row is None:
-                return None
-            return self._row_to_public_dict(row)
-
-    async def _get_internal_record(
-        self,
-        artifact_id: str,
-    ) -> AgentArtifact | None:
-        """Get full artifact record including internal fields.
-
-        Used internally by read_artifact to access storage_key/storage_type.
-
-        Args:
-            artifact_id: Artifact ID
-
-        Returns:
-            AgentArtifact ORM instance, or None if not found
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(AgentArtifact).where(
-                    AgentArtifact.artifact_id == artifact_id
-                )
-            )
-            return result.scalars().first()
-
-    async def read_artifact(
-        self,
-        artifact_id: str,
-        offset: int = 0,
-        limit: int | None = None,
-    ) -> dict[str, typing.Any] | None:
-        """Read artifact content.
-
-        For small artifacts, returns content_base64 directly.
-        For large artifacts, returns file_key for chunked transfer.
-
-        Args:
-            artifact_id: Artifact ID
-            offset: Byte offset to start reading from (must be >= 0)
-            limit: Maximum bytes to read (must be > 0 if provided)
-
-        Returns:
-            ArtifactReadResult dict, or None if not found
-
-        Raises:
-            ValueError: If offset < 0 or limit <= 0
-        """
-        # Validate offset and limit
-        if offset < 0:
-            raise ValueError("offset must be >= 0")
-
-        if limit is not None and limit <= 0:
-            raise ValueError("limit must be > 0")
-
-        # Get internal record (includes storage_key/storage_type)
-        record = await self._get_internal_record(artifact_id)
-        if record is None:
-            return None
-
-        storage_type = record.storage_type or 'binary_storage'
-        storage_key = record.storage_key
-        size_bytes = record.size_bytes or 0
-
-        # Cap limit at hard limit
-        if limit is None:
-            limit = self.MAX_INLINE_READ_BYTES
-        limit = min(limit, self.MAX_RANGE_READ_BYTES)
-
-        # For binary_storage, read content
-        if storage_type == 'binary_storage' and storage_key:
-            content = await self._read_binary_storage(storage_key)
-            if content is None:
-                return None
-
-            # Apply offset and limit
-            if offset > 0:
-                content = content[offset:]
-            if limit and len(content) > limit:
-                content = content[:limit]
-                has_more = True
-            else:
-                has_more = False
-
-            return {
-                'artifact_id': artifact_id,
-                'mime_type': record.mime_type,
-                'size_bytes': size_bytes,
-                'offset': offset,
-                'length': len(content),
-                'content_base64': base64.b64encode(content).decode('utf-8'),
-                'file_key': None,
-                'has_more': has_more,
-            }
-
-        if storage_type == 'file':
-            return self._read_file_storage(record, artifact_id, offset, limit)
-
-        # For other storage types, return storage reference
-        # (caller can use file_key for chunked transfer)
-        return {
-            'artifact_id': artifact_id,
-            'mime_type': record.mime_type,
-            'size_bytes': size_bytes,
-            'offset': offset,
-            'length': None,
-            'content_base64': None,
-            'file_key': storage_key,
-            'has_more': False,
-        }
-
-    async def _read_binary_storage(self, key: str) -> bytes | None:
-        """Read content from BinaryStorage.
-
-        Uses unique_key for isolation to prevent cross-artifact access.
-
-        Args:
-            key: The unique_key used when storing the artifact
-
-        Returns:
-            Content bytes, or None if not found
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(BinaryStorage).where(BinaryStorage.unique_key == key)
-            )
-            row = result.scalars().first()
-            if row is None:
-                return None
-            return row.value
-
-    def _read_file_storage(
-        self,
-        record: AgentArtifact,
-        artifact_id: str,
-        offset: int,
-        limit: int,
-    ) -> dict[str, typing.Any] | None:
-        metadata = self._load_metadata(record.metadata_json)
-        file_info = metadata.get(_FILE_ARTIFACT_METADATA_KEY)
-        if not isinstance(file_info, dict):
-            return None
-
-        host_path = file_info.get('path')
-        host_root = file_info.get('root')
-        if not isinstance(host_path, str) or not isinstance(host_root, str):
-            return None
-
-        real_path, _ = self._validate_file_artifact_path(host_path, host_root)
-        if not os.path.isfile(real_path):
-            return None
-
-        file_size = os.path.getsize(real_path)
-        if offset >= file_size:
-            content = b''
-        else:
-            with open(real_path, 'rb') as f:
-                f.seek(offset)
-                content = f.read(limit)
-
-        return {
-            'artifact_id': artifact_id,
-            'mime_type': record.mime_type,
-            'size_bytes': file_size,
-            'offset': offset,
-            'length': len(content),
-            'content_base64': base64.b64encode(content).decode('utf-8'),
-            'file_key': None,
-            'has_more': offset + len(content) < file_size,
-        }
-
-    @staticmethod
-    def _validate_file_artifact_path(host_path: str, host_root: str) -> tuple[str, str]:
-        real_path = os.path.realpath(host_path)
-        real_root = os.path.realpath(host_root)
-        if not real_root:
-            raise ValueError('file artifact root is required')
-        if not (real_path == real_root or real_path.startswith(real_root + os.sep)):
-            raise ValueError('file artifact path escapes allowed root')
-        return real_path, real_root
-
-    @staticmethod
-    def _load_metadata(metadata_json: str | None) -> dict[str, typing.Any]:
-        if not metadata_json:
-            return {}
-        try:
-            metadata = json.loads(metadata_json)
-        except Exception:
-            return {}
-        return metadata if isinstance(metadata, dict) else {}
-
-    @staticmethod
-    def _public_metadata(metadata_json: str | None) -> dict[str, typing.Any]:
-        metadata = ArtifactStore._load_metadata(metadata_json)
-        metadata.pop(_FILE_ARTIFACT_METADATA_KEY, None)
-        return metadata
-
-    def _row_to_public_dict(self, row: AgentArtifact) -> dict[str, typing.Any]:
-        """Convert an AgentArtifact row to public dict.
-
-        Returns only fields that match SDK ArtifactMetadata entity.
-        Host-only fields (bot_id, workspace_id, storage_key, storage_type) are excluded.
-        """
-        return {
-            'artifact_id': row.artifact_id,
-            'artifact_type': row.artifact_type,
-            'mime_type': row.mime_type,
-            'name': row.name,
-            'size_bytes': row.size_bytes,
-            'sha256': row.sha256,
-            'source': row.source,
-            'conversation_id': row.conversation_id,
-            'run_id': row.run_id,
-            'runner_id': row.runner_id,
-            'created_at': int(row.created_at.timestamp()) if row.created_at else None,
-            'expires_at': int(row.expires_at.timestamp()) if row.expires_at else None,
-            'metadata': self._public_metadata(row.metadata_json),
-        }

src/langbot/pkg/agent/runner/binding_resolver.py

@@ -1,63 +0,0 @@
-"""Resolve host events to one effective Agent binding."""
-
-from __future__ import annotations
-
-from .host_models import AgentConfig, AgentBinding, AgentEventEnvelope, BindingScope
-
-
-class AgentBindingResolutionError(Exception):
-    """Raised when an event cannot resolve to exactly one Agent binding."""
-
-
-class AgentBindingResolver:
-    """Resolve an event to a single AgentBinding.
-
-    The target product model is one bot / IM channel -> one Agent. Fan-out,
-    observer agents, or multi-runner arbitration require separate delivery and
-    state semantics and are intentionally not hidden in this resolver.
-    """
-
-    def resolve_one(
-        self,
-        event: AgentEventEnvelope,
-        agents: list[AgentConfig],
-    ) -> AgentBinding:
-        """Resolve exactly one enabled Agent for the event."""
-        matches = [
-            agent
-            for agent in agents
-            if agent.enabled and event.event_type in agent.event_types
-        ]
-
-        if not matches:
-            raise AgentBindingResolutionError(
-                f'No Agent binding matches event_type={event.event_type}'
-            )
-
-        if len(matches) > 1:
-            agent_ids = ', '.join(agent.agent_id or '<anonymous>' for agent in matches)
-            raise AgentBindingResolutionError(
-                f'Multiple Agent bindings match event_type={event.event_type}: {agent_ids}'
-            )
-
-        return self._to_binding(matches[0])
-
-    def _to_binding(self, agent: AgentConfig) -> AgentBinding:
-        """Project product-level Agent config into the run-time binding model."""
-        scope = BindingScope(
-            scope_type='agent',
-            scope_id=agent.agent_id,
-        )
-
-        return AgentBinding(
-            binding_id=f"agent_{agent.agent_id or 'default'}_{agent.runner_id}",
-            scope=scope,
-            event_types=list(agent.event_types),
-            runner_id=agent.runner_id,
-            runner_config=agent.runner_config,
-            resource_policy=agent.resource_policy,
-            state_policy=agent.state_policy,
-            delivery_policy=agent.delivery_policy,
-            enabled=agent.enabled,
-            agent_id=agent.agent_id,
-        )

src/langbot/pkg/agent/runner/config_migration.py

@@ -1,95 +0,0 @@
-"""Helpers for the current AgentRunner config shape."""
-
-from __future__ import annotations
-
-import typing
-
-
-class ConfigMigration:
-    """Configuration helper for agent runner IDs.
-
-    Responsibilities:
-    - Resolve runner ID from ai.runner.id
-    - Extract current Agent/runner config from ai.runner_config
-    - Keep the current config container shape stable on save
-    """
-
-    @staticmethod
-    def resolve_runner_id(pipeline_config: dict[str, typing.Any]) -> str | None:
-        """Resolve runner ID from current configuration.
-
-        Args:
-            pipeline_config: Current configuration container
-
-        Returns:
-            Runner ID string, or None if not configured
-        """
-        ai_config = pipeline_config.get('ai', {})
-        runner_config = ai_config.get('runner', {})
-
-        runner_id = runner_config.get('id')
-        if runner_id:
-            return runner_id
-
-        return None
-
-    @staticmethod
-    def resolve_runner_config(
-        pipeline_config: dict[str, typing.Any],
-        runner_id: str,
-    ) -> dict[str, typing.Any]:
-        """Resolve Agent/runner configuration from the current container.
-
-        Args:
-            pipeline_config: Current configuration container
-            runner_id: Resolved runner ID
-
-        Returns:
-            Runner configuration dict (empty if not found)
-        """
-        ai_config = pipeline_config.get('ai', {})
-
-        runner_configs = ai_config.get('runner_config', {})
-        if runner_id in runner_configs:
-            return runner_configs[runner_id]
-
-        return {}
-
-    @staticmethod
-    def get_expire_time(pipeline_config: dict[str, typing.Any]) -> int:
-        """Get conversation expire time from configuration.
-
-        Args:
-            pipeline_config: Current configuration container
-
-        Returns:
-            Expire time in seconds (0 means no expiry)
-        """
-        ai_config = pipeline_config.get('ai', {})
-        runner_config = ai_config.get('runner', {})
-        return runner_config.get('expire-time', 0)
-
-    @staticmethod
-    def migrate_pipeline_config(pipeline_config: dict[str, typing.Any]) -> dict[str, typing.Any]:
-        """Normalize the current config container before saving.
-
-        Args:
-            pipeline_config: Original configuration
-
-        Returns:
-            Configuration with explicit ai.runner and ai.runner_config containers
-        """
-        new_config = dict(pipeline_config)
-        if 'ai' not in new_config:
-            return new_config
-
-        ai_config = dict(new_config.get('ai', {}))
-
-        runner_config = dict(ai_config.get('runner', {}))
-        runner_configs = dict(ai_config.get('runner_config', {}))
-
-        ai_config['runner'] = runner_config
-        ai_config['runner_config'] = runner_configs
-        new_config['ai'] = ai_config
-
-        return new_config

src/langbot/pkg/agent/runner/config_schema.py

@@ -1,215 +0,0 @@
-"""Helpers for interpreting AgentRunner DynamicForm configuration."""
-from __future__ import annotations
-
-import typing
-
-from .descriptor import AgentRunnerDescriptor
-
-
-LLM_MODEL_SELECTOR_TYPES = {'model-fallback-selector', 'llm-model-selector'}
-KB_SELECTOR_TYPES = {'knowledge-base-multi-selector'}
-PROMPT_EDITOR_TYPES = {'prompt-editor'}
-NONE_SENTINELS = {'', '__none__', '__none'}
-
-
-def iter_schema_items(
-    descriptor: AgentRunnerDescriptor | None,
-    field_types: set[str],
-) -> typing.Iterator[dict[str, typing.Any]]:
-    """Yield descriptor config schema items whose type is in field_types."""
-    if descriptor is None:
-        return
-    for item in descriptor.config_schema or []:
-        if not isinstance(item, dict):
-            continue
-        if item.get('type') in field_types:
-            yield item
-
-
-def has_permission(
-    descriptor: AgentRunnerDescriptor | None,
-    name: str,
-    actions: set[str],
-) -> bool:
-    """Return whether a runner descriptor requests one of the given actions."""
-    if descriptor is None:
-        return False
-    configured_actions = descriptor.permissions.get(name, [])
-    return any(action in configured_actions for action in actions)
-
-
-def uses_host_models(descriptor: AgentRunnerDescriptor | None) -> bool:
-    """Return whether LangBot should resolve model resources for this runner."""
-    return (
-        has_permission(descriptor, 'models', {'invoke', 'stream', 'list'})
-        and any(True for _ in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES))
-    )
-
-
-def uses_host_tools(descriptor: AgentRunnerDescriptor | None) -> bool:
-    """Return whether LangBot should expose tool resources to this runner."""
-    return (
-        descriptor is not None
-        and descriptor.supports_tool_calling()
-        and has_permission(descriptor, 'tools', {'list', 'detail', 'call'})
-    )
-
-
-def uses_host_knowledge_bases(descriptor: AgentRunnerDescriptor | None) -> bool:
-    """Return whether LangBot should expose knowledge-base resources to this runner."""
-    return (
-        descriptor is not None
-        and descriptor.supports_knowledge_retrieval()
-        and has_permission(descriptor, 'knowledge_bases', {'list', 'retrieve'})
-    )
-
-
-def supports_skill_authoring(descriptor: AgentRunnerDescriptor | None) -> bool:
-    """Return whether the runner wants Host skill-authoring tools."""
-    if descriptor is None:
-        return False
-    return bool(descriptor.capabilities.get('skill_authoring', False))
-
-
-def extract_prompt_config(
-    descriptor: AgentRunnerDescriptor | None,
-    runner_config: dict[str, typing.Any],
-    default_prompt: list[dict[str, typing.Any]],
-) -> list[dict[str, typing.Any]]:
-    """Extract the prompt-editor value selected by the runner schema."""
-    for item in iter_schema_items(descriptor, PROMPT_EDITOR_TYPES):
-        field_name = item.get('name')
-        if field_name and field_name in runner_config:
-            configured_prompt = runner_config[field_name]
-            if isinstance(configured_prompt, list):
-                return configured_prompt
-        default_value = item.get('default')
-        if isinstance(default_value, list):
-            return default_value
-    return default_prompt
-
-
-def extract_model_selection(
-    descriptor: AgentRunnerDescriptor | None,
-    runner_config: dict[str, typing.Any],
-) -> tuple[str, list[str]]:
-    """Extract primary/fallback LLM selections from schema-defined fields."""
-    primary_uuid = ''
-    fallback_uuids: list[str] = []
-
-    for item in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES):
-        field_name = item.get('name')
-        if not field_name:
-            continue
-
-        value = runner_config.get(field_name, item.get('default'))
-        if item.get('type') == 'model-fallback-selector':
-            if isinstance(value, str):
-                primary_uuid = value
-            elif isinstance(value, dict):
-                primary_uuid = value.get('primary') or ''
-                fallbacks = value.get('fallbacks', [])
-                if isinstance(fallbacks, list):
-                    fallback_uuids = [fallback for fallback in fallbacks if isinstance(fallback, str)]
-            break
-
-        if item.get('type') == 'llm-model-selector' and isinstance(value, str):
-            primary_uuid = value
-            break
-
-    return primary_uuid, fallback_uuids
-
-
-def extract_knowledge_base_uuids(
-    descriptor: AgentRunnerDescriptor | None,
-    runner_config: dict[str, typing.Any],
-) -> list[str]:
-    """Extract configured knowledge-base UUIDs from schema-defined fields."""
-    if not uses_host_knowledge_bases(descriptor):
-        return []
-
-    kb_uuids: list[str] = []
-    for item in iter_schema_items(descriptor, KB_SELECTOR_TYPES):
-        field_name = item.get('name')
-        if not field_name:
-            continue
-        value = runner_config.get(field_name, item.get('default', []))
-        if isinstance(value, list):
-            kb_uuids.extend(
-                kb_uuid for kb_uuid in value if isinstance(kb_uuid, str) and kb_uuid not in NONE_SENTINELS
-            )
-
-    return list(dict.fromkeys(kb_uuids))
-
-
-def iter_config_model_refs(
-    descriptor: AgentRunnerDescriptor,
-    runner_config: dict[str, typing.Any],
-) -> typing.Iterator[tuple[str, str]]:
-    """Yield model references declared by schema-defined model selector fields."""
-    for item in descriptor.config_schema or []:
-        if not isinstance(item, dict):
-            continue
-
-        field_name = item.get('name')
-        field_type = item.get('type')
-        if not field_name or field_name not in runner_config:
-            continue
-
-        value = runner_config.get(field_name)
-        if field_type == 'model-fallback-selector':
-            if isinstance(value, str) and value not in NONE_SENTINELS:
-                yield 'llm', value
-            elif isinstance(value, dict):
-                primary = value.get('primary')
-                if isinstance(primary, str) and primary not in NONE_SENTINELS:
-                    yield 'llm', primary
-                fallbacks = value.get('fallbacks', [])
-                if isinstance(fallbacks, list):
-                    for fallback_uuid in fallbacks:
-                        if isinstance(fallback_uuid, str) and fallback_uuid not in NONE_SENTINELS:
-                            yield 'llm', fallback_uuid
-        elif field_type == 'llm-model-selector':
-            if isinstance(value, str) and value not in NONE_SENTINELS:
-                yield 'llm', value
-        elif field_type == 'rerank-model-selector':
-            if isinstance(value, str) and value not in NONE_SENTINELS:
-                yield 'rerank', value
-
-
-def set_empty_llm_model_selection(
-    descriptor: AgentRunnerDescriptor,
-    runner_config: dict[str, typing.Any],
-    model_uuid: str,
-) -> bool:
-    """Set the first empty schema-defined LLM selector to model_uuid."""
-    for item in iter_schema_items(descriptor, LLM_MODEL_SELECTOR_TYPES):
-        field_name = item.get('name')
-        field_type = item.get('type')
-        if not field_name:
-            continue
-
-        value = runner_config.get(field_name, item.get('default'))
-        if field_type == 'model-fallback-selector':
-            if isinstance(value, dict):
-                primary = value.get('primary') or ''
-                if primary not in NONE_SENTINELS:
-                    return False
-                fallbacks = value.get('fallbacks', [])
-                runner_config[field_name] = {
-                    'primary': model_uuid,
-                    'fallbacks': fallbacks if isinstance(fallbacks, list) else [],
-                }
-                return True
-            if isinstance(value, str) and value not in NONE_SENTINELS:
-                return False
-            runner_config[field_name] = {'primary': model_uuid, 'fallbacks': []}
-            return True
-
-        if field_type == 'llm-model-selector':
-            if isinstance(value, str) and value not in NONE_SENTINELS:
-                return False
-            runner_config[field_name] = model_uuid
-            return True
-
-    return False

src/langbot/pkg/agent/runner/context_builder.py

@@ -1,429 +0,0 @@
-"""Agent run context builder for provisioning AgentRunContext envelopes."""
-
-from __future__ import annotations
-
-import uuid
-import time
-import typing
-
-from ...core import app
-from .descriptor import AgentRunnerDescriptor
-from .persistent_state_store import get_persistent_state_store
-from .host_models import AgentEventEnvelope, AgentBinding
-
-
-DEFAULT_RUNNER_TIMEOUT_SECONDS = 300
-
-
-# Internal models for the agent runner context protocol.
-
-
-class AgentTrigger(typing.TypedDict):
-    """Agent trigger information."""
-
-    type: str
-    source: str
-    timestamp: int | None
-
-
-class ConversationContext(typing.TypedDict):
-    """Conversation context."""
-
-    conversation_id: str | None
-    thread_id: str | None
-    launcher_type: str | None
-    launcher_id: str | None
-    sender_id: str | None
-    bot_id: str | None
-    workspace_id: str | None
-    session_id: str | None
-
-
-class AgentInput(typing.TypedDict):
-    """Agent input."""
-
-    text: str | None
-    contents: list[dict[str, typing.Any]]
-    message_chain: dict[str, typing.Any] | None
-    attachments: list[dict[str, typing.Any]]
-
-
-class AgentRunState(typing.TypedDict):
-    """Agent run state with 4 scopes."""
-
-    conversation: dict[str, typing.Any]
-    actor: dict[str, typing.Any]
-    subject: dict[str, typing.Any]
-    runner: dict[str, typing.Any]
-
-
-# Resource payload models matching langbot-plugin-sdk/resources.py.
-
-
-class ModelResource(typing.TypedDict):
-    """Model resource payload."""
-
-    model_id: str
-    model_type: str | None
-    provider: str | None
-
-
-class ToolResource(typing.TypedDict):
-    """Tool resource payload."""
-
-    tool_name: str
-    tool_type: str | None
-    description: str | None
-
-
-class KnowledgeBaseResource(typing.TypedDict):
-    """Knowledge base resource payload."""
-
-    kb_id: str
-    kb_name: str | None
-    kb_type: str | None
-
-
-class SkillResource(typing.TypedDict):
-    """Skill resource payload."""
-
-    skill_name: str
-    display_name: str | None
-    description: str | None
-
-
-class FileResource(typing.TypedDict):
-    """File resource payload."""
-
-    file_id: str
-    file_name: str | None
-    mime_type: str | None
-    source: str | None
-
-
-class StorageResource(typing.TypedDict):
-    """Storage resource payload."""
-
-    plugin_storage: bool
-    workspace_storage: bool
-
-
-class AgentResources(typing.TypedDict):
-    """Agent resources payload."""
-
-    models: list[ModelResource]
-    tools: list[ToolResource]
-    knowledge_bases: list[KnowledgeBaseResource]
-    skills: list[SkillResource]
-    files: list[FileResource]
-    storage: StorageResource
-    platform_capabilities: dict[str, typing.Any]
-
-
-class AgentRuntimeContext(typing.TypedDict):
-    """Agent runtime context."""
-
-    langbot_version: str | None
-    protocol_version: str
-    trace_id: str | None
-    deadline_at: float | None
-    metadata: dict[str, typing.Any]
-
-
-class AgentRunContextPayload(typing.TypedDict):
-    """AgentRunContext payload passed to an agent runner.
-
-    Protocol v1 structure - matches SDK AgentRunContext.
-
-    Note: The 'config' field contains the current Agent/runner config
-    from ai.runner_config[runner_id] while the current Query entry remains
-    a temporary configuration container. It is not plugin instance config.
-    """
-
-    run_id: str
-    trigger: AgentTrigger
-    conversation: ConversationContext | None
-    event: dict[str, typing.Any]  # REQUIRED for Protocol v1
-    actor: dict[str, typing.Any] | None
-    subject: dict[str, typing.Any] | None
-    input: AgentInput
-    delivery: dict[str, typing.Any]  # REQUIRED for Protocol v1
-    resources: AgentResources
-    context: dict[str, typing.Any]  # ContextAccess - REQUIRED for Protocol v1
-    state: AgentRunState
-    runtime: AgentRuntimeContext
-    config: dict[str, typing.Any]  # Agent/runner config from ai.runner_config[runner_id]
-    adapter: dict[str, typing.Any] | None  # Entry adapter context
-    metadata: dict[str, typing.Any]  # Additional metadata
-
-
-class AgentRunContextBuilder:
-    """Builder for provisioning AgentRunContext.
-
-    Responsibilities:
-    - Generate new run_id (UUID, not query id)
-    - Set trigger type based on event source
-    - Build conversation context from event
-    - Build input from event
-    - Build state snapshot from PersistentStateStore
-    - Build runtime context with host info, trace_id, deadline
-    - Set config from current Agent/runner configuration.
-
-    Query adaptation belongs to QueryEntryAdapter, not this builder.
-    """
-
-    ap: app.Application
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def build_context_from_event(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-        resources: AgentResources,
-    ) -> AgentRunContextPayload:
-        """Build AgentRunContext from event-first envelope.
-
-        This is the main entry point for Protocol v1.
-        Does NOT inline full history by default.
-
-        Args:
-            event: Event envelope
-            binding: Agent binding
-            descriptor: Runner descriptor
-            resources: Built resources
-
-        Returns:
-            AgentRunContextPayload for the runner
-        """
-        # Generate new run_id
-        run_id = str(uuid.uuid4())
-
-        # Build trigger from event
-        trigger: AgentTrigger = {
-            'type': event.event_type,
-            'source': event.source,
-            'timestamp': event.event_time or int(time.time()),
-        }
-
-        # Build conversation context from event
-        conversation: ConversationContext | None = None
-        if event.conversation_id:
-            conversation = {
-                'session_id': None,
-                'conversation_id': event.conversation_id,
-                'thread_id': event.thread_id,
-                'launcher_type': None,  # Will be filled from actor/subject if needed
-                'launcher_id': None,
-                'sender_id': event.actor.actor_id if event.actor else None,
-                'bot_id': event.bot_id,
-                'workspace_id': event.workspace_id,
-            }
-
-        # Build event context (Protocol v1 event-first)
-        event_context = {
-            'event_id': event.event_id,
-            'event_type': event.event_type,
-            'event_time': event.event_time,
-            'source': event.source,
-            'source_event_type': event.source_event_type,
-            'raw_ref': event.raw_ref.model_dump(mode='json') if event.raw_ref else None,
-            'data': event.data,
-        }
-
-        # Build actor context
-        actor_context = None
-        if event.actor:
-            actor_context = {
-                'actor_type': event.actor.actor_type,
-                'actor_id': event.actor.actor_id,
-                'actor_name': event.actor.actor_name,
-            }
-
-        # Build subject context
-        subject_context = None
-        if event.subject:
-            subject_context = {
-                'subject_type': event.subject.subject_type,
-                'subject_id': event.subject.subject_id,
-                'data': event.subject.data,
-            }
-
-        # Build input from event
-        input: AgentInput = {
-            'text': event.input.text,
-            'contents': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents],
-            'message_chain': event.input.message_chain,
-            'attachments': [
-                a.model_dump(mode='json') if hasattr(a, 'model_dump') else a for a in event.input.attachments
-            ],
-        }
-
-        # Build context access (no history inlined by default for Protocol v1)
-        # Populate with actual values from stores
-        context_access = await self._build_context_access(event, descriptor, binding)
-
-        # Build state snapshot from persistent state store (event-first Protocol v1)
-        persistent_state_store = get_persistent_state_store(self.ap.persistence_mgr.get_db_engine())
-        state: AgentRunState = await persistent_state_store.build_snapshot_from_event(event, binding, descriptor)
-
-        # Build runtime context
-        runtime: AgentRuntimeContext = {
-            'langbot_version': self.ap.ver_mgr.get_current_version(),
-            'protocol_version': descriptor.protocol_version,
-            'trace_id': run_id,
-            'deadline_at': self._build_deadline_from_binding(binding),
-            'metadata': {
-                'bot_id': event.bot_id,
-                'workspace_id': event.workspace_id,
-                'streaming_supported': event.delivery.supports_streaming,
-                'model_context_window_tokens': None,
-                # TODO(model-info): populate model_context_window_tokens after
-                # LiteLLM/model metadata lands. Runners fall back to their
-                # ctx.config until Host can provide the real window.
-            },
-        }
-
-        # Build delivery context
-        delivery_context = {
-            'surface': event.delivery.surface,
-            'reply_target': event.delivery.reply_target,
-            'supports_streaming': event.delivery.supports_streaming,
-            'supports_edit': event.delivery.supports_edit,
-            'supports_reaction': event.delivery.supports_reaction,
-            'max_message_size': event.delivery.max_message_size,
-            'platform_capabilities': event.delivery.platform_capabilities,
-        }
-
-        # Build adapter context (empty for event-first)
-        adapter_context = {
-            'extra': {},
-        }
-
-        # Build full context - Protocol v1 structure
-        context: AgentRunContextPayload = {
-            'run_id': run_id,
-            'trigger': trigger,
-            'conversation': conversation,
-            'event': event_context,  # REQUIRED
-            'actor': actor_context,
-            'subject': subject_context,
-            'input': input,
-            'delivery': delivery_context,  # REQUIRED
-            'resources': resources,
-            'context': context_access,  # ContextAccess - REQUIRED
-            'state': state,
-            'runtime': runtime,
-            'config': binding.runner_config,
-            'adapter': adapter_context,
-            'metadata': {},  # Additional metadata
-        }
-
-        return context
-
-    def _build_deadline_from_binding(self, binding: AgentBinding) -> float | None:
-        """Build deadline timestamp from binding timeout config.
-
-        Args:
-            binding: Agent binding with runner_config
-
-        Returns:
-            Deadline timestamp or None
-        """
-        timeout = binding.runner_config.get('timeout', DEFAULT_RUNNER_TIMEOUT_SECONDS)
-        if timeout is None:
-            return None
-
-        try:
-            timeout_seconds = float(timeout)
-        except (TypeError, ValueError):
-            return None
-
-        if timeout_seconds <= 0:
-            return None
-
-        return time.time() + timeout_seconds
-
-    async def _build_context_access(
-        self,
-        event: AgentEventEnvelope,
-        descriptor: AgentRunnerDescriptor,
-        binding: AgentBinding | None = None,
-    ) -> dict[str, typing.Any]:
-        """Build ContextAccess with actual values from stores.
-
-        Args:
-            event: Event envelope
-            descriptor: Runner descriptor
-            binding: Agent binding (required for state_policy in event-first mode)
-
-        Returns:
-            ContextAccess dict
-        """
-        conversation_id = event.conversation_id
-
-        # Check if history APIs are available for this runner
-        # Based on runner permissions
-        permissions = descriptor.permissions or {}
-        history_permissions = permissions.get('history', [])
-        event_permissions = permissions.get('events', [])
-        artifact_permissions = permissions.get('artifacts', [])
-
-        history_page_enabled = 'page' in history_permissions and conversation_id is not None
-        history_search_enabled = 'search' in history_permissions and conversation_id is not None
-        event_get_enabled = 'get' in event_permissions
-        event_page_enabled = 'page' in event_permissions and conversation_id is not None
-        artifact_metadata_enabled = 'metadata' in artifact_permissions
-        artifact_read_enabled = 'read' in artifact_permissions
-
-        # Determine state API availability based on binding state_policy.
-        state_enabled = False
-        if binding is not None:
-            state_policy = binding.state_policy
-            if state_policy.enable_state and state_policy.state_scopes:
-                state_enabled = True
-
-        # Get latest cursor and has_history_before if conversation exists
-        latest_cursor = None
-        has_history_before = False
-
-        if conversation_id:
-            try:
-                from .transcript_store import TranscriptStore
-
-                store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
-
-                latest_cursor = await store.get_latest_cursor(conversation_id)
-                if latest_cursor:
-                    has_history_before = True
-            except Exception as e:
-                self.ap.logger.warning(f'Failed to get transcript cursor: {e}')
-
-        return {
-            'conversation_id': conversation_id,
-            'thread_id': event.thread_id,
-            'latest_cursor': latest_cursor,
-            'event_seq': None,  # Will be populated when EventLog is written
-            'transcript_seq': int(latest_cursor) if latest_cursor else None,
-            'has_history_before': has_history_before,
-            'inline_policy': {
-                'mode': 'current_event',
-                'delivered_count': 0,
-                'source_total_count': None,
-                'messages_complete': False,
-                'reason': 'self_managed_context',
-            },
-            'available_apis': {
-                'history_page': history_page_enabled,
-                'history_search': history_search_enabled,
-                'event_get': event_get_enabled,
-                'event_page': event_page_enabled,
-                'artifact_metadata': artifact_metadata_enabled,
-                'artifact_read': artifact_read_enabled,
-                'state': state_enabled,
-                'storage': True,
-                'prompt_get': False,
-            },
-        }

src/langbot/pkg/agent/runner/default_config.py

@@ -1,72 +0,0 @@
-"""Default AgentRunner binding configuration helpers."""
-
-from __future__ import annotations
-
-import sqlalchemy
-
-from ...core import app
-from ...entity.persistence import pipeline as persistence_pipeline
-from . import config_schema
-from .config_migration import ConfigMigration
-
-
-class AgentRunnerDefaultConfigService:
-    """Apply AgentRunner schema-defined defaults to host binding config."""
-
-    ap: app.Application
-
-    def __init__(self, ap: app.Application) -> None:
-        self.ap = ap
-
-    async def _get_runner_descriptor(self, runner_id: str):
-        registry = getattr(self.ap, 'agent_runner_registry', None)
-        if registry is None:
-            return None
-        try:
-            return await registry.get(runner_id, bound_plugins=None)
-        except Exception as e:
-            logger = getattr(self.ap, 'logger', None)
-            if logger:
-                logger.warning(f'Failed to load AgentRunner descriptor while setting default model: {e}')
-            return None
-
-    async def auto_set_default_pipeline_llm_model(self, model_uuid: str) -> bool:
-        """Set model_uuid into the default pipeline runner config when the selector is empty."""
-        result = await self.ap.persistence_mgr.execute_async(
-            sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
-                persistence_pipeline.LegacyPipeline.is_default == True
-            )
-        )
-        pipeline = result.first()
-        if pipeline is None:
-            return False
-
-        return await self.set_pipeline_llm_model_if_empty(pipeline, model_uuid)
-
-    async def set_pipeline_llm_model_if_empty(
-        self,
-        pipeline: persistence_pipeline.LegacyPipeline,
-        model_uuid: str,
-    ) -> bool:
-        """Set model_uuid into a pipeline's schema-defined LLM selector if it is empty."""
-        pipeline_config = pipeline.config
-        if not isinstance(pipeline_config, dict):
-            return False
-
-        runner_id = ConfigMigration.resolve_runner_id(pipeline_config)
-        if not runner_id:
-            return False
-
-        descriptor = await self._get_runner_descriptor(runner_id)
-        if descriptor is None:
-            return False
-
-        ai_config = pipeline_config.setdefault('ai', {})
-        runner_configs = ai_config.setdefault('runner_config', {})
-        runner_config = runner_configs.setdefault(runner_id, {})
-
-        if not config_schema.set_empty_llm_model_selection(descriptor, runner_config, model_uuid):
-            return False
-
-        await self.ap.pipeline_service.update_pipeline(pipeline.uuid, {'config': pipeline_config})
-        return True

src/langbot/pkg/agent/runner/descriptor.py

@@ -1,72 +0,0 @@
-"""Agent runner descriptor."""
-from __future__ import annotations
-
-import typing
-import pydantic
-
-
-class AgentRunnerDescriptor(pydantic.BaseModel):
-    """Descriptor for an agent runner.
-
-    Represents the discovered metadata for a runner, including
-    its identity, capabilities, permissions, and configuration schema.
-    """
-
-    id: str
-    """Unique runner ID: plugin:author/plugin_name/runner_name"""
-
-    source: typing.Literal['plugin']
-    """Runner source type"""
-
-    label: dict[str, str]
-    """Display labels keyed by locale (e.g., en_US, zh_Hans)"""
-
-    description: dict[str, str] | None = None
-    """Optional description keyed by locale"""
-
-    plugin_author: str
-    """Plugin author from manifest"""
-
-    plugin_name: str
-    """Plugin name from manifest"""
-
-    runner_name: str
-    """AgentRunner component name from manifest"""
-
-    plugin_version: str | None = None
-    """Optional plugin version"""
-
-    protocol_version: str = '1'
-    """SDK protocol version, default '1'"""
-
-    config_schema: list[dict[str, typing.Any]] = []
-    """Configuration schema using DynamicForm format"""
-
-    capabilities: dict[str, bool] = {}
-    """Runner capabilities: streaming, tool_calling, knowledge_retrieval, etc."""
-
-    permissions: dict[str, list[str]] = {}
-    """Requested permissions: models, tools, knowledge_bases, storage, files, platform_api"""
-
-    raw_manifest: dict[str, typing.Any] = {}
-    """Original manifest for reference"""
-
-    model_config = pydantic.ConfigDict(
-        extra='allow',
-    )
-
-    def get_plugin_id(self) -> str:
-        """Return plugin identifier as author/name."""
-        return f'{self.plugin_author}/{self.plugin_name}'
-
-    def supports_streaming(self) -> bool:
-        """Check if runner supports streaming output."""
-        return self.capabilities.get('streaming', False)
-
-    def supports_tool_calling(self) -> bool:
-        """Check if runner supports tool calling."""
-        return self.capabilities.get('tool_calling', False)
-
-    def supports_knowledge_retrieval(self) -> bool:
-        """Check if runner supports knowledge retrieval."""
-        return self.capabilities.get('knowledge_retrieval', False)

src/langbot/pkg/agent/runner/errors.py

@@ -1,37 +0,0 @@
-"""Agent runner errors."""
-from __future__ import annotations
-
-
-class AgentRunnerError(Exception):
-    """Base error for agent runner operations."""
-    pass
-
-
-class RunnerNotFoundError(AgentRunnerError):
-    """Runner not found in registry."""
-    def __init__(self, runner_id: str):
-        self.runner_id = runner_id
-        super().__init__(f'Agent runner not found: {runner_id}')
-
-
-class RunnerNotAuthorizedError(AgentRunnerError):
-    """Runner not authorized for this binding."""
-    def __init__(self, runner_id: str, bound_plugins: list[str] | None):
-        self.runner_id = runner_id
-        self.bound_plugins = bound_plugins
-        super().__init__(f'Agent runner {runner_id} not authorized for bound_plugins={bound_plugins}')
-
-
-class RunnerProtocolError(AgentRunnerError):
-    """Runner protocol version mismatch or invalid manifest."""
-    def __init__(self, runner_id: str, message: str):
-        self.runner_id = runner_id
-        super().__init__(f'Agent runner protocol error for {runner_id}: {message}')
-
-
-class RunnerExecutionError(AgentRunnerError):
-    """Runner execution failed."""
-    def __init__(self, runner_id: str, message: str, retryable: bool = False):
-        self.runner_id = runner_id
-        self.retryable = retryable
-        super().__init__(f'Agent runner {runner_id} execution failed: {message}')

src/langbot/pkg/agent/runner/event_log_store.py

@@ -1,255 +0,0 @@
-"""EventLog store for writing and querying event records."""
-from __future__ import annotations
-
-import json
-import datetime
-import typing
-import uuid
-
-import sqlalchemy
-from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession
-from sqlalchemy.orm import sessionmaker
-
-from ...entity.persistence.event_log import EventLog
-
-
-class EventLogStore:
-    """Store for EventLog records.
-
-    Handles writing events to the event log and querying them.
-    All methods are async and use the provided database engine.
-    """
-
-    engine: AsyncEngine
-
-    # Hard limits
-    MAX_INPUT_SUMMARY_LENGTH = 1000
-
-    def __init__(self, engine: AsyncEngine):
-        self.engine = engine
-        self._session_factory = sessionmaker(
-            engine, class_=AsyncSession, expire_on_commit=False
-        )
-
-    async def append_event(
-        self,
-        event_id: str | None,
-        event_type: str,
-        source: str,
-        bot_id: str | None = None,
-        workspace_id: str | None = None,
-        conversation_id: str | None = None,
-        thread_id: str | None = None,
-        actor_type: str | None = None,
-        actor_id: str | None = None,
-        actor_name: str | None = None,
-        subject_type: str | None = None,
-        subject_id: str | None = None,
-        input_summary: str | None = None,
-        input_json: dict[str, typing.Any] | None = None,
-        raw_ref: str | None = None,
-        run_id: str | None = None,
-        runner_id: str | None = None,
-        event_time: datetime.datetime | None = None,
-        metadata: dict[str, typing.Any] | None = None,
-    ) -> str:
-        """Append an event to the event log.
-
-        Args:
-            event_id: Unique event ID (generated if None)
-            event_type: Event type
-            source: Event source
-            bot_id: Bot UUID
-            workspace_id: Workspace ID
-            conversation_id: Conversation ID
-            thread_id: Thread ID
-            actor_type: Actor type
-            actor_id: Actor ID
-            actor_name: Actor display name
-            subject_type: Subject type
-            subject_id: Subject ID
-            input_summary: Brief input summary
-            input_json: Full input JSON
-            raw_ref: Reference to raw event payload
-            run_id: Run ID processing this event
-            runner_id: Runner ID processing this event
-            event_time: When the event occurred
-            metadata: Additional metadata
-
-        Returns:
-            The event_id
-        """
-        if event_id is None:
-            event_id = str(uuid.uuid4())
-
-        # Truncate input summary if too long
-        if input_summary and len(input_summary) > self.MAX_INPUT_SUMMARY_LENGTH:
-            input_summary = input_summary[:self.MAX_INPUT_SUMMARY_LENGTH - 3] + "..."
-
-        async with self._session_factory() as session:
-            event = EventLog(
-                event_id=event_id,
-                event_type=event_type,
-                event_time=event_time,
-                source=source,
-                bot_id=bot_id,
-                workspace_id=workspace_id,
-                conversation_id=conversation_id,
-                thread_id=thread_id,
-                actor_type=actor_type,
-                actor_id=actor_id,
-                actor_name=actor_name,
-                subject_type=subject_type,
-                subject_id=subject_id,
-                input_summary=input_summary,
-                input_json=json.dumps(input_json) if input_json else None,
-                raw_ref=raw_ref,
-                run_id=run_id,
-                runner_id=runner_id,
-                metadata_json=json.dumps(metadata) if metadata else None,
-                created_at=datetime.datetime.utcnow(),
-            )
-            session.add(event)
-            await session.commit()
-
-        return event_id
-
-    async def get_event(
-        self,
-        event_id: str,
-    ) -> dict[str, typing.Any] | None:
-        """Get a single event by ID.
-
-        Args:
-            event_id: Event ID
-
-        Returns:
-            Event record as dict, or None if not found
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(EventLog).where(EventLog.event_id == event_id)
-            )
-            row = result.scalars().first()
-            if row is None:
-                return None
-            return self._row_to_dict(row)
-
-    async def page_events(
-        self,
-        conversation_id: str | None = None,
-        event_types: list[str] | None = None,
-        before_seq: int | None = None,
-        limit: int = 50,
-    ) -> tuple[list[dict[str, typing.Any]], int | None, bool]:
-        """Page through event records.
-
-        Args:
-            conversation_id: Filter by conversation ID
-            event_types: Filter by event types
-            before_seq: Get events before this sequence number
-            limit: Maximum items to return (capped at 100)
-
-        Returns:
-            Tuple of (items, next_seq, has_more)
-        """
-        limit = min(limit, 100)  # Hard cap
-
-        async with self._session_factory() as session:
-            query = sqlalchemy.select(EventLog)
-
-            if conversation_id is not None:
-                query = query.where(EventLog.conversation_id == conversation_id)
-
-            if event_types:
-                query = query.where(EventLog.event_type.in_(event_types))
-
-            if before_seq is not None:
-                query = query.where(EventLog.id < before_seq)
-
-            query = query.order_by(EventLog.id.desc()).limit(limit + 1)
-
-            result = await session.execute(query)
-            rows = result.scalars().all()
-
-            items = [self._row_to_dict(row) for row in rows[:limit]]
-            has_more = len(rows) > limit
-            next_seq = items[-1]['id'] if items and has_more else None
-
-            return items, next_seq, has_more
-
-    async def get_latest_cursor(
-        self,
-        conversation_id: str,
-    ) -> str | None:
-        """Get the latest cursor for a conversation.
-
-        Args:
-            conversation_id: Conversation ID
-
-        Returns:
-            Cursor string (seq number), or None if no events
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(EventLog.id)
-                .where(EventLog.conversation_id == conversation_id)
-                .order_by(EventLog.id.desc())
-                .limit(1)
-            )
-            row = result.scalars().first()
-            if row is None:
-                return None
-            return str(row)
-
-    async def has_events_before(
-        self,
-        conversation_id: str,
-        seq: int,
-    ) -> bool:
-        """Check if there are events before a sequence number.
-
-        Args:
-            conversation_id: Conversation ID
-            seq: Sequence number
-
-        Returns:
-            True if there are events before
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(sqlalchemy.func.count())
-                .select_from(EventLog)
-                .where(
-                    EventLog.conversation_id == conversation_id,
-                    EventLog.id < seq,
-                )
-            )
-            count = result.scalar()
-            return count > 0
-
-    def _row_to_dict(self, row: EventLog) -> dict[str, typing.Any]:
-        """Convert an EventLog row to dict."""
-        return {
-            'id': row.id,
-            'event_id': row.event_id,
-            'event_type': row.event_type,
-            'event_time': int(row.event_time.timestamp()) if row.event_time else None,
-            'source': row.source,
-            'bot_id': row.bot_id,
-            'workspace_id': row.workspace_id,
-            'conversation_id': row.conversation_id,
-            'thread_id': row.thread_id,
-            'actor_type': row.actor_type,
-            'actor_id': row.actor_id,
-            'actor_name': row.actor_name,
-            'subject_type': row.subject_type,
-            'subject_id': row.subject_id,
-            'input_summary': row.input_summary,
-            'input_json': json.loads(row.input_json) if row.input_json else None,
-            'raw_ref': row.raw_ref,
-            'run_id': row.run_id,
-            'runner_id': row.runner_id,
-            'created_at': int(row.created_at.timestamp()) if row.created_at else None,
-            'metadata': json.loads(row.metadata_json) if row.metadata_json else {},
-        }

src/langbot/pkg/agent/runner/events.py

@@ -1,25 +0,0 @@
-"""Canonical AgentRunner event names reserved for future EBA integration."""
-from __future__ import annotations
-
-
-MESSAGE_RECEIVED = 'message.received'
-"""A normal message entered the current Pipeline."""
-
-MESSAGE_RECALLED = 'message.recalled'
-"""A platform message was recalled or deleted."""
-
-GROUP_MEMBER_JOINED = 'group.member_joined'
-"""A new member joined a group/channel conversation."""
-
-FRIEND_REQUEST_RECEIVED = 'friend.request_received'
-"""A new friend/contact request was received."""
-
-
-RESERVED_EVENT_TYPES = frozenset(
-    {
-        MESSAGE_RECEIVED,
-        MESSAGE_RECALLED,
-        GROUP_MEMBER_JOINED,
-        FRIEND_REQUEST_RECEIVED,
-    }
-)

src/langbot/pkg/agent/runner/host_models.py

@@ -1,210 +0,0 @@
-"""Agent event envelope and binding models for LangBot Host.
-
-These are Host-internal models, not exposed to SDK.
-"""
-from __future__ import annotations
-
-import typing
-import pydantic
-
-from langbot_plugin.api.entities.builtin.agent_runner.event import (
-    ActorContext,
-    SubjectContext,
-    RawEventRef,
-)
-from langbot_plugin.api.entities.builtin.agent_runner.input import AgentInput
-from langbot_plugin.api.entities.builtin.agent_runner.delivery import DeliveryContext
-
-
-class AgentEventEnvelope(pydantic.BaseModel):
-    """Event envelope for LangBot Host event gateway.
-
-    This is the unified input model that replaces Query-first approach.
-    IM / WebUI / API / EventRouter all produce this envelope.
-    """
-
-    event_id: str
-    """Unique event identifier."""
-
-    event_type: str
-    """Event type (message.received, message.recalled, etc.)."""
-
-    event_time: int | None = None
-    """Event timestamp (epoch seconds)."""
-
-    source: str
-    """Event source (platform, webui, api, scheduler, system)."""
-
-    source_event_type: str | None = None
-    """Original source event type, when available."""
-
-    bot_id: str | None = None
-    """Bot UUID handling this event."""
-
-    workspace_id: str | None = None
-    """Workspace ID (for multi-tenant)."""
-
-    conversation_id: str | None = None
-    """Conversation ID."""
-
-    thread_id: str | None = None
-    """Thread ID (for platforms supporting threads)."""
-
-    actor: ActorContext | None = None
-    """Actor (who triggered the event)."""
-
-    subject: SubjectContext | None = None
-    """Subject (what the event is about)."""
-
-    input: AgentInput
-    """Event input."""
-
-    delivery: DeliveryContext
-    """Delivery context."""
-
-    raw_ref: RawEventRef | None = None
-    """Reference to raw event payload."""
-
-    data: dict[str, typing.Any] = pydantic.Field(default_factory=dict)
-    """Small structured event payload. Large payloads should be referenced via raw_ref/artifacts."""
-
-
-# Binding scope types
-class BindingScope(pydantic.BaseModel):
-    """Scope for agent binding."""
-
-    scope_type: typing.Literal["agent", "bot", "workspace", "global"] = "agent"
-    """Scope type."""
-
-    scope_id: str | None = None
-    """Scope identifier (agent_id, bot_uuid, etc.)."""
-
-
-class ResourcePolicy(pydantic.BaseModel):
-    """Resource policy for agent binding.
-
-    Controls what resources the runner can access.
-    """
-
-    allowed_model_uuids: list[str] | None = None
-    """Additional model UUID grants. None means no additional model grants."""
-
-    allowed_tool_names: list[str] | None = None
-    """Additional tool name grants. None means no additional tool grants."""
-
-    allowed_kb_uuids: list[str] | None = None
-    """Additional knowledge base UUID grants. None means no additional KB grants."""
-
-    allowed_skill_names: list[str] | None = None
-    """Allowed skill names. None means all currently visible skills are allowed."""
-
-    allow_plugin_storage: bool = True
-    """Whether plugin storage is allowed."""
-
-    allow_workspace_storage: bool = False
-    """Whether workspace storage is allowed."""
-
-
-class StatePolicy(pydantic.BaseModel):
-    """State policy for agent binding.
-
-    Controls state management behavior.
-    """
-
-    enable_state: bool = True
-    """Whether host-owned state is enabled."""
-
-    state_scopes: list[typing.Literal["conversation", "actor", "subject", "runner"]] = (
-        pydantic.Field(default_factory=lambda: ["conversation", "actor"])
-    )
-    """Enabled state scopes."""
-
-
-class DeliveryPolicy(pydantic.BaseModel):
-    """Delivery policy for agent binding.
-
-    Controls how results are delivered.
-    """
-
-    enable_streaming: bool = True
-    """Whether streaming output is enabled."""
-
-    enable_reply: bool = True
-    """Whether reply is enabled."""
-
-    max_message_size: int | None = None
-    """Maximum message size."""
-
-
-class AgentConfig(pydantic.BaseModel):
-    """Host-side Agent configuration.
-
-    Product-level Agent is the target replacement for Pipeline-owned agent
-    config. Current Pipeline entry paths can project their config into this
-    model during migration.
-    """
-
-    agent_id: str | None = None
-    """Host-side Agent/config identifier."""
-
-    runner_id: str
-    """Runner ID to invoke."""
-
-    runner_config: dict[str, typing.Any] = pydantic.Field(default_factory=dict)
-    """Agent/runner binding configuration."""
-
-    resource_policy: ResourcePolicy = pydantic.Field(default_factory=ResourcePolicy)
-    """Resource policy for this Agent."""
-
-    state_policy: StatePolicy = pydantic.Field(default_factory=StatePolicy)
-    """State policy for this Agent."""
-
-    delivery_policy: DeliveryPolicy = pydantic.Field(default_factory=DeliveryPolicy)
-    """Delivery policy for this Agent."""
-
-    event_types: list[str] = pydantic.Field(default_factory=lambda: ["message.received"])
-    """Event types this Agent handles."""
-
-    enabled: bool = True
-    """Whether this Agent can be selected by a binding resolver."""
-
-    metadata: dict[str, typing.Any] = pydantic.Field(default_factory=dict)
-    """Non-protocol diagnostic metadata, such as legacy config source."""
-
-
-class AgentBinding(pydantic.BaseModel):
-    """Binding configuration for mapping events to runners.
-
-    This is Host-internal model for event-to-runner binding.
-    It replaces the old Pipeline runner config role.
-    """
-
-    binding_id: str
-    """Unique binding identifier."""
-
-    scope: BindingScope = pydantic.Field(default_factory=BindingScope)
-    """Binding scope."""
-
-    event_types: list[str] = pydantic.Field(default_factory=lambda: ["message.received"])
-    """Event types this binding handles."""
-
-    runner_id: str
-    """Runner ID to invoke."""
-
-    runner_config: dict[str, typing.Any] = pydantic.Field(default_factory=dict)
-    """Current Agent/runner configuration."""
-
-    resource_policy: ResourcePolicy = pydantic.Field(default_factory=ResourcePolicy)
-    """Resource policy."""
-
-    state_policy: StatePolicy = pydantic.Field(default_factory=StatePolicy)
-    """State policy."""
-
-    delivery_policy: DeliveryPolicy = pydantic.Field(default_factory=DeliveryPolicy)
-    """Delivery policy."""
-
-    enabled: bool = True
-    """Whether binding is enabled."""
-
-    agent_id: str | None = None
-    """Host-side Agent/config identifier for this binding."""

src/langbot/pkg/agent/runner/id.py

@@ -1,91 +0,0 @@
-"""Agent runner ID parsing and formatting."""
-from __future__ import annotations
-
-import dataclasses
-
-
-@dataclasses.dataclass(frozen=True)
-class RunnerIdParts:
-    """Parsed runner ID components."""
-    source: str  # 'plugin' (future: 'builtin')
-    plugin_author: str
-    plugin_name: str
-    runner_name: str
-
-    def to_plugin_id(self) -> str:
-        """Return plugin identifier as author/name."""
-        return f'{self.plugin_author}/{self.plugin_name}'
-
-
-def parse_runner_id(runner_id: str) -> RunnerIdParts:
-    """Parse runner ID string into components.
-
-    Args:
-        runner_id: Runner ID in format 'plugin:author/plugin_name/runner_name'
-
-    Returns:
-        RunnerIdParts with parsed components
-
-    Raises:
-        ValueError: If runner_id format is invalid
-    """
-    if runner_id.startswith('plugin:'):
-        parts = runner_id[7:].split('/')
-        if len(parts) != 3:
-            raise ValueError(
-                f'Invalid plugin runner ID format: {runner_id}. '
-                f'Expected: plugin:author/plugin_name/runner_name'
-            )
-        plugin_author, plugin_name, runner_name = parts
-        if not plugin_author or not plugin_name or not runner_name:
-            raise ValueError(
-                f'Invalid plugin runner ID: {runner_id}. '
-                f'author, plugin_name, and runner_name must be non-empty'
-            )
-        return RunnerIdParts(
-            source='plugin',
-            plugin_author=plugin_author,
-            plugin_name=plugin_name,
-            runner_name=runner_name,
-        )
-    else:
-        # Only plugin runner IDs are valid at the protocol boundary.
-        raise ValueError(
-            f'Invalid runner ID format: {runner_id}. '
-            f'Expected: plugin:author/plugin_name/runner_name'
-        )
-
-
-def format_runner_id(
-    source: str,
-    plugin_author: str,
-    plugin_name: str,
-    runner_name: str,
-) -> str:
-    """Format runner ID from components.
-
-    Args:
-        source: Runner source ('plugin')
-        plugin_author: Plugin author
-        plugin_name: Plugin name
-        runner_name: Runner component name
-
-    Returns:
-        Runner ID string
-    """
-    if source == 'plugin':
-        return f'plugin:{plugin_author}/{plugin_name}/{runner_name}'
-    else:
-        raise ValueError(f'Invalid runner source: {source}')
-
-
-def is_plugin_runner_id(runner_id: str) -> bool:
-    """Check if runner ID is a plugin runner.
-
-    Args:
-        runner_id: Runner ID string
-
-    Returns:
-        True if runner ID starts with 'plugin:'
-    """
-    return runner_id.startswith('plugin:')

src/langbot/pkg/agent/runner/invoker.py

@@ -1,131 +0,0 @@
-"""Plugin-runtime invocation for AgentRunner executions."""
-
-from __future__ import annotations
-
-import asyncio
-import time
-import traceback
-import typing
-
-from langbot_plugin.entities.io.errors import ActionCallTimeoutError
-
-from ...core import app
-from .context_builder import AgentRunContextPayload
-from .descriptor import AgentRunnerDescriptor
-from .errors import RunnerExecutionError
-
-
-class AgentRunnerInvoker:
-    """Invoke an AgentRunner through the plugin runtime.
-
-    This keeps runtime transport, deadline enforcement, and transport error
-    mapping out of the orchestration state machine.
-    """
-
-    ap: app.Application
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def invoke(
-        self,
-        descriptor: AgentRunnerDescriptor,
-        context: AgentRunContextPayload,
-    ) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
-        """Invoke the runner and yield raw result dictionaries."""
-        if not self.ap.plugin_connector.is_enable_plugin:
-            raise RunnerExecutionError(
-                descriptor.id,
-                'Plugin system is disabled',
-                retryable=False,
-            )
-
-        try:
-            gen = self.ap.plugin_connector.run_agent(
-                plugin_author=descriptor.plugin_author,
-                plugin_name=descriptor.plugin_name,
-                runner_name=descriptor.runner_name,
-                context=context,
-            )
-
-            while True:
-                try:
-                    result_dict = await self._next_with_deadline(gen, descriptor, context)
-                except StopAsyncIteration:
-                    break
-                yield result_dict
-
-        except asyncio.TimeoutError as e:
-            raise RunnerExecutionError(
-                descriptor.id,
-                'Runner timed out (code: runner.timeout)',
-                retryable=True,
-            ) from e
-        except ActionCallTimeoutError as e:
-            raise RunnerExecutionError(
-                descriptor.id,
-                f'{e} (code: runner.timeout)',
-                retryable=True,
-            ) from e
-        except RunnerExecutionError:
-            raise
-        except Exception as e:
-            self.ap.logger.error(
-                f'Runner {descriptor.id} unexpected error: {traceback.format_exc()}'
-            )
-            raise RunnerExecutionError(
-                descriptor.id,
-                str(e),
-                retryable=False,
-            )
-
-    async def _next_with_deadline(
-        self,
-        gen: typing.AsyncGenerator[dict[str, typing.Any], None],
-        descriptor: AgentRunnerDescriptor,
-        context: AgentRunContextPayload,
-    ) -> dict[str, typing.Any]:
-        """Read the next runner result while enforcing the run deadline."""
-        remaining = self._remaining_deadline_seconds(context)
-        if remaining is not None and remaining <= 0:
-            await self._close_generator(gen, descriptor)
-            raise asyncio.TimeoutError
-
-        try:
-            if remaining is None:
-                return await anext(gen)
-            return await asyncio.wait_for(anext(gen), timeout=remaining)
-        except StopAsyncIteration:
-            if self._is_deadline_exhausted(context):
-                raise asyncio.TimeoutError
-            raise
-        except asyncio.TimeoutError:
-            await self._close_generator(gen, descriptor)
-            raise
-
-    def _remaining_deadline_seconds(
-        self,
-        context: AgentRunContextPayload,
-    ) -> float | None:
-        runtime = context.get('runtime') or {}
-        deadline_at = runtime.get('deadline_at')
-        if deadline_at is None:
-            return None
-        try:
-            return float(deadline_at) - time.time()
-        except (TypeError, ValueError):
-            return None
-
-    def _is_deadline_exhausted(self, context: AgentRunContextPayload) -> bool:
-        remaining = self._remaining_deadline_seconds(context)
-        return remaining is not None and remaining <= 0
-
-    async def _close_generator(
-        self,
-        gen: typing.AsyncGenerator[dict[str, typing.Any], None],
-        descriptor: AgentRunnerDescriptor,
-    ) -> None:
-        try:
-            await gen.aclose()
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to close timed-out runner {descriptor.id}: {e}')

src/langbot/pkg/agent/runner/orchestrator.py

@@ -1,302 +0,0 @@
-"""Agent run orchestrator for coordinating runner execution."""
-
-from __future__ import annotations
-
-import typing
-
-from langbot_plugin.api.entities.builtin.provider import message as provider_message
-from langbot_plugin.api.entities.builtin.pipeline import query as pipeline_query
-
-from ...core import app
-from .binding_resolver import AgentBindingResolver
-from .context_builder import AgentRunContextBuilder, AgentRunContextPayload
-from .descriptor import AgentRunnerDescriptor
-from .host_models import AgentBinding, AgentEventEnvelope
-from .invoker import AgentRunnerInvoker
-from .query_bridge import QueryRunBridge
-from .registry import AgentRunnerRegistry
-from .resource_builder import AgentResourceBuilder
-from .result_normalizer import AgentResultNormalizer
-from .run_journal import AgentRunJournal, MAX_ARTIFACT_INLINE_BYTES as _MAX_ARTIFACT_INLINE_BYTES
-from .session_registry import AgentRunSessionRegistry, get_session_registry
-from .state_scope import build_state_context
-from ...provider.tools.loaders import skill as skill_loader
-
-
-MAX_ARTIFACT_INLINE_BYTES = _MAX_ARTIFACT_INLINE_BYTES
-
-
-class AgentRunOrchestrator:
-    """Coordinate one AgentRunner execution.
-
-    The orchestrator keeps the run state machine readable and delegates
-    transport, Query bridging, and persistence side effects to narrower
-    collaborators.
-    """
-
-    ap: app.Application
-    registry: AgentRunnerRegistry
-    context_builder: AgentRunContextBuilder
-    resource_builder: AgentResourceBuilder
-    result_normalizer: AgentResultNormalizer
-    binding_resolver: AgentBindingResolver
-    query_bridge: QueryRunBridge
-    invoker: AgentRunnerInvoker
-    journal: AgentRunJournal
-    _session_registry: AgentRunSessionRegistry
-
-    def __init__(
-        self,
-        ap: app.Application,
-        registry: AgentRunnerRegistry,
-    ):
-        self.ap = ap
-        self.registry = registry
-        self.context_builder = AgentRunContextBuilder(ap)
-        self.resource_builder = AgentResourceBuilder(ap)
-        self.result_normalizer = AgentResultNormalizer(ap)
-        self.binding_resolver = AgentBindingResolver()
-        self.query_bridge = QueryRunBridge(self.binding_resolver)
-        self.invoker = AgentRunnerInvoker(ap)
-        self.journal = AgentRunJournal(ap)
-        self._session_registry = get_session_registry()
-
-    async def run(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        bound_plugins: list[str] | None = None,
-        adapter_context: dict[str, typing.Any] | None = None,
-    ) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
-        """Run an AgentRunner from an event-first envelope."""
-        runner_id = binding.runner_id
-        descriptor = await self.registry.get(runner_id, bound_plugins)
-
-        resources = await self.resource_builder.build_resources_from_binding(
-            event=event,
-            binding=binding,
-            descriptor=descriptor,
-        )
-
-        context = await self.context_builder.build_context_from_event(
-            event=event,
-            binding=binding,
-            descriptor=descriptor,
-            resources=resources,
-        )
-
-        session_query_id = None
-        if adapter_context:
-            query = adapter_context.get('_query')
-            if query is not None:
-                skill_loader.restore_activated_skills_from_state(
-                    self.ap,
-                    query,
-                    context.get('state', {}),
-                )
-            session_query_id = adapter_context.get('query_id')
-            if 'params' in adapter_context:
-                context['adapter']['extra']['params'] = adapter_context['params']
-            if adapter_context.get('prompt_get'):
-                context['context']['available_apis']['prompt_get'] = True
-
-        state_context = build_state_context(event, binding, descriptor)
-        run_id = context['run_id']
-        await self._session_registry.register(
-            run_id=run_id,
-            runner_id=descriptor.id,
-            query_id=session_query_id,
-            plugin_identity=descriptor.get_plugin_id(),
-            resources=resources,
-            permissions=descriptor.permissions or {},
-            conversation_id=event.conversation_id,
-            state_policy={
-                'enable_state': binding.state_policy.enable_state,
-                'state_scopes': list(binding.state_policy.state_scopes),
-            },
-            state_context=state_context,
-        )
-
-        event_log_id = await self.journal.write_event_log(
-            event=event,
-            binding=binding,
-            run_id=run_id,
-            runner_id=descriptor.id,
-        )
-        await self.journal.register_input_artifacts(
-            event=event,
-            run_id=run_id,
-            runner_id=descriptor.id,
-        )
-        if event.event_type == 'message.received' and event.conversation_id:
-            await self.journal.write_user_transcript(
-                event=event,
-                event_log_id=event_log_id,
-            )
-
-        pending_artifact_refs: list[dict[str, typing.Any]] = []
-
-        try:
-            async for result_dict in self.invoker.invoke(descriptor, context):
-                result_type = result_dict.get('type')
-
-                if result_type == 'artifact.created':
-                    artifact_ref = await self.journal.handle_artifact_created(
-                        result_dict=result_dict,
-                        event=event,
-                        run_id=run_id,
-                        runner_id=descriptor.id,
-                    )
-                    pending_artifact_refs.append(artifact_ref)
-                    await self.result_normalizer.normalize(result_dict, descriptor)
-                    continue
-
-                if result_type == 'state.updated':
-                    await self.journal.handle_state_updated_event(result_dict, event, binding, descriptor)
-                    await self.result_normalizer.normalize(result_dict, descriptor)
-                    continue
-
-                if result_type == 'message.completed' and event.conversation_id:
-                    merged_refs = self.journal.merge_artifact_refs(
-                        pending_artifact_refs,
-                        result_dict,
-                    )
-                    pending_artifact_refs.clear()
-
-                    await self.journal.write_assistant_transcript(
-                        result_dict=result_dict,
-                        event=event,
-                        run_id=run_id,
-                        runner_id=descriptor.id,
-                        artifact_refs=merged_refs if merged_refs else None,
-                    )
-
-                result = await self.result_normalizer.normalize(result_dict, descriptor)
-                if result is not None:
-                    yield result
-        finally:
-            await self._session_registry.unregister(run_id)
-
-    async def run_from_query(
-        self,
-        query: pipeline_query.Query,
-    ) -> typing.AsyncGenerator[provider_message.Message | provider_message.MessageChunk, None]:
-        """Run an AgentRunner from the current Pipeline Query entry point."""
-        plan = self.query_bridge.build_plan(query)
-        adapter_context = dict(plan.adapter_context)
-        adapter_context['_query'] = query
-        async for result in self.run(
-            plan.event,
-            plan.binding,
-            bound_plugins=plan.bound_plugins,
-            adapter_context=adapter_context,
-        ):
-            yield result
-
-    def resolve_runner_id_for_telemetry(self, query: pipeline_query.Query) -> str | None:
-        """Resolve runner ID for telemetry/logging without full execution."""
-        return self.query_bridge.resolve_runner_id_for_telemetry(query)
-
-    async def _invoke_runner(
-        self,
-        descriptor: AgentRunnerDescriptor,
-        context: AgentRunContextPayload,
-    ) -> typing.AsyncGenerator[dict[str, typing.Any], None]:
-        """Compatibility delegate for older tests and internal callers."""
-        async for result in self.invoker.invoke(descriptor, context):
-            yield result
-
-    async def _next_with_deadline(
-        self,
-        gen: typing.AsyncGenerator[dict[str, typing.Any], None],
-        descriptor: AgentRunnerDescriptor,
-        context: AgentRunContextPayload,
-    ) -> dict[str, typing.Any]:
-        return await self.invoker._next_with_deadline(gen, descriptor, context)
-
-    def _remaining_deadline_seconds(
-        self,
-        context: AgentRunContextPayload,
-    ) -> float | None:
-        return self.invoker._remaining_deadline_seconds(context)
-
-    def _is_deadline_exhausted(self, context: AgentRunContextPayload) -> bool:
-        return self.invoker._is_deadline_exhausted(context)
-
-    async def _close_generator(
-        self,
-        gen: typing.AsyncGenerator[dict[str, typing.Any], None],
-        descriptor: AgentRunnerDescriptor,
-    ) -> None:
-        await self.invoker._close_generator(gen, descriptor)
-
-    async def _handle_state_updated_event(
-        self,
-        result_dict: dict[str, typing.Any],
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-    ) -> None:
-        await self.journal.handle_state_updated_event(result_dict, event, binding, descriptor)
-
-    async def _write_event_log(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        run_id: str,
-        runner_id: str,
-    ) -> str:
-        return await self.journal.write_event_log(event, binding, run_id, runner_id)
-
-    async def _register_input_artifacts(
-        self,
-        event: AgentEventEnvelope,
-        run_id: str,
-        runner_id: str,
-    ) -> None:
-        await self.journal.register_input_artifacts(event, run_id, runner_id)
-
-    def _decode_attachment_content(
-        self,
-        content: typing.Any,
-    ) -> tuple[bytes | None, str | None]:
-        return self.journal.decode_attachment_content(content)
-
-    async def _write_user_transcript(
-        self,
-        event: AgentEventEnvelope,
-        event_log_id: str,
-    ) -> None:
-        await self.journal.write_user_transcript(event, event_log_id)
-
-    async def _handle_artifact_created(
-        self,
-        result_dict: dict[str, typing.Any],
-        event: AgentEventEnvelope,
-        run_id: str,
-        runner_id: str,
-    ) -> dict[str, typing.Any]:
-        return await self.journal.handle_artifact_created(result_dict, event, run_id, runner_id)
-
-    def _merge_artifact_refs(
-        self,
-        pending_refs: list[dict[str, typing.Any]],
-        result_dict: dict[str, typing.Any],
-    ) -> list[dict[str, typing.Any]]:
-        return self.journal.merge_artifact_refs(pending_refs, result_dict)
-
-    async def _write_assistant_transcript(
-        self,
-        result_dict: dict[str, typing.Any],
-        event: AgentEventEnvelope,
-        run_id: str,
-        runner_id: str,
-        artifact_refs: list[dict[str, typing.Any]] | None = None,
-    ) -> None:
-        await self.journal.write_assistant_transcript(
-            result_dict=result_dict,
-            event=event,
-            run_id=run_id,
-            runner_id=runner_id,
-            artifact_refs=artifact_refs,
-        )

src/langbot/pkg/agent/runner/persistent_state_store.py

@@ -1,431 +0,0 @@
-"""Persistent state store for AgentRunner protocol state.
-
-This module provides a database-backed state store for event-first Protocol v1.
-"""
-from __future__ import annotations
-
-import typing
-import json
-import threading
-from datetime import datetime
-
-import sqlalchemy
-from sqlalchemy.ext.asyncio import AsyncEngine
-from sqlalchemy import select, delete, update
-
-from .descriptor import AgentRunnerDescriptor
-from .host_models import AgentEventEnvelope, AgentBinding
-from .state_scope import (
-    VALID_STATE_SCOPES,
-    build_state_scope_key,
-    get_binding_identity,
-    normalize_state_key,
-)
-from ...entity.persistence.agent_runner_state import AgentRunnerState
-
-
-# Maximum value_json size (256KB)
-MAX_VALUE_JSON_BYTES = 256 * 1024
-
-
-class PersistentStateStore:
-    """Database-backed state store for AgentRunner protocol state.
-
-    IMPORTANT: This is HOST-OWNED protocol state, NOT plugin instance state.
-
-    This store provides:
-    1. Persistent storage across runs via database
-    2. Scope isolation by runner_id + binding_identity + scope
-    3. Policy enforcement (enable_state, state_scopes)
-    4. JSON value validation and size limits
-
-    Used by:
-    - Event-first Protocol v1 (async methods)
-    - State API handlers (get/set/delete/list)
-    """
-
-    def __init__(self, db_engine: AsyncEngine):
-        self._db_engine = db_engine
-
-    def _get_scope_key(
-        self,
-        scope: str,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-    ) -> str | None:
-        """Get scope key for given scope."""
-        return build_state_scope_key(scope, event, binding, descriptor)
-
-    def _check_scope_enabled(self, scope: str, binding: AgentBinding) -> bool:
-        """Check if scope is enabled by binding's state_policy."""
-        state_policy = binding.state_policy
-        if not state_policy.enable_state:
-            return False
-        return scope in state_policy.state_scopes
-
-    def _validate_json_value(
-        self,
-        value: typing.Any,
-        logger: typing.Any = None,
-    ) -> tuple[str | None, str | None]:
-        """Validate and serialize value to JSON.
-
-        Returns:
-            Tuple of (json_string, error_message). If error_message is not None,
-            json_string will be None.
-        """
-        try:
-            json_str = json.dumps(value, ensure_ascii=False)
-        except (TypeError, ValueError) as e:
-            return None, f'Value is not JSON-serializable: {e}'
-
-        # Check size limit
-        json_bytes = len(json_str.encode('utf-8'))
-        if json_bytes > MAX_VALUE_JSON_BYTES:
-            return None, f'Value size {json_bytes} bytes exceeds limit {MAX_VALUE_JSON_BYTES} bytes'
-
-        return json_str, None
-
-    # ========== Async DB Operations ==========
-
-    async def build_snapshot_from_event(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-    ) -> dict[str, dict[str, typing.Any]]:
-        """Build state snapshot for all scopes from event and binding.
-
-        Reads from database, respects state_policy.
-        """
-        state_policy = binding.state_policy
-
-        # If state is disabled, return all empty scopes
-        if not state_policy.enable_state:
-            return {
-                'conversation': {},
-                'actor': {},
-                'subject': {},
-                'runner': {},
-            }
-
-        snapshot: dict[str, dict[str, typing.Any]] = {
-            'conversation': {},
-            'actor': {},
-            'subject': {},
-            'runner': {},
-        }
-
-        async with self._db_engine.connect() as conn:
-            for scope in VALID_STATE_SCOPES:
-                if not self._check_scope_enabled(scope, binding):
-                    continue
-
-                scope_key = self._get_scope_key(scope, event, binding, descriptor)
-                if not scope_key:
-                    continue
-
-                # Query all state entries for this scope_key
-                result = await conn.execute(
-                    select(AgentRunnerState.state_key, AgentRunnerState.value_json)
-                    .where(AgentRunnerState.scope_key == scope_key)
-                )
-                rows = result.fetchall()
-
-                for row in rows:
-                    key = row.state_key
-                    value_json = row.value_json
-                    if value_json:
-                        try:
-                            snapshot[scope][key] = json.loads(value_json)
-                        except json.JSONDecodeError:
-                            pass  # Skip invalid JSON
-
-        # Seed external.conversation_id from event.conversation_id if not set
-        if self._check_scope_enabled('conversation', binding) and event.conversation_id:
-            if 'external.conversation_id' not in snapshot['conversation']:
-                snapshot['conversation']['external.conversation_id'] = event.conversation_id
-
-        return snapshot
-
-    async def apply_update_from_event(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-        scope: str,
-        key: str,
-        value: typing.Any,
-        logger: typing.Any = None,
-    ) -> tuple[bool, str | None]:
-        """Apply a state update from event context.
-
-        Returns:
-            Tuple of (success, error_message). If success is False, error_message
-            contains the reason.
-        """
-        state_policy = binding.state_policy
-
-        # Check if state is disabled
-        if not state_policy.enable_state:
-            return False, 'State is disabled by binding policy'
-
-        # Validate scope
-        if scope not in VALID_STATE_SCOPES:
-            return False, f'Invalid scope: {scope}'
-
-        # Check if scope is enabled
-        if not self._check_scope_enabled(scope, binding):
-            return False, f'Scope "{scope}" not enabled by binding policy'
-
-        # Map accepted key aliases
-        key = normalize_state_key(key)
-
-        # Get scope key
-        scope_key = self._get_scope_key(scope, event, binding, descriptor)
-        if not scope_key:
-            return False, f'Missing identity for scope "{scope}"'
-
-        # Validate and serialize value
-        value_json, error = self._validate_json_value(value, logger)
-        if error:
-            return False, error
-
-        # Build context fields
-        binding_identity = get_binding_identity(binding)
-
-        async with self._db_engine.begin() as conn:
-            # Check if entry exists
-            result = await conn.execute(
-                select(AgentRunnerState.id)
-                .where(AgentRunnerState.scope_key == scope_key)
-                .where(AgentRunnerState.state_key == key)
-            )
-            existing = result.first()
-
-            now = datetime.utcnow()
-
-            if existing:
-                # Update existing entry
-                await conn.execute(
-                    update(AgentRunnerState)
-                    .where(AgentRunnerState.id == existing.id)
-                    .values(
-                        value_json=value_json,
-                        updated_at=now,
-                    )
-                )
-            else:
-                # Insert new entry
-                await conn.execute(
-                    sqlalchemy.insert(AgentRunnerState).values(
-                        runner_id=descriptor.id,
-                        binding_identity=binding_identity,
-                        scope=scope,
-                        scope_key=scope_key,
-                        state_key=key,
-                        value_json=value_json,
-                        bot_id=event.bot_id,
-                        workspace_id=event.workspace_id,
-                        conversation_id=event.conversation_id,
-                        thread_id=event.thread_id,
-                        actor_type=event.actor.actor_type if event.actor else None,
-                        actor_id=event.actor.actor_id if event.actor else None,
-                        subject_type=event.subject.subject_type if event.subject else None,
-                        subject_id=event.subject.subject_id if event.subject else None,
-                        created_at=now,
-                        updated_at=now,
-                    )
-                )
-
-        return True, None
-
-    async def state_get(
-        self,
-        scope_key: str,
-        state_key: str,
-    ) -> typing.Any:
-        """Get a single state value by scope_key and state_key.
-
-        Used by State API handlers.
-        """
-        state_key = normalize_state_key(state_key)
-
-        async with self._db_engine.connect() as conn:
-            result = await conn.execute(
-                select(AgentRunnerState.value_json)
-                .where(AgentRunnerState.scope_key == scope_key)
-                .where(AgentRunnerState.state_key == state_key)
-            )
-            row = result.first()
-
-            if not row or not row.value_json:
-                return None
-
-            try:
-                return json.loads(row.value_json)
-            except json.JSONDecodeError:
-                return None
-
-    async def state_set(
-        self,
-        scope_key: str,
-        state_key: str,
-        value: typing.Any,
-        runner_id: str,
-        binding_identity: str,
-        scope: str,
-        context: dict[str, typing.Any] | None = None,
-        logger: typing.Any = None,
-    ) -> tuple[bool, str | None]:
-        """Set a state value.
-
-        Used by State API handlers.
-        Context contains optional fields like bot_id, conversation_id, etc.
-        """
-        state_key = normalize_state_key(state_key)
-
-        # Validate and serialize value
-        value_json, error = self._validate_json_value(value, logger)
-        if error:
-            return False, error
-
-        context = context or {}
-
-        async with self._db_engine.begin() as conn:
-            # Check if entry exists
-            result = await conn.execute(
-                select(AgentRunnerState.id)
-                .where(AgentRunnerState.scope_key == scope_key)
-                .where(AgentRunnerState.state_key == state_key)
-            )
-            existing = result.first()
-
-            now = datetime.utcnow()
-
-            if existing:
-                # Update existing entry
-                await conn.execute(
-                    update(AgentRunnerState)
-                    .where(AgentRunnerState.id == existing.id)
-                    .values(
-                        value_json=value_json,
-                        updated_at=now,
-                    )
-                )
-            else:
-                # Insert new entry
-                await conn.execute(
-                    sqlalchemy.insert(AgentRunnerState).values(
-                        runner_id=runner_id,
-                        binding_identity=binding_identity,
-                        scope=scope,
-                        scope_key=scope_key,
-                        state_key=state_key,
-                        value_json=value_json,
-                        bot_id=context.get('bot_id'),
-                        workspace_id=context.get('workspace_id'),
-                        conversation_id=context.get('conversation_id'),
-                        thread_id=context.get('thread_id'),
-                        actor_type=context.get('actor_type'),
-                        actor_id=context.get('actor_id'),
-                        subject_type=context.get('subject_type'),
-                        subject_id=context.get('subject_id'),
-                        created_at=now,
-                        updated_at=now,
-                    )
-                )
-
-        return True, None
-
-    async def state_delete(
-        self,
-        scope_key: str,
-        state_key: str,
-    ) -> bool:
-        """Delete a state value.
-
-        Returns True if deleted, False if not found.
-        """
-        state_key = normalize_state_key(state_key)
-
-        async with self._db_engine.begin() as conn:
-            result = await conn.execute(
-                delete(AgentRunnerState)
-                .where(AgentRunnerState.scope_key == scope_key)
-                .where(AgentRunnerState.state_key == state_key)
-                .returning(AgentRunnerState.id)
-            )
-            deleted = result.first()
-            return deleted is not None
-
-    async def state_list(
-        self,
-        scope_key: str,
-        prefix: str | None = None,
-        limit: int = 100,
-    ) -> tuple[list[str], bool]:
-        """List state keys in a scope.
-
-        Returns tuple of (keys, has_more).
-        """
-        # Enforce limit cap
-        limit = min(limit, 100)
-
-        async with self._db_engine.connect() as conn:
-            query = (
-                select(AgentRunnerState.state_key)
-                .where(AgentRunnerState.scope_key == scope_key)
-                .order_by(AgentRunnerState.state_key)
-                .limit(limit + 1)  # Fetch one extra to check has_more
-            )
-
-            if prefix:
-                prefix = normalize_state_key(prefix)
-                query = query.where(
-                    AgentRunnerState.state_key.like(f'{prefix}%')
-                )
-
-            result = await conn.execute(query)
-            rows = result.fetchall()
-
-            keys = [row.state_key for row in rows[:limit]]
-            has_more = len(rows) > limit
-
-            return keys, has_more
-
-    async def clear_all(self) -> None:
-        """Clear all state entries (for testing)."""
-        async with self._db_engine.begin() as conn:
-            await conn.execute(delete(AgentRunnerState))
-
-
-# Global singleton persistent state store
-_persistent_state_store: PersistentStateStore | None = None
-_persistent_state_store_lock = threading.Lock()
-
-
-def get_persistent_state_store(db_engine: AsyncEngine | None = None) -> PersistentStateStore:
-    """Get the global persistent state store singleton.
-
-    Args:
-        db_engine: Database engine (required on first call)
-
-    Returns:
-        PersistentStateStore singleton
-    """
-    global _persistent_state_store
-    with _persistent_state_store_lock:
-        if _persistent_state_store is None:
-            if db_engine is None:
-                raise RuntimeError("db_engine required for first call to get_persistent_state_store")
-            _persistent_state_store = PersistentStateStore(db_engine)
-        return _persistent_state_store
-
-
-def reset_persistent_state_store() -> None:
-    """Reset the global persistent state store (for testing)."""
-    global _persistent_state_store
-    with _persistent_state_store_lock:
-        _persistent_state_store = None

src/langbot/pkg/agent/runner/query_bridge.py

@@ -1,56 +0,0 @@
-"""Pipeline Query bridge for AgentRunner execution."""
-
-from __future__ import annotations
-
-import dataclasses
-import typing
-
-from langbot_plugin.api.entities.builtin.pipeline import query as pipeline_query
-
-from .binding_resolver import AgentBindingResolver
-from .config_migration import ConfigMigration
-from .errors import RunnerNotFoundError
-from .host_models import AgentBinding, AgentEventEnvelope
-from .query_entry_adapter import QueryEntryAdapter
-
-
-@dataclasses.dataclass(frozen=True)
-class QueryRunPlan:
-    """Projected event-first execution request for a Query-backed run."""
-
-    event: AgentEventEnvelope
-    binding: AgentBinding
-    bound_plugins: list[str] | None
-    adapter_context: dict[str, typing.Any]
-
-
-class QueryRunBridge:
-    """Project the current Pipeline Query entry point into Protocol v1 inputs."""
-
-    binding_resolver: AgentBindingResolver
-
-    def __init__(self, binding_resolver: AgentBindingResolver):
-        self.binding_resolver = binding_resolver
-
-    def build_plan(self, query: pipeline_query.Query) -> QueryRunPlan:
-        """Build an event-first run plan from a Pipeline Query."""
-        runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
-        if not runner_id:
-            raise RunnerNotFoundError('no runner configured')
-
-        event = QueryEntryAdapter.query_to_event(query)
-        agent_config = QueryEntryAdapter.config_to_agent_config(query, runner_id)
-        binding = self.binding_resolver.resolve_one(event, [agent_config])
-        bound_plugins = query.variables.get('_pipeline_bound_plugins')
-        adapter_context = QueryEntryAdapter.build_adapter_context(query, binding)
-
-        return QueryRunPlan(
-            event=event,
-            binding=binding,
-            bound_plugins=bound_plugins,
-            adapter_context=adapter_context,
-        )
-
-    def resolve_runner_id_for_telemetry(self, query: pipeline_query.Query) -> str | None:
-        """Resolve runner ID for telemetry/logging without full execution."""
-        return ConfigMigration.resolve_runner_id(query.pipeline_config)

src/langbot/pkg/agent/runner/query_entry_adapter.py

@@ -1,602 +0,0 @@
-"""Query entry adapter for converting Query to event-first envelope.
-
-This adapter bridges the current Query entry point with the event-first
-Protocol v1 architecture without exposing Query internals to runners.
-"""
-from __future__ import annotations
-
-import hashlib
-import typing
-
-from langbot_plugin.api.entities.builtin.pipeline import query as pipeline_query
-from langbot_plugin.api.entities.builtin.platform import message as platform_message
-from langbot_plugin.api.entities.builtin.agent_runner.event import (
-    AgentEventContext,
-    ConversationContext,
-    ActorContext,
-    SubjectContext,
-    RawEventRef,
-)
-from langbot_plugin.api.entities.builtin.agent_runner.input import AgentInput
-from langbot_plugin.api.entities.builtin.agent_runner.delivery import DeliveryContext
-
-from .host_models import (
-    AgentConfig,
-    AgentEventEnvelope,
-    ResourcePolicy,
-    StatePolicy,
-    DeliveryPolicy,
-)
-from . import events as runner_events
-
-
-class QueryEntryAdapter:
-    """Adapter for converting Query to event-first envelope.
-
-    This adapter is responsible for:
-    - Converting Query to AgentEventEnvelope
-    - Projecting current Pipeline config to temporary AgentConfig
-    - Putting Query-only fields into adapter context
-    """
-
-    INTERNAL_PREFIX = '_'
-    SENSITIVE_PATTERNS = ('secret', 'token', 'key', 'password', 'credential', 'api_key', 'apikey')
-    PERMISSION_VARS = ('_pipeline_bound_plugins', '_authorized', '_permission')
-
-    @classmethod
-    def query_to_event(
-        cls,
-        query: pipeline_query.Query,
-    ) -> AgentEventEnvelope:
-        """Convert Query to AgentEventEnvelope.
-
-        Args:
-            query: Current entry query
-
-        Returns:
-            AgentEventEnvelope for event-first processing
-        """
-        # Build event context
-        event = cls._build_event_context(query)
-
-        # Build conversation context
-        conversation = cls._build_conversation_context(query)
-
-        # Build actor context
-        actor = cls._build_actor_context(query)
-
-        # Build subject context
-        subject = cls._build_subject_context(query)
-
-        # Build input
-        input = cls._build_input(query)
-
-        # Build delivery context
-        delivery = cls._build_delivery_context(query)
-
-        # Build raw ref
-        raw_ref = cls._build_raw_ref(query)
-
-        return AgentEventEnvelope(
-            event_id=event.event_id or str(query.query_id),
-            event_type=event.event_type or runner_events.MESSAGE_RECEIVED,
-            event_time=event.event_time,
-            source="host_adapter",
-            source_event_type=event.source_event_type,
-            bot_id=query.bot_uuid,
-            workspace_id=None,  # Not available in Query
-            conversation_id=conversation.conversation_id,
-            thread_id=conversation.thread_id,
-            actor=actor,
-            subject=subject,
-            input=input,
-            delivery=delivery,
-            raw_ref=raw_ref,
-            data=event.data,
-        )
-
-    @classmethod
-    def config_to_agent_config(
-        cls,
-        query: pipeline_query.Query,
-        runner_id: str,
-    ) -> AgentConfig:
-        """Project the current Pipeline config container into target Agent config."""
-        pipeline_config = query.pipeline_config or {}
-        ai_config = pipeline_config.get('ai', {})
-        runner_config = ai_config.get('runner_config', {}).get(runner_id, {})
-        agent_id = getattr(query, 'pipeline_uuid', None)
-
-        # Build resource policy from current config
-        resource_policy = ResourcePolicy(
-            allowed_model_uuids=cls._extract_allowed_models(query),
-            allowed_tool_names=cls._extract_allowed_tools(query),
-            allowed_kb_uuids=cls._extract_allowed_kbs(query),
-            allowed_skill_names=cls._extract_allowed_skills(query),
-        )
-
-        # Build state policy
-        state_policy = StatePolicy(
-            enable_state=True,
-            state_scopes=["conversation", "actor", "subject", "runner"],
-        )
-
-        # Build delivery policy
-        delivery_policy = DeliveryPolicy(
-            enable_streaming=True,
-            enable_reply=True,
-        )
-
-        return AgentConfig(
-            agent_id=agent_id,
-            runner_id=runner_id,
-            runner_config=runner_config,
-            resource_policy=resource_policy,
-            state_policy=state_policy,
-            delivery_policy=delivery_policy,
-            event_types=[runner_events.MESSAGE_RECEIVED],
-            enabled=True,
-            metadata={'source': 'pipeline_adapter'},
-        )
-
-    @classmethod
-    def build_adapter_context(
-        cls,
-        query: pipeline_query.Query,
-        binding: AgentBinding,
-    ) -> dict[str, typing.Any]:
-        """Build Query-derived fields for the current entry adapter."""
-        return {
-            'params': cls.build_params(query),
-            'query_id': getattr(query, 'query_id', None),
-            'prompt_get': cls._has_effective_prompt(query),
-        }
-
-    @classmethod
-    def build_params(cls, query: pipeline_query.Query) -> dict[str, typing.Any]:
-        """Build adapter params from Pipeline variables with host filtering."""
-        params: dict[str, typing.Any] = {}
-        variables = getattr(query, 'variables', None)
-        if not variables:
-            return params
-
-        for key, value in variables.items():
-            if key.startswith(cls.INTERNAL_PREFIX):
-                continue
-            key_lower = key.lower()
-            if any(pattern in key_lower for pattern in cls.SENSITIVE_PATTERNS):
-                continue
-            if any(key == perm_var or key.startswith(perm_var) for perm_var in cls.PERMISSION_VARS):
-                continue
-            if cls.is_json_serializable(value):
-                params[key] = value
-
-        return params
-
-    @classmethod
-    def is_json_serializable(cls, value: typing.Any) -> bool:
-        """Return whether a value can safely cross the adapter boundary as JSON."""
-        if value is None or isinstance(value, (str, int, float, bool)):
-            return True
-        if isinstance(value, (list, tuple)):
-            return all(cls.is_json_serializable(item) for item in value)
-        if isinstance(value, dict):
-            return all(
-                isinstance(k, str) and cls.is_json_serializable(v)
-                for k, v in value.items()
-            )
-        return False
-
-    @classmethod
-    def _has_effective_prompt(cls, query: pipeline_query.Query) -> bool:
-        prompt = getattr(query, 'prompt', None)
-        messages = getattr(prompt, 'messages', None) if prompt is not None else None
-        return isinstance(messages, list)
-
-    # Private helper methods
-
-    @classmethod
-    def _build_event_context(
-        cls,
-        query: pipeline_query.Query,
-    ) -> AgentEventContext:
-        """Build AgentEventContext from Query."""
-        message_event = getattr(query, 'message_event', None)
-
-        event_data: dict[str, typing.Any] = {}
-        if message_event and hasattr(message_event, 'model_dump'):
-            try:
-                event_data = message_event.model_dump(mode='json')
-            except TypeError:
-                event_data = message_event.model_dump()
-            except Exception:
-                event_data = {}
-            event_data.pop('source_platform_object', None)
-
-        source_event_type = None
-        if message_event:
-            source_event_type = getattr(message_event, 'type', None)
-
-        message_chain = getattr(query, 'message_chain', None)
-        message_id = getattr(message_chain, 'message_id', None)
-        if message_id == -1:
-            message_id = None
-
-        event_time = None
-        if message_event:
-            event_time = getattr(message_event, 'time', None)
-        if isinstance(event_time, (int, float)):
-            event_time = int(event_time)
-
-        source_event_id = str(message_id or query.query_id)
-        return AgentEventContext(
-            event_id=cls._build_scoped_event_id(query, source_event_id, event_time),
-            event_type=runner_events.MESSAGE_RECEIVED,
-            event_time=event_time,
-            source="host_adapter",
-            source_event_type=source_event_type,
-            data=event_data,
-        )
-
-    @classmethod
-    def _build_scoped_event_id(
-        cls,
-        query: pipeline_query.Query,
-        source_event_id: str,
-        event_time: int | None,
-    ) -> str:
-        """Build a globally unique host event id from pipeline-local ids."""
-        launcher_type = getattr(query, 'launcher_type', None)
-        launcher_type_value = getattr(launcher_type, 'value', launcher_type) if launcher_type is not None else None
-        scope_parts = [
-            'host_adapter',
-            getattr(query, 'pipeline_uuid', None),
-            getattr(query, 'bot_uuid', None),
-            launcher_type_value,
-            getattr(query, 'launcher_id', None),
-            getattr(query, 'sender_id', None),
-            source_event_id,
-            event_time,
-        ]
-        scoped = '|'.join('' if part is None else str(part) for part in scope_parts)
-        digest = hashlib.sha256(scoped.encode('utf-8')).hexdigest()[:32]
-        return f'host:{digest}'
-
-    @classmethod
-    def _build_conversation_context(
-        cls,
-        query: pipeline_query.Query,
-    ) -> ConversationContext:
-        """Build ConversationContext from Query."""
-        # Handle launcher_type safely
-        launcher_type = getattr(query, 'launcher_type', None)
-        launcher_type_value = None
-        if launcher_type is not None:
-            launcher_type_value = getattr(launcher_type, 'value', launcher_type)
-
-        # Handle launcher_id
-        launcher_id = getattr(query, 'launcher_id', None)
-
-        # Build session_id from launcher info if available
-        session_id = None
-        if launcher_type_value and launcher_id:
-            session_id = f'{launcher_type_value}_{launcher_id}'
-
-        # Handle session and conversation_id
-        conversation_id = None
-        session = getattr(query, 'session', None)
-        if session:
-            conversation = getattr(session, 'using_conversation', None)
-            if conversation:
-                conversation_id = getattr(conversation, 'uuid', None)
-
-        if not conversation_id:
-            variables = getattr(query, 'variables', None) or {}
-            conversation_id = variables.get('conversation_id') or None
-
-        if not conversation_id:
-            conversation_id = session_id
-
-        # Handle sender_id
-        sender_id = getattr(query, 'sender_id', None)
-        if sender_id is not None:
-            sender_id = str(sender_id)
-
-        # Handle bot_uuid
-        bot_uuid = getattr(query, 'bot_uuid', None)
-
-        return ConversationContext(
-            conversation_id=str(conversation_id) if conversation_id is not None else None,
-            thread_id=None,
-            launcher_type=launcher_type_value,
-            launcher_id=launcher_id,
-            sender_id=sender_id,
-            bot_id=bot_uuid,
-            workspace_id=None,
-            session_id=session_id,
-        )
-
-    @classmethod
-    def _build_actor_context(
-        cls,
-        query: pipeline_query.Query,
-    ) -> ActorContext:
-        """Build ActorContext from Query."""
-        message_event = getattr(query, 'message_event', None)
-        sender = getattr(message_event, 'sender', None) if message_event else None
-        sender_id = getattr(query, 'sender_id', None)
-        actor_id = getattr(sender, 'id', None) if sender else None
-        if actor_id is None:
-            actor_id = sender_id
-        actor_name = sender.get_name() if sender and hasattr(sender, 'get_name') else None
-
-        return ActorContext(
-            actor_type="user",
-            actor_id=str(actor_id) if actor_id is not None else None,
-            actor_name=actor_name,
-            metadata={},
-        )
-
-    @classmethod
-    def _build_subject_context(
-        cls,
-        query: pipeline_query.Query,
-    ) -> SubjectContext:
-        """Build SubjectContext from Query."""
-        message_chain = getattr(query, 'message_chain', None)
-        message_id = getattr(message_chain, 'message_id', None) if message_chain else None
-        if message_id == -1:
-            message_id = None
-
-        query_id = getattr(query, 'query_id', None)
-
-        # Safely get launcher_type
-        launcher_type = getattr(query, 'launcher_type', None)
-        launcher_type_value = None
-        if launcher_type is not None:
-            launcher_type_value = getattr(launcher_type, 'value', launcher_type)
-
-        return SubjectContext(
-            subject_type="message",
-            subject_id=str(message_id or query_id or ''),
-            data={
-                "launcher_type": launcher_type_value,
-                "launcher_id": getattr(query, 'launcher_id', None),
-                "sender_id": str(getattr(query, 'sender_id', '')) if getattr(query, 'sender_id', None) else None,
-                "bot_uuid": getattr(query, 'bot_uuid', None),
-            },
-        )
-
-    @classmethod
-    def _build_input(
-        cls,
-        query: pipeline_query.Query,
-    ) -> AgentInput:
-        """Build AgentInput from Query."""
-        text = None
-        text_parts: list[str] = []
-        contents: list[dict[str, typing.Any]] = []
-
-        user_message = getattr(query, 'user_message', None)
-        if user_message:
-            content = getattr(user_message, 'content', None)
-            if isinstance(content, list):
-                for elem in content:
-                    elem_dict = None
-                    if hasattr(elem, 'model_dump'):
-                        elem_dict = elem.model_dump(mode='json')
-                    elif isinstance(elem, dict):
-                        elem_dict = elem
-
-                    if not isinstance(elem_dict, dict):
-                        continue
-
-                    contents.append(elem_dict)
-                    if elem_dict.get('type') == 'text':
-                        elem_text = elem_dict.get('text')
-                        if elem_text:
-                            text_parts.append(elem_text)
-            elif content is not None:
-                text = str(content)
-                contents.append({'type': 'text', 'text': text})
-
-        if text_parts:
-            text = ''.join(text_parts)
-
-        message_chain_dict = None
-        message_chain = getattr(query, 'message_chain', None)
-        if message_chain:
-            if hasattr(message_chain, 'model_dump'):
-                message_chain_dict = message_chain.model_dump(mode='json')
-
-        attachments = cls._build_attachments(query, contents)
-
-        return AgentInput(
-            text=text,
-            contents=contents,
-            message_chain=message_chain_dict,
-            attachments=attachments,
-        )
-
-    @classmethod
-    def _build_attachments(
-        cls,
-        query: pipeline_query.Query,
-        contents: list[dict[str, typing.Any]],
-    ) -> list[dict[str, typing.Any]]:
-        """Extract attachments from query."""
-        import uuid
-
-        attachments: list[dict[str, typing.Any]] = []
-
-        for elem in contents:
-            elem_type = elem.get('type')
-            artifact_id = str(uuid.uuid4())  # Generate unique ID
-
-            if elem_type == 'image_url':
-                image_url = elem.get('image_url') or {}
-                attachments.append({
-                    'artifact_id': artifact_id,
-                    'artifact_type': 'image',
-                    'source': 'url',
-                    'url': image_url.get('url') if isinstance(image_url, dict) else str(image_url),
-                })
-            elif elem_type == 'image_base64':
-                attachments.append({
-                    'artifact_id': artifact_id,
-                    'artifact_type': 'image',
-                    'source': 'base64',
-                    'content': elem.get('image_base64'),
-                })
-            elif elem_type == 'file_url':
-                attachments.append({
-                    'artifact_id': artifact_id,
-                    'artifact_type': 'file',
-                    'source': 'url',
-                    'url': elem.get('file_url'),
-                    'name': elem.get('file_name'),
-                })
-            elif elem_type == 'file_base64':
-                attachments.append({
-                    'artifact_id': artifact_id,
-                    'artifact_type': 'file',
-                    'source': 'base64',
-                    'content': elem.get('file_base64'),
-                    'name': elem.get('file_name'),
-                })
-
-        message_chain = getattr(query, 'message_chain', None)
-        if message_chain:
-            try:
-                message_components = iter(message_chain)
-            except TypeError:
-                message_components = iter(())
-
-            for component in message_components:
-                artifact_id = str(uuid.uuid4())  # Generate unique ID
-
-                if isinstance(component, platform_message.Image):
-                    attachments.append({
-                        'artifact_id': artifact_id,
-                        'artifact_type': 'image',
-                        'source': 'message_chain',
-                        'id': component.image_id or None,
-                        'url': component.url or None,
-                    })
-                elif isinstance(component, platform_message.File):
-                    attachments.append({
-                        'artifact_id': artifact_id,
-                        'artifact_type': 'file',
-                        'source': 'message_chain',
-                        'id': component.id or None,
-                        'name': component.name or None,
-                    })
-                elif isinstance(component, platform_message.Voice):
-                    attachments.append({
-                        'artifact_id': artifact_id,
-                        'artifact_type': 'voice',
-                        'source': 'message_chain',
-                        'id': component.voice_id or None,
-                        'url': component.url or None,
-                    })
-
-        return attachments
-
-    @classmethod
-    def _build_delivery_context(
-        cls,
-        query: pipeline_query.Query,
-    ) -> DeliveryContext:
-        """Build DeliveryContext from Query."""
-        message_chain = getattr(query, 'message_chain', None)
-        return DeliveryContext(
-            surface="platform",
-            reply_target={
-                "message_id": getattr(message_chain, 'message_id', None),
-            },
-            supports_streaming=True,
-            supports_edit=False,
-            supports_reaction=False,
-            platform_capabilities={},
-        )
-
-    @classmethod
-    def _build_raw_ref(
-        cls,
-        query: pipeline_query.Query,
-    ) -> RawEventRef | None:
-        """Build RawEventRef from Query."""
-        # For now, we don't store raw event payload
-        return None
-
-    @classmethod
-    def _extract_allowed_models(
-        cls,
-        query: pipeline_query.Query,
-    ) -> list[str] | None:
-        """Extract allowed model UUIDs from query."""
-        model_uuids: list[str] = []
-        model_uuid = getattr(query, 'use_llm_model_uuid', None)
-        if model_uuid:
-            model_uuids.append(model_uuid)
-
-        variables = getattr(query, 'variables', None) or {}
-        for fallback_uuid in variables.get('_fallback_model_uuids', []) or []:
-            if fallback_uuid and fallback_uuid not in model_uuids:
-                model_uuids.append(fallback_uuid)
-
-        return model_uuids or None
-
-    @classmethod
-    def _extract_allowed_tools(
-        cls,
-        query: pipeline_query.Query,
-    ) -> list[str] | None:
-        """Extract allowed tool names from query."""
-        use_funcs = getattr(query, 'use_funcs', None)
-        if not use_funcs:
-            return None
-        try:
-            tool_names = []
-            for func in use_funcs:
-                if isinstance(func, dict):
-                    name = func.get('name')
-                elif hasattr(func, 'name'):
-                    name = func.name
-                else:
-                    continue
-                if name:
-                    tool_names.append(name)
-            return tool_names if tool_names else None
-        except (TypeError, AttributeError):
-            return None
-
-    @classmethod
-    def _extract_allowed_kbs(
-        cls,
-        query: pipeline_query.Query,
-    ) -> list[str] | None:
-        """Extract allowed knowledge base UUIDs from query."""
-        variables = getattr(query, 'variables', None)
-        if not variables:
-            return None
-        kb_uuids = variables.get('_knowledge_base_uuids')
-        if kb_uuids:
-            return kb_uuids
-        return None
-
-    @classmethod
-    def _extract_allowed_skills(
-        cls,
-        query: pipeline_query.Query,
-    ) -> list[str] | None:
-        """Extract pipeline-visible skill names from query."""
-        variables = getattr(query, 'variables', None)
-        if not variables or '_pipeline_bound_skills' not in variables:
-            return None
-        bound_skills = variables.get('_pipeline_bound_skills')
-        if bound_skills is None:
-            return None
-        if not isinstance(bound_skills, list):
-            return []
-        return [str(skill_name) for skill_name in bound_skills if skill_name]

src/langbot/pkg/agent/runner/registry.py

@@ -1,293 +0,0 @@
-"""Agent runner registry for discovering and caching runner descriptors."""
-
-from __future__ import annotations
-
-import typing
-import asyncio
-
-from ...core import app
-from .descriptor import AgentRunnerDescriptor
-from .id import parse_runner_id, format_runner_id
-from .errors import RunnerNotFoundError, RunnerNotAuthorizedError
-
-
-class AgentRunnerRegistry:
-    """Registry for discovering and managing agent runners.
-
-    Responsibilities:
-    - Discover runners from plugin runtime via LIST_AGENT_RUNNERS
-    - Validate runner manifests (kind, metadata, spec)
-    - Cache discovered runners for performance
-    - Filter runners by bound plugins
-    - Handle manifest errors gracefully (log warning, skip runner)
-    """
-
-    ap: app.Application
-
-    _cache: dict[str, AgentRunnerDescriptor] | None
-    """Cached runner descriptors keyed by runner ID"""
-
-    _cache_lock: asyncio.Lock
-    """Lock for cache refresh operations"""
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-        self._cache = None
-        self._cache_lock = asyncio.Lock()
-
-    async def _discover_runners(self) -> dict[str, AgentRunnerDescriptor]:
-        """Discover runners from plugin runtime.
-
-        Always discovers ALL runners (no bound_plugins filter).
-        The cache should contain unfiltered discovery results.
-
-        Returns:
-            Dict of runner descriptors keyed by runner ID
-        """
-        if not self.ap.plugin_connector.is_enable_plugin:
-            return {}
-
-        runners: dict[str, AgentRunnerDescriptor] = {}
-
-        try:
-            # Always list all runners (bound_plugins=None)
-            plugin_runners = await self.ap.plugin_connector.list_agent_runners(None)
-
-            for runner_data in plugin_runners:
-                try:
-                    descriptor = self._validate_and_build_descriptor(runner_data)
-                    if descriptor is not None:
-                        runners[descriptor.id] = descriptor
-                except Exception as e:
-                    plugin_author = runner_data.get('plugin_author', 'unknown')
-                    plugin_name = runner_data.get('plugin_name', 'unknown')
-                    runner_name = runner_data.get('runner_name', 'unknown')
-                    self.ap.logger.warning(
-                        f'Invalid runner manifest for plugin:{plugin_author}/{plugin_name}/{runner_name}: {e}'
-                    )
-                    continue
-
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to list agent runners from plugin runtime: {e}')
-            return {}
-
-        return runners
-
-    def _validate_and_build_descriptor(self, runner_data: dict[str, typing.Any]) -> AgentRunnerDescriptor | None:
-        """Validate runner manifest and build descriptor.
-
-        Args:
-            runner_data: Raw runner data from plugin runtime with fields:
-                - plugin_author, plugin_name, runner_name
-                - manifest (full component manifest dict)
-                - protocol_version, capabilities, permissions, config (extracted from spec)
-
-        Returns:
-            AgentRunnerDescriptor if valid, None if invalid
-        """
-        plugin_author = runner_data.get('plugin_author', '')
-        plugin_name = runner_data.get('plugin_name', '')
-        runner_name = runner_data.get('runner_name', '')
-
-        if not plugin_author or not plugin_name or not runner_name:
-            return None
-
-        manifest = runner_data.get('manifest', {})
-
-        # Validate kind
-        kind = manifest.get('kind', '')
-        if kind != 'AgentRunner':
-            return None
-
-        # Validate metadata
-        metadata = manifest.get('metadata', {})
-        name = metadata.get('name', '')
-        if not name:
-            return None
-
-        # metadata.label must exist
-        label = metadata.get('label', {})
-        if not label:
-            label = {name: name}  # fallback
-
-        spec = manifest.get('spec', {})
-
-        # SDK now provides these directly extracted from spec. Fall back to
-        # manifest.spec for older runtimes/tests that return the raw manifest.
-        protocol_version = runner_data.get('protocol_version') or spec.get('protocol_version', '1')
-        config_schema = runner_data.get('config') or spec.get('config', [])
-        capabilities = runner_data.get('capabilities') or spec.get('capabilities', {})
-        permissions = runner_data.get('permissions') or spec.get('permissions', {})
-
-        # Build descriptor
-        runner_id = format_runner_id(
-            source='plugin',
-            plugin_author=plugin_author,
-            plugin_name=plugin_name,
-            runner_name=runner_name,
-        )
-
-        return AgentRunnerDescriptor(
-            id=runner_id,
-            source='plugin',
-            label=label,
-            description=metadata.get('description') or runner_data.get('runner_description'),
-            plugin_author=plugin_author,
-            plugin_name=plugin_name,
-            runner_name=runner_name,
-            plugin_version=runner_data.get('plugin_version'),
-            protocol_version=protocol_version,
-            config_schema=config_schema,
-            capabilities=capabilities,
-            permissions=permissions,
-            raw_manifest=manifest,
-        )
-
-    async def refresh(self) -> None:
-        """Refresh runner cache.
-
-        Always discovers ALL runners (no bound_plugins filter).
-        The cache contains unfiltered discovery results.
-        """
-        async with self._cache_lock:
-            self._cache = await self._discover_runners()
-
-    async def list_runners(
-        self,
-        bound_plugins: list[str] | None = None,
-        use_cache: bool = True,
-    ) -> list[AgentRunnerDescriptor]:
-        """List available runners.
-
-        Args:
-            bound_plugins: Optional filter for bound plugins (applied locally)
-            use_cache: Use cached data if available
-
-        Returns:
-            List of runner descriptors
-        """
-        if use_cache and self._cache is not None:
-            # Filter from cache
-            return self._filter_runners_by_bound_plugins(self._cache, bound_plugins)
-
-        # Discover fresh (always full list)
-        runners = await self._discover_runners()
-
-        # Update cache (full list, unfiltered)
-        async with self._cache_lock:
-            self._cache = runners
-
-        # Filter locally
-        return self._filter_runners_by_bound_plugins(runners, bound_plugins)
-
-    def _filter_runners_by_bound_plugins(
-        self,
-        runners: dict[str, AgentRunnerDescriptor],
-        bound_plugins: list[str] | None,
-    ) -> list[AgentRunnerDescriptor]:
-        """Filter runners by bound plugins.
-
-        Args:
-            runners: Dict of runner descriptors
-            bound_plugins: Optional filter (None means all plugins allowed)
-
-        Returns:
-            Filtered list of runner descriptors
-        """
-        if bound_plugins is None:
-            # All plugins allowed
-            return list(runners.values())
-
-        allowed_plugin_ids = set(bound_plugins)
-        filtered = []
-        for descriptor in runners.values():
-            plugin_id = descriptor.get_plugin_id()
-            if plugin_id in allowed_plugin_ids:
-                filtered.append(descriptor)
-
-        return filtered
-
-    async def get(
-        self,
-        runner_id: str,
-        bound_plugins: list[str] | None = None,
-    ) -> AgentRunnerDescriptor:
-        """Get a specific runner descriptor.
-
-        Args:
-            runner_id: Runner ID to lookup
-            bound_plugins: Optional bound plugins filter
-
-        Returns:
-            AgentRunnerDescriptor
-
-        Raises:
-            RunnerNotFoundError: If runner not found
-            RunnerNotAuthorizedError: If runner not in bound plugins
-        """
-        # Parse and validate runner ID format
-        try:
-            parse_runner_id(runner_id)
-        except ValueError as e:
-            raise RunnerNotFoundError(runner_id) from e
-
-        # Get from cache or discover (always full list)
-        if self._cache is None:
-            await self.refresh()
-
-        if self._cache is None:
-            raise RunnerNotFoundError(runner_id)
-
-        descriptor = self._cache.get(runner_id)
-        if descriptor is None:
-            raise RunnerNotFoundError(runner_id)
-
-        # Check authorization
-        if bound_plugins is not None:
-            plugin_id = descriptor.get_plugin_id()
-            if plugin_id not in bound_plugins:
-                raise RunnerNotAuthorizedError(runner_id, bound_plugins)
-
-        return descriptor
-
-    async def get_runner_metadata_for_pipeline(self) -> list[dict[str, typing.Any]]:
-        """Get runner metadata for pipeline configuration UI.
-
-        Returns runner options and their config schemas for the DynamicForm.
-        """
-        # Get all runners (no bound plugin filter for metadata listing)
-        runners = await self.list_runners(bound_plugins=None)
-
-        options = []
-        stages = []
-
-        for descriptor in runners:
-            config_schema = []
-            for index, config_item in enumerate(descriptor.config_schema):
-                item = dict(config_item)
-                if not item.get('id'):
-                    item_name = item.get('name') or str(index)
-                    item['id'] = f'{descriptor.id}.{item_name}'
-                config_schema.append(item)
-
-            # Add runner option
-            options.append(
-                {
-                    'name': descriptor.id,
-                    'label': descriptor.label,
-                    'description': descriptor.description,
-                }
-            )
-
-            # Add config schema as stage if not empty
-            if descriptor.config_schema:
-                stages.append(
-                    {
-                        'name': descriptor.id,
-                        'label': descriptor.label,
-                        'description': descriptor.description,
-                        'config': config_schema,
-                    }
-                )
-
-        return options, stages

src/langbot/pkg/agent/runner/resource_builder.py

@@ -1,304 +0,0 @@
-"""Agent resource builder for constructing authorized resources."""
-from __future__ import annotations
-
-import typing
-
-from ...core import app
-from .descriptor import AgentRunnerDescriptor
-from .context_builder import (
-    AgentResources,
-    ModelResource,
-    ToolResource,
-    KnowledgeBaseResource,
-    SkillResource,
-    StorageResource,
-)
-from . import config_schema
-from .host_models import AgentEventEnvelope, AgentBinding
-
-
-class AgentResourceBuilder:
-    """Builder for constructing AgentResources with permission filtering.
-
-    Responsibilities:
-    - Apply 3-layer permission filtering:
-        1. Runner manifest declared permissions
-        2. Pipeline extensions_preference (bound plugins/MCP servers)
-        3. Agent/runner config selected resources
-    - Build models list from authorized models
-    - Build tools list from bound plugins/MCP servers
-    - Build knowledge_bases list from config
-    - Build storage and files permissions summary
-
-    Note: This only builds the resource declaration. The actual proxy actions
-    in handler.py must still validate against ctx.resources at runtime.
-
-    Resource field names match the plugin SDK payload:
-    - ModelResource: model_id, model_type, provider
-    - ToolResource: tool_name, tool_type, description
-    - KnowledgeBaseResource: kb_id, kb_name, kb_type
-    - SkillResource: skill_name, display_name, description
-    - StorageResource: plugin_storage, workspace_storage
-    """
-
-    ap: app.Application
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def build_resources_from_binding(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-    ) -> AgentResources:
-        """Build AgentResources from event and binding.
-
-        This is the main entry point for Protocol v1.
-
-        Args:
-            event: Event envelope
-            binding: Agent binding with resource policy
-            descriptor: Runner descriptor with permissions and capabilities
-
-        Returns:
-            AgentResources dict with filtered resource lists
-        """
-        # Layer 1: Runner manifest permissions
-        manifest_perms = descriptor.permissions
-
-        # Layer 2: Binding resource policy
-        resource_policy = binding.resource_policy
-
-        # Layer 3: Agent/runner config
-        runner_config = binding.runner_config
-
-        # Build each resource category
-        models = await self._build_models_from_binding(
-            manifest_perms, resource_policy, descriptor, runner_config
-        )
-        tools = await self._build_tools_from_binding(
-            manifest_perms, resource_policy, binding
-        )
-        knowledge_bases = await self._build_knowledge_bases_from_binding(
-            manifest_perms, resource_policy, descriptor, runner_config
-        )
-        skills = self._build_skills_from_binding(
-            resource_policy, descriptor
-        )
-        storage = self._build_storage_from_binding(manifest_perms, binding)
-
-        return {
-            'models': models,
-            'tools': tools,
-            'knowledge_bases': knowledge_bases,
-            'skills': skills,
-            'files': [],  # Files are populated at runtime
-            'storage': storage,
-            'platform_capabilities': {},  # Reserved for EBA
-        }
-
-    async def _build_models_from_binding(
-        self,
-        manifest_perms: dict[str, list[str]],
-        resource_policy: typing.Any,
-        descriptor: AgentRunnerDescriptor,
-        runner_config: dict[str, typing.Any],
-    ) -> list[ModelResource]:
-        """Build models list from binding."""
-        models: list[ModelResource] = []
-        seen_model_ids: set[str] = set()
-
-        model_perms = manifest_perms.get('models', [])
-        allow_llm = 'invoke' in model_perms or 'stream' in model_perms
-        allow_rerank = 'rerank' in model_perms
-        if not allow_llm and not allow_rerank:
-            return models
-
-        # Get additional model UUID grants from resource policy.
-        allowed_uuids = resource_policy.allowed_model_uuids
-
-        # Add model resources from Agent/runner config schema
-        await self._append_config_declared_model_resources(
-            models=models,
-            seen_model_ids=seen_model_ids,
-            descriptor=descriptor,
-            runner_config=runner_config,
-            include_llm=allow_llm,
-            include_rerank=allow_rerank,
-        )
-
-        # Add explicitly allowed models
-        if allowed_uuids and allow_llm:
-            for model_uuid in allowed_uuids:
-                await self._append_llm_model_resource(models, seen_model_ids, model_uuid)
-
-        return models
-
-    async def _build_tools_from_binding(
-        self,
-        manifest_perms: dict[str, list[str]],
-        resource_policy: typing.Any,
-        binding: AgentBinding,
-    ) -> list[ToolResource]:
-        """Build tools list from binding."""
-        tools: list[ToolResource] = []
-
-        # Check manifest permission
-        tool_perms = manifest_perms.get('tools', [])
-        if 'detail' not in tool_perms and 'call' not in tool_perms:
-            return tools
-
-        # Get tool names from resource policy
-        allowed_names = resource_policy.allowed_tool_names
-
-        if allowed_names:
-            for tool_name in allowed_names:
-                tools.append({
-                    'tool_name': tool_name,
-                    'tool_type': None,
-                    'description': None,
-                })
-
-        return tools
-
-    async def _build_knowledge_bases_from_binding(
-        self,
-        manifest_perms: dict[str, list[str]],
-        resource_policy: typing.Any,
-        descriptor: AgentRunnerDescriptor,
-        runner_config: dict[str, typing.Any],
-    ) -> list[KnowledgeBaseResource]:
-        """Build knowledge bases list from binding."""
-        kb_resources: list[KnowledgeBaseResource] = []
-
-        # Check manifest permission
-        kb_perms = manifest_perms.get('knowledge_bases', [])
-        if 'list' not in kb_perms and 'retrieve' not in kb_perms:
-            return kb_resources
-
-        # Get KB UUID grants from schema-defined config fields.
-        kb_uuids = config_schema.extract_knowledge_base_uuids(descriptor, runner_config)
-
-        # Also include resource policy grants.
-        allowed_uuids = resource_policy.allowed_kb_uuids
-        if allowed_uuids:
-            kb_uuids = list(dict.fromkeys([*kb_uuids, *allowed_uuids]))
-
-        for kb_uuid in kb_uuids:
-            try:
-                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
-                if kb:
-                    kb_resources.append({
-                        'kb_id': kb_uuid,
-                        'kb_name': kb.get_name(),
-                        'kb_type': kb.knowledge_base_entity.kb_type if hasattr(kb.knowledge_base_entity, 'kb_type') else None,
-                    })
-            except Exception as e:
-                self.ap.logger.warning(f'Failed to build knowledge base resource {kb_uuid}: {e}')
-
-        return kb_resources
-
-    def _build_skills_from_binding(
-        self,
-        resource_policy: typing.Any,
-        descriptor: AgentRunnerDescriptor,
-    ) -> list[SkillResource]:
-        """Build pipeline-visible skill resource facts."""
-        if not config_schema.supports_skill_authoring(descriptor):
-            return []
-
-        skill_mgr = getattr(self.ap, 'skill_mgr', None)
-        if skill_mgr is None:
-            return []
-
-        loaded_skills = getattr(skill_mgr, 'skills', {}) or {}
-        allowed_names = resource_policy.allowed_skill_names
-        if allowed_names is None:
-            names = sorted(loaded_skills.keys())
-        else:
-            names = sorted(name for name in allowed_names if name in loaded_skills)
-
-        skills: list[SkillResource] = []
-        for skill_name in names:
-            skill_data = loaded_skills.get(skill_name) or {}
-            skills.append({
-                'skill_name': skill_name,
-                'display_name': skill_data.get('display_name') or skill_data.get('name') or skill_name,
-                'description': skill_data.get('description') or None,
-            })
-        return skills
-
-    def _build_storage_from_binding(
-        self,
-        manifest_perms: dict[str, list[str]],
-        binding: AgentBinding,
-    ) -> StorageResource:
-        """Build storage permissions from binding."""
-        storage_perms = manifest_perms.get('storage', [])
-        resource_policy = binding.resource_policy
-
-        return {
-            'plugin_storage': 'plugin' in storage_perms and resource_policy.allow_plugin_storage,
-            'workspace_storage': 'workspace' in storage_perms and resource_policy.allow_workspace_storage,
-        }
-
-    async def _append_config_declared_model_resources(
-        self,
-        models: list[ModelResource],
-        seen_model_ids: set[str],
-        descriptor: AgentRunnerDescriptor,
-        runner_config: dict[str, typing.Any],
-        include_llm: bool,
-        include_rerank: bool,
-    ) -> None:
-        """Authorize model-like values selected through DynamicForm fields."""
-        for model_type, model_uuid in config_schema.iter_config_model_refs(descriptor, runner_config):
-            if model_type == 'llm' and include_llm:
-                await self._append_llm_model_resource(models, seen_model_ids, model_uuid)
-            elif model_type == 'rerank' and include_rerank:
-                await self._append_rerank_model_resource(models, seen_model_ids, model_uuid)
-
-    async def _append_llm_model_resource(
-        self,
-        models: list[ModelResource],
-        seen_model_ids: set[str],
-        model_uuid: str | None,
-    ) -> None:
-        """Append an LLM model resource if it exists and has not been added."""
-        if not model_uuid or model_uuid == '__none__' or model_uuid in seen_model_ids:
-            return
-
-        try:
-            model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
-            if model and model.model_entity:
-                models.append({
-                    'model_id': model_uuid,
-                    'model_type': getattr(model.model_entity, 'model_type', None),
-                    'provider': getattr(model.provider_entity, 'name', None) if hasattr(model, 'provider_entity') else None,
-                })
-                seen_model_ids.add(model_uuid)
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to build LLM model resource {model_uuid}: {e}')
-
-    async def _append_rerank_model_resource(
-        self,
-        models: list[ModelResource],
-        seen_model_ids: set[str],
-        model_uuid: str | None,
-    ) -> None:
-        """Append a rerank model resource if it exists and has not been added."""
-        if not model_uuid or model_uuid == '__none__' or model_uuid in seen_model_ids:
-            return
-
-        try:
-            model = await self.ap.model_mgr.get_rerank_model_by_uuid(model_uuid)
-            if model and model.model_entity:
-                models.append({
-                    'model_id': model_uuid,
-                    'model_type': getattr(model.model_entity, 'model_type', 'rerank') or 'rerank',
-                    'provider': getattr(model.provider_entity, 'name', None) if hasattr(model, 'provider_entity') else None,
-                })
-                seen_model_ids.add(model_uuid)
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to build rerank model resource {model_uuid}: {e}')

src/langbot/pkg/agent/runner/result_normalizer.py

@@ -1,193 +0,0 @@
-"""Agent result normalizer for converting AgentRunResult to Pipeline messages."""
-from __future__ import annotations
-
-import typing
-
-from langbot_plugin.api.entities.builtin.provider import message as provider_message
-
-from ...core import app
-from .descriptor import AgentRunnerDescriptor
-from .errors import RunnerExecutionError, RunnerProtocolError
-
-
-# Maximum size for a single result payload (prevent memory exhaustion)
-MAX_RESULT_SIZE_BYTES = 1024 * 1024  # 1 MB
-
-
-class AgentResultNormalizer:
-    """Normalizer for converting AgentRunResult to Pipeline messages.
-
-    Responsibilities:
-    - Accept only supported result types (message.delta, message.completed, etc.)
-    - Map message.delta -> MessageChunk
-    - Map message.completed -> Message
-    - Map run.completed (with message) -> Message
-    - Handle run.failed as controlled error
-    - Ignore unknown types with warning
-    - Validate result size
-    - Validate message schema
-
-    Accepted result types:
-    - message.delta
-    - message.completed
-    - tool.call.started
-    - tool.call.completed
-    - state.updated
-    - run.completed
-    - run.failed
-    - action.requested (log only, don't execute)
-    """
-
-    ap: app.Application
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-
-    async def normalize(
-        self,
-        result_dict: dict[str, typing.Any],
-        descriptor: AgentRunnerDescriptor,
-    ) -> provider_message.Message | provider_message.MessageChunk | None:
-        """Normalize AgentRunResult to Message or MessageChunk.
-
-        Args:
-            result_dict: Raw result dict from plugin runtime
-            descriptor: Runner descriptor for error context
-
-        Returns:
-            Message, MessageChunk, or None (for non-message events)
-
-        Raises:
-            RunnerExecutionError: On run.failed
-            RunnerProtocolError: On invalid result format
-        """
-        # Validate result type
-        result_type = result_dict.get('type')
-        if not result_type:
-            raise RunnerProtocolError(descriptor.id, 'Missing result type')
-
-        # Validate result size
-        try:
-            import json
-            result_json = json.dumps(result_dict)
-            if len(result_json) > MAX_RESULT_SIZE_BYTES:
-                self.ap.logger.warning(
-                    f'Runner {descriptor.id} result too large ({len(result_json)} bytes), truncating'
-                )
-                # Truncate content if possible
-                data = result_dict.get('data', {})
-                if 'chunk' in data or 'message' in data:
-                    content = data.get('chunk', {}).get('content', '') or data.get('message', {}).get('content', '')
-                    if isinstance(content, str) and len(content) > 10000:
-                        # Keep reasonable length
-                        data['chunk'] = {'role': 'assistant', 'content': content[:10000] + '...[truncated]'}
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to validate runner {descriptor.id} result size: {e}')
-
-        # Handle each result type
-        data = result_dict.get('data', {})
-
-        if result_type == 'message.delta':
-            return self._normalize_message_delta(data, descriptor)
-
-        elif result_type == 'message.completed':
-            return self._normalize_message_completed(data, descriptor)
-
-        elif result_type == 'tool.call.started':
-            # Log only, don't yield to pipeline
-            self.ap.logger.debug(
-                f'Runner {descriptor.id} tool call started: {data.get("tool_name", "unknown")}'
-            )
-            return None
-
-        elif result_type == 'tool.call.completed':
-            # Log only, don't yield to pipeline
-            self.ap.logger.debug(
-                f'Runner {descriptor.id} tool call completed: {data.get("tool_name", "unknown")}'
-            )
-            return None
-
-        elif result_type == 'state.updated':
-            # Log for telemetry, don't yield to pipeline
-            # Orchestrator already handles the actual PersistentStateStore update.
-            scope = data.get('scope', 'unknown')
-            key = data.get('key', 'unknown')
-            value_repr = repr(data.get('value', '...'))[:100]  # Truncate for log
-            self.ap.logger.debug(
-                f'Runner {descriptor.id} state.updated logged: scope={scope}, key={key}, value={value_repr}'
-            )
-            return None
-
-        elif result_type == 'run.completed':
-            # May include final message
-            if 'message' in data:
-                return self._normalize_message_completed(data, descriptor)
-            # If no message, it's just completion signal
-            return None
-
-        elif result_type == 'run.failed':
-            error_msg = data.get('error', 'Unknown error')
-            error_code = data.get('code', 'unknown')
-            retryable = data.get('retryable', False)
-            raise RunnerExecutionError(
-                descriptor.id,
-                f'{error_msg} (code: {error_code})',
-                retryable=retryable,
-            )
-
-        elif result_type == 'action.requested':
-            # Reserved for EBA - log only, don't execute
-            self.ap.logger.info(
-                f'Runner {descriptor.id} requested action (not executed in current phase): '
-                f'{data.get("action", "unknown")}'
-            )
-            return None
-
-        elif result_type == 'artifact.created':
-            # Log for telemetry, consumed by orchestrator
-            artifact_id = data.get('artifact_id', 'unknown')
-            artifact_type = data.get('artifact_type', 'unknown')
-            self.ap.logger.debug(
-                f'Runner {descriptor.id} artifact.created logged: artifact_id={artifact_id}, type={artifact_type}'
-            )
-            return None
-
-        else:
-            # Unknown type - warn and ignore.
-            self.ap.logger.warning(
-                f'Runner {descriptor.id} returned unknown result type: {result_type}. '
-                f'Expected supported types (message.delta, message.completed, run.completed, run.failed, etc.)'
-            )
-            return None
-
-    def _normalize_message_delta(
-        self,
-        data: dict[str, typing.Any],
-        descriptor: AgentRunnerDescriptor,
-    ) -> provider_message.MessageChunk:
-        """Normalize message.delta to MessageChunk."""
-        chunk_data = data.get('chunk', {})
-        if not chunk_data:
-            raise RunnerProtocolError(descriptor.id, 'message.delta missing chunk data')
-
-        try:
-            chunk = provider_message.MessageChunk.model_validate(chunk_data)
-            return chunk
-        except Exception as e:
-            raise RunnerProtocolError(descriptor.id, f'Invalid chunk schema: {e}')
-
-    def _normalize_message_completed(
-        self,
-        data: dict[str, typing.Any],
-        descriptor: AgentRunnerDescriptor,
-    ) -> provider_message.Message:
-        """Normalize message.completed to Message."""
-        message_data = data.get('message', {})
-        if not message_data:
-            raise RunnerProtocolError(descriptor.id, 'message.completed missing message data')
-
-        try:
-            msg = provider_message.Message.model_validate(message_data)
-            return msg
-        except Exception as e:
-            raise RunnerProtocolError(descriptor.id, f'Invalid message schema: {e}')

src/langbot/pkg/agent/runner/run_journal.py

@@ -1,437 +0,0 @@
-"""Run-side effects for AgentRunner executions."""
-
-from __future__ import annotations
-
-import typing
-
-from ...core import app
-from .descriptor import AgentRunnerDescriptor
-from .errors import RunnerProtocolError
-from .host_models import AgentBinding, AgentEventEnvelope
-from .persistent_state_store import PersistentStateStore, get_persistent_state_store
-
-
-# Maximum inline artifact content size (1MB)
-MAX_ARTIFACT_INLINE_BYTES = 1 * 1024 * 1024
-
-
-class AgentRunJournal:
-    """Persist run events, transcript records, artifacts, and state updates."""
-
-    ap: app.Application
-
-    _persistent_state_store: PersistentStateStore | None
-
-    def __init__(self, ap: app.Application):
-        self.ap = ap
-        self._persistent_state_store = None
-
-    async def handle_state_updated_event(
-        self,
-        result_dict: dict[str, typing.Any],
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        descriptor: AgentRunnerDescriptor,
-    ) -> None:
-        """Handle state.updated result in event-first mode."""
-        data = result_dict.get('data', {})
-
-        scope = data.get('scope')
-        if not scope:
-            raise RunnerProtocolError(
-                descriptor.id,
-                'state.updated missing required field: scope',
-            )
-
-        key = data.get('key')
-        value = data.get('value')
-
-        if not key:
-            raise RunnerProtocolError(
-                descriptor.id,
-                'state.updated missing required field: key',
-            )
-
-        if self._persistent_state_store is None:
-            self._persistent_state_store = get_persistent_state_store(
-                self.ap.persistence_mgr.get_db_engine()
-            )
-
-        success, error = await self._persistent_state_store.apply_update_from_event(
-            event=event,
-            binding=binding,
-            descriptor=descriptor,
-            scope=scope,
-            key=key,
-            value=value,
-            logger=self.ap.logger,
-        )
-
-        if success:
-            self.ap.logger.debug(
-                f'Runner {descriptor.id} state.updated (event mode): scope={scope}, key={key}'
-            )
-        elif error:
-            self.ap.logger.warning(
-                f'Runner {descriptor.id} state.updated rejected: {error}'
-            )
-
-    async def write_event_log(
-        self,
-        event: AgentEventEnvelope,
-        binding: AgentBinding,
-        run_id: str,
-        runner_id: str,
-    ) -> str:
-        """Write incoming event to EventLog."""
-        import datetime
-
-        from .event_log_store import EventLogStore
-
-        store = EventLogStore(self.ap.persistence_mgr.get_db_engine())
-
-        input_summary = None
-        input_json = None
-        if event.input:
-            if event.input.text:
-                input_summary = event.input.text[:1000]
-            input_json = {
-                'text': event.input.text,
-                'contents': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents],
-                'attachments': [a.model_dump(mode='json') if hasattr(a, 'model_dump') else a for a in event.input.attachments],
-            }
-
-        return await store.append_event(
-            event_id=event.event_id,
-            event_type=event.event_type,
-            source=event.source,
-            bot_id=event.bot_id,
-            workspace_id=event.workspace_id,
-            conversation_id=event.conversation_id,
-            thread_id=event.thread_id,
-            actor_type=event.actor.actor_type if event.actor else None,
-            actor_id=event.actor.actor_id if event.actor else None,
-            actor_name=event.actor.actor_name if event.actor else None,
-            subject_type=event.subject.subject_type if event.subject else None,
-            subject_id=event.subject.subject_id if event.subject else None,
-            input_summary=input_summary,
-            input_json=input_json,
-            run_id=run_id,
-            runner_id=runner_id,
-            event_time=datetime.datetime.fromtimestamp(event.event_time) if event.event_time else None,
-        )
-
-    async def register_input_artifacts(
-        self,
-        event: AgentEventEnvelope,
-        run_id: str,
-        runner_id: str,
-    ) -> None:
-        """Register current-event attachments referenced by AgentInput."""
-        if not event.input or not event.input.attachments:
-            return
-
-        from .artifact_store import ArtifactStore
-
-        store = ArtifactStore(self.ap.persistence_mgr.get_db_engine())
-
-        for attachment in event.input.attachments:
-            data = attachment.model_dump(mode='json') if hasattr(attachment, 'model_dump') else attachment
-            if not isinstance(data, dict):
-                continue
-
-            artifact_id = data.get('artifact_id')
-            artifact_type = data.get('artifact_type') or 'file'
-            if not artifact_id:
-                continue
-
-            content, parsed_mime_type = self.decode_attachment_content(data.get('content'))
-            url = data.get('url')
-            platform_ref_id = data.get('id')
-            storage_key = None
-            storage_type = 'metadata_only'
-            if content is None:
-                if url:
-                    storage_key = url
-                    storage_type = 'url'
-                elif platform_ref_id:
-                    storage_key = platform_ref_id
-                    storage_type = 'platform_ref'
-
-            metadata = {
-                'input_attachment': True,
-                'input_source': data.get('source') or 'platform',
-            }
-            if url:
-                metadata['url'] = url
-            if platform_ref_id:
-                metadata['platform_ref_id'] = platform_ref_id
-
-            try:
-                await store.register_artifact(
-                    artifact_id=artifact_id,
-                    artifact_type=artifact_type,
-                    source='platform',
-                    storage_key=storage_key,
-                    storage_type=storage_type,
-                    mime_type=data.get('mime_type') or parsed_mime_type,
-                    name=data.get('name'),
-                    size_bytes=data.get('size') or (len(content) if content is not None else None),
-                    conversation_id=event.conversation_id,
-                    run_id=run_id,
-                    runner_id=runner_id,
-                    bot_id=event.bot_id,
-                    workspace_id=event.workspace_id,
-                    metadata=metadata,
-                    content=content,
-                )
-            except Exception as e:
-                self.ap.logger.warning(
-                    f'Failed to register input artifact {artifact_id}: {e}'
-                )
-
-    def decode_attachment_content(
-        self,
-        content: typing.Any,
-    ) -> tuple[bytes | None, str | None]:
-        """Decode base64 attachment content, including data URLs."""
-        if not isinstance(content, str) or not content:
-            return None, None
-
-        import base64
-        import binascii
-
-        mime_type = None
-        payload = content
-        if content.startswith('data:') and ',' in content:
-            header, payload = content.split(',', 1)
-            if ';base64' in header:
-                mime_type = header[5:].split(';', 1)[0] or None
-
-        try:
-            return base64.b64decode(payload, validate=False), mime_type
-        except (binascii.Error, ValueError):
-            return None, mime_type
-
-    async def write_user_transcript(
-        self,
-        event: AgentEventEnvelope,
-        event_log_id: str,
-    ) -> None:
-        """Write user message to Transcript."""
-        from .transcript_store import TranscriptStore
-
-        store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
-
-        content = event.input.text if event.input else None
-        content_json = None
-        if event.input:
-            content_json = {
-                'role': 'user',
-                'content': [c.model_dump(mode='json') if hasattr(c, 'model_dump') else c for c in event.input.contents] if event.input.contents else [],
-            }
-
-        artifact_refs = []
-        if event.input and event.input.attachments:
-            for a in event.input.attachments:
-                artifact_refs.append(a.model_dump(mode='json') if hasattr(a, 'model_dump') else a)
-
-        await store.append_transcript(
-            transcript_id=None,
-            event_id=event_log_id,
-            conversation_id=event.conversation_id,
-            role='user',
-            content=content,
-            content_json=content_json,
-            artifact_refs=artifact_refs if artifact_refs else None,
-            thread_id=event.thread_id,
-            item_type='message',
-            metadata={
-                'actor_type': event.actor.actor_type if event.actor else None,
-                'actor_id': event.actor.actor_id if event.actor else None,
-            },
-        )
-
-    async def handle_artifact_created(
-        self,
-        result_dict: dict[str, typing.Any],
-        event: AgentEventEnvelope,
-        run_id: str,
-        runner_id: str,
-    ) -> dict[str, typing.Any]:
-        """Handle artifact.created result, register artifact, and write EventLog."""
-        import base64
-        import uuid
-
-        from .artifact_store import ArtifactStore
-        from .event_log_store import EventLogStore
-
-        data = result_dict.get('data', {})
-
-        result_run_id = result_dict.get('run_id')
-        if result_run_id and result_run_id != run_id:
-            raise RunnerProtocolError(
-                runner_id,
-                f'artifact.created run_id mismatch: expected {run_id}, got {result_run_id}',
-            )
-
-        artifact_id = data.get('artifact_id') or str(uuid.uuid4())
-        artifact_type = data.get('artifact_type')
-        if not artifact_type:
-            raise RunnerProtocolError(
-                runner_id,
-                'artifact.created missing required field: artifact_type',
-            )
-
-        mime_type = data.get('mime_type')
-        name = data.get('name')
-        size_bytes = data.get('size_bytes')
-        sha256 = data.get('sha256')
-        metadata = data.get('metadata')
-        content_base64 = data.get('content_base64')
-
-        content: bytes | None = None
-        if content_base64:
-            try:
-                content = base64.b64decode(content_base64, validate=True)
-            except Exception as e:
-                raise RunnerProtocolError(
-                    runner_id,
-                    f'artifact.created invalid base64 content: {e}',
-                )
-
-            if len(content) > MAX_ARTIFACT_INLINE_BYTES:
-                raise RunnerProtocolError(
-                    runner_id,
-                    f'artifact.created content size {len(content)} bytes exceeds limit {MAX_ARTIFACT_INLINE_BYTES} bytes',
-                )
-
-        artifact_store = ArtifactStore(self.ap.persistence_mgr.get_db_engine())
-        try:
-            registered_id = await artifact_store.register_artifact(
-                artifact_id=artifact_id,
-                artifact_type=artifact_type,
-                source='runner',
-                mime_type=mime_type,
-                name=name,
-                size_bytes=size_bytes,
-                sha256=sha256,
-                conversation_id=event.conversation_id,
-                run_id=run_id,
-                runner_id=runner_id,
-                bot_id=event.bot_id,
-                workspace_id=event.workspace_id,
-                metadata=metadata,
-                content=content,
-            )
-        except Exception as e:
-            raise RunnerProtocolError(
-                runner_id,
-                f'artifact.created failed to register artifact: {e}',
-            )
-
-        event_log_store = EventLogStore(self.ap.persistence_mgr.get_db_engine())
-        await event_log_store.append_event(
-            event_id=str(uuid.uuid4()),
-            event_type='artifact.created',
-            source='runner',
-            bot_id=event.bot_id,
-            workspace_id=event.workspace_id,
-            conversation_id=event.conversation_id,
-            thread_id=event.thread_id,
-            actor_type=event.actor.actor_type if event.actor else None,
-            actor_id=event.actor.actor_id if event.actor else None,
-            actor_name=event.actor.actor_name if event.actor else None,
-            input_summary=f'Artifact created: {artifact_type}',
-            input_json={
-                'artifact_id': registered_id,
-                'artifact_type': artifact_type,
-                'mime_type': mime_type,
-                'name': name,
-                'size_bytes': size_bytes,
-            },
-            run_id=run_id,
-            runner_id=runner_id,
-        )
-
-        return {
-            'artifact_id': registered_id,
-            'artifact_type': artifact_type,
-            'mime_type': mime_type,
-            'name': name,
-        }
-
-    def merge_artifact_refs(
-        self,
-        pending_refs: list[dict[str, typing.Any]],
-        result_dict: dict[str, typing.Any],
-    ) -> list[dict[str, typing.Any]]:
-        """Merge pending artifact refs with a message's own refs."""
-        merged = list(pending_refs)
-        seen_ids = {ref.get('artifact_id') for ref in pending_refs if ref.get('artifact_id')}
-
-        data = result_dict.get('data', {})
-        message = data.get('message', {})
-        message_refs = message.get('artifact_refs', [])
-
-        if isinstance(message_refs, list):
-            for ref in message_refs:
-                if isinstance(ref, dict):
-                    artifact_id = ref.get('artifact_id')
-                    if artifact_id and artifact_id not in seen_ids:
-                        merged.append(ref)
-                        seen_ids.add(artifact_id)
-
-        return merged
-
-    async def write_assistant_transcript(
-        self,
-        result_dict: dict[str, typing.Any],
-        event: AgentEventEnvelope,
-        run_id: str,
-        runner_id: str,
-        artifact_refs: list[dict[str, typing.Any]] | None = None,
-    ) -> None:
-        """Write assistant message to Transcript."""
-        import uuid
-
-        from .transcript_store import TranscriptStore
-
-        store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
-
-        data = result_dict.get('data', {})
-        message = data.get('message', {})
-
-        content = None
-        content_json = None
-
-        if isinstance(message.get('content'), str):
-            content = message['content']
-            content_json = message
-        elif isinstance(message.get('content'), list):
-            text_parts = []
-            for c in message['content']:
-                if isinstance(c, dict) and c.get('type') == 'text':
-                    text_parts.append(c.get('text', ''))
-            content = ' '.join(text_parts) if text_parts else None
-            content_json = message
-
-        assistant_event_id = str(uuid.uuid4())
-
-        await store.append_transcript(
-            transcript_id=str(uuid.uuid4()),
-            event_id=assistant_event_id,
-            conversation_id=event.conversation_id,
-            role='assistant',
-            content=content,
-            content_json=content_json,
-            artifact_refs=artifact_refs,
-            thread_id=event.thread_id,
-            item_type='message',
-            run_id=run_id,
-            runner_id=runner_id,
-            metadata={
-                'run_id': run_id,
-                'runner_id': runner_id,
-            },
-        )

src/langbot/pkg/agent/runner/session_registry.py

@@ -1,264 +0,0 @@
-"""Agent run session registry for proxy action permission validation."""
-from __future__ import annotations
-
-import asyncio
-import copy
-import typing
-import time
-import threading
-
-from .context_builder import AgentResources
-
-
-class AgentRunSessionStatus(typing.TypedDict):
-    """Status tracking for agent run session."""
-    started_at: int
-    last_activity_at: int
-
-
-class RunAuthorizationSnapshot(typing.TypedDict):
-    """Frozen authorization data for one active run.
-
-    ResourceBuilder creates the authorized resource list once before runner
-    execution. Runtime proxy handlers must validate against this run-scoped
-    snapshot instead of recomputing resource policy.
-    """
-
-    resources: AgentResources
-    permissions: dict[str, list[str]]
-    conversation_id: str | None
-    state_policy: dict[str, typing.Any]
-    state_context: dict[str, typing.Any]
-    authorized_ids: dict[str, set[str]]
-
-
-class AgentRunSession(typing.TypedDict):
-    """Session for an active agent runner execution.
-
-    Stored in AgentRunSessionRegistry for proxy action permission validation.
-
-    Fields:
-        run_id: Unique run identifier (UUID from AgentRunContext)
-        runner_id: Runner descriptor ID (plugin:author/name/runner)
-        query_id: Host entry query ID, only present for query-based adapters
-        plugin_identity: Plugin identifier (author/name) of the runner
-        authorization: Run-scoped authorization snapshot; runtime auth truth
-        status: Session status tracking
-    """
-    run_id: str
-    runner_id: str
-    query_id: int | None
-    plugin_identity: str  # author/name
-    authorization: RunAuthorizationSnapshot
-    status: AgentRunSessionStatus
-
-
-class AgentRunSessionRegistry:
-    """Registry for active agent run sessions.
-
-    Host-owned registry for tracking active AgentRunner executions.
-    Used by proxy actions in handler.py to validate resource access.
-
-    Key: run_id (UUID from AgentRunContext)
-    Value: AgentRunSession with authorized resources
-
-    Thread-safe via asyncio.Lock.
-    """
-
-    _sessions: dict[str, AgentRunSession]
-    _lock: asyncio.Lock
-
-    def __init__(self):
-        self._sessions = {}
-        self._lock = asyncio.Lock()
-
-    async def register(
-        self,
-        run_id: str,
-        runner_id: str,
-        query_id: int | None,
-        plugin_identity: str,
-        resources: AgentResources,
-        conversation_id: str | None = None,
-        permissions: dict[str, list[str]] | None = None,
-        state_policy: dict[str, typing.Any] | None = None,
-        state_context: dict[str, typing.Any] | None = None,
-    ) -> None:
-        """Register a new agent run session.
-
-        Args:
-            run_id: Unique run identifier
-            runner_id: Runner descriptor ID
-            query_id: Host entry query ID, only present for query-based adapters
-            plugin_identity: Plugin identifier (author/name)
-            resources: Authorized resources for this run
-            conversation_id: Conversation ID for history/event access
-            permissions: Runner permissions from descriptor (artifacts, history, events, etc.)
-            state_policy: State policy from binding (enable_state, state_scopes)
-            state_context: Context for state API (scope_keys, binding_identity, etc.)
-        """
-        now = int(time.time())
-
-        # Normalize permissions to empty dict if None
-        permissions = permissions or {}
-
-        # Normalize state_policy to defaults if None
-        if state_policy is None:
-            state_policy = {'enable_state': True, 'state_scopes': ['conversation', 'actor']}
-
-        # Normalize state_context to empty dict if None
-        state_context = state_context or {}
-
-        resources_snapshot = copy.deepcopy(resources)
-        authorization: RunAuthorizationSnapshot = {
-            'resources': resources_snapshot,
-            'permissions': copy.deepcopy(permissions),
-            'conversation_id': conversation_id,
-            'state_policy': copy.deepcopy(state_policy),
-            'state_context': copy.deepcopy(state_context),
-            'authorized_ids': self._build_authorized_ids(resources_snapshot),
-        }
-
-        session: AgentRunSession = {
-            'run_id': run_id,
-            'runner_id': runner_id,
-            'query_id': query_id,
-            'plugin_identity': plugin_identity,
-            'authorization': authorization,
-            'status': {
-                'started_at': now,
-                'last_activity_at': now,
-            },
-        }
-
-        async with self._lock:
-            self._sessions[run_id] = session
-
-    def _build_authorized_ids(self, resources: AgentResources) -> dict[str, set[str]]:
-        """Pre-compute authorized resource IDs for O(1) lookup."""
-        return {
-            'model': {m.get('model_id') for m in resources.get('models', [])},
-            'tool': {t.get('tool_name') for t in resources.get('tools', [])},
-            'knowledge_base': {kb.get('kb_id') for kb in resources.get('knowledge_bases', [])},
-            'skill': {s.get('skill_name') for s in resources.get('skills', [])},
-            'file': {f.get('file_id') for f in resources.get('files', [])},
-        }
-
-    async def unregister(self, run_id: str) -> None:
-        """Unregister an agent run session.
-
-        Args:
-            run_id: Unique run identifier
-        """
-        async with self._lock:
-            if run_id in self._sessions:
-                del self._sessions[run_id]
-
-    async def get(self, run_id: str) -> AgentRunSession | None:
-        """Get session by run_id.
-
-        Args:
-            run_id: Unique run identifier
-
-        Returns:
-            AgentRunSession if found, None otherwise
-        """
-        async with self._lock:
-            return self._sessions.get(run_id)
-
-    async def update_activity(self, run_id: str) -> None:
-        """Update last activity timestamp for session.
-
-        Args:
-            run_id: Unique run identifier
-        """
-        async with self._lock:
-            if run_id in self._sessions:
-                self._sessions[run_id]['status']['last_activity_at'] = int(time.time())
-
-    def is_resource_allowed(
-        self,
-        session: AgentRunSession,
-        resource_type: str,
-        resource_id: str,
-    ) -> bool:
-        """Check if resource access is allowed for this session.
-
-        Uses pre-computed authorized IDs for O(1) lookup.
-
-        Args:
-            session: AgentRunSession to check
-            resource_type: Resource type ('model', 'tool', 'knowledge_base', 'storage', 'file')
-            resource_id: Resource identifier (model_id, tool_name, kb_id, 'plugin'/'workspace', file_key)
-
-        Returns:
-            True if resource is authorized, False otherwise
-        """
-        authorization = session['authorization']
-        authorized_ids = authorization['authorized_ids']
-        resources = authorization['resources']
-
-        if resource_type in ('model', 'tool', 'knowledge_base', 'skill', 'file'):
-            return resource_id in authorized_ids.get(resource_type, set())
-
-        if resource_type == 'storage':
-            storage = resources.get('storage', {})
-            if resource_id == 'plugin':
-                return storage.get('plugin_storage', False)
-            elif resource_id == 'workspace':
-                return storage.get('workspace_storage', False)
-            return False
-
-        return False
-
-    async def list_active_runs(self) -> list[AgentRunSession]:
-        """List all active run sessions.
-
-        Returns:
-            List of active AgentRunSession dicts
-        """
-        async with self._lock:
-            return list(self._sessions.values())
-
-    async def cleanup_stale_sessions(self, max_age_seconds: int = 3600) -> int:
-        """Cleanup sessions that have been inactive for too long.
-
-        Args:
-            max_age_seconds: Maximum inactivity time in seconds (default 1 hour)
-
-        Returns:
-            Number of sessions cleaned up
-        """
-        now = int(time.time())
-        cleaned = 0
-
-        async with self._lock:
-            stale_run_ids = []
-            for run_id, session in self._sessions.items():
-                last_activity = session['status'].get('last_activity_at', 0)
-                if now - last_activity > max_age_seconds:
-                    stale_run_ids.append(run_id)
-
-            for run_id in stale_run_ids:
-                del self._sessions[run_id]
-                cleaned += 1
-
-        return cleaned
-
-
-# Global registry instance (singleton)
-_global_registry: AgentRunSessionRegistry | None = None
-_global_registry_lock = threading.Lock()
-
-
-def get_session_registry() -> AgentRunSessionRegistry:
-    """Get global session registry instance (thread-safe singleton).
-
-    Returns:
-        AgentRunSessionRegistry singleton
-    """
-    global _global_registry
-    with _global_registry_lock:
-        if _global_registry is None:
-            _global_registry = AgentRunSessionRegistry()
-        return _global_registry

src/langbot/pkg/agent/runner/state_scope.py

@@ -1,113 +0,0 @@
-"""State scope key helpers for AgentRunner host-owned state."""
-from __future__ import annotations
-
-import typing
-
-from .descriptor import AgentRunnerDescriptor
-from .host_models import AgentBinding, AgentEventEnvelope
-
-
-VALID_STATE_SCOPES = ('conversation', 'actor', 'subject', 'runner')
-
-STATE_KEY_ALIASES = {
-    'conversation_id': 'external.conversation_id',
-}
-
-
-def normalize_state_key(key: str) -> str:
-    """Map accepted public aliases to protocol state keys."""
-    return STATE_KEY_ALIASES.get(key, key)
-
-
-def get_binding_identity(binding: AgentBinding) -> str:
-    """Return the stable binding identity used for state isolation."""
-    if binding.binding_id:
-        return binding.binding_id
-
-    scope = binding.scope
-    if scope.scope_type and scope.scope_id:
-        return f'{scope.scope_type}:{scope.scope_id}'
-
-    return 'unknown_binding'
-
-
-def build_state_scope_key(
-    scope: str,
-    event: AgentEventEnvelope,
-    binding: AgentBinding,
-    descriptor: AgentRunnerDescriptor,
-) -> str | None:
-    """Build the storage key for one state scope.
-
-    Returns None when the event lacks the identity required by that scope.
-    """
-    binding_identity = get_binding_identity(binding)
-
-    if scope == 'conversation':
-        if not event.conversation_id:
-            return None
-        parts = [descriptor.id, binding_identity, event.conversation_id]
-        if event.thread_id:
-            parts.append(event.thread_id)
-        return f'conversation:{":".join(parts)}'
-
-    if scope == 'actor':
-        if not event.actor or not event.actor.actor_id:
-            return None
-        parts = [
-            descriptor.id,
-            binding_identity,
-            event.actor.actor_type or 'user',
-            event.actor.actor_id,
-        ]
-        return f'actor:{":".join(parts)}'
-
-    if scope == 'subject':
-        if not event.subject or not event.subject.subject_id:
-            return None
-        parts = [
-            descriptor.id,
-            binding_identity,
-            event.subject.subject_type or 'unknown',
-            event.subject.subject_id,
-        ]
-        return f'subject:{":".join(parts)}'
-
-    if scope == 'runner':
-        return f'runner:{descriptor.id}:{binding_identity}'
-
-    return None
-
-
-def build_state_scope_keys(
-    event: AgentEventEnvelope,
-    binding: AgentBinding,
-    descriptor: AgentRunnerDescriptor,
-) -> dict[str, str]:
-    """Build all available scope keys for an event/binding pair."""
-    scope_keys: dict[str, str] = {}
-    for scope in VALID_STATE_SCOPES:
-        scope_key = build_state_scope_key(scope, event, binding, descriptor)
-        if scope_key:
-            scope_keys[scope] = scope_key
-    return scope_keys
-
-
-def build_state_context(
-    event: AgentEventEnvelope,
-    binding: AgentBinding,
-    descriptor: AgentRunnerDescriptor,
-) -> dict[str, typing.Any]:
-    """Build the State API context stored in the run session."""
-    return {
-        'scope_keys': build_state_scope_keys(event, binding, descriptor),
-        'binding_identity': get_binding_identity(binding),
-        'bot_id': event.bot_id,
-        'workspace_id': event.workspace_id,
-        'conversation_id': event.conversation_id,
-        'thread_id': event.thread_id,
-        'actor_type': event.actor.actor_type if event.actor else None,
-        'actor_id': event.actor.actor_id if event.actor else None,
-        'subject_type': event.subject.subject_type if event.subject else None,
-        'subject_id': event.subject.subject_id if event.subject else None,
-    }

src/langbot/pkg/agent/runner/transcript_store.py

@@ -1,341 +0,0 @@
-"""Transcript store for writing and querying conversation history."""
-from __future__ import annotations
-
-import json
-import datetime
-import typing
-import uuid
-
-import sqlalchemy
-from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession
-from sqlalchemy.orm import sessionmaker
-
-from ...entity.persistence.transcript import Transcript
-from langbot_plugin.api.entities.builtin.provider import message as provider_message
-
-
-class TranscriptStore:
-    """Store for Transcript records.
-
-    Handles writing transcript items and querying them for history API.
-    All methods are async and use the provided database engine.
-    """
-
-    engine: AsyncEngine
-
-    # Hard limits
-    MAX_CONTENT_LENGTH = 4000
-    HARD_LIMIT = 100
-
-    def __init__(self, engine: AsyncEngine):
-        self.engine = engine
-        self._session_factory = sessionmaker(
-            engine, class_=AsyncSession, expire_on_commit=False
-        )
-
-    async def append_transcript(
-        self,
-        transcript_id: str | None,
-        event_id: str,
-        conversation_id: str,
-        role: str,
-        content: str | None = None,
-        content_json: dict[str, typing.Any] | None = None,
-        artifact_refs: list[dict[str, typing.Any]] | None = None,
-        thread_id: str | None = None,
-        item_type: str = "message",
-        run_id: str | None = None,
-        runner_id: str | None = None,
-        metadata: dict[str, typing.Any] | None = None,
-    ) -> str:
-        """Append a transcript item.
-
-        Args:
-            transcript_id: Unique transcript ID (generated if None)
-            event_id: Source event ID
-            conversation_id: Conversation ID
-            role: Message role (user, assistant, system, tool)
-            content: Text content
-            content_json: Full structured content
-            artifact_refs: Artifact references
-            thread_id: Thread ID
-            item_type: Item type
-            run_id: Run ID that generated this
-            runner_id: Runner ID that generated this
-            metadata: Additional metadata
-
-        Returns:
-            The transcript_id
-        """
-        if transcript_id is None:
-            transcript_id = str(uuid.uuid4())
-
-        # Truncate content if too long
-        if content and len(content) > self.MAX_CONTENT_LENGTH:
-            content = content[:self.MAX_CONTENT_LENGTH - 3] + "..."
-
-        async with self._session_factory() as session:
-            item = Transcript(
-                transcript_id=transcript_id,
-                event_id=event_id,
-                conversation_id=conversation_id,
-                thread_id=thread_id,
-                role=role,
-                item_type=item_type,
-                content=content,
-                content_json=json.dumps(content_json) if content_json else None,
-                artifact_refs_json=json.dumps(artifact_refs) if artifact_refs else None,
-                seq=0,
-                run_id=run_id,
-                runner_id=runner_id,
-                created_at=datetime.datetime.utcnow(),
-                metadata_json=json.dumps(metadata) if metadata else None,
-            )
-            session.add(item)
-            await session.flush()
-            item.seq = item.id or await self._get_next_seq(conversation_id)
-            await session.commit()
-
-        return transcript_id
-
-    async def page_transcript(
-        self,
-        conversation_id: str,
-        before_seq: int | None = None,
-        after_seq: int | None = None,
-        limit: int = 50,
-        direction: str = "backward",
-        include_artifacts: bool = False,
-    ) -> tuple[list[dict[str, typing.Any]], int | None, int | None, bool]:
-        """Page through transcript items.
-
-        Args:
-            conversation_id: Conversation ID
-            before_seq: Get items before this sequence (backward)
-            after_seq: Get items after this sequence (forward)
-            limit: Maximum items to return (capped at 100)
-            direction: 'backward' (older) or 'forward' (newer)
-            include_artifacts: Include artifact refs
-
-        Returns:
-            Tuple of (items, next_seq, prev_seq, has_more)
-        """
-        limit = min(limit, self.HARD_LIMIT)
-
-        async with self._session_factory() as session:
-            query = sqlalchemy.select(Transcript).where(
-                Transcript.conversation_id == conversation_id
-            )
-
-            if direction == "backward" and before_seq is not None:
-                query = query.where(Transcript.seq < before_seq)
-                query = query.order_by(Transcript.seq.desc())
-            elif direction == "forward" and after_seq is not None:
-                query = query.where(Transcript.seq > after_seq)
-                query = query.order_by(Transcript.seq.asc())
-            else:
-                # Default: most recent items first (backward from latest)
-                query = query.order_by(Transcript.seq.desc())
-
-            query = query.limit(limit + 1)
-
-            result = await session.execute(query)
-            rows = result.scalars().all()
-
-            items = [self._row_to_dict(row, include_artifacts) for row in rows[:limit]]
-            has_more = len(rows) > limit
-
-            # Calculate cursors
-            next_seq = None
-            prev_seq = None
-
-            if direction == "backward":
-                # Items are in descending order
-                if items:
-                    next_seq = items[-1].get('seq') if has_more else None
-                    prev_seq = items[0].get('seq')
-            else:
-                # Items are in ascending order
-                if items:
-                    next_seq = items[-1].get('seq') if has_more else None
-                    prev_seq = items[0].get('seq')
-
-            return items, next_seq, prev_seq, has_more
-
-    async def search_transcript(
-        self,
-        conversation_id: str,
-        query_text: str,
-        filters: dict[str, typing.Any] | None = None,
-        top_k: int = 10,
-    ) -> list[dict[str, typing.Any]]:
-        """Search transcript items.
-
-        Basic implementation using LIKE filtering.
-
-        Args:
-            conversation_id: Conversation ID
-            query_text: Search query
-            filters: Optional filters
-            top_k: Maximum results
-
-        Returns:
-            List of matching items
-        """
-        async with self._session_factory() as session:
-            query = sqlalchemy.select(Transcript).where(
-                Transcript.conversation_id == conversation_id,
-                Transcript.content.ilike(f"%{query_text}%"),
-            )
-
-            # Apply additional filters
-            if filters:
-                if 'roles' in filters:
-                    query = query.where(Transcript.role.in_(filters['roles']))
-                if 'item_types' in filters:
-                    query = query.where(Transcript.item_type.in_(filters['item_types']))
-
-            query = query.order_by(Transcript.seq.desc()).limit(top_k)
-
-            result = await session.execute(query)
-            rows = result.scalars().all()
-
-            return [self._row_to_dict(row, include_artifacts=True) for row in rows]
-
-    async def get_latest_cursor(
-        self,
-        conversation_id: str,
-    ) -> str | None:
-        """Get the latest cursor for a conversation.
-
-        Args:
-            conversation_id: Conversation ID
-
-        Returns:
-            Cursor string (seq number), or None if no items
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(Transcript.seq)
-                .where(Transcript.conversation_id == conversation_id)
-                .order_by(Transcript.seq.desc())
-                .limit(1)
-            )
-            row = result.scalars().first()
-            if row is None:
-                return None
-            return str(row)
-
-    async def get_legacy_provider_messages(
-        self,
-        conversation_id: str,
-        limit: int = HARD_LIMIT,
-    ) -> list[provider_message.Message]:
-        """Project Transcript rows into the legacy provider Message view.
-
-        AgentRunner history is canonical in Transcript. This view exists for
-        legacy Pipeline readers such as PromptPreProcessing that still expect
-        query.messages.
-        """
-        items, _, _, _ = await self.page_transcript(
-            conversation_id=conversation_id,
-            limit=limit,
-            direction="backward",
-        )
-
-        messages: list[provider_message.Message] = []
-        for item in reversed(items):
-            message = self._transcript_item_to_provider_message(item)
-            if message is not None:
-                messages.append(message)
-        return messages
-
-    async def has_history_before(
-        self,
-        conversation_id: str,
-        seq: int,
-    ) -> bool:
-        """Check if there is history before a sequence number.
-
-        Args:
-            conversation_id: Conversation ID
-            seq: Sequence number
-
-        Returns:
-            True if there are items before
-        """
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(sqlalchemy.func.count())
-                .select_from(Transcript)
-                .where(
-                    Transcript.conversation_id == conversation_id,
-                    Transcript.seq < seq,
-                )
-            )
-            count = result.scalar()
-            return count > 0
-
-    async def _get_next_seq(self, conversation_id: str) -> int:
-        """Fallback next sequence number for stores that cannot expose autoincrement IDs."""
-        async with self._session_factory() as session:
-            result = await session.execute(
-                sqlalchemy.select(sqlalchemy.func.max(Transcript.seq))
-                .where(Transcript.conversation_id == conversation_id)
-            )
-            max_seq = result.scalar()
-            return (max_seq or 0) + 1
-
-    def _row_to_dict(
-        self,
-        row: Transcript,
-        include_artifacts: bool = False,
-    ) -> dict[str, typing.Any]:
-        """Convert a Transcript row to dict."""
-        result = {
-            'transcript_id': row.transcript_id,
-            'event_id': row.event_id,
-            'conversation_id': row.conversation_id,
-            'thread_id': row.thread_id,
-            'role': row.role,
-            'item_type': row.item_type,
-            'content': row.content,
-            'content_json': json.loads(row.content_json) if row.content_json else None,
-            'seq': row.seq,
-            'cursor': str(row.seq),
-            'created_at': int(row.created_at.timestamp()) if row.created_at else None,
-            'metadata': json.loads(row.metadata_json) if row.metadata_json else {},
-        }
-
-        if include_artifacts and row.artifact_refs_json:
-            result['artifact_refs'] = json.loads(row.artifact_refs_json)
-        else:
-            result['artifact_refs'] = []
-
-        return result
-
-    def _transcript_item_to_provider_message(
-        self,
-        item: dict[str, typing.Any],
-    ) -> provider_message.Message | None:
-        """Convert one Transcript API item into a provider Message."""
-        if item.get('item_type') != 'message':
-            return None
-
-        role = item.get('role')
-        if role not in {'user', 'assistant'}:
-            return None
-
-        content_json = item.get('content_json')
-        if isinstance(content_json, dict):
-            message_data = dict(content_json)
-            message_data['role'] = role
-            try:
-                return provider_message.Message.model_validate(message_data)
-            except Exception:
-                pass
-
-        content = item.get('content')
-        if content is None:
-            return None
-        return provider_message.Message(role=role, content=content)

src/langbot/pkg/api/http/controller/groups/resources/mcp.py

@@ -12,7 +12,7 @@ class MCPRouterGroup(group.RouterGroup):
     async def initialize(self) -> None:
         @self.route('/servers', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN)
         async def _() -> str:
-            """List MCP servers or create a new MCP server."""
+            """获取MCP服务器列表"""
             if quart.request.method == 'GET':
                 servers = await self.ap.mcp_service.get_mcp_servers(contain_runtime_info=True)
 
@@ -30,7 +30,7 @@ class MCPRouterGroup(group.RouterGroup):
 
         @self.route('/servers/<server_name>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN)
         async def _(server_name: str) -> str:
-            """Get, update, or delete an MCP server configuration."""
+            """获取、更新或删除MCP服务器配置"""
             from urllib.parse import unquote
 
             server_name = unquote(server_name)
@@ -59,7 +59,7 @@ class MCPRouterGroup(group.RouterGroup):
 
         @self.route('/servers/<server_name>/test', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
         async def _(server_name: str) -> str:
-            """Test an MCP server connection."""
+            """测试MCP服务器连接"""
             from urllib.parse import unquote
 
             server_name = unquote(server_name)

src/langbot/pkg/api/http/service/mcp.py

@@ -137,7 +137,7 @@ class MCPService:
                 await self.ap.tool_mgr.mcp_tool_loader.remove_mcp_server(server_name)
 
     async def test_mcp_server(self, server_name: str, server_data: dict) -> int:
-        """Test an MCP server connection and return the task ID."""
+        """测试 MCP 服务器连接并返回任务 ID"""
 
         runtime_mcp_session: RuntimeMCPSession | None = None
 

src/langbot/pkg/api/http/service/model.py

@@ -7,6 +7,7 @@ from langbot_plugin.api.entities.builtin.provider import message as provider_mes
 
 from ....core import app
 from ....entity.persistence import model as persistence_model
+from ....entity.persistence import pipeline as persistence_pipeline
 from ....provider.modelmgr import requester as model_requester
 
 
@@ -108,9 +109,23 @@ class LLMModelsService:
         self.ap.model_mgr.llm_models.append(runtime_llm_model)
 
         if auto_set_to_default_pipeline:
-            default_config_service = getattr(self.ap, 'agent_runner_default_config_service', None)
-            if default_config_service is not None:
-                await default_config_service.auto_set_default_pipeline_llm_model(model_data['uuid'])
+            # set the default pipeline model to this model
+            result = await self.ap.persistence_mgr.execute_async(
+                sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
+                    persistence_pipeline.LegacyPipeline.is_default == True
+                )
+            )
+            pipeline = result.first()
+            if pipeline is not None:
+                model_config = pipeline.config.get('ai', {}).get('local-agent', {}).get('model', {})
+                if not model_config.get('primary', ''):
+                    pipeline_config = pipeline.config
+                    pipeline_config['ai']['local-agent']['model'] = {
+                        'primary': model_data['uuid'],
+                        'fallbacks': [],
+                    }
+                    pipeline_data = {'config': pipeline_config}
+                    await self.ap.pipeline_service.update_pipeline(pipeline.uuid, pipeline_data)
 
         return model_data['uuid']
 

src/langbot/pkg/api/http/service/pipeline.py

@@ -3,7 +3,6 @@ from __future__ import annotations
 import uuid
 import json
 import sqlalchemy
-import typing
 
 from ....core import app
 from ....entity.persistence import pipeline as persistence_pipeline
@@ -14,6 +13,7 @@ default_stage_order = [
     'BanSessionCheckStage',  # 封禁会话检查
     'PreContentFilterStage',  # 内容过滤前置阶段
     'PreProcessor',  # 预处理器
+    'ConversationMessageTruncator',  # 会话消息截断器
     'RequireRateLimitOccupancy',  # 请求速率限制占用
     'MessageProcessor',  # 处理器
     'ReleaseRateLimitOccupancy',  # 释放速率限制占用
@@ -30,100 +30,11 @@ class PipelineService:
     def __init__(self, ap: app.Application) -> None:
         self.ap = ap
 
-    def _get_default_values_from_schema(self, config_schema: list[dict[str, typing.Any]]) -> dict[str, typing.Any]:
-        """Build runner config defaults from a DynamicForm schema."""
-        defaults: dict[str, typing.Any] = {}
-        for item in config_schema:
-            name = item.get('name')
-            if not name:
-                continue
-            if 'default' in item:
-                defaults[name] = item['default']
-        return defaults
-
-    async def get_default_pipeline_config(self) -> dict[str, typing.Any]:
-        """Get the default pipeline config, rendering runner defaults from installed plugins."""
-        from ....utils import paths as path_utils
-
-        template_path = path_utils.get_resource_path('templates/default-pipeline-config.json')
-        with open(template_path, 'r', encoding='utf-8') as f:
-            config = json.load(f)
-
-        agent_runner_registry = getattr(self.ap, 'agent_runner_registry', None)
-        if agent_runner_registry is None:
-            return config
-
-        try:
-            runners = await agent_runner_registry.list_runners(bound_plugins=None)
-        except Exception as e:
-            logger = getattr(self.ap, 'logger', None)
-            if logger:
-                logger.warning(f'Failed to load plugin agent runners for default pipeline config: {e}')
-            return config
-
-        if not runners:
-            return config
-
-        selected_runner = runners[0]
-        ai_config = config.setdefault('ai', {})
-        runner_config = ai_config.setdefault('runner', {})
-        runner_config['id'] = selected_runner.id
-        runner_config.setdefault('expire-time', 0)
-
-        ai_config['runner_config'] = {
-            selected_runner.id: self._get_default_values_from_schema(selected_runner.config_schema),
-        }
-
-        return config
-
     async def get_pipeline_metadata(self) -> list[dict]:
-        """Get pipeline metadata with dynamically loaded plugin runners from registry"""
-        import copy
-
-        # Deep copy AI metadata to avoid modifying the original
-        ai_metadata = copy.deepcopy(self.ap.pipeline_config_meta_ai)
-
-        # Find the runner stage
-        runner_stage = None
-        for stage in ai_metadata.get('stages', []):
-            if stage.get('name') == 'runner':
-                runner_stage = stage
-                break
-
-        if runner_stage:
-            # Find the runner select config (now uses 'id' field)
-            for config_item in runner_stage.get('config', []):
-                if config_item.get('name') == 'id':
-                    # Get plugin agent runners from registry
-                    try:
-                        (
-                            runner_options,
-                            runner_stages,
-                        ) = await self.ap.agent_runner_registry.get_runner_metadata_for_pipeline()
-
-                        # Replace options entirely with registry options
-                        # Only installed/available runners should be shown
-                        config_item['options'] = runner_options
-
-                        # Use the registry order as the default order. If no runner is available, leave
-                        # the default unset so the UI can recommend installing an AgentRunner plugin.
-                        if runner_options and 'default' not in config_item:
-                            config_item['default'] = runner_options[0]['name']
-
-                        # Add corresponding stage configuration for each runner
-                        for stage_config in runner_stages:
-                            # Avoid duplicate stages
-                            existing_stage_names = {s.get('name') for s in ai_metadata.get('stages', [])}
-                            if stage_config['name'] not in existing_stage_names:
-                                ai_metadata['stages'].append(stage_config)
-
-                    except Exception as e:
-                        self.ap.logger.warning(f'Failed to load plugin agent runners from registry: {e}')
-
         return [
             self.ap.pipeline_config_meta_trigger,
             self.ap.pipeline_config_meta_safety,
-            ai_metadata,
+            self.ap.pipeline_config_meta_ai,
             self.ap.pipeline_config_meta_output,
         ]
 
@@ -163,6 +74,8 @@ class PipelineService:
         return self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
 
     async def create_pipeline(self, pipeline_data: dict, default: bool = False) -> str:
+        from ....utils import paths as path_utils
+
         # Check limitation
         limitation = self.ap.instance_config.data.get('system', {}).get('limitation', {})
         max_pipelines = limitation.get('max_pipelines', -1)
@@ -176,7 +89,9 @@ class PipelineService:
         pipeline_data['stages'] = default_stage_order.copy()
         pipeline_data['is_default'] = default
 
-        pipeline_data['config'] = await self.get_default_pipeline_config()
+        template_path = path_utils.get_resource_path('templates/default-pipeline-config.json')
+        with open(template_path, 'r', encoding='utf-8') as f:
+            pipeline_data['config'] = json.load(f)
 
         # Ensure extensions_preferences is set with enable_all_plugins and enable_all_mcp_servers=True by default
         if 'extensions_preferences' not in pipeline_data:
@@ -198,16 +113,10 @@ class PipelineService:
         return pipeline_data['uuid']
 
     async def update_pipeline(self, pipeline_uuid: str, pipeline_data: dict) -> None:
-        from ....agent.runner.config_migration import ConfigMigration
-
         pipeline_data = pipeline_data.copy()
         for protected_field in ('uuid', 'for_version', 'stages', 'is_default'):
             pipeline_data.pop(protected_field, None)
 
-        # Migrate config to new format before saving
-        if 'config' in pipeline_data:
-            pipeline_data['config'] = ConfigMigration.migrate_pipeline_config(pipeline_data['config'])
-
         await self.ap.persistence_mgr.execute_async(
             sqlalchemy.update(persistence_pipeline.LegacyPipeline)
             .where(persistence_pipeline.LegacyPipeline.uuid == pipeline_uuid)

src/langbot/pkg/box/workspace.py

@@ -146,19 +146,13 @@ def wrap_python_command_with_env(command: str, *, mount_path: str = '/workspace'
         _LB_PIP_CACHE_DIR="{mount_path}/.cache/pip"
 
         mkdir -p "$_LB_META_DIR" "$_LB_TMP_DIR" "$_LB_PIP_CACHE_DIR"
-        _LB_SYSTEM_PYTHON="$(command -v python3 || command -v python || true)"
-        if [ -z "$_LB_SYSTEM_PYTHON" ]; then
-          echo "python3 or python is required to prepare the workspace Python environment" >&2
-          exit 127
-        fi
-
         export TMPDIR="$_LB_TMP_DIR"
         export TEMP="$_LB_TMP_DIR"
         export TMP="$_LB_TMP_DIR"
         export PIP_CACHE_DIR="$_LB_PIP_CACHE_DIR"
 
         _lb_python_meta() {{
-          "$_LB_SYSTEM_PYTHON" - <<'PY'
+          python - <<'PY'
         import hashlib
         import json
         import os
@@ -231,7 +225,7 @@ def wrap_python_command_with_env(command: str, *, mount_path: str = '/workspace'
 
           if [ "$_LB_NEEDS_BOOTSTRAP" -eq 1 ]; then
             rm -rf "$_LB_VENV_DIR"
-            "$_LB_SYSTEM_PYTHON" -m venv "$_LB_VENV_DIR"
+            python -m venv "$_LB_VENV_DIR"
             . "$_LB_VENV_DIR/bin/activate"
             python -m pip install --upgrade pip setuptools wheel
             if [ -f "{mount_path}/requirements.txt" ]; then

src/langbot/pkg/core/app.py

@@ -4,7 +4,6 @@ import logging
 import asyncio
 import traceback
 import os
-from typing import TYPE_CHECKING
 
 from ..platform import botmgr as im_mgr
 from ..platform.webhook_pusher import WebhookPusher
@@ -47,9 +46,6 @@ from ..telemetry import telemetry as telemetry_module
 from ..survey import manager as survey_module
 from ..skill import manager as skill_mgr
 
-if TYPE_CHECKING:
-    from ..agent.runner import AgentRunnerRegistry, AgentRunOrchestrator, AgentRunnerDefaultConfigService
-
 
 class Application:
     """Runtime application object and context"""
@@ -169,13 +165,6 @@ class Application:
 
     maintenance_service: maintenance_service.MaintenanceService = None
 
-    # Agent runner subsystem
-    agent_runner_registry: AgentRunnerRegistry = None
-
-    agent_runner_default_config_service: AgentRunnerDefaultConfigService = None
-
-    agent_run_orchestrator: AgentRunOrchestrator = None
-
     def __init__(self):
         pass
 

src/langbot/pkg/core/migrations/m009_msg_truncator_cfg.py

@@ -0,0 +1,22 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class('msg-truncator-cfg-migration', 9)
+class MsgTruncatorConfigMigration(migration.Migration):
+    """迁移"""
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移"""
+        return 'msg-truncate' not in self.ap.pipeline_cfg.data
+
+    async def run(self):
+        """执行迁移"""
+
+        self.ap.pipeline_cfg.data['msg-truncate'] = {
+            'method': 'round',
+            'round': {'max-round': 10},
+        }
+
+        await self.ap.pipeline_cfg.dump_config()

src/langbot/pkg/core/migrations/m042_weknora_api.py

@@ -0,0 +1,27 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class('weknora-api-config', 42)
+class WeKnoraAPICfgMigration(migration.Migration):
+    """WeKnora API 配置迁移"""
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移"""
+        return 'weknora-api' not in self.ap.provider_cfg.data
+
+    async def run(self):
+        """执行迁移"""
+        self.ap.provider_cfg.data['weknora-api'] = {
+            'base-url': 'http://localhost:8080/api/v1',
+            'app-type': 'agent',
+            'api-key': '',
+            'agent-id': 'builtin-smart-reasoning',
+            'knowledge-base-ids': [],
+            'web-search-enabled': False,
+            'timeout': 120,
+            'base-prompt': '请回答用户的问题。',
+        }
+
+        await self.ap.provider_cfg.dump_config()

src/langbot/pkg/core/migrations/m043_deerflow_api.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from .. import migration
+
+
+@migration.migration_class('deerflow-api-config', 43)
+class DeerFlowAPICfgMigration(migration.Migration):
+    """DeerFlow API 配置迁移"""
+
+    async def need_migrate(self) -> bool:
+        """判断当前环境是否需要运行此迁移"""
+        return 'deerflow-api' not in self.ap.provider_cfg.data
+
+    async def run(self):
+        """执行迁移"""
+        self.ap.provider_cfg.data['deerflow-api'] = {
+            'api-base': 'http://127.0.0.1:2026',
+            'api-key': '',
+            'auth-header': '',
+            'assistant-id': 'lead_agent',
+            'model-name': '',
+            'thinking-enabled': False,
+            'plan-mode': False,
+            'subagent-enabled': False,
+            'max-concurrent-subagents': 3,
+            'timeout': 300,
+            'recursion-limit': 1000,
+        }
+
+        await self.ap.provider_cfg.dump_config()

src/langbot/pkg/core/stages/build_app.py

@@ -39,7 +39,6 @@ from ...vector import mgr as vectordb_mgr
 from .. import taskmgr
 from ...telemetry import telemetry as telemetry_module
 from ...survey import manager as survey_module
-from ...agent.runner import AgentRunnerRegistry, AgentRunOrchestrator, AgentRunnerDefaultConfigService
 
 
 @stage.stage_class('BuildAppStage')
@@ -195,15 +194,5 @@ class BuildAppStage(stage.BootingStage):
         await plugin_connector_inst.initialize()
         ap.plugin_connector = plugin_connector_inst
 
-        # Initialize agent runner subsystem
-        agent_runner_registry_inst = AgentRunnerRegistry(ap)
-        ap.agent_runner_registry = agent_runner_registry_inst
-
-        agent_runner_default_config_service_inst = AgentRunnerDefaultConfigService(ap)
-        ap.agent_runner_default_config_service = agent_runner_default_config_service_inst
-
-        agent_run_orchestrator_inst = AgentRunOrchestrator(ap, agent_runner_registry_inst)
-        ap.agent_run_orchestrator = agent_run_orchestrator_inst
-
         ctrl = controller.Controller(ap)
         ap.ctrl = ctrl

src/langbot/pkg/entity/persistence/agent_runner_state.py

@@ -1,88 +0,0 @@
-"""Agent runner state persistence entity for host-owned state."""
-from __future__ import annotations
-
-import sqlalchemy
-import datetime
-
-from .base import Base
-
-
-class AgentRunnerState(Base):
-    """AgentRunnerState stores host-owned state for AgentRunner protocol.
-
-    State is:
-    - Host-owned: Managed by LangBot, not by plugin instances
-    - Scope-isolated: Separated by runner_id + binding_identity + scope
-    - Policy-enforced: Controlled by StatePolicy (enable_state, state_scopes)
-
-    Scope key design:
-    - conversation: runner_id + binding_id + conversation_id [+ thread_id]
-    - actor: runner_id + binding_id + actor_type + actor_id
-    - subject: runner_id + binding_id + subject_type + subject_id
-    - runner: runner_id + binding_id
-
-    This table is the production store for AgentRunner state.
-    """
-
-    __tablename__ = 'agent_runner_state'
-
-    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
-    """Auto-increment ID for sequencing."""
-
-    # Identity
-    runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
-    """Runner descriptor ID (plugin:author/name/runner)."""
-
-    binding_identity = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
-    """Binding identity for isolation (binding_id or scope_type:scope_id)."""
-
-    scope = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, index=True)
-    """State scope: 'conversation', 'actor', 'subject', or 'runner'."""
-
-    scope_key = sqlalchemy.Column(sqlalchemy.String(512), nullable=False, index=True)
-    """Full scope key for unique lookup (includes all identity parts)."""
-
-    state_key = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
-    """State key within scope (should use namespace prefix like external.*)."""
-
-    value_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """State value as JSON string (size-limited by host)."""
-
-    # Context fields for querying/filtering
-    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Bot UUID if applicable."""
-
-    workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Workspace ID for multi-tenant."""
-
-    conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Conversation ID for conversation scope."""
-
-    thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Thread ID for thread-scoped conversation state."""
-
-    actor_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
-    """Actor type for actor scope."""
-
-    actor_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Actor ID for actor scope."""
-
-    subject_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
-    """Subject type for subject scope."""
-
-    subject_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Subject ID for subject scope."""
-
-    # Lifecycle
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
-    """When this state entry was created."""
-
-    updated_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow, onupdate=datetime.datetime.utcnow)
-    """When this state entry was last updated."""
-
-    # Unique constraint: scope_key + state_key
-    __table_args__ = (
-        sqlalchemy.UniqueConstraint('scope_key', 'state_key', name='uq_agent_runner_state_scope_key_state_key'),
-        sqlalchemy.Index('ix_agent_runner_state_runner_binding', 'runner_id', 'binding_identity'),
-        sqlalchemy.Index('ix_agent_runner_state_scope_key_lookup', 'scope_key'),
-    )

src/langbot/pkg/entity/persistence/artifact.py

@@ -1,77 +0,0 @@
-"""Artifact persistence entity for Host-owned artifact store."""
-from __future__ import annotations
-
-import sqlalchemy
-import datetime
-
-from .base import Base
-
-
-class AgentArtifact(Base):
-    """AgentArtifact stores metadata for large files, images, tool results, etc.
-
-    This table only stores metadata. The actual blob content is stored in
-    BinaryStorage or external storage, referenced by storage_key.
-
-    Artifacts are accessed via artifact_metadata and artifact_read APIs
-    with run_id authorization.
-    """
-
-    __tablename__ = 'agent_artifact'
-
-    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
-    """Auto-increment ID for sequencing."""
-
-    artifact_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
-    """Unique artifact identifier."""
-
-    artifact_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
-    """Artifact type: 'image', 'file', 'voice', 'tool_result', 'platform_attachment', etc."""
-
-    mime_type = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """MIME type of the content."""
-
-    name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Original file name (if applicable)."""
-
-    size_bytes = sqlalchemy.Column(sqlalchemy.BigInteger, nullable=True)
-    """Size in bytes."""
-
-    sha256 = sqlalchemy.Column(sqlalchemy.String(64), nullable=True)
-    """SHA256 hash of content (for integrity verification)."""
-
-    source = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
-    """Source of artifact: 'platform', 'runner', 'tool', 'system'."""
-
-    # Storage reference (points to BinaryStorage or external storage)
-    storage_key = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Key in BinaryStorage or external storage reference."""
-
-    storage_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, default='binary_storage')
-    """Storage type: 'binary_storage', 'file', 'url', etc."""
-
-    # Context
-    conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Conversation this artifact belongs to."""
-
-    run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Run ID that created this artifact."""
-
-    runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Runner ID that created this artifact."""
-
-    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Bot UUID that handled this artifact."""
-
-    workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Workspace ID for multi-tenant deployments."""
-
-    # Lifecycle
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
-    """When this artifact was created."""
-
-    expires_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
-    """When this artifact expires (optional)."""
-
-    metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Additional metadata as JSON string."""

src/langbot/pkg/entity/persistence/event_log.py

@@ -1,85 +0,0 @@
-"""EventLog persistence entity for storing auditable event facts."""
-from __future__ import annotations
-
-import sqlalchemy
-import datetime
-
-from .base import Base
-
-
-class EventLog(Base):
-    """EventLog stores auditable event records for AgentRunner.
-
-    This is the fact source for events - messages, tool calls, system events, etc.
-    Large payloads are stored separately as artifacts; this table stores
-    references and summaries.
-    """
-
-    __tablename__ = 'event_log'
-
-    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
-    """Auto-increment ID for sequencing."""
-
-    event_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
-    """Unique event identifier."""
-
-    event_type = sqlalchemy.Column(sqlalchemy.String(100), nullable=False, index=True)
-    """Event type (message.received, tool.call.started, etc.)."""
-
-    event_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
-    """When the event occurred."""
-
-    source = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
-    """Event source (platform, webui, api, scheduler, system, pipeline_adapter)."""
-
-    bot_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Bot UUID that handled this event."""
-
-    workspace_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Workspace ID for multi-tenant deployments."""
-
-    conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Conversation ID this event belongs to."""
-
-    thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Thread ID if platform supports threads."""
-
-    # Actor information
-    actor_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
-    """Actor type (user, system, runner)."""
-
-    actor_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Actor identifier."""
-
-    actor_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Actor display name."""
-
-    # Subject information
-    subject_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
-    """Subject type (message, tool_call, artifact)."""
-
-    subject_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Subject identifier."""
-
-    # Input information
-    input_summary = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Brief summary of input (truncated text, max 1000 chars)."""
-
-    input_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Full input JSON if reasonably sized (AgentInput as JSON string)."""
-
-    # Raw event reference
-    raw_ref = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Reference to raw event payload in ArtifactStore."""
-
-    run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Run ID that processed this event."""
-
-    runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Runner ID that processed this event."""
-
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
-    """When this record was created."""
-
-    metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Additional metadata as JSON string."""

src/langbot/pkg/entity/persistence/mcp.py

@@ -11,6 +11,10 @@ class MCPServer(Base):
     enable = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=False)
     mode = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)  # stdio, sse, http
     extra_args = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
+    # Markdown documentation captured from LangBot Space at install time so the
+    # detail page can show docs even when the server is offline / has no tools.
+    # Empty string for manually-created servers that have no marketplace README.
+    readme = sqlalchemy.Column(sqlalchemy.Text, nullable=False, server_default='', default='')
     created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
     updated_at = sqlalchemy.Column(
         sqlalchemy.DateTime,

src/langbot/pkg/entity/persistence/transcript.py

@@ -1,72 +0,0 @@
-"""Transcript persistence entity for conversation history projection."""
-from __future__ import annotations
-
-import sqlalchemy
-import datetime
-
-from .base import Base
-
-
-class Transcript(Base):
-    """Transcript stores conversation-oriented message projection for history API.
-
-    This is a projection of EventLog, optimized for agent history retrieval.
-    It includes message content and artifact refs, but not raw platform payloads.
-    """
-
-    __tablename__ = 'transcript'
-
-    id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
-    """Auto-increment ID for sequencing."""
-
-    transcript_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, unique=True, index=True)
-    """Unique transcript item identifier."""
-
-    event_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
-    """Reference to the source event in EventLog."""
-
-    conversation_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
-    """Conversation this item belongs to."""
-
-    thread_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Thread ID if platform supports threads."""
-
-    role = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
-    """Message role: 'user', 'assistant', 'system', or 'tool'."""
-
-    item_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False, default='message')
-    """Item type: 'message', 'tool_call', 'tool_result', 'system'."""
-
-    # Content
-    content = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Text content summary (may be truncated for large messages, max 4000 chars)."""
-
-    content_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Full structured content as JSON string (Message model dump)."""
-
-    # Artifact references
-    artifact_refs_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Artifact references as JSON string (list of ArtifactRef)."""
-
-    # Sequence for cursor-based pagination
-    seq = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, index=True)
-    """Monotonic cursor sequence for pagination."""
-
-    # Context
-    run_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True, index=True)
-    """Run ID that generated this item (for assistant messages)."""
-
-    runner_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
-    """Runner ID that generated this item."""
-
-    created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, default=datetime.datetime.utcnow)
-    """When this item was created."""
-
-    metadata_json = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
-    """Additional metadata as JSON string (sender_id, platform, etc.)."""
-
-    # Indexes
-    __table_args__ = (
-        sqlalchemy.Index('ix_transcript_conversation_seq', 'conversation_id', 'seq'),
-        sqlalchemy.Index('ix_transcript_conversation_created', 'conversation_id', 'created_at'),
-    )

src/langbot/pkg/persistence/alembic/env.py

@@ -13,28 +13,6 @@ from sqlalchemy.engine import Connection
 
 from langbot.pkg.entity.persistence.base import Base
 
-# Import all ORM models so they are registered with Base.metadata
-# This is required for autogenerate to detect model changes
-from langbot.pkg.entity.persistence import (
-    agent_runner_state,
-    apikey,
-    artifact,
-    bot,
-    bstorage,
-    event_log,
-    mcp,
-    metadata,
-    model,
-    monitoring,
-    pipeline,
-    plugin,
-    rag,
-    transcript,
-    user,
-    vector,
-    webhook,
-)
-
 target_metadata = Base.metadata
 
 

src/langbot/pkg/persistence/alembic/versions/0004_add_mcp_readme.py

@@ -0,0 +1,34 @@
+"""add readme column to mcp_servers
+
+Revision ID: 0004_add_mcp_readme
+Revises: 0003_add_rerank_models
+Create Date: 2026-06-06
+"""
+
+import sqlalchemy as sa
+from alembic import op
+
+revision = '0004_add_mcp_readme'
+down_revision = '0003_add_rerank_models'
+branch_labels = None
+depends_on = None
+
+
+def upgrade() -> None:
+    # Add ``readme`` to mcp_servers if the table exists and the column is missing
+    # (the table may have been created by create_all() with the column already
+    # present on fresh installs, so guard against duplicate-add).
+    conn = op.get_bind()
+    inspector = sa.inspect(conn)
+    if 'mcp_servers' not in inspector.get_table_names():
+        return
+    columns = {col['name'] for col in inspector.get_columns('mcp_servers')}
+    if 'readme' not in columns:
+        op.add_column(
+            'mcp_servers',
+            sa.Column('readme', sa.Text(), nullable=False, server_default=''),
+        )
+
+
+def downgrade() -> None:
+    op.drop_column('mcp_servers', 'readme')

src/langbot/pkg/persistence/alembic/versions/0004_migrate_runner_config.py

@@ -1,67 +0,0 @@
-"""Normalize AgentRunner config containers
-
-Revision ID: 0004_migrate_runner_config
-Revises: 0003_add_rerank_models
-Create Date: 2026-05-10
-"""
-
-import json
-import sqlalchemy as sa
-from alembic import op
-
-revision = '0004_migrate_runner_config'
-down_revision = '0003_add_rerank_models'
-branch_labels = None
-depends_on = None
-
-def migrate_pipeline_config(config: dict) -> dict:
-    """Keep current AgentRunner config containers explicit."""
-    new_config = dict(config)
-    if 'ai' not in new_config:
-        return new_config
-
-    ai_config = dict(new_config.get('ai', {}))
-
-    ai_config['runner'] = dict(ai_config.get('runner', {}))
-    ai_config['runner_config'] = dict(ai_config.get('runner_config', {}))
-    new_config['ai'] = ai_config
-
-    return new_config
-
-
-def upgrade() -> None:
-    """Normalize existing pipeline config containers."""
-    conn = op.get_bind()
-    inspector = sa.inspect(conn)
-
-    # Check if pipelines table exists (may not exist in fresh install)
-    if 'pipelines' not in inspector.get_table_names():
-        return
-
-    # Get all pipelines
-    result = conn.execute(sa.text('SELECT uuid, config FROM pipelines'))
-    pipelines = result.fetchall()
-
-    for pipeline_uuid, config_json in pipelines:
-        if not config_json:
-            continue
-
-        try:
-            config = json.loads(config_json)
-            migrated_config = migrate_pipeline_config(config)
-
-            # Only update if config changed
-            if json.dumps(config, sort_keys=True) != json.dumps(migrated_config, sort_keys=True):
-                conn.execute(
-                    sa.text('UPDATE pipelines SET config = :config WHERE uuid = :uuid'),
-                    {'config': json.dumps(migrated_config), 'uuid': pipeline_uuid},
-                )
-        except Exception:
-            # Skip invalid configs
-            continue
-
-
-def downgrade() -> None:
-    """Downgrade is not supported for data migration."""
-    # No downgrade - keep configs in new format
-    pass

src/langbot/pkg/persistence/alembic/versions/58846a8d7a81_add_event_log_and_transcript_tables.py

@@ -1,102 +0,0 @@
-"""add_event_log_and_transcript_tables
-
-Revision ID: 58846a8d7a81
-Revises: 0004_migrate_runner_config
-Create Date: 2026-05-23 15:41:47.030841
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers
-revision = '58846a8d7a81'
-down_revision = '0004_migrate_runner_config'
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Create event_log table
-    op.create_table(
-        'event_log',
-        sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
-        sa.Column('event_id', sa.String(255), nullable=False, unique=True),
-        sa.Column('event_type', sa.String(100), nullable=False),
-        sa.Column('event_time', sa.DateTime(), nullable=True),
-        sa.Column('source', sa.String(50), nullable=False),
-        sa.Column('bot_id', sa.String(255), nullable=True),
-        sa.Column('workspace_id', sa.String(255), nullable=True),
-        sa.Column('conversation_id', sa.String(255), nullable=True),
-        sa.Column('thread_id', sa.String(255), nullable=True),
-        sa.Column('actor_type', sa.String(50), nullable=True),
-        sa.Column('actor_id', sa.String(255), nullable=True),
-        sa.Column('actor_name', sa.String(255), nullable=True),
-        sa.Column('subject_type', sa.String(50), nullable=True),
-        sa.Column('subject_id', sa.String(255), nullable=True),
-        sa.Column('input_summary', sa.Text(), nullable=True),
-        sa.Column('input_json', sa.Text(), nullable=True),
-        sa.Column('raw_ref', sa.String(255), nullable=True),
-        sa.Column('run_id', sa.String(255), nullable=True),
-        sa.Column('runner_id', sa.String(255), nullable=True),
-        sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.text('(CURRENT_TIMESTAMP)')),
-        sa.Column('metadata_json', sa.Text(), nullable=True),
-    )
-
-    # Create indexes for event_log
-    with op.batch_alter_table('event_log', schema=None) as batch_op:
-        batch_op.create_index('ix_event_log_event_id', ['event_id'], unique=True)
-        batch_op.create_index('ix_event_log_event_type', ['event_type'], unique=False)
-        batch_op.create_index('ix_event_log_bot_id', ['bot_id'], unique=False)
-        batch_op.create_index('ix_event_log_conversation_id', ['conversation_id'], unique=False)
-        batch_op.create_index('ix_event_log_run_id', ['run_id'], unique=False)
-
-    # Create transcript table
-    op.create_table(
-        'transcript',
-        sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
-        sa.Column('transcript_id', sa.String(255), nullable=False, unique=True),
-        sa.Column('event_id', sa.String(255), nullable=False),
-        sa.Column('conversation_id', sa.String(255), nullable=False),
-        sa.Column('thread_id', sa.String(255), nullable=True),
-        sa.Column('role', sa.String(50), nullable=False),
-        sa.Column('item_type', sa.String(50), nullable=False, server_default='message'),
-        sa.Column('content', sa.Text(), nullable=True),
-        sa.Column('content_json', sa.Text(), nullable=True),
-        sa.Column('artifact_refs_json', sa.Text(), nullable=True),
-        sa.Column('seq', sa.Integer(), nullable=False),
-        sa.Column('run_id', sa.String(255), nullable=True),
-        sa.Column('runner_id', sa.String(255), nullable=True),
-        sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.text('(CURRENT_TIMESTAMP)')),
-        sa.Column('metadata_json', sa.Text(), nullable=True),
-    )
-
-    # Create indexes for transcript
-    with op.batch_alter_table('transcript', schema=None) as batch_op:
-        batch_op.create_index('ix_transcript_transcript_id', ['transcript_id'], unique=True)
-        batch_op.create_index('ix_transcript_event_id', ['event_id'], unique=False)
-        batch_op.create_index('ix_transcript_conversation_id', ['conversation_id'], unique=False)
-        batch_op.create_index('ix_transcript_conversation_seq', ['conversation_id', 'seq'], unique=False)
-        batch_op.create_index('ix_transcript_conversation_created', ['conversation_id', 'created_at'], unique=False)
-        batch_op.create_index('ix_transcript_run_id', ['run_id'], unique=False)
-
-
-def downgrade() -> None:
-    # Drop transcript table
-    with op.batch_alter_table('transcript', schema=None) as batch_op:
-        batch_op.drop_index('ix_transcript_run_id')
-        batch_op.drop_index('ix_transcript_conversation_created')
-        batch_op.drop_index('ix_transcript_conversation_seq')
-        batch_op.drop_index('ix_transcript_conversation_id')
-        batch_op.drop_index('ix_transcript_event_id')
-        batch_op.drop_index('ix_transcript_transcript_id')
-
-    op.drop_table('transcript')
-
-    # Drop event_log table
-    with op.batch_alter_table('event_log', schema=None) as batch_op:
-        batch_op.drop_index('ix_event_log_run_id')
-        batch_op.drop_index('ix_event_log_conversation_id')
-        batch_op.drop_index('ix_event_log_bot_id')
-        batch_op.drop_index('ix_event_log_event_type')
-        batch_op.drop_index('ix_event_log_event_id')
-
-    op.drop_table('event_log')

src/langbot/pkg/persistence/alembic/versions/6dfd3dd7f0c7_add_agent_runner_state_table_for_host_.py

@@ -1,68 +0,0 @@
-# Alembic script.py.mako — template for auto-generated revisions
-"""add agent_runner_state table for host-owned persistent state
-
-Revision ID: 6dfd3dd7f0c7
-Revises: a1b2c3d4e5f6
-Create Date: 2026-05-23 19:49:08.529110
-"""
-from alembic import op
-import sqlalchemy as sa
-
-
-# revision identifiers
-revision = '6dfd3dd7f0c7'
-down_revision = 'a1b2c3d4e5f6'
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    op.create_table('agent_runner_state',
-    sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
-    sa.Column('runner_id', sa.String(length=255), nullable=False),
-    sa.Column('binding_identity', sa.String(length=255), nullable=False),
-    sa.Column('scope', sa.String(length=50), nullable=False),
-    sa.Column('scope_key', sa.String(length=512), nullable=False),
-    sa.Column('state_key', sa.String(length=255), nullable=False),
-    sa.Column('value_json', sa.Text(), nullable=True),
-    sa.Column('bot_id', sa.String(length=255), nullable=True),
-    sa.Column('workspace_id', sa.String(length=255), nullable=True),
-    sa.Column('conversation_id', sa.String(length=255), nullable=True),
-    sa.Column('thread_id', sa.String(length=255), nullable=True),
-    sa.Column('actor_type', sa.String(length=50), nullable=True),
-    sa.Column('actor_id', sa.String(length=255), nullable=True),
-    sa.Column('subject_type', sa.String(length=50), nullable=True),
-    sa.Column('subject_id', sa.String(length=255), nullable=True),
-    sa.Column('created_at', sa.DateTime(), nullable=False),
-    sa.Column('updated_at', sa.DateTime(), nullable=False),
-    sa.PrimaryKeyConstraint('id'),
-    sa.UniqueConstraint('scope_key', 'state_key', name='uq_agent_runner_state_scope_key_state_key')
-    )
-    with op.batch_alter_table('agent_runner_state', schema=None) as batch_op:
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_actor_id'), ['actor_id'], unique=False)
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_binding_identity'), ['binding_identity'], unique=False)
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_bot_id'), ['bot_id'], unique=False)
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_conversation_id'), ['conversation_id'], unique=False)
-        batch_op.create_index('ix_agent_runner_state_runner_binding', ['runner_id', 'binding_identity'], unique=False)
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_runner_id'), ['runner_id'], unique=False)
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_scope'), ['scope'], unique=False)
-        batch_op.create_index(batch_op.f('ix_agent_runner_state_scope_key'), ['scope_key'], unique=False)
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table('agent_runner_state', schema=None) as batch_op:
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_scope_key'))
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_scope'))
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_runner_id'))
-        batch_op.drop_index('ix_agent_runner_state_runner_binding')
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_conversation_id'))
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_bot_id'))
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_binding_identity'))
-        batch_op.drop_index(batch_op.f('ix_agent_runner_state_actor_id'))
-
-    op.drop_table('agent_runner_state')
-    # ### end Alembic commands ###

src/langbot/pkg/persistence/alembic/versions/a1b2c3d4e5f6_add_agent_artifact_table.py

@@ -1,55 +0,0 @@
-"""add_agent_artifact_table
-
-Revision ID: a1b2c3d4e5f6
-Revises: 58846a8d7a81
-Create Date: 2026-05-23 20:00:00.000000
-"""
-from alembic import op
-import sqlalchemy as sa
-
-# revision identifiers
-revision = 'a1b2c3d4e5f6'
-down_revision = '58846a8d7a81'
-branch_labels = None
-depends_on = None
-
-
-def upgrade() -> None:
-    # Create agent_artifact table
-    op.create_table(
-        'agent_artifact',
-        sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
-        sa.Column('artifact_id', sa.String(255), nullable=False, unique=True),
-        sa.Column('artifact_type', sa.String(50), nullable=False),
-        sa.Column('mime_type', sa.String(255), nullable=True),
-        sa.Column('name', sa.String(255), nullable=True),
-        sa.Column('size_bytes', sa.BigInteger(), nullable=True),
-        sa.Column('sha256', sa.String(64), nullable=True),
-        sa.Column('source', sa.String(50), nullable=False),
-        sa.Column('storage_key', sa.String(255), nullable=True),
-        sa.Column('storage_type', sa.String(50), nullable=False, server_default='binary_storage'),
-        sa.Column('conversation_id', sa.String(255), nullable=True),
-        sa.Column('run_id', sa.String(255), nullable=True),
-        sa.Column('runner_id', sa.String(255), nullable=True),
-        sa.Column('bot_id', sa.String(255), nullable=True),
-        sa.Column('workspace_id', sa.String(255), nullable=True),
-        sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.text('(CURRENT_TIMESTAMP)')),
-        sa.Column('expires_at', sa.DateTime(), nullable=True),
-        sa.Column('metadata_json', sa.Text(), nullable=True),
-    )
-
-    # Create indexes for agent_artifact
-    with op.batch_alter_table('agent_artifact', schema=None) as batch_op:
-        batch_op.create_index('ix_agent_artifact_artifact_id', ['artifact_id'], unique=True)
-        batch_op.create_index('ix_agent_artifact_conversation_id', ['conversation_id'], unique=False)
-        batch_op.create_index('ix_agent_artifact_run_id', ['run_id'], unique=False)
-
-
-def downgrade() -> None:
-    # Drop agent_artifact table
-    with op.batch_alter_table('agent_artifact', schema=None) as batch_op:
-        batch_op.drop_index('ix_agent_artifact_run_id')
-        batch_op.drop_index('ix_agent_artifact_conversation_id')
-        batch_op.drop_index('ix_agent_artifact_artifact_id')
-
-    op.drop_table('agent_artifact')

src/langbot/pkg/persistence/migrations/dbm001_migrate_v3_config.py

@@ -118,6 +118,9 @@ class DBMigrateV3Config(migration.DBMigration):
                 'runner': self.ap.provider_cfg.data['runner'],
             }
             pipeline_config['ai']['local-agent']['model'] = model_uuid
+            pipeline_config['ai']['local-agent']['max-round'] = self.ap.pipeline_cfg.data['msg-truncate']['round'][
+                'max-round'
+            ]
 
             pipeline_config['ai']['local-agent']['prompt'] = [
                 {

src/langbot/pkg/pipeline/msgtrun/msgtrun.py

@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from .. import stage, entities
+from . import truncator
+from ...utils import importutil
+import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+from . import truncators
+
+importutil.import_modules_in_pkg(truncators)
+
+
+@stage.stage_class('ConversationMessageTruncator')
+class ConversationMessageTruncator(stage.PipelineStage):
+    """Conversation message truncator
+
+    Used to truncate the conversation message chain to adapt to the LLM message length limit.
+    """
+
+    trun: truncator.Truncator
+
+    async def initialize(self, pipeline_config: dict):
+        use_method = 'round'
+
+        for trun in truncator.preregistered_truncators:
+            if trun.name == use_method:
+                self.trun = trun(self.ap)
+                break
+        else:
+            raise ValueError(f'Unknown truncator: {use_method}')
+
+    async def process(self, query: pipeline_query.Query, stage_inst_name: str) -> entities.StageProcessResult:
+        """处理"""
+        query = await self.trun.truncate(query)
+
+        return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/pipeline/msgtrun/truncator.py

@@ -0,0 +1,56 @@
+from __future__ import annotations
+
+import typing
+import abc
+
+from ...core import app
+import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+
+preregistered_truncators: list[typing.Type[Truncator]] = []
+
+
+def truncator_class(
+    name: str,
+) -> typing.Callable[[typing.Type[Truncator]], typing.Type[Truncator]]:
+    """截断器类装饰器
+
+    Args:
+        name (str): 截断器名称
+
+    Returns:
+        typing.Callable[[typing.Type[Truncator]], typing.Type[Truncator]]: 装饰器
+    """
+
+    def decorator(cls: typing.Type[Truncator]) -> typing.Type[Truncator]:
+        assert issubclass(cls, Truncator)
+
+        cls.name = name
+
+        preregistered_truncators.append(cls)
+
+        return cls
+
+    return decorator
+
+
+class Truncator(abc.ABC):
+    """消息截断器基类"""
+
+    name: str
+
+    ap: app.Application
+
+    def __init__(self, ap: app.Application):
+        self.ap = ap
+
+    async def initialize(self):
+        pass
+
+    @abc.abstractmethod
+    async def truncate(self, query: pipeline_query.Query) -> pipeline_query.Query:
+        """截断
+
+        一般只需要操作query.messages，也可以扩展操作query.prompt, query.user_message。
+        请勿操作其他字段。
+        """
+        pass

src/langbot/pkg/pipeline/msgtrun/truncators/round.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from .. import truncator
+import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
+
+
+@truncator.truncator_class('round')
+class RoundTruncator(truncator.Truncator):
+    """Truncate the conversation message chain to adapt to the LLM message length limit."""
+
+    async def truncate(self, query: pipeline_query.Query) -> pipeline_query.Query:
+        """截断"""
+        max_round = query.pipeline_config['ai']['local-agent']['max-round']
+
+        temp_messages = []
+
+        current_round = 0
+
+        # Traverse from back to front
+        for msg in query.messages[::-1]:
+            if current_round < max_round:
+                temp_messages.append(msg)
+                if msg.role == 'user':
+                    current_round += 1
+            else:
+                break
+
+        query.messages = temp_messages[::-1]
+
+        return query

src/langbot/pkg/pipeline/pipelinemgr.py

@@ -28,6 +28,7 @@ from . import (
     wrapper,
     preproc,
     ratelimit,
+    msgtrun,
 )
 
 importutil.import_modules_in_pkgs(
@@ -41,6 +42,7 @@ importutil.import_modules_in_pkgs(
         wrapper,
         preproc,
         ratelimit,
+        msgtrun,
     ]
 )
 
@@ -436,9 +438,6 @@ class PipelineManager:
         # initialize stage containers according to pipeline_entity.stages
         stage_containers: list[StageInstContainer] = []
         for stage_name in pipeline_entity.stages:
-            if stage_name not in self.stage_dict:
-                self.ap.logger.warning(f'Pipeline stage {stage_name} is not registered; skipping')
-                continue
             stage_containers.append(StageInstContainer(inst_name=stage_name, inst=self.stage_dict[stage_name](self.ap)))
 
         for stage_container in stage_containers:

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import datetime
-import typing
 
 from .. import stage, entities
 from langbot_plugin.api.entities.builtin.provider import message as provider_message
@@ -10,15 +9,6 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.platform.events as platform_events
 
-from ...agent.runner.descriptor import AgentRunnerDescriptor
-from ...agent.runner.config_migration import ConfigMigration
-from ...agent.runner import config_schema
-
-
-DEFAULT_PROMPT_CONFIG = [
-    {'role': 'system', 'content': 'You are a helpful assistant.'},
-]
-
 
 @stage.stage_class('PreProcessor')
 class PreProcessor(stage.PipelineStage):
@@ -35,156 +25,55 @@ class PreProcessor(stage.PipelineStage):
         - use_funcs
     """
 
-    async def _get_runner_descriptor(
-        self,
-        runner_id: str | None,
-        bound_plugins: list[str] | None,
-    ) -> AgentRunnerDescriptor | None:
-        if not runner_id:
-            return None
-
-        registry = getattr(self.ap, 'agent_runner_registry', None)
-        if registry is None:
-            return None
-
-        try:
-            return await registry.get(runner_id, bound_plugins)
-        except Exception as e:
-            self.ap.logger.debug(f'Unable to load AgentRunner descriptor for {runner_id}: {e}')
-            return None
-
-    async def _resolve_llm_model(
-        self,
-        primary_uuid: str,
-    ) -> typing.Any | None:
-        if primary_uuid in config_schema.NONE_SENTINELS:
-            return None
-        try:
-            return await self.ap.model_mgr.get_model_by_uuid(primary_uuid)
-        except ValueError:
-            self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured')
-            return None
-
-    async def _resolve_fallback_models(self, fallback_uuids: list[str]) -> list[str]:
-        valid_fallbacks = []
-        for fallback_uuid in fallback_uuids:
-            if fallback_uuid in config_schema.NONE_SENTINELS:
-                continue
-            try:
-                await self.ap.model_mgr.get_model_by_uuid(fallback_uuid)
-                valid_fallbacks.append(fallback_uuid)
-            except ValueError:
-                self.ap.logger.warning(f'Fallback model {fallback_uuid} not found, skipping')
-        return valid_fallbacks
-
-    def _runner_accepts_multimodal_input(self, descriptor: AgentRunnerDescriptor | None) -> bool:
-        if descriptor is None:
-            return True
-        return descriptor.capabilities.get('multimodal_input', False)
-
-    def _model_supports_vision(self, llm_model: typing.Any | None) -> bool:
-        if not llm_model:
-            return False
-        abilities = getattr(getattr(llm_model, 'model_entity', None), 'abilities', [])
-        return 'vision' in abilities
-
-    def _should_keep_image_inputs(
-        self,
-        descriptor: AgentRunnerDescriptor | None,
-        uses_host_models: bool,
-        llm_model: typing.Any | None,
-    ) -> bool:
-        if not self._runner_accepts_multimodal_input(descriptor):
-            return False
-        if uses_host_models:
-            return self._model_supports_vision(llm_model)
-        return True
-
-    def _strip_images_from_history(self, query: pipeline_query.Query) -> None:
-        for msg in query.messages:
-            if isinstance(msg.content, list):
-                msg.content = [elem for elem in msg.content if elem.type != 'image_url']
-
-    def _has_declared_db_engine(self) -> bool:
-        persistence_mgr = getattr(self.ap, 'persistence_mgr', None)
-        if persistence_mgr is None:
-            return False
-        if 'get_db_engine' in getattr(persistence_mgr, '__dict__', {}):
-            return True
-        return hasattr(type(persistence_mgr), 'get_db_engine')
-
-    async def _load_agent_runner_history_messages(
-        self,
-        runner_id: str | None,
-        conversation_uuid: str | None,
-    ) -> list[provider_message.Message] | None:
-        if not runner_id or not conversation_uuid or not self._has_declared_db_engine():
-            return None
-
-        try:
-            from ...agent.runner.transcript_store import TranscriptStore
-
-            store = TranscriptStore(self.ap.persistence_mgr.get_db_engine())
-            messages = await store.get_legacy_provider_messages(str(conversation_uuid))
-        except Exception as e:
-            self.ap.logger.warning(
-                f'Unable to load Transcript history view for conversation {conversation_uuid}: {e}'
-            )
-            return None
-
-        return messages or None
-
-    async def _resolve_history_messages(
-        self,
-        runner_id: str | None,
-        conversation: typing.Any,
-    ) -> list[provider_message.Message]:
-        transcript_messages = await self._load_agent_runner_history_messages(
-            runner_id,
-            getattr(conversation, 'uuid', None),
-        )
-        if transcript_messages is not None:
-            return transcript_messages
-        return conversation.messages.copy()
-
     async def process(
         self,
         query: pipeline_query.Query,
         stage_inst_name: str,
     ) -> entities.StageProcessResult:
         """Process"""
-        # Resolve runner ID from the current ai.runner.id shape.
-        runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
-
-        # Get runner config from ai.runner_config[runner_id].
-        runner_config = ConfigMigration.resolve_runner_config(query.pipeline_config, runner_id) if runner_id else {}
-        query.variables = query.variables or {}
-        bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
-        bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
-        descriptor = await self._get_runner_descriptor(runner_id, bound_plugins)
+        selected_runner = query.pipeline_config['ai']['runner']['runner']
+        include_skill_authoring = (
+            selected_runner == 'local-agent' and getattr(self.ap, 'skill_service', None) is not None
+        )
 
         session = await self.ap.sess_mgr.get_session(query)
 
-        uses_host_models = config_schema.uses_host_models(descriptor)
-        uses_host_tools = config_schema.uses_host_tools(descriptor)
-        include_skill_authoring = (
-            config_schema.supports_skill_authoring(descriptor)
-            and getattr(self.ap, 'skill_service', None) is not None
-        )
+        # When not local-agent, llm_model is None
         llm_model = None
-        if uses_host_models:
-            primary_uuid, fallback_uuids = config_schema.extract_model_selection(descriptor, runner_config)
-            llm_model = await self._resolve_llm_model(primary_uuid)
-            valid_fallbacks = await self._resolve_fallback_models(fallback_uuids)
-            if valid_fallbacks:
-                query.variables['_fallback_model_uuids'] = valid_fallbacks
+        if selected_runner == 'local-agent':
+            # Read model config — new format is { primary: str, fallbacks: [str] },
+            # but handle legacy plain string for backward compatibility
+            model_config = query.pipeline_config['ai']['local-agent'].get('model', {})
+            if isinstance(model_config, str):
+                # Legacy format: plain UUID string
+                primary_uuid = model_config
+                fallback_uuids = []
+            else:
+                primary_uuid = model_config.get('primary', '')
+                fallback_uuids = model_config.get('fallbacks', [])
 
-        prompt_config = config_schema.extract_prompt_config(descriptor, runner_config, DEFAULT_PROMPT_CONFIG)
+            if primary_uuid:
+                try:
+                    llm_model = await self.ap.model_mgr.get_model_by_uuid(primary_uuid)
+                except ValueError:
+                    self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured')
+
+            # Resolve fallback model UUIDs
+            if fallback_uuids:
+                valid_fallbacks = []
+                for fb_uuid in fallback_uuids:
+                    try:
+                        await self.ap.model_mgr.get_model_by_uuid(fb_uuid)
+                        valid_fallbacks.append(fb_uuid)
+                    except ValueError:
+                        self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping')
+                if valid_fallbacks:
+                    query.variables['_fallback_model_uuids'] = valid_fallbacks
 
         conversation = await self.ap.sess_mgr.get_conversation(
             query,
             session,
-            prompt_config,
+            query.pipeline_config['ai']['local-agent']['prompt'],
             query.pipeline_uuid,
             query.bot_uuid,
         )
@@ -193,7 +82,7 @@ class PreProcessor(stage.PipelineStage):
         # been idle for longer than the configured conversation expire time.
         # The idle window is measured from the last preprocess/update time, not
         # from the conversation creation time.
-        conversation_expire_time = ConfigMigration.get_expire_time(query.pipeline_config)
+        conversation_expire_time = query.pipeline_config.get('ai', {}).get('runner', {}).get('expire-time', None)
         now = datetime.datetime.now()
         if conversation_expire_time is not None and conversation_expire_time > 0:
             last_update_time = getattr(conversation, 'update_time', None) or getattr(conversation, 'create_time', None)
@@ -210,17 +99,20 @@ class PreProcessor(stage.PipelineStage):
         # time instead of the first message/creation time.
         conversation.update_time = now
 
-        # Attach resolved session state to the query.
+        # 设置query
         query.session = session
         query.prompt = conversation.prompt.copy()
-        query.messages = await self._resolve_history_messages(runner_id, conversation)
+        query.messages = conversation.messages.copy()
 
-        if uses_host_models:
+        if selected_runner == 'local-agent':
             query.use_funcs = []
             if llm_model:
                 query.use_llm_model_uuid = llm_model.model_entity.uuid
 
-                if uses_host_tools and llm_model.model_entity.abilities.__contains__('func_call'):
+                if llm_model.model_entity.abilities.__contains__('func_call'):
+                    # Get bound plugins and MCP servers for filtering tools
+                    bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
+                    bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
                     query.use_funcs = await self.ap.tool_mgr.get_all_tools(
                         bound_plugins,
                         bound_mcp_servers,
@@ -233,22 +125,14 @@ class PreProcessor(stage.PipelineStage):
 
             # If primary model doesn't support func_call but fallback models exist,
             # load tools anyway since fallback models may support them
-            if uses_host_tools and not query.use_funcs and query.variables.get('_fallback_model_uuids'):
+            if not query.use_funcs and query.variables.get('_fallback_model_uuids'):
+                bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
+                bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None)
                 query.use_funcs = await self.ap.tool_mgr.get_all_tools(
                     bound_plugins,
                     bound_mcp_servers,
                     include_skill_authoring=include_skill_authoring,
                 )
-        elif uses_host_tools:
-            query.use_funcs = await self.ap.tool_mgr.get_all_tools(
-                bound_plugins,
-                bound_mcp_servers,
-                include_skill_authoring=include_skill_authoring,
-            )
-
-            self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
-            self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
-            self.ap.logger.debug(f'Use funcs: {query.use_funcs}')
 
         sender_name = ''
 
@@ -273,25 +157,36 @@ class PreProcessor(stage.PipelineStage):
         }
         query.variables.update(variables)
 
-        keep_image_inputs = self._should_keep_image_inputs(descriptor, uses_host_models, llm_model)
-        if not keep_image_inputs:
-            self._strip_images_from_history(query)
+        # Check if this model supports vision, if not, remove all images
+        # TODO this checking should be performed in runner, and in this stage, the image should be reserved
+        if (
+            selected_runner == 'local-agent'
+            and llm_model
+            and not llm_model.model_entity.abilities.__contains__('vision')
+        ):
+            for msg in query.messages:
+                if isinstance(msg.content, list):
+                    for me in msg.content:
+                        if me.type == 'image_url':
+                            msg.content.remove(me)
 
         content_list: list[provider_message.ContentElement] = []
 
         plain_text = ''
-        quote_msg = query.pipeline_config['trigger'].get('misc', {}).get('combine-quote-message', False)
+        quote_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message')
 
         for me in query.message_chain:
             if isinstance(me, platform_message.Plain):
                 content_list.append(provider_message.ContentElement.from_text(me.text))
                 plain_text += me.text
             elif isinstance(me, platform_message.Image):
-                if keep_image_inputs:
+                if selected_runner != 'local-agent' or (
+                    llm_model and llm_model.model_entity.abilities.__contains__('vision')
+                ):
                     if me.base64 is not None:
                         content_list.append(provider_message.ContentElement.from_image_base64(me.base64))
             elif isinstance(me, platform_message.Voice):
-                # Convert voice input into file content for downstream model upload.
+                # 转成文件链接，让下游 runner 上传到目标模型
                 if me.base64:
                     content_list.append(provider_message.ContentElement.from_file_base64(me.base64, 'voice.silk'))
                 elif me.url:
@@ -306,7 +201,9 @@ class PreProcessor(stage.PipelineStage):
                     if isinstance(msg, platform_message.Plain):
                         content_list.append(provider_message.ContentElement.from_text(msg.text))
                     elif isinstance(msg, platform_message.Image):
-                        if keep_image_inputs:
+                        if selected_runner != 'local-agent' or (
+                            llm_model and llm_model.model_entity.abilities.__contains__('vision')
+                        ):
                             if msg.base64 is not None:
                                 content_list.append(provider_message.ContentElement.from_image_base64(msg.base64))
                     elif isinstance(msg, platform_message.File):
@@ -326,14 +223,16 @@ class PreProcessor(stage.PipelineStage):
 
         query.user_message = provider_message.Message(role='user', content=content_list)
 
-        # Extract configured KB UUIDs into query variables so PromptPreProcessing
-        # plugins can still adjust the authorized retrieval set before run_agent.
-        query.variables['_knowledge_base_uuids'] = config_schema.extract_knowledge_base_uuids(
-            descriptor,
-            runner_config,
-        )
+        # Extract knowledge base UUIDs into query variables so plugins can modify them
+        # during PromptPreProcessing before the runner performs retrieval.
+        kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', [])
+        if not kb_uuids:
+            old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '')
+            if old_kb_uuid and old_kb_uuid != '__none__':
+                kb_uuids = [old_kb_uuid]
+        query.variables['_knowledge_base_uuids'] = list(kb_uuids)
 
-        # Emit PromptPreProcessing before the runner receives the query.
+        # =========== 触发事件 PromptPreProcessing
 
         event = events.PromptPreProcessing(
             session_name=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
@@ -349,7 +248,19 @@ class PreProcessor(stage.PipelineStage):
         query.prompt.messages = event_ctx.event.default_prompt
         query.messages = event_ctx.event.prompt
 
-        if include_skill_authoring and getattr(self.ap, 'skill_mgr', None) is not None:
+        # =========== Skill awareness for the local-agent runner ===========
+        # The actual activation goes through the ``activate`` Tool Call so the
+        # LLM doesn't see full SKILL.md instructions until it commits to a
+        # skill (Claude Code's progressive disclosure). But the LLM still has
+        # to KNOW which skills exist to make that choice, so we:
+        #   1. resolve the pipeline's bound skills and stash them in
+        #      ``query.variables['_pipeline_bound_skills']`` for downstream
+        #      visibility checks (skill loader, native exec workdir);
+        #   2. inject a short ``Available Skills`` index (name + description
+        #      only) into the system prompt. The contributor's original PR
+        #      relied on this injection; without it the LLM never discovers
+        #      the skills are there and just calls native tools instead.
+        if selected_runner == 'local-agent' and self.ap.skill_mgr:
             pipeline_data = await self.ap.pipeline_service.get_pipeline(query.pipeline_uuid)
             extensions_prefs = (pipeline_data or {}).get('extensions_preferences', {})
             enable_all_skills = extensions_prefs.get('enable_all_skills', True)
@@ -361,4 +272,43 @@ class PreProcessor(stage.PipelineStage):
 
             query.variables['_pipeline_bound_skills'] = bound_skills
 
+            skill_addition = self.ap.skill_mgr.build_skill_aware_prompt_addition(
+                bound_skills=bound_skills,
+            )
+            if skill_addition:
+                # Append to the first system message; create one if the
+                # prompt has none. Handles both plain-string and
+                # content-element (list) message bodies.
+                if query.prompt.messages and query.prompt.messages[0].role == 'system':
+                    head = query.prompt.messages[0]
+                    if isinstance(head.content, str):
+                        head.content = head.content + skill_addition
+                    elif isinstance(head.content, list):
+                        appended = False
+                        for ce in head.content:
+                            if getattr(ce, 'type', None) == 'text':
+                                ce.text = (ce.text or '') + skill_addition
+                                appended = True
+                                break
+                        if not appended:
+                            head.content.append(provider_message.ContentElement(type='text', text=skill_addition))
+                else:
+                    query.prompt.messages.insert(
+                        0,
+                        provider_message.Message(role='system', content=skill_addition.strip()),
+                    )
+                self.ap.logger.debug(
+                    f'Skill index injected into system prompt: '
+                    f'pipeline={query.pipeline_uuid} '
+                    f'bound_skills={bound_skills or "all"} '
+                    f'loaded_skills={len(self.ap.skill_mgr.skills)}'
+                )
+            else:
+                self.ap.logger.debug(
+                    f'No skills available for prompt injection: '
+                    f'pipeline={query.pipeline_uuid} '
+                    f'loaded_skills={len(self.ap.skill_mgr.skills)} '
+                    f'bound_skills={bound_skills}'
+                )
+
         return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/pipeline/process/handlers/chat.py

@@ -9,35 +9,29 @@ from datetime import datetime
 
 from .. import handler
 from ... import entities
+from ....provider import runner as runner_module
 
 import langbot_plugin.api.entities.events as events
-from ....agent.runner.config_migration import ConfigMigration
-from ....agent.runner import config_schema
-from ....utils import constants, runner as runner_utils
+from ....utils import importutil, constants, runner as runner_utils
+from ....provider import runners
 import langbot_plugin.api.entities.builtin.provider.session as provider_session
 import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
 import langbot_plugin.api.entities.builtin.provider.message as provider_message
 
 
-DEFAULT_PROMPT_CONFIG = [
-    {'role': 'system', 'content': 'You are a helpful assistant.'},
-]
+importutil.import_modules_in_pkg(runners)
 
 
 class ChatMessageHandler(handler.MessageHandler):
-    """Chat message handler using AgentRunOrchestrator.
-
-    This handler delegates all runner execution to the agent_run_orchestrator,
-    which resolves runner ID, builds context, invokes plugin runtime,
-    and normalizes results.
-    """
-
     async def handle(
         self,
         query: pipeline_query.Query,
     ) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
-        """Handle chat message by delegating to AgentRunOrchestrator."""
-        # Trigger plugin event
+        """处理"""
+        # 调API
+        #   生成器
+
+        # 触发插件事件
         event_class = (
             events.PersonNormalMessageReceived
             if query.launcher_type == provider_session.LauncherTypes.PERSON
@@ -58,7 +52,7 @@ class ChatMessageHandler(handler.MessageHandler):
         bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
         event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)
 
-        is_create_card = False  # Track if streaming card was created
+        is_create_card = False  # 判断下是否需要创建流式卡片
 
         if event_ctx.is_prevented_default():
             if event_ctx.event.reply_message_chain is not None:
@@ -89,37 +83,35 @@ class ChatMessageHandler(handler.MessageHandler):
                 is_stream = False
 
             try:
+                for r in runner_module.preregistered_runners:
+                    if r.name == query.pipeline_config['ai']['runner']['runner']:
+                        runner = r(self.ap, query.pipeline_config)
+                        break
+                else:
+                    raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
                 # Mark start time for telemetry
                 start_ts = time.time()
 
-                # Create a single resp_message_id for the entire streaming response
-                resp_message_id = uuid.uuid4()
-                chunk_count = 0
+                if is_stream:
+                    resp_message_id = uuid.uuid4()
+                    chunk_count = 0  # Track streaming chunks to reduce excessive logging
 
-                # Use AgentRunOrchestrator to run the agent
-                # This replaces direct runner lookup and PluginAgentRunnerWrapper
-                async for result in self.ap.agent_run_orchestrator.run_from_query(query):
-                    result.resp_message_id = str(resp_message_id)
-
-                    # For streaming mode, pop previous response before adding new chunk
-                    # This allows incremental card updates
-                    if is_stream:
+                    async for result in runner.run(query):
+                        result.resp_message_id = str(resp_message_id)
                         if query.resp_messages:
                             query.resp_messages.pop()
                         if query.resp_message_chain:
                             query.resp_message_chain.pop()
-
-                        # Create streaming card on first result (connection established)
-                        if not is_create_card:
+                        # 此时连接外部 AI 服务正常,创建卡片
+                        if not is_create_card:  # 只有不是第一次才创建卡片
                             await query.adapter.create_message_card(str(resp_message_id), query.message_event)
                             is_create_card = True
+                        query.resp_messages.append(result)
 
-                    query.resp_messages.append(result)
-
-                    if is_stream:
                         chunk_count += 1
-                        # Only log every 10th chunk to reduce excessive logging during streaming.
-                        # First chunk uses INFO level to confirm connection establishment.
+                        # Only log every 10th chunk to reduce excessive logging during streaming
+                        # This prevents memory overflow from thousands of log entries per conversation
+                        # First chunk uses INFO level to confirm connection establishment
                         if chunk_count == 1:
                             summary = self.format_result_log(result)
                             if summary is not None:
@@ -130,59 +122,46 @@ class ChatMessageHandler(handler.MessageHandler):
                             self.ap.logger.debug(
                                 f'Conversation({query.query_id}) Streaming chunk {chunk_count}: {self.cut_str(result.readable_str())}'
                             )
-                    else:
-                        summary = self.format_result_log(result)
-                        if summary is not None:
-                            self.ap.logger.info(f'Conversation({query.query_id}) Response: {summary}')
 
-                    if result.content is not None:
-                        text_length += len(result.content)
+                        if result.content is not None:
+                            text_length += len(result.content)
 
-                    yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
+                        yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
 
-                # Log final summary after streaming completes
-                if is_stream:
+                    # Log final summary after streaming completes
                     self.ap.logger.info(
                         f'Conversation({query.query_id}) Streaming completed: {chunk_count} chunks, {text_length} chars'
                     )
 
-                # Keep a conversation object available for downstream legacy
-                # readers, but do not mirror AgentRunner history into
-                # conversation.messages. TranscriptStore is the canonical
-                # history source for this path.
-                await self._ensure_conversation_for_history(query)
+                else:
+                    async for result in runner.run(query):
+                        query.resp_messages.append(result)
 
+                        summary = self.format_result_log(result)
+                        if summary is not None:
+                            self.ap.logger.info(f'Conversation({query.query_id}) Response: {summary}')
+
+                        if result.content is not None:
+                            text_length += len(result.content)
+
+                        yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
+
+                query.session.using_conversation.messages.append(query.user_message)
+
+                query.session.using_conversation.messages.extend(query.resp_messages)
             except Exception as e:
-                # Import orchestrator errors for specific handling
-                from ....agent.runner.errors import (
-                    RunnerNotFoundError,
-                    RunnerNotAuthorizedError,
-                    RunnerExecutionError,
-                )
-
                 error_info = f'{traceback.format_exc()}'
                 self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
+                traceback.print_exc()
 
-                # Handle specific runner errors with appropriate messages
-                if isinstance(e, RunnerNotFoundError):
-                    user_notice = f'Agent runner not found: {e.runner_id}'
-                elif isinstance(e, RunnerNotAuthorizedError):
-                    user_notice = 'Agent runner not authorized for this pipeline'
-                elif isinstance(e, RunnerExecutionError):
-                    if e.retryable:
-                        user_notice = 'Agent runner temporarily unavailable. Please try again.'
-                    else:
-                        user_notice = 'Agent runner execution failed.'
-                else:
-                    # Use existing exception handling
-                    exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
+                exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
 
-                    if exception_handling == 'show-error':
-                        user_notice = f'{e}'
-                    elif exception_handling == 'show-hint':
-                        user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
-                    else:  # hide
-                        user_notice = None
+                if exception_handling == 'show-error':
+                    user_notice = f'{e}'
+                elif exception_handling == 'show-hint':
+                    user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
+                else:  # hide
+                    user_notice = None
 
                 yield entities.StageProcessResult(
                     result_type=entities.ResultType.INTERRUPT,
@@ -192,7 +171,7 @@ class ChatMessageHandler(handler.MessageHandler):
                     debug_notice=traceback.format_exc(),
                 )
             finally:
-                # Telemetry reporting
+                # Telemetry reporting: collect minimal per-query execution info and send asynchronously
                 try:
                     end_ts = time.time()
                     duration_ms = None
@@ -200,14 +179,16 @@ class ChatMessageHandler(handler.MessageHandler):
                         duration_ms = int((end_ts - start_ts) * 1000)
 
                     adapter_name = query.adapter.__class__.__name__ if hasattr(query, 'adapter') else None
+                    runner_name = (
+                        query.pipeline_config.get('ai', {}).get('runner', {}).get('runner')
+                        if query.pipeline_config
+                        else None
+                    )
 
-                    # Use orchestrator to resolve runner ID for telemetry
-                    runner_name = self.ap.agent_run_orchestrator.resolve_runner_id_for_telemetry(query)
-
-                    # Model name if available
+                    # Model name if using localagent
                     model_name = None
                     try:
-                        if getattr(query, 'use_llm_model_uuid', None):
+                        if runner_name == 'local-agent' and getattr(query, 'use_llm_model_uuid', None):
                             m = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
                             if m and getattr(m, 'model_entity', None):
                                 model_name = getattr(m.model_entity, 'name', None)
@@ -217,7 +198,7 @@ class ChatMessageHandler(handler.MessageHandler):
                     pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)
 
                     runner_category = runner_utils.get_runner_category_from_runner(
-                        runner_name, None, query.pipeline_config
+                        runner_name, runner, query.pipeline_config
                     )
 
                     payload = {
@@ -235,6 +216,7 @@ class ChatMessageHandler(handler.MessageHandler):
                         'timestamp': datetime.utcnow().isoformat(),
                     }
 
+                    # Send telemetry asynchronously and do not block pipeline via app's telemetry manager
                     await self.ap.telemetry.start_send_task(payload)
 
                     # Trigger survey event on first successful non-WebSocket response
@@ -242,70 +224,5 @@ class ChatMessageHandler(handler.MessageHandler):
                         if self.ap.survey:
                             await self.ap.survey.trigger_event('first_bot_response_success')
                 except Exception as ex:
+                    # Ensure telemetry issues do not affect normal flow
                     self.ap.logger.warning(f'Failed to send telemetry: {ex}')
-
-    async def _ensure_conversation_for_history(
-        self,
-        query: pipeline_query.Query,
-    ) -> provider_session.Conversation:
-        session = getattr(query, 'session', None)
-        conversation = getattr(session, 'using_conversation', None)
-        if conversation is not None:
-            return conversation
-
-        if session is None or getattr(self.ap, 'sess_mgr', None) is None:
-            raise RuntimeError('Conversation is not available for history update')
-
-        prompt_config = await self._build_history_prompt_config(query)
-        conversation = await self.ap.sess_mgr.get_conversation(
-            query,
-            session,
-            prompt_config,
-            query.pipeline_uuid,
-            query.bot_uuid,
-        )
-        if conversation is None:
-            raise RuntimeError('Conversation manager did not return a conversation')
-
-        if getattr(session, 'using_conversation', None) is None:
-            session.using_conversation = conversation
-        return conversation
-
-    async def _build_history_prompt_config(
-        self,
-        query: pipeline_query.Query,
-    ) -> list[dict[str, typing.Any]]:
-        prompt_messages = getattr(getattr(query, 'prompt', None), 'messages', None)
-        if prompt_messages:
-            prompt_config = []
-            for message in prompt_messages:
-                if hasattr(message, 'model_dump'):
-                    prompt_config.append(message.model_dump(mode='python'))
-                elif isinstance(message, dict):
-                    prompt_config.append(message)
-            if prompt_config:
-                return prompt_config
-
-        runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
-        runner_config = ConfigMigration.resolve_runner_config(query.pipeline_config, runner_id) if runner_id else {}
-        bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
-        descriptor = await self._get_runner_descriptor(runner_id, bound_plugins)
-        return config_schema.extract_prompt_config(descriptor, runner_config, DEFAULT_PROMPT_CONFIG)
-
-    async def _get_runner_descriptor(
-        self,
-        runner_id: str | None,
-        bound_plugins: list[str] | None,
-    ) -> typing.Any | None:
-        if not runner_id:
-            return None
-
-        registry = getattr(self.ap, 'agent_runner_registry', None)
-        if registry is None:
-            return None
-
-        try:
-            return await registry.get(runner_id, bound_plugins)
-        except Exception as e:
-            self.ap.logger.debug(f'Unable to load AgentRunner descriptor for {runner_id}: {e}')
-            return None

src/langbot/pkg/platform/sources/web_page_bot_adapter.py

@@ -84,20 +84,6 @@ class WebPageBotAdapter(abstract_platform_adapter.AbstractMessagePlatformAdapter
     ):
         self.listeners.pop(event_type, None)
 
-    async def is_stream_output_supported(self) -> bool:
-        """Delegate stream output check to ws_adapter."""
-        if self._ws_adapter is not None:
-            return await self._ws_adapter.is_stream_output_supported()
-        return False
-
-    async def create_message_card(
-        self, message_id: str | int, event: platform_events.MessageEvent
-    ) -> bool:
-        """Delegate create_message_card to ws_adapter."""
-        if self._ws_adapter is not None:
-            return await self._ws_adapter.create_message_card(message_id, event)
-        return False
-
     async def is_muted(self, group_id: int) -> bool:
         return False
 

src/langbot/pkg/plugin/connector.py

@@ -187,15 +187,6 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
     async def initialize_plugins(self):
         pass
 
-    async def _refresh_agent_runner_registry(self) -> None:
-        registry = getattr(self.ap, 'agent_runner_registry', None)
-        if registry is None:
-            return
-        try:
-            await registry.refresh()
-        except Exception as e:
-            self.ap.logger.warning(f'Failed to refresh agent runner registry: {e}')
-
     async def ping_plugin_runtime(self):
         if not hasattr(self, 'handler'):
             raise PluginRuntimeNotConnectedError('Plugin runtime is not connected')
@@ -257,6 +248,9 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
 
         mode = mcp_data.get('mode') or 'stdio'
         extra_args = mcp_data.get('extra_args') or {}
+        # Marketplace records carry the rendered README markdown; persist it so
+        # the detail page Docs tab works offline and without a marketplace round-trip.
+        readme = mcp_data.get('readme') or ''
         # Use __ instead of / to avoid URL routing issues with slashes
         name = f'{mcp_data.get("author", "")}__{mcp_data.get("name", "")}'
 
@@ -276,6 +270,7 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
             'enable': True,
             'mode': mode,
             'extra_args': extra_args,
+            'readme': readme,
         }
 
         await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_mcp.MCPServer).values(server_data))
@@ -468,12 +463,7 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
                                         )
 
                                     file_bytes = download_resp.content
-                                    plugin_author, plugin_name = self._inspect_plugin_package(
-                                        file_bytes,
-                                        task_context,
-                                    )
-                                    if task_context is not None and plugin_author and plugin_name:
-                                        task_context.metadata['plugin_name'] = f'{plugin_author}/{plugin_name}'
+                                    self._inspect_plugin_package(file_bytes, task_context)
                                     file_key = await self.handler.send_file(file_bytes, 'lbpkg')
                                     install_info['plugin_file_key'] = file_key
                                     self.ap.logger.info(f'Transfered file {file_key} to plugin runtime')
@@ -560,7 +550,6 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
                 task_context.metadata.update(metadata)
 
         await self._wait_for_installed_plugin_ready(plugin_author, plugin_name, task_context)
-        await self._refresh_agent_runner_registry()
 
     async def upgrade_plugin(
         self,
@@ -579,8 +568,6 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
                 if task_context is not None:
                     task_context.trace(trace)
 
-        await self._refresh_agent_runner_registry()
-
     async def delete_plugin(
         self,
         plugin_author: str,
@@ -605,8 +592,6 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
                 task_context.trace('Cleaning up plugin configuration and storage...')
             await self.handler.cleanup_plugin_data(plugin_author, plugin_name)
 
-        await self._refresh_agent_runner_registry()
-
     async def list_plugins(self, component_kinds: list[str] | None = None) -> list[dict[str, Any]]:
         """List plugins, optionally filtered by component kinds.
 
@@ -797,53 +782,6 @@ class PluginRuntimeConnector(ManagedRuntimeConnector):
 
             yield cmd_ret
 
-    # AgentRunner methods
-    async def list_agent_runners(self, bound_plugins: list[str] | None = None) -> list[dict[str, Any]]:
-        """List all available AgentRunner components.
-
-        Returns list of dicts with plugin_author, plugin_name, runner_name, manifest, etc.
-        """
-        if not self.is_enable_plugin:
-            return []
-
-        runners_data = await self.handler.list_agent_runners(include_plugins=bound_plugins)
-        return runners_data
-
-    async def run_agent(
-        self,
-        plugin_author: str,
-        plugin_name: str,
-        runner_name: str,
-        context: dict[str, Any],
-    ) -> typing.AsyncGenerator[dict[str, Any], None]:
-        """Run an AgentRunner from a plugin.
-
-        Args:
-            plugin_author: Plugin author
-            plugin_name: Plugin name
-            runner_name: AgentRunner component name
-            context: AgentRunContext as dict
-
-        Yields:
-            AgentRunResult dicts
-        """
-        if not self.is_enable_plugin:
-            # Return a protocol-level failure result.
-            yield {
-                'type': 'run.failed',
-                'data': {
-                    'error': 'Plugin system is disabled',
-                    'code': 'plugin.disabled',
-                    'retryable': False,
-                },
-            }
-            return
-
-        gen = self.handler.run_agent(plugin_author, plugin_name, runner_name, context)
-
-        async for ret in gen:
-            yield ret
-
     async def retrieve_knowledge(
         self,
         plugin_author: str,

src/langbot/pkg/provider/modelmgr/modelmgr.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import asyncio
 import sqlalchemy
 import traceback
 
@@ -55,19 +54,8 @@ class ModelManager:
             self.ap.logger.info('LangBot Space Models service is disabled, skipping sync.')
             return
 
-        sync_timeout = space_config.get('models_sync_timeout')
         try:
-            if sync_timeout:
-                await asyncio.wait_for(
-                    self.sync_new_models_from_space(),
-                    timeout=float(sync_timeout),
-                )
-            else:
-                await self.sync_new_models_from_space()
-        except asyncio.TimeoutError:
-            self.ap.logger.warning(
-                f'LangBot Space model sync timed out after {sync_timeout}s, skipping startup sync.'
-            )
+            await self.sync_new_models_from_space()
         except Exception as e:
             self.ap.logger.warning('Failed to sync new models from LangBot Space, model list may not be updated.')
             self.ap.logger.warning(f'  - Error: {e}')
@@ -155,49 +143,83 @@ class ModelManager:
         # get the latest models from space
         space_models = await self.ap.space_service.get_models()
 
-        exists_llm_models_uuids = [m['uuid'] for m in await self.ap.llm_model_service.get_llm_models()]
-        exists_embedding_models_uuids = [
-            m['uuid'] for m in await self.ap.embedding_models_service.get_embedding_models()
-        ]
+        # Index existing models by uuid. Space reuses a model's uuid across
+        # renames / re-specs (e.g. the uuid that used to be ``claude-opus-4-6``
+        # may later become ``claude-opus-4-7``). So for Space-managed models we
+        # upsert: create when the uuid is new, otherwise update name/abilities/
+        # ranking to track Space. Models owned by other providers are never
+        # touched, even on an (unexpected) uuid collision.
+        existing_llm_models = {m['uuid']: m for m in await self.ap.llm_model_service.get_llm_models()}
+        existing_embedding_models = {
+            m['uuid']: m for m in await self.ap.embedding_models_service.get_embedding_models()
+        }
+
+        created = 0
+        updated = 0
 
         for space_model in space_models:
             if space_model.category == 'chat':
-                uuid = space_model.uuid
-
-                if uuid in exists_llm_models_uuids:
-                    continue
-
-                # model will be automatically loaded
-                await self.ap.llm_model_service.create_llm_model(
-                    {
-                        'uuid': space_model.uuid,
+                existing = existing_llm_models.get(space_model.uuid)
+                if existing is None:
+                    # model will be automatically loaded
+                    await self.ap.llm_model_service.create_llm_model(
+                        {
+                            'uuid': space_model.uuid,
+                            'name': space_model.model_id,
+                            'provider_uuid': space_model_provider.uuid,
+                            'abilities': space_model.llm_abilities or [],
+                            'extra_args': {},
+                            'prefered_ranking': space_model.featured_order,
+                        },
+                        preserve_uuid=True,
+                        auto_set_to_default_pipeline=False,
+                    )
+                    created += 1
+                elif existing.get('provider_uuid') == space_model_provider.uuid:
+                    desired = {
                         'name': space_model.model_id,
                         'provider_uuid': space_model_provider.uuid,
                         'abilities': space_model.llm_abilities or [],
-                        'extra_args': {},
                         'prefered_ranking': space_model.featured_order,
-                    },
-                    preserve_uuid=True,
-                    auto_set_to_default_pipeline=False,
-                )
+                    }
+                    if (
+                        existing.get('name') != desired['name']
+                        or list(existing.get('abilities') or []) != list(desired['abilities'])
+                        or existing.get('prefered_ranking') != desired['prefered_ranking']
+                    ):
+                        await self.ap.llm_model_service.update_llm_model(space_model.uuid, dict(desired))
+                        updated += 1
 
             elif space_model.category == 'embedding':
-                uuid = space_model.uuid
-
-                if uuid in exists_embedding_models_uuids:
-                    continue
-
-                # model will be automatically loaded
-                await self.ap.embedding_models_service.create_embedding_model(
-                    {
-                        'uuid': space_model.uuid,
+                existing = existing_embedding_models.get(space_model.uuid)
+                if existing is None:
+                    # model will be automatically loaded
+                    await self.ap.embedding_models_service.create_embedding_model(
+                        {
+                            'uuid': space_model.uuid,
+                            'name': space_model.model_id,
+                            'provider_uuid': space_model_provider.uuid,
+                            'extra_args': {},
+                            'prefered_ranking': space_model.featured_order,
+                        },
+                        preserve_uuid=True,
+                    )
+                    created += 1
+                elif existing.get('provider_uuid') == space_model_provider.uuid:
+                    desired = {
                         'name': space_model.model_id,
                         'provider_uuid': space_model_provider.uuid,
-                        'extra_args': {},
                         'prefered_ranking': space_model.featured_order,
-                    },
-                    preserve_uuid=True,
-                )
+                    }
+                    if (
+                        existing.get('name') != desired['name']
+                        or existing.get('prefered_ranking') != desired['prefered_ranking']
+                    ):
+                        await self.ap.embedding_models_service.update_embedding_model(space_model.uuid, dict(desired))
+                        updated += 1
+
+        if created or updated:
+            self.ap.logger.info(f'Synced models from LangBot Space: {created} added, {updated} updated.')
 
     async def init_temporary_runtime_llm_model(
         self,

src/langbot/pkg/provider/modelmgr/requesters/bailianchatcmpl.py

@@ -171,8 +171,7 @@ class BailianChatCompletions(modelscopechatcmpl.ModelScopeChatCompletions):
                 # 解析 chunk 数据
                 if hasattr(chunk, 'choices') and chunk.choices:
                     choice = chunk.choices[0]
-                    delta_obj = getattr(choice, 'delta', None)
-                    delta = delta_obj.model_dump() if delta_obj is not None else {}
+                    delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
                     finish_reason = getattr(choice, 'finish_reason', None)
                 else:
                     delta = {}

src/langbot/pkg/provider/modelmgr/requesters/chatcmpl.py

@@ -359,8 +359,7 @@ class OpenAIChatCompletions(requester.ProviderAPIRequester):
 
             if hasattr(chunk, 'choices') and chunk.choices:
                 choice = chunk.choices[0]
-                delta_obj = getattr(choice, 'delta', None)
-                delta = delta_obj.model_dump() if delta_obj is not None else {}
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
 
                 finish_reason = getattr(choice, 'finish_reason', None)
             else:

src/langbot/pkg/provider/modelmgr/requesters/geminichatcmpl.py

@@ -132,8 +132,7 @@ class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
 
             if hasattr(chunk, 'choices') and chunk.choices:
                 choice = chunk.choices[0]
-                delta_obj = getattr(choice, 'delta', None)
-                delta = delta_obj.model_dump() if delta_obj is not None else {}
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
 
                 finish_reason = getattr(choice, 'finish_reason', None)
             else:

src/langbot/pkg/provider/modelmgr/requesters/jiekouaichatcmpl.py

@@ -144,8 +144,7 @@ class JieKouAIChatCompletions(chatcmpl.OpenAIChatCompletions):
             # 解析 chunk 数据
             if hasattr(chunk, 'choices') and chunk.choices:
                 choice = chunk.choices[0]
-                delta_obj = getattr(choice, 'delta', None)
-                delta = delta_obj.model_dump() if delta_obj is not None else {}
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
                 finish_reason = getattr(choice, 'finish_reason', None)
             else:
                 delta = {}
@@ -160,7 +159,7 @@ class JieKouAIChatCompletions(chatcmpl.OpenAIChatCompletions):
             # reasoning_content = delta.get('reasoning_content', '')
 
             if remove_think:
-                if delta.get('content') is not None:
+                if delta['content'] is not None:
                     if '<think>' in delta['content'] and not thinking_started and not thinking_ended:
                         thinking_started = True
                         continue

src/langbot/pkg/provider/modelmgr/requesters/modelscopechatcmpl.py

@@ -391,8 +391,7 @@ class ModelScopeChatCompletions(requester.ProviderAPIRequester):
             # 解析 chunk 数据
             if hasattr(chunk, 'choices') and chunk.choices:
                 choice = chunk.choices[0]
-                delta_obj = getattr(choice, 'delta', None)
-                delta = delta_obj.model_dump() if delta_obj is not None else {}
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
                 finish_reason = getattr(choice, 'finish_reason', None)
             else:
                 delta = {}

src/langbot/pkg/provider/modelmgr/requesters/ppiochatcmpl.py

@@ -144,8 +144,7 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
             # 解析 chunk 数据
             if hasattr(chunk, 'choices') and chunk.choices:
                 choice = chunk.choices[0]
-                delta_obj = getattr(choice, 'delta', None)
-                delta = delta_obj.model_dump() if delta_obj is not None else {}
+                delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
                 finish_reason = getattr(choice, 'finish_reason', None)
             else:
                 delta = {}
@@ -160,7 +159,7 @@ class PPIOChatCompletions(chatcmpl.OpenAIChatCompletions):
             # reasoning_content = delta.get('reasoning_content', '')
 
             if remove_think:
-                if delta.get('content') is not None:
+                if delta['content'] is not None:
                     if '<think>' in delta['content'] and not thinking_started and not thinking_ended:
                         thinking_started = True
                         continue