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66 Commits

Author SHA1 Message Date
RockChinQ 59538fd5cd enforce workflow data edge order 2026-07-02 00:51:01 +08:00
RockChinQ 235b33c335 separate workflow control and data edges 2026-07-01 22:01:06 +08:00
RockChinQ c588f9dfc1 fix: port workflow schema migration to alembic 2026-07-01 20:42:30 +08:00
RockChinQ d770de7f7f Merge remote-tracking branch 'origin/master' into feat/workflow
# Conflicts:
#	pyproject.toml
#	src/langbot/pkg/api/http/controller/main.py
#	src/langbot/pkg/core/app.py
#	src/langbot/pkg/core/stages/build_app.py
#	src/langbot/pkg/provider/modelmgr/requester.py
#	uv.lock
#	web/package-lock.json
#	web/package.json
#	web/pnpm-lock.yaml
#	web/src/app/home/components/dynamic-form/DynamicFormComponent.tsx
#	web/src/app/home/components/dynamic-form/DynamicFormItemComponent.tsx
#	web/src/app/home/components/home-sidebar/HomeSidebar.tsx
#	web/src/app/infra/entities/api/index.ts
#	web/src/app/infra/http/BackendClient.ts
#	web/src/i18n/locales/en-US.ts
#	web/src/i18n/locales/es-ES.ts
#	web/src/i18n/locales/ja-JP.ts
#	web/src/i18n/locales/ru-RU.ts
#	web/src/i18n/locales/th-TH.ts
#	web/src/i18n/locales/vi-VN.ts
#	web/src/i18n/locales/zh-Hans.ts
#	web/src/i18n/locales/zh-Hant.ts
#	web/src/router.tsx
2026-07-01 20:33:05 +08:00
彼方 cc7a13158e fix(aiocqhttp): resolve group member metadata (#2298)
* fix(aiocqhttp): resolve group member metadata

* style: ruff format
2026-07-01 20:00:47 +08:00
RockChinQ 7de7c7f714 docs: update Product Hunt badge to featured 2026-06-30 08:23:55 -04:00
Junyan Qin 85923e3d7b fix(migrations): shorten mcp resource revision id 2026-06-30 19:39:23 +08:00
Junyan Qin c7c6c5dc51 fix(i18n): add skill tools tooltip translations 2026-06-30 19:37:57 +08:00
Junyan Qin b056646696 fix(migrations): add mcp resource preferences 2026-06-30 19:36:04 +08:00
advancer-young 096ec1a8ce feat(mcp): support mcp resources (#2215)
* feat(mcp): support mcp resources

* feat(web): split MCP resources into tab

* docs: add MCP resources PR review

* feat(mcp): productionize resource support

* feat(mcp): scope local agent tools and resources

* fix(web): gate space embedding models behind login

* fix(web): prevent clipped space model CTA

* test: update preproc resource tool expectations

* fix(web): expose skill authoring tools in selector

---------

Co-authored-by: yang.xiang <yang.xiang@advancegroup.com>
Co-authored-by: Hyu <chenhyu@proton.me>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>
2026-06-30 19:16:30 +08:00
RockChinQ 2618e06492 docs: update 302.AI referral link to share.302ai.cn 2026-06-30 05:10:17 -04:00
彼方 5c8c0eb17b test: use package import paths (#2297) 2026-06-30 10:51:34 +08:00
彼方 ccc51522cf fix(aiocqhttp): normalize base64 media payloads (#2296)
* fix(aiocqhttp): normalize image and voice base64 payloads

* fix(aiocqhttp): support file messages from base64 payloads

* test(aiocqhttp): use package import path
2026-06-30 10:51:09 +08:00
彼方 6a99a83f2d chore(scripts): mark scripts executable (#2295) 2026-06-30 10:50:26 +08:00
Hyu ebab5343cf fix(i18n): add bots.admins keys to all locale files (#2292)
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
2026-06-27 17:38:30 +08:00
Hyu b97cc800d3 feat(platform): migrate bot admins from config.yaml to database (#2289)
* feat(platform): migrate bot admins from config.yaml to database

- Add BotAdmin ORM model (bot_admins table) scoped per bot_uuid
- Add Alembic migration 0007 to create table and migrate legacy config admins
- Remove top-level admins key from config.yaml template
- Add GET/POST/DELETE /api/v1/platform/bots/<uuid>/admins endpoints
- Update cmdmgr privilege check to query bot_admins table (bot-scoped)
- Add BotAdminsPanel frontend component in bot detail sessions tab
- Add i18n keys (zh-Hans, en-US)

* fix(ci): ruff/prettier format, fix test_importutil assertion

* fix(ci): prettier format BackendClient.ts and i18n locales

* fix(ci): eslint-prettier fix BackendClient.ts single-param formatting

* refactor: move admin management into session monitor, fix session_id enum format

- Remove standalone admins tab, replace with BotAdminsDialog in session monitor
- Add admin toggle button inline in chat header next to Active status
- Add BotAdminsDialog component with useBotAdmins hook
- Fix session_id written as LauncherTypes.PERSON_xxx instead of person_xxx
  (monitoring_helper.py x4, pipelinemgr.py x1 missing .value on launcher_type)
- Fix duplicate platform/person label in session list and chat header
- Migrate existing malformed session_id records in DB

---------

Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
2026-06-27 17:18:19 +08:00
彼方 48905ea080 feat(plugin): report deferred response delivery failures (#2287)
* feat(plugin): report deferred response delivery failures

* style: fix ruff format issues in plugin_diagnostics and test_handler_actions

---------

Co-authored-by: RockChinQ <45992437+RockChinQ@users.noreply.github.com>
2026-06-26 23:45:10 +08:00
Hyu ddb77fc43c fix(api): guard /set-password with allow_modify_login_info (#2288)
The /change-password and /bind-space endpoints already refuse when
system.allow_modify_login_info is false, but /set-password did not,
leaving a path to alter login credentials on locked-down deployments
(e.g. public demo instances). Apply the same guard.

Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
2026-06-26 16:35:50 +08:00
huanghuoguoguo 5b2826fa49 Add performance and reliability QA gates (#2283)
* Add performance and reliability QA gates

* test(skills): prepare user path performance gate

* test(skills): add debug chat load gate

* test(skills): extend fake provider load profiles

* test(skills): add debug chat timing and isolation probes

* test(skills): clarify manual QA perf gates
2026-06-25 21:02:44 +08:00
Hyu 20636ac432 Merge pull request #2284 from langbot-app/fix/api-password-thread-offload
fix(api): offload password hashing from event loop
2026-06-25 20:31:44 +08:00
Hyu af42602547 Merge pull request #2285 from langbot-app/fix/monitoring-null-payloads
fix(monitoring): tolerate null API payloads
2026-06-25 20:26:25 +08:00
dadachann 53b20e2b13 fix(monitoring): tolerate null API payloads
Normalize monitoring API responses before rendering so empty or error payloads with data:null cannot crash the dashboard. Also guard chart, token, and box session arrays before reading length/map.
2026-06-25 08:22:01 -04:00
dadachann 1242dc2d21 fix(api): offload password hashing from event loop 2026-06-25 06:29:16 -04:00
RockChinQ 04628d93cb docs: add architecture guide for agents 2026-06-25 04:17:19 -04:00
RockChinQ 9c22a1521c fix(box): defer separated workspace ownership to runtime 2026-06-25 00:09:40 -04:00
RockChinQ c8d5039580 feat(box): expose Docker CPU limit toggle 2026-06-24 23:31:53 -04:00
dadachann 85d8d9304e fix(web): keep feedback dialog interactive 2026-06-24 10:10:19 -04:00
Hyu 76471af179 feat(web): add sidebar feedback popover
Co-authored-by: dadachann <185672915+dadachann@users.noreply.github.com>
2026-06-24 16:43:50 +08:00
RockChinQ 59b2a7cd51 fix(monitoring): hide disabled box status on cloud 2026-06-23 06:40:05 -04:00
RockChinQ a43978ff24 chore(release): bump version to 4.10.4 2026-06-22 21:15:53 -04:00
RockChinQ e3417dd20b fix(release): derive package version from metadata 2026-06-22 21:10:33 -04:00
RockChinQ 2982e7c553 chore(release): bump version to 4.10.3 2026-06-22 11:12:15 -04:00
RockChinQ e1e14e9269 chore(deps): bump langbot-plugin to 0.4.6 2026-06-22 11:08:08 -04:00
Junyan Chin 1c128a1524 docs(skills/langbot-plugin-dev): document marketplace README i18n convention (root README.md must be English; other langs in readme/) 2026-06-22 02:39:15 -04:00
RockChinQ 8ad1203fd5 docs(examples): add web-page-bot embed demo, drop stray test-embed.html
Move the Page Bot (web_page_bot) embed test page out of the repo root into
examples/web-page-bot/ as a proper, LangBot-styled demo: a self-contained
index.html that loads the live widget.js against a running instance, plus
bilingual READMEs mirroring examples/http-bot/.
2026-06-22 02:17:26 -04:00
Junyan Chin 144bec371c feat(platform): standalone HTTP Bot adapter (server-to-server) (#2274)
* docs(platform): add HTTP Bot adapter design (RFC)

Standalone server-to-server HTTP adapter for driving a pipeline from external
systems (LangBot Space ticketing et al). Inbound via the existing unified
webhook route; outbound via signed callback POSTs. Preserves pipeline-native
N->1 aggregation and 1->M multi-reply without a long-lived WebSocket.

No core changes required (router/aggregator/pipeline untouched).

* feat(platform): add standalone HTTP Bot adapter

A first-class, vendor-neutral message-platform adapter (http_bot) for
server-to-server integrations (LangBot Space ticketing et al). Drives a
pipeline over plain HTTP with no long-lived connection:

- Inbound: signed POST to the existing unified webhook route /bots/<uuid>,
  carrying a caller-defined session_id mapped to the LangBot launcher id via
  get_launcher_id -> per-session isolation. Preserves pipeline-native N->1
  aggregation for free.
- Outbound: each reply_message / reply_message_chunk becomes one signed
  callback POST to the config-only callback_url, delivered in per-session
  sequence order with retry/backoff -> 1->M multi-reply.
- Sub-paths: /reset (drop a session) and /sync (block for the collapsed reply).
- Auth: symmetric HMAC-SHA256 both directions (timestamp + replay window),
  no JWT/Turnstile, no socket.

Decisions: callback URL is config-only (SSRF closed); reset + sync shipped;
Python + TS reference clients shipped (signing verified byte-identical 3-way).

No core changes: the unified webhook router, aggregator, query pool and
pipeline are untouched. Adapter is auto-discovered from platform/sources/.

Adds:
  src/langbot/pkg/platform/sources/http_bot.{py,yaml,svg}
  src/langbot/pkg/platform/sources/http_bot_signing.py
  docs/platforms/http-bot.md, docs/http-bot-openapi.json
  examples/http-bot/{client.py,client.ts,README.md}
Updates docs/HTTP_BOT_ADAPTER_DESIGN.md (status: implemented).

* docs(examples): add interactive HTTP Bot playground (browser debug console)

A single-file aiohttp web app (examples/http-bot/playground.py) that lets you
chat with a RUNNING http_bot bot from the browser and watch the protocol live:
signed inbound POST -> 202 ack -> 1->M signed callbacks streamed back via SSE,
with a debug panel showing the signature, HTTP status, and per-callback
sequence/verification. Light LangBot-styled UI.

On startup it reads the API key + http_bot bot from data/langbot.db and points
the bot's callback_url + secrets back at itself via the LangBot API (live
reload, no restart). README updated with a playground section.

* docs(examples): add Chinese README for http-bot reference clients

* style(platform): use </> code icon for http_bot adapter logo

* docs(examples): point http-bot guide links to docs.langbot.app

* style(platform): make http_bot icon a transparent monochrome </> so WebUI tints it like other adapters

* Revert to colorful </> badge for http_bot icon (WebUI renders it as-is)
2026-06-22 13:38:00 +08:00
Typer_Body 417cca016c cold 2026-06-05 22:35:57 +08:00
Typer_Body cb0cc4d06a cold 2026-06-05 21:20:46 +08:00
Typer_Body cb0bb44db8 update 2026-06-05 02:49:09 +08:00
Typer_Body 6609bebeec platfrom 2026-06-04 03:13:32 +08:00
Typer_Body 192b69b0fb fix 2026-06-02 02:29:35 +08:00
Typer_Body 543fbd8ca0 new 2026-06-02 02:08:07 +08:00
Typer_Body 804448b6cd yaml 2026-05-27 00:24:07 +08:00
Typer_Body 5d4e40459f shit 2026-05-26 02:28:01 +08:00
Typer_Body b5c43cc113 new nofe、、 2026-05-23 03:21:31 +08:00
Typer_Body 8ebfcd963a new node 2026-05-23 02:58:17 +08:00
Typer_Body 127198675e end3 2026-05-23 01:31:42 +08:00
Typer_Body 44fb188994 end1 2026-05-23 01:00:10 +08:00
Typer_Body 265385a563 end 2026-05-23 00:51:24 +08:00
Typer_Body 253cc6cbea shit 2026-05-22 02:07:48 +08:00
Typer_Body f99d3022e8 Merge master into feat/workflow: resolve conflicts by keeping workflow branch changes 2026-05-21 00:51:09 +08:00
Typer_Body 5c5614667a change 2026-05-20 02:49:44 +08:00
Typer_Body 313d553271 bezier 2026-05-18 02:00:31 +08:00
Typer_Body bb7db53447 backend 2026-05-18 01:47:13 +08:00
Typer_Body 27c0d344bf unified standard 2026-05-16 00:09:57 +08:00
Typer_Body c088dc114f clean the shit of ai 2026-05-15 02:11:21 +08:00
Typer_Body 37c74b0622 update icon i18n 2026-05-15 01:05:29 +08:00
Typer_Body c9f7911efe update 2026-05-08 01:27:45 +08:00
Typer_Body 75fdfe6806 ruff 2026-05-08 00:56:27 +08:00
Typer_Body eb9f38b102 针对已被移除的路由规则功能的。根据 botmgr.py:74-100 中的注释,路由规则已被移除,Bot 现在直接绑定到 Pipeline 或 Workflow。 2026-05-08 00:53:35 +08:00
Typer_Body fc40d3c949 ruff 2026-05-07 23:33:54 +08:00
Typer_Body d176a448e0 Merge remote-tracking branch 'origin/master' into feat/workflow 2026-05-07 00:57:56 +08:00
Typer_Body ada4c30f85 1111 2026-05-06 01:03:34 +08:00
Typer_Body 32c9eaff45 还是修不好 2026-05-05 16:16:33 +08:00
Typer_Body 9706ee2d53 没修好 2026-05-05 16:16:04 +08:00
Typer_Body e7c9bc69d3 后端没修完版 2026-05-05 15:08:04 +08:00
298 changed files with 47215 additions and 1684 deletions
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# AGENTS.md
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.
This file guides code agents working in the LangBot main repository. `CLAUDE.md` is a symlink to this file.
## Project Overview
Read `ARCHITECTURE.md` before non-trivial backend, frontend, runtime, plugin, Box, MCP, persistence, or cross-repo SDK changes. This file is the working checklist; `ARCHITECTURE.md` is the system map.
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.
## Quick Facts
LangBot has a comprehensive web frontend — almost every operation can be performed through it.
- Python backend: `>=3.11,<4.0`, dependencies managed by `uv`.
- Frontend: `web/` is Vite + React Router 7 + shadcn/ui + Tailwind, managed by `pnpm`.
- Backend framework: Quart served by Hypercorn on `api.port`, default `5300`.
- Frontend dev server: `web/` on `3000`, with `VITE_API_BASE_URL` pointing at the backend.
- Plugin/Box/runtime contracts live in sibling repo `langbot-plugin-sdk`, pinned as `langbot-plugin` in `pyproject.toml`.
- **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`.
## Repository Layout
```
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 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
```
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.
### Frontend
Requires Node.js + [pnpm](https://pnpm.io/installation).
```bash
cd web
cp .env.example .env # Windows: copy .env.example .env
pnpm install
pnpm dev # http://127.0.0.1:3000 (npm install / npm run dev also work)
```
`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.
### Code formatting
The repo runs lint + format checks in CI. Install the pre-commit hooks so the same checks run locally before each commit:
## Essential Commands
```bash
uv sync --dev
uv run main.py
uv run pre-commit install
cd web
pnpm install
pnpm dev
pnpm build
```
## Plugin System
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`.
### Architecture (what to know inside this repo)
- 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:
Useful focused tests:
```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"
uv run pytest tests/unit_tests -q
uv run pytest tests/integration -q
uv run pytest tests/integration/persistence -q
uv run pytest tests/manual/mcp_smoke.py
cd web
pnpm lint
pnpm test:e2e
```
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).
Run the narrowest useful test first, then broader checks when confidence is needed.
When writing a migration, follow these rules:
## Where to Look
- **Revision id ≤ 32 characters.** PostgreSQL stores `alembic_version.version_num` as `varchar(32)`; a longer id raises `StringDataRightTruncationError` at runtime. Prefer short, descriptive ids like `0005_add_llm_context_length`.
- **Guard every operation against missing tables/columns.** Fresh installs build the schema via `create_all()` and then stamp the Alembic baseline, so a migration may run against a table that already has the change — or, in tests, against an empty database. Check `inspector.get_table_names()` / `inspector.get_columns(...)` before `add_column` / `drop_column`, mirroring the existing migrations.
- **Keep a single linear head.** Chain `down_revision` to the current head; do not create branches. Run the migration tests after adding one: `uv run pytest tests/integration/persistence/ -q` (the PostgreSQL test needs a running PG via `TEST_POSTGRES_URL`).
- Architecture map: `ARCHITECTURE.md`.
- Dev environment guide: https://docs.langbot.app/zh/develop/dev-config.
- Plugin runtime / CLI / SDK debugging: https://docs.langbot.app/zh/develop/plugin-runtime.
- API-key auth: `docs/API_KEY_AUTH.md`.
- Box deep-dive notes: `docs/review/box-architecture.md` and related files.
- In-repo skills: `skills/` is the single source of truth for LangBot agent skills.
- SDK repo: `../langbot-plugin-sdk/` when changing shared entities, plugin APIs, action protocol, `lbp rt`, or `lbp box`.
> **Legacy migration system (deprecated — do not extend).** The old 3.x migration system under `src/langbot/pkg/persistence/migrations/` (`DBMigration` subclasses in `dbmXXX_*.py`, run from `pkg/persistence/mgr.py`) is **frozen**. Do **not** add new `dbmXXX_*.py` files. The chain is capped at `required_database_version = 25` (`pkg/utils/constants.py`); those files only exist to upgrade pre-existing 3.x databases up to the Alembic baseline and are kept read-only. All new schema changes go through Alembic.
## Cross-Repo SDK Work
## Agent-Facing Surfaces (MCP + Skills)
When changing SDK contracts used by LangBot:
LangBot is built to be **agent-friendly**. Three surfaces let AI agents work
with LangBot, and they MUST be kept in lockstep with the HTTP API:
```bash
# from langbot-plugin-sdk, with LangBot's .venv active
uv pip install .
1. **MCP server**`src/langbot/pkg/api/mcp/` exposes a curated subset of the
API as MCP tools at `/mcp` (API-key authenticated, including the
`api.global_api_key` from config.yaml). `server.py` defines the tools (they
call the service layer directly); `mount.py` is the ASGI dispatcher.
2. **In-repo skills**`skills/` is the **single source of truth** for agent
skills (plugin/core/deploy/e2e/MCP-ops). Docs and the landing page link here
rather than embedding their own copies.
3. **API-key auth**`api.global_api_key` (config.yaml) authenticates the API
and MCP without a login session; see `docs/API_KEY_AUTH.md`.
# from LangBot, preserve the locally installed SDK
uv run --no-sync main.py
```
> **Maintenance rule (important).** When you add, remove, or change an HTTP API
> endpoint that should be agent-accessible, you MUST update **both** the matching
> MCP tool in `src/langbot/pkg/api/mcp/server.py` **and** the relevant skill under
> `skills/` (especially `skills/skills/langbot-mcp-ops`). The API, the MCP tool
> surface, and the skills are one system — drift between them is a bug.
For standalone runtime debugging:
## Some Principles
```bash
# in langbot-plugin-sdk
uv run --no-sync lbp rt
uv run --no-sync lbp box
# in LangBot
uv run --no-sync main.py --standalone-runtime
uv run --no-sync main.py --standalone-box
```
Config keys to verify in `data/config.yaml` / `src/langbot/templates/config.yaml`:
- Plugin runtime: `plugin.runtime_ws_url`, default Docker host `langbot_plugin_runtime:5400/control/ws`.
- Box runtime: `box.enabled`, `box.backend`, `box.runtime.endpoint`, Docker host `langbot_box:5410`.
- API/MCP auth: `api.global_api_key`.
## Change Rules
- HTTP API changes that should be agent-accessible must update the matching MCP tool in `src/langbot/pkg/api/mcp/server.py` and the relevant skill under `skills/` in the same pass.
- New schema changes use Alembic under `src/langbot/pkg/persistence/alembic/versions/`; do not add legacy `dbmXXX` migrations.
- New platform behavior belongs in platform adapters only for platform translation; pipeline/business logic belongs in `pkg/pipeline/` or services.
- User-facing strings must support i18n (`en_US`, `zh_Hans`; include `ja_JP` where the repo already does).
- Code comments and docstrings must be English.
- Keep compatibility and security in mind; LangBot is used in both self-hosted/community and toB deployments.
- Commit message format: `<type>(<scope>): <subject>`.
## Runtime Pitfalls
- Local stdio Plugin Runtime disconnects do not auto-reconnect; restart LangBot if that path breaks.
- Orphan runtime processes on `5400`/`5401` commonly break plugin debugging.
- Use `uv run --no-sync` after locally installing the SDK, or `uv` may restore the pinned package.
- A false Box “no backend” often means Docker is running but the current user lacks Docker socket permission.
- Do not confuse external MCP servers LangBot connects to (`pkg/provider/tools/loaders/mcp.py`) with LangBot's own `/mcp` server (`pkg/api/mcp/`).
- `CLAUDE.md` is a symlink to this file; edit `AGENTS.md`, not the symlink.
## Principles
- Keep it simple, stupid.
- Entities should not be multiplied unnecessarily.
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@@ -0,0 +1,250 @@
# Architecture
This document is a map of LangBot's moving parts. It is intentionally more stable than a feature guide and more concrete than the README: when you need to change behavior, start here, then follow the file references into the code.
For agent-specific working rules, see `AGENTS.md`. For plugin-runtime and Box-runtime implementation details, also read the sibling SDK repo: [`langbot-plugin-sdk`](https://github.com/langbot-app/langbot-plugin-sdk).
## What LangBot Is
LangBot is an open-source platform for building production IM bots backed by LLMs, agents, RAG, plugins, MCP tools, and a web management panel.
At runtime, one LangBot process owns:
- a Quart/Hypercorn HTTP service and the built web UI on `:5300`;
- messaging-platform adapters such as Discord, Telegram, Slack, WeChat, QQ, WeCom, Lark, DingTalk, KOOK, LINE, Satori, Matrix, and HTTP/WebSocket bots;
- a pipeline engine that turns inbound platform messages into LLM/tool/plugin work and replies;
- persistence, storage, vector database, telemetry, monitoring, and configuration managers;
- bridges to the Plugin Runtime and Box Runtime provided by `langbot-plugin-sdk`;
- an MCP server at `/mcp` exposing a curated agent-facing subset of the service layer.
## Repository Boundary
LangBot is not a single-repo system.
- `LangBot/` is the main product: backend, web UI, platform adapters, pipeline engine, HTTP API, MCP server, RAG, persistence, skills integration, and the bridge code that talks to runtimes.
- `langbot-plugin-sdk/` is published as `langbot-plugin` and pinned in `LangBot/pyproject.toml`. It contains plugin developer APIs, shared entities, `lbp`, the Plugin Runtime (`lbp rt`), and the Box Runtime (`lbp box`).
- Plugins import SDK APIs from `langbot_plugin.*`; the LangBot main process imports the same package for shared entities and runtime protocols.
This split matters. If a change modifies SDK entities, component APIs, action protocols, `lbp rt`, or `lbp box`, verify the sibling SDK repo and install the local SDK into LangBot's virtualenv when testing cross-repo behavior.
## Startup Path
The process entrypoint is small and layered:
1. `main.py` delegates to `langbot.__main__.main()`.
2. `src/langbot/__main__.py` parses `--standalone-runtime`, `--standalone-box`, and `--debug`, checks dependencies, generates missing config/data files, and calls `pkg.core.boot.main()`.
3. `pkg/core/boot.py` executes startup stages in order: `LoadConfigStage`, `GenKeysStage`, `SetupLoggerStage`, `BuildAppStage`, `ShowNotesStage`.
4. `BuildAppStage` constructs the `Application` object by wiring managers, services, runtime connectors, and controllers.
5. `Application.run()` starts the platform manager, query controller, HTTP controller, telemetry/cleanup loops, and plugin initialization.
The central runtime object is `pkg/core/app.py::Application`. It is a service locator for long-lived managers. That is not elegant, but it is the current architectural center; most subsystems receive `ap: Application` and collaborate through it.
## Top-Level Layout
```text
LangBot/
├── main.py # Entrypoint shim
├── pyproject.toml # Python package, deps, pinned langbot-plugin
├── src/langbot/
│ ├── __main__.py # CLI entrypoint and boot handoff
│ ├── pkg/
│ │ ├── core/ # Application, boot stages, task manager
│ │ ├── api/ # HTTP API + MCP server mount
│ │ ├── platform/ # IM adapters and runtime bot manager
│ │ ├── pipeline/ # Message routing and pipeline stages
│ │ ├── provider/ # LLM runners, model manager, tools
│ │ ├── plugin/ # LangBot-side Plugin Runtime connector/handler
│ │ ├── box/ # LangBot-side Box service/connector
│ │ ├── skill/ # Skill metadata/activation integration
│ │ ├── rag/ , vector/ # Knowledge-base and vector DB integration
│ │ ├── persistence/ # SQLAlchemy/SQLModel, Alembic, legacy migrations
│ │ ├── storage/ # Local/S3 file storage abstraction
│ │ └── config/, entity/, utils/, telemetry/, survey/
│ ├── libs/ # Vendored third-party platform SDKs
│ └── templates/ # Default config and component metadata
├── web/ # Vite + React Router + shadcn/ui + Tailwind SPA
├── docker/ # Deployment manifests
├── skills/ # In-repo agent skills, single source of truth
└── tests/ # Unit/integration/e2e/manual tests
```
## The Runtime Graph
The most useful mental model is this graph:
```text
Platform adapter
→ RuntimeBot
→ MessageAggregator
→ QueryPool
→ Controller
→ RuntimePipeline
→ PipelineStage chain
→ RequestRunner / ToolManager / PluginRuntimeConnector / BoxService
→ response via adapter
```
The HTTP and MCP surfaces are parallel entrypoints into the same service layer:
```text
HTTP client / Web UI
→ Quart route group
→ api/http/service/*
→ Application managers / persistence / runtime connectors
MCP client
→ /mcp mount
→ api/mcp/server.py tools
→ the same service layer directly
```
## Message Flow
Inbound platform messages enter through adapter-specific SDK callbacks. The common path is:
1. A platform adapter under `pkg/platform/sources/` converts platform-specific events into SDK message/event entities.
2. `RuntimeBot` in `pkg/platform/botmgr.py` applies pipeline routing rules and either discards the message, pushes it to webhooks, or sends it to the message aggregator.
3. `MessageAggregator` batches/normalizes messages before adding a `Query` to `QueryPool`.
4. `Controller` in `pkg/pipeline/controller.py` selects queries subject to global pipeline concurrency and per-session concurrency.
5. `RuntimePipeline` in `pkg/pipeline/pipelinemgr.py` runs configured pipeline stages using a responsibility-chain style executor that supports generator stages.
6. The chat stage emits plugin events, calls a configured `RequestRunner`, handles streaming/non-streaming responses, records telemetry, and appends conversation history.
7. Output stages send text, cards, chunks, files, or error notices back through the original platform adapter.
Pipeline components are registered by decorators and package import side effects. When adding a new stage, loader, runner, or adapter, check the corresponding preregistration mechanism instead of inventing a second registry.
## Platform Layer
Platform code lives under `pkg/platform/`.
- `botmgr.py` owns runtime bots, routing rules, event logging, webhook pushing, and adapter lifecycle.
- `sources/` contains adapter implementations. Each adapter subclasses `langbot_plugin.api.definition.abstract.platform.adapter.AbstractMessagePlatformAdapter` from the SDK.
- Platform entities such as `MessageChain`, `Image`, `At`, `Voice`, and events come from `langbot-plugin-sdk`, not from this repo.
The platform layer should translate between external platform APIs and LangBot's shared message/event model. It should not contain LLM-provider logic or pipeline business logic.
## Pipeline Layer
Pipeline code lives under `pkg/pipeline/`.
Important pieces:
- `pool.py::QueryPool` stores pending queries and cached in-flight queries for plugin backward-compatible calls.
- `controller.py::Controller` schedules query processing and enforces concurrency.
- `pipelinemgr.py::RuntimePipeline` materializes database pipeline config into a runtime stage chain.
- `process/handlers/chat.py::ChatMessageHandler` is the main LLM conversation handler.
- Stage families include response rules, banned sessions, content filters, preprocessors, rate limits, message truncation, long text handling, response-back, command handling, and wrappers.
Pipelines are configuration-driven. Prefer adding a stage or extending an existing stage family over hard-coding behavior in platform adapters.
## Provider, RAG, and Tools
Provider code lives under `pkg/provider/`.
- `modelmgr/` manages configured model providers and requesters.
- `runners/` implements request runners such as the local agent runner and external workflow integrations.
- `tools/toolmgr.py` aggregates tools from native tools, plugin tools, external MCP servers, and skill-authoring tools.
- `tools/loaders/mcp.py` is the MCP client side: external MCP servers that LangBot connects to for agent tools.
- RAG lives across `pkg/rag/`, `pkg/vector/`, model services, and plugin KnowledgeEngine actions.
Do not confuse LangBot's MCP client side with LangBot's own MCP server at `/mcp`; they are different surfaces.
## Plugin System
The plugin system crosses the repo boundary.
In this repo:
- `pkg/plugin/connector.py` connects LangBot to the Plugin Runtime over stdio or WebSocket.
- `pkg/plugin/handler.py` exposes LangBot actions to the runtime and calls runtime actions for plugin operations.
- `pkg/provider/tools/loaders/plugin.py` exposes plugin Tool components to LLM runners.
- Pipeline handlers emit SDK events such as normal-message events and prompt-processing events.
In `langbot-plugin-sdk`:
- `src/langbot_plugin/api/` defines `BasePlugin`, component base classes, message/event entities, contexts, proxies, and manifests.
- `src/langbot_plugin/runtime/` implements `lbp rt`, plugin discovery, dependency installation, process launching, and control/debug connections.
- `src/langbot_plugin/entities/io/` defines the action protocol shared by LangBot, runtime, and plugin processes.
The Plugin Runtime supports stdio and WebSocket control transports. Direct local LangBot runs usually spawn the runtime over stdio. Containerized/standalone deployments connect over WebSocket using `plugin.runtime_ws_url` and `--standalone-runtime`.
## Box Runtime and Skills
Box is the sandbox subsystem used by native agent tools, stdio MCP servers, skill authoring, and managed processes.
In this repo:
- `pkg/box/service.py` is the application-facing facade for exec, sessions, managed processes, skill CRUD, status, reconnects, quotas, mounts, and sandbox profiles.
- `pkg/box/connector.py` connects to the Box Runtime over stdio, Windows subprocess+WebSocket, or remote WebSocket.
- `pkg/provider/tools/loaders/native.py`, `mcp_stdio.py`, and skill loaders depend on Box availability.
- `pkg/skill/manager.py` loads skills from the Box runtime, falling back to local `data/skills` when needed.
In `langbot-plugin-sdk`:
- `src/langbot_plugin/box/server.py` implements `lbp box` and the WebSocket endpoints on `:5410`.
- `src/langbot_plugin/box/runtime.py` owns sandbox sessions and managed processes.
- `backend.py`, `nsjail_backend.py`, and `e2b_backend.py` implement sandbox backends.
- `skill_store.py` manages skill packages from the Box side.
Important config keys live under `box:` in `src/langbot/templates/config.yaml`: `box.enabled`, `box.backend`, `box.runtime.endpoint`, and `box.local.*`. Start LangBot with `--standalone-box` when connecting to an externally launched Box runtime.
## HTTP API, Web UI, and MCP Server
`pkg/api/http/controller/main.py` builds a Quart app, registers route groups, serves the built SPA, and wraps the ASGI app with the MCP dispatcher.
- HTTP route groups live under `pkg/api/http/controller/groups/`.
- Service-layer logic lives under `pkg/api/http/service/`.
- The built web UI is served from the frontend build path with SPA fallback.
- The MCP server lives under `pkg/api/mcp/` and is mounted at `/mcp`.
The MCP server intentionally exposes a curated subset of the API. Tools call service classes directly rather than making HTTP requests back into LangBot.
Maintenance rule: when adding, removing, or changing an HTTP endpoint that should be agent-accessible, update the matching MCP tool and the relevant in-repo skill under `skills/` in the same pass.
## Persistence and Configuration
Persistence is centered on `pkg/persistence/mgr.py`.
- SQLite is the default database; PostgreSQL is supported.
- Models live under `pkg/entity/persistence/`.
- Fresh schemas are created from metadata, then legacy migrations run up to the frozen 3.x baseline, then Alembic migrations run to head.
- New schema changes should use Alembic under `pkg/persistence/alembic/versions/`; do not extend the frozen legacy migration chain.
Configuration starts from `src/langbot/templates/config.yaml` and is generated into `data/config.yaml` on first run. Most long-lived managers read from `ap.instance_config.data`.
## Frontend
The frontend lives in `web/` and is a Vite SPA using React Router 7, shadcn/ui, Tailwind CSS, and pnpm. It is not Next.js, despite some historical filenames.
In development, `pnpm dev` serves the UI on `:3000` and reads `VITE_API_BASE_URL` to call the backend on `:5300`. In production, the built frontend is packaged into the Python distribution and served by the backend.
Keep frontend API behavior aligned with `pkg/api/http/service/` and route groups. User-facing strings must go through the existing i18n setup.
## Agent-Facing Surfaces
LangBot is deliberately agent-friendly. The agent-facing surfaces are part of the architecture, not extra docs.
- `skills/` is the single source of truth for in-repo skills.
- `pkg/api/mcp/server.py` exposes the LangBot MCP server at `/mcp`.
- `api.global_api_key` authenticates API/MCP access without a browser login.
- `AGENTS.md` and `ARCHITECTURE.md` tell coding agents how the repo works.
When one of these changes, update the others if the behavior or contract changed. API, MCP tools, and skills are one system; drift is a bug.
## Where to Change Things
- New HTTP API: add/adjust a service in `pkg/api/http/service/`, a route group in `pkg/api/http/controller/groups/`, tests, and MCP/skills if agent-accessible.
- New platform adapter: add a `pkg/platform/sources/*` adapter, component metadata/templates as needed, i18n, docs, and tests/smoke coverage.
- New pipeline behavior: add or extend a pipeline stage family under `pkg/pipeline/`; avoid putting pipeline rules in adapters.
- New LLM provider/requester: work under `pkg/provider/modelmgr/` and related service/UI surfaces.
- New LLM tool source: extend `pkg/provider/tools/loaders/` and `ToolManager` intentionally.
- New plugin component/API/protocol: change `langbot-plugin-sdk` first or in lockstep, then update LangBot bridge code.
- New Box capability: change both `pkg/box/` and `langbot-plugin-sdk/src/langbot_plugin/box/`, plus config and tests.
- New database schema: add an Alembic migration, not a legacy `dbmXXX` migration.
## Design Biases
- Keep platform translation, pipeline orchestration, provider execution, and runtime protocols separate.
- Reuse existing registries and service layers instead of adding parallel paths.
- Prefer small, explicit agent surfaces over exposing every internal API.
- Treat cross-repo contracts with the SDK as public interfaces.
- Test behavior at the narrowest useful layer first, then add integration/e2e coverage for runtime or platform changes.
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@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>Production-grade platform for building agentic IM bots.</h3>
<h4>Quickly build, debug, and ship AI bots to Slack, Discord, Telegram, WeChat, and more.</h4>
@@ -136,7 +136,7 @@ docker compose --profile all up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU Platform | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU Platform | ✅ |
| [接口 AI](https://jiekou.ai/) | Gateway | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Gateway | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | Gateway | ✅ |
| [Qiniu](https://www.qiniu.com/ai/agent) | Gateway | ✅ |
[→ View all integrations](https://link.langbot.app/en/docs/features)
+1 -1
View File
@@ -136,7 +136,7 @@ docker compose --profile all up -d
| [优云智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
| [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | 聚合平台 | ✅ |
| [小马算力](https://www.tokenpony.cn/453z1) | 聚合平台 | ✅ |
| [百宝箱Tbox](https://www.tbox.cn/open) | 智能体平台 | ✅ |
| [七牛云Qiniu](https://www.qiniu.com/ai/agent) | 聚合平台 | ✅ |
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@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>Plataforma de grado de producción para construir bots de mensajería instantánea con agentes de IA.</h3>
<h4>Construya, depure y despliegue bots de IA rápidamente en Slack, Discord, Telegram, WeChat y más.</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plataforma GPU | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plataforma GPU | ✅ |
| [接口 AI](https://jiekou.ai/) | Pasarela | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Pasarela | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | Pasarela | ✅ |
| [Qiniu](https://www.qiniu.com/ai/agent) | Pasarela | ✅ |
[→ Ver todas las integraciones](https://link.langbot.app/en/docs/features)
+2 -2
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@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>Plateforme de niveau production pour construire des bots de messagerie instantanée avec agents IA.</h3>
<h4>Créez, déboguez et déployez rapidement des bots IA sur Slack, Discord, Telegram, WeChat et plus.</h4>
@@ -132,7 +132,7 @@ docker compose --profile all up -d
| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Passerelle | ✅ |
| [GiteeAI](https://ai.gitee.com/) | Passerelle | ✅ |
| [接口 AI](https://jiekou.ai/) | Passerelle | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Passerelle | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | Passerelle | ✅ |
| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Plateforme GPU | ✅ |
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Plateforme GPU | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Plateforme GPU | ✅ |
+2 -2
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@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>AIエージェント搭載IMボットを構築するための本番グレードプラットフォーム。</h3>
<h4>Slack、Discord、Telegram、WeChat などに AI ボットを素早く構築、デバッグ、デプロイ。</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPUプラットフォーム | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPUプラットフォーム | ✅ |
| [接口 AI](https://jiekou.ai/) | ゲートウェイ | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | ゲートウェイ | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | ゲートウェイ | ✅ |
| [Qiniu](https://www.qiniu.com/ai/agent) | ゲートウェイ | ✅ |
[→ すべての統合を表示](https://link.langbot.app/en/docs/features)
+2 -2
View File
@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>AI 에이전트 IM 봇 구축을 위한 프로덕션 등급 플랫폼.</h3>
<h4>Slack, Discord, Telegram, WeChat 등에 AI 봇을 빠르게 구축, 디버그 및 배포.</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 플랫폼 | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | GPU 플랫폼 | ✅ |
| [接口 AI](https://jiekou.ai/) | 게이트웨이 | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | 게이트웨이 | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | 게이트웨이 | ✅ |
| [Qiniu](https://www.qiniu.com/ai/agent) | 게이트웨이 | ✅ |
[→ 모든 통합 보기](https://link.langbot.app/en/docs/features)
+2 -2
View File
@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>Платформа производственного уровня для создания агентных IM-ботов.</h3>
<h4>Быстро создавайте, отлаживайте и развертывайте ИИ-ботов в Slack, Discord, Telegram, WeChat и других платформах.</h4>
@@ -131,7 +131,7 @@ docker compose --profile all up -d
| [Volc Engine Ark](https://console.volcengine.com/ark/region:ark+cn-beijing/model?vendor=Bytedance&view=LIST_VIEW) | Шлюз | ✅ |
| [ModelScope](https://modelscope.cn/docs/model-service/API-Inference/intro) | Шлюз | ✅ |
| [GiteeAI](https://ai.gitee.com/) | Шлюз | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Шлюз | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | Шлюз | ✅ |
| [接口 AI](https://jiekou.ai/) | Шлюз | ✅ |
| [CompShare](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | Платформа GPU | ✅ |
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Платформа GPU | ✅ |
+1 -1
View File
@@ -137,7 +137,7 @@ docker compose --profile all up -d
| [優雲智算](https://www.compshare.cn/?ytag=GPU_YY-gh_langbot) | GPU 平台 | ✅ |
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | GPU 平台 | ✅ |
| [接口 AI](https://jiekou.ai/) | 聚合平台 | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | 聚合平台 | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | 聚合平台 | ✅ |
| [Qiniu](https://www.qiniu.com/ai/agent) | 聚合平台 | ✅ |
### TTS(語音合成)
+2 -2
View File
@@ -5,7 +5,7 @@
<div align="center">
<a href="https://www.producthunt.com/products/langbot?utm_source=badge-follow&utm_medium=badge&utm_source=badge-langbot" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/follow.svg?product_id=1077185&theme=light" alt="LangBot - Production&#0045;grade&#0032;IM&#0032;bot&#0032;made&#0032;easy&#0046; | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a>
<a href="https://www.producthunt.com/products/langbot/launches/langbot?embed=true&amp;utm_source=badge-featured&amp;utm_medium=badge&amp;utm_campaign=badge-langbot" target="_blank" rel="noopener noreferrer"><img alt="LangBot - Easy-to-use global IM bot platform designed for the LLM era | Product Hunt" width="250" height="54" src="https://api.producthunt.com/widgets/embed-image/v1/featured.svg?post_id=979554&amp;theme=light&amp;t=1782822143403"></a>
<h3>Nền tảng cấp sản xuất để xây dựng bot IM với AI agent.</h3>
<h4>Xây dựng, gỡ lỗi và triển khai bot AI nhanh chóng trên Slack, Discord, Telegram, WeChat và nhiều nền tảng khác.</h4>
@@ -135,7 +135,7 @@ docker compose --profile all up -d
| [PPIO](https://ppinfra.com/user/register?invited_by=QJKFYD&utm_source=github_langbot) | Nền tảng GPU | ✅ |
| [ShengSuanYun](https://www.shengsuanyun.com/?from=CH_KYIPP758) | Nền tảng GPU | ✅ |
| [接口 AI](https://jiekou.ai/) | Cổng | ✅ |
| [302.AI](https://share.302.ai/SuTG99) | Cổng | ✅ |
| [302.AI](https://share.302ai.cn/SuTG99) | Cổng | ✅ |
| [Qiniu](https://www.qiniu.com/ai/agent) | Cổng | ✅ |
[→ Xem tất cả tích hợp](https://link.langbot.app/en/docs/features)
+163
View File
@@ -0,0 +1,163 @@
#!/usr/bin/env python3
"""Compare YAML node definitions with frontend node-configs."""
import yaml
import os
import re
import json
# 1. Parse YAML files
yaml_dir = 'src/langbot/templates/metadata/nodes'
yaml_nodes = {}
for filename in sorted(os.listdir(yaml_dir)):
if filename.endswith('.yaml'):
filepath = os.path.join(yaml_dir, filename)
with open(filepath, 'r') as f:
data = yaml.safe_load(f)
node_name = data.get('name', filename.replace('.yaml', ''))
yaml_nodes[node_name] = {
'category': data.get('category', ''),
'inputs': [i['name'] for i in data.get('inputs', [])],
'outputs': [o['name'] for o in data.get('outputs', [])],
'config': [c['name'] for c in data.get('config', [])]
}
# 2. Parse frontend node-configs TypeScript files
node_configs_dir = 'web/src/app/home/workflows/components/workflow-editor/node-configs'
frontend_nodes = {}
def parse_ts_file(filepath):
"""Parse a TypeScript file to extract node configurations."""
with open(filepath, 'r') as f:
content = f.read()
# Find all node type definitions
# Pattern: nodeType: 'xxx'
node_type_pattern = r"nodeType:\s*'([^']+)'"
node_types = re.findall(node_type_pattern, content)
# For each node type, extract inputs, outputs, and config
for node_type in node_types:
# Find the config object for this node type
# Look for the section between this nodeType and the next one or end of object
pattern = rf"nodeType:\s*'({re.escape(node_type)})'.*?(?=nodeType:|export\s+(const|function)|$)"
match = re.search(pattern, content, re.DOTALL)
if match:
section = match.group(0)
# Extract inputs
inputs = re.findall(r"createInput\('([^']+)'", section)
# Extract outputs
outputs = re.findall(r"createOutput\('([^']+)'", section)
# Extract config names
config_names = re.findall(r"name:\s*'([^']+)'", section)
# Remove duplicates while preserving order
seen = set()
unique_config = []
for c in config_names:
if c not in seen:
seen.add(c)
unique_config.append(c)
frontend_nodes[node_type] = {
'inputs': inputs,
'outputs': outputs,
'config': unique_config
}
# Parse all config files
for filename in os.listdir(node_configs_dir):
if filename.endswith('.ts') and filename != 'types.ts' and filename != 'index.ts':
filepath = os.path.join(node_configs_dir, filename)
parse_ts_file(filepath)
# 3. Compare and report differences
print("=" * 80)
print("WORKFLOW NODE COMPARISON REPORT: YAML vs Frontend")
print("=" * 80)
all_node_types = sorted(set(list(yaml_nodes.keys()) + list(frontend_nodes.keys())))
discrepancies = []
for node_type in all_node_types:
yaml_def = yaml_nodes.get(node_type)
frontend_def = frontend_nodes.get(node_type)
node_discrepancies = []
if not yaml_def:
print(f"\n⚠️ {node_type}: ONLY in frontend (not in YAML)")
continue
if not frontend_def:
print(f"\n⚠️ {node_type}: ONLY in YAML (not in frontend)")
continue
# Compare inputs
yaml_inputs = set(yaml_def['inputs'])
frontend_inputs = set(frontend_def['inputs'])
if yaml_inputs != frontend_inputs:
only_yaml = yaml_inputs - frontend_inputs
only_frontend = frontend_inputs - yaml_inputs
node_discrepancies.append({
'type': 'inputs',
'only_yaml': list(only_yaml),
'only_frontend': list(only_frontend)
})
# Compare outputs
yaml_outputs = set(yaml_def['outputs'])
frontend_outputs = set(frontend_def['outputs'])
if yaml_outputs != frontend_outputs:
only_yaml = yaml_outputs - frontend_outputs
only_frontend = frontend_outputs - yaml_outputs
node_discrepancies.append({
'type': 'outputs',
'only_yaml': list(only_yaml),
'only_frontend': list(only_frontend)
})
# Compare config
yaml_config = set(yaml_def['config'])
frontend_config = set(frontend_def['config'])
if yaml_config != frontend_config:
only_yaml = yaml_config - frontend_config
only_frontend = frontend_config - yaml_config
node_discrepancies.append({
'type': 'config',
'only_yaml': list(only_yaml),
'only_frontend': list(only_frontend)
})
if node_discrepancies:
print(f"\n{node_type} ({yaml_def['category']}): HAS DISCREPANCIES")
for d in node_discrepancies:
print(f" {d['type']}:")
if d['only_yaml']:
print(f" Only in YAML: {d['only_yaml']}")
if d['only_frontend']:
print(f" Only in Frontend: {d['only_frontend']}")
discrepancies.append((node_type, node_discrepancies))
else:
print(f"\n{node_type} ({yaml_def['category']}): OK")
print(f"\n{'=' * 80}")
print(f"SUMMARY: {len(discrepancies)} nodes with discrepancies out of {len(all_node_types)} total")
print(f"{'=' * 80}")
# Output as JSON for further processing
output = {
'yaml_nodes': {k: v for k, v in yaml_nodes.items()},
'frontend_nodes': {k: v for k, v in frontend_nodes.items()},
'discrepancies': {k: v for k, v in discrepancies}
}
with open('node_comparison.json', 'w') as f:
json.dump(output, f, indent=2)
print(f"\nDetailed comparison saved to node_comparison.json")
+2 -1
View File
@@ -62,11 +62,12 @@ services:
- TZ=Asia/Shanghai
# Unified env-override convention: SECTION__SUBSECTION__KEY overrides the
# matching config.yaml field (see LoadConfigStage). These map onto
# box.local.* and are forwarded to the Box runtime via INIT RPC.
# box.* and are forwarded to the Box runtime via INIT RPC.
- BOX__LOCAL__HOST_ROOT=${LANGBOT_BOX_ROOT:-${PWD}/data/box}
- BOX__LOCAL__DEFAULT_WORKSPACE=default
- BOX__LOCAL__SKILLS_ROOT=skills
- BOX__LOCAL__ALLOWED_MOUNT_ROOTS=${LANGBOT_BOX_ROOT:-${PWD}/data/box}
- BOX__DOCKER__CPU_LIMIT_ENABLED=${LANGBOT_BOX_DOCKER_CPU_LIMIT_ENABLED:-true}
ports:
- 5300:5300 # For web ui and webhook callback
- 2280-2285:2280-2285 # For platform reverse connection
+575
View File
@@ -0,0 +1,575 @@
# HTTP Bot Adapter — Design Document
> Status: **Implemented** · Branch: `feat/http-bot-adapter` · Author: LangBot core
>
> A first-class, **standalone** message-platform adapter (`http_bot`) that lets
> any external system (e.g. LangBot Space ticketing, an internal back-office, a
> CRM, a custom web app) talk to a LangBot pipeline over plain HTTP — **inbound**
> by POSTing messages in, **outbound** by receiving replies on a callback URL —
> with full support for the pipeline's native N→1 aggregation and 1→M
> multi-reply semantics, and **without** holding a long-lived WebSocket
> connection.
>
> **Shipped in this branch:**
> - `src/langbot/pkg/platform/sources/http_bot.yaml` — adapter manifest (auto-discovered)
> - `src/langbot/pkg/platform/sources/http_bot.py``HttpBotAdapter`
> - `src/langbot/pkg/platform/sources/http_bot_signing.py` — HMAC helpers
> - `src/langbot/pkg/platform/sources/http_bot.svg` — icon
> - `docs/platforms/http-bot.md` — integration guide
> - `docs/http-bot-openapi.json` — machine-readable contract
> - `examples/http-bot/` — Python + TypeScript reference clients
>
> **Final decisions (resolving the original open questions):**
> 1. Callback URL is **config-only** — never accepted per-message (SSRF closed).
> 2. **Session reset is provided**`POST /bots/<uuid>/reset` keyed by `session_id`.
> 3. Reference **clients are provided**`examples/http-bot/client.py` + `client.ts`.
> 4. **Sync convenience mode is included**`POST /bots/<uuid>/sync` (opt-in, lossy).
---
## 1. Background & Motivation
### 1.1 The concrete need
LangBot Space wants to use a LangBot pipeline as the brain for **ticket
handling**. The integration is **server-to-server**: Space's backend pushes a
user's ticket messages into LangBot and renders LangBot's replies back into the
ticket thread.
This interaction is **not** request/response shaped:
- **N → 1**: a user may fire several messages in a row ("the app crashed" …
"when I click export" … "here's a screenshot"). The pipeline's
**message aggregation** feature should debounce and merge these into one turn.
- **1 → N**: a single turn may yield **multiple** outbound messages — a tool/
function call narrating progress, a plugin emitting several cards, a streamed
answer split into chunks.
### 1.2 Why the existing options don't fit
LangBot today exposes exactly one externally-reachable way to drive a pipeline
that is **not** tied to a specific IM vendor: the **WebSocket** path
(`/api/v1/pipelines/<uuid>/ws/connect` for dashboard debug, and
`/api/v1/embed/<bot_uuid>/ws/connect` for the embeddable web widget).
For a server-to-server integration the WebSocket path has real friction:
| Problem | Detail |
|---|---|
| Long-lived connection | Caller must maintain a socket, heartbeats, and reconnect logic for what is fundamentally a fire-and-collect workload. |
| Session identity | Inbound messages are keyed by the transient `connection_id` (`websocket_{connection_id}`); the caller **cannot supply a stable, business-meaningful session id** (e.g. a ticket number). Multi-ticket isolation is not expressible. |
| Auth mismatch | The debug socket is gated by the **dashboard JWT** (must not be handed to an external service); the embed socket is gated by **Cloudflare Turnstile** (a *browser* human-check that a backend cannot satisfy). Neither is a server-to-server credential. |
| In-memory, single-process state | Session history lives in process memory and is lost on restart. |
> **Key realisation.** The N→1 / 1→M behaviour the caller wants is **not**
> provided by WebSocket — it is provided by the **pipeline** (aggregation +
> the adapter being free to call `reply_message` any number of times). It is
> therefore **transport-independent**. We can deliver the exact same semantics
> over a far lighter HTTP transport.
### 1.3 Why a *new, standalone* adapter (not a refactor of an existing one)
The brief is explicit: **do not reuse / fork an existing vendor adapter.** The
vendor adapters (`lark`, `wecom`, `qqofficial`, `slack`, …) carry vendor-specific
signature schemes, payload shapes, and message-segment mappings. Bending one of
them into a "generic" mode would couple a public integration surface to one
vendor's quirks and make the developer experience worse for everyone.
Instead we ship `http_bot` as a clean, independent adapter whose **entire
contract is LangBot's own** — documented, versioned, and designed front-to-back
around *integrator* developer experience.
---
## 2. Goals & Non-Goals
### Goals
- **G1** A standalone `http_bot` adapter, selectable like any other platform
adapter in the dashboard, with its own config schema and docs.
- **G2** **Inbound**: external systems POST messages to a stable LangBot URL,
carrying a **caller-defined `session_id`** that maps 1:1 to a LangBot session.
- **G3** **Outbound**: LangBot delivers each reply by POSTing to a
caller-configured **callback URL**; one turn may produce **many** callbacks.
- **G4** Preserve pipeline-native **N→1 aggregation** and **1→M multi-reply**.
- **G5** Server-to-server **auth**: shared-secret HMAC request signing both
directions (no JWT, no Turnstile, no long-lived socket).
- **G6** **Great DX**: copy-pasteable curl, a tiny reference client, an OpenAPI
fragment, idempotency, clear error envelope, and a local echo-server recipe.
### Non-Goals
- Not replacing or deprecating the WebSocket / embed widget path (that remains
the right tool for *browser*, real-time, streaming chat UIs).
- Not a synchronous "one request → one response" RPC (explicitly rejected: it
cannot express 1→M; see §9 for the optional sync convenience mode).
- No built-in message **persistence/replay** in v1 (callbacks are at-least-once
best-effort; durability is the caller's responsibility — see §8).
- No multi-tenant API-key management UI in v1 (one secret per bot; see §11).
---
## 3. How LangBot routes a message (the parts we plug into)
Understanding the existing flow is what makes this adapter cheap. A message
flows through these stages (verified against current `master`):
```
INBOUND OUTBOUND
external POST ─┐ ┌─ reply_message()
▼ │ reply_message_chunk()
POST /bots/<bot_uuid> (unified webhook router, AuthType.NONE)
│ webhooks.py → adapter.handle_unified_webhook(bot_uuid, path, request)
▼ │
HttpBotAdapter.handle_unified_webhook │ (called 0..N times
• verify HMAC signature │ per turn by the
• parse {session_id, message[]} │ pipeline / plugins)
• build FriendMessage / GroupMessage │
• fire registered listener ───────────────┐ │
│ │ │
▼ ▼ │
botmgr.on_friend_message / on_group_message │
• (optional) webhook_pusher fan-out │
• msg_aggregator.add_message(...) ── N→1 debounce ──►│
│ │
▼ │
query_pool → pipeline.run() ─── invokes adapter ─────┘
reply methods 1..M times
```
Two framework facts we rely on:
1. **N→1 aggregation is free.** `botmgr` hands every inbound event to
`self.ap.msg_aggregator.add_message(...)`, which debounces per
`session_id` and merges consecutive messages into one pipeline turn
(`pkg/pipeline/aggregator.py`). The adapter does nothing special.
2. **1→M is free.** The pipeline (and any plugin in the chain) calls
`adapter.reply_message()` / `reply_message_chunk()` **as many times as it
wants** per turn. The adapter's only job is to deliver each call outward.
For `http_bot` that means: **one outbound callback POST per call.**
3. **A unified inbound route already exists.** `WebhookRouterGroup`
(`pkg/api/http/controller/groups/webhooks.py`) maps
`POST /bots/<bot_uuid>[/<path>]` (auth `NONE`) to
`adapter.handle_unified_webhook(bot_uuid, path, request)`. `http_bot`
implements that method and is reachable **without registering any new
route** — it does its own signature verification, exactly like the vendor
webhook adapters do.
> Net new code is essentially: one `http_bot.py` adapter, one `http_bot.yaml`
> schema, signing helpers, and docs. No router, aggregator, or pipeline changes.
---
## 4. Architecture Overview
```
┌────────────────────┐ (1) inbound: POST signed message
│ External system │ ──────────────────────────────────────────────► ┌──────────────────────┐
│ (LangBot Space, │ POST /bots/<bot_uuid> │ LangBot │
│ CRM, web app …) │ X-LB-Signature, X-LB-Timestamp │ │
│ │ { session_id, message:[...] } │ HttpBotAdapter │
│ - callback server │ ◄────────────────────────────────────────────── │ (platform/sources) │
│ (receives │ (4) outbound: POST signed reply(s) │ │
│ replies) │ POST <callback_url> │ pipeline + aggregator│
└────────────────────┘ X-LB-Signature, X-LB-Timestamp └──────────────────────┘
{ session_id, sequence, is_final,
message:[...] } (sent 1..M times)
```
- The adapter is **stateless across requests** at the HTTP layer; session
continuity is carried by `session_id` and resolved by LangBot's normal
session manager.
- **Inbound** and **outbound** are **independent HTTP exchanges**. LangBot does
not answer the inbound POST with the pipeline result; it `202 Accepts` it and
later POSTs the reply(s) to the callback URL. This is what makes 1→M natural.
---
## 5. Configuration Schema (`http_bot.yaml`)
Follows the existing `MessagePlatformAdapter` manifest convention (cf.
`slack.yaml`). Fields:
| field | type | required | purpose |
|---|---|---|---|
| `inbound_secret` | string (secret) | yes | HMAC key the **caller** uses to sign inbound POSTs; LangBot verifies. |
| `callback_url` | string (url) | no* | Where LangBot POSTs replies. *Optional if the caller supplies `callback_url` per-message (see §6.1); a static default lives here. |
| `outbound_secret` | string (secret) | no | HMAC key LangBot uses to sign outbound callbacks; caller verifies. Defaults to `inbound_secret` if empty. |
| `default_session_type` | enum `person`/`group` | no | Default when a message omits `session_type`. Default `person`. |
| `signature_required` | bool | no | If `false`, skip inbound signature check (dev only; logs a warning). Default `true`. |
| `callback_timeout` | int (seconds) | no | Per-callback HTTP timeout. Default `15`. |
| `callback_max_retries` | int | no | Retries on 5xx/timeout with backoff. Default `3`. |
| `webhook_url` | webhook-url (display) | — | Read-only field rendering the inbound URL `…/bots/<bot_uuid>` for copy-paste, like other webhook adapters. |
Manifest sketch (i18n labels elided for brevity):
```yaml
apiVersion: v1
kind: MessagePlatformAdapter
metadata:
name: http_bot
label: { en_US: "HTTP Bot", zh_Hans: "HTTP 通用接入" }
description:
en_US: "Integrate any backend over plain HTTP. Push messages in, receive replies on a callback URL. Server-to-server, no long-lived connection."
zh_Hans: "通过 HTTP 接入任意后端系统。推入消息、在回调地址接收回复。面向服务间集成,无需长连接。"
icon: http_bot.svg
spec:
categories: [popular, global]
help_links:
zh: https://docs.langbot.app/zh/platforms/http-bot
en: https://docs.langbot.app/en/platforms/http-bot
config:
- { name: inbound_secret, type: string, required: true, default: "" }
- { name: callback_url, type: string, required: false, default: "" }
- { name: outbound_secret, type: string, required: false, default: "" }
- { name: default_session_type, type: select, required: false, default: "person",
options: [person, group] }
- { name: signature_required, type: boolean, required: false, default: true }
- { name: callback_timeout, type: integer, required: false, default: 15 }
- { name: callback_max_retries, type: integer, required: false, default: 3 }
- { name: webhook_url, type: webhook-url, required: false, default: "" }
execution:
python:
path: ./http_bot.py
attr: HttpBotAdapter
```
---
## 6. The HTTP Contract (this is the DX surface)
### 6.1 Inbound — push a message into LangBot
```
POST /bots/{bot_uuid}
Content-Type: application/json
X-LB-Timestamp: 1718000000
X-LB-Signature: sha256=<hex hmac>
X-LB-Idempotency-Key: <uuid> # optional, dedup window
```
Body:
```jsonc
{
"session_id": "ticket-10293", // REQUIRED. Caller-defined. Maps 1:1 to a LangBot session.
"session_type": "person", // optional, "person" | "group"; default from config
"sender": { // optional metadata, surfaced to pipeline/plugins
"id": "user-5567",
"name": "Alice"
},
"message": [ // REQUIRED. A LangBot MessageChain (list of segments).
{ "type": "Plain", "text": "Export keeps failing on the dashboard." },
{ "type": "Image", "url": "https://.../screenshot.png" }
]
}
```
Response (LangBot does **not** block on the pipeline):
```jsonc
// 202 Accepted
{
"code": 0,
"msg": "accepted",
"data": {
"session_id": "ticket-10293",
"accepted_message_id": "in_01H....", // server-assigned id for this inbound message
"aggregating": true // true if buffered by the aggregator
}
}
```
**N→1 in practice.** Fire three POSTs with the same `session_id` inside the
aggregation window → the pipeline runs **once** with the three messages merged.
No special flag needed; this is the aggregator's default behaviour when enabled
on the pipeline.
### 6.2 Outbound — LangBot delivers replies to your callback
For each `reply_message` / `reply_message_chunk` the pipeline emits, LangBot
POSTs to `callback_url`:
```
POST {callback_url}
Content-Type: application/json
X-LB-Timestamp: 1718000001
X-LB-Signature: sha256=<hex hmac over body>
```
Body:
```jsonc
{
"session_id": "ticket-10293", // echoes the inbound session
"reply_to": "in_01H....", // the inbound message id this answers
"sequence": 1, // 1-based ordinal within this turn (for 1→M ordering)
"is_final": false, // false for intermediate/streamed parts
"stream": false, // true when this is a streamed chunk
"message": [
{ "type": "Plain", "text": "Looking into it — checking your export logs…" }
],
"timestamp": "2026-06-22T09:00:01Z"
}
```
**1→M in practice.** A turn that fires a function call then a final answer
produces e.g.:
```
POST callback → { sequence: 1, is_final: false, message: ["Checking logs…"] }
POST callback → { sequence: 2, is_final: false, message: ["Found 2 failed exports."] }
POST callback → { sequence: 3, is_final: true, message: ["Fixed. Try again now."] }
```
The caller stitches by `session_id` + `sequence`, and knows the turn is complete
when `is_final: true` arrives.
Your callback endpoint should return `200` quickly. A non-2xx triggers retry
with backoff (`callback_max_retries`).
### 6.3 Error envelope (inbound)
Consistent, machine-readable; never leak internals:
```jsonc
{ "code": 40101, "msg": "invalid signature", "data": null }
```
| HTTP | code | meaning |
|---|---|---|
| 202 | 0 | accepted |
| 400 | 40001 | malformed body / missing `session_id` or `message` |
| 401 | 40101 | bad/expired signature |
| 403 | 40301 | bot disabled |
| 404 | 40401 | bot_uuid not found / not an `http_bot` adapter |
| 409 | 40901 | duplicate idempotency key (already accepted) |
| 413 | 41301 | message too large |
| 500 | 50001 | internal error |
---
## 7. Signing scheme (both directions)
Symmetric, dependency-free HMAC-SHA256 — trivial to implement in any language.
```
signing_string = "{timestamp}.{raw_request_body}"
signature = "sha256=" + hex(HMAC_SHA256(secret, signing_string))
```
Verification rules:
- Reject if `|now - timestamp| > 300s` (replay window).
- Constant-time compare (`hmac.compare_digest`).
- Inbound verified with `inbound_secret`; outbound signed with
`outbound_secret` (falls back to `inbound_secret`).
- `signature_required: false` bypasses verification **and logs a warning**
intended only for local development behind a trusted network.
Reference (Python, ~6 lines):
```python
import hmac, hashlib, time
def sign(secret: str, body: bytes, ts: int | None = None) -> tuple[str, str]:
ts = ts or int(time.time())
mac = hmac.new(secret.encode(), f"{ts}.".encode() + body, hashlib.sha256)
return str(ts), "sha256=" + mac.hexdigest()
```
---
## 8. Delivery semantics & reliability
- **Inbound**: `202 Accepted` means *queued*, not *processed*. Use
`X-LB-Idempotency-Key` to make client retries safe (dedup window, e.g. 10 min).
- **Outbound**: **at-least-once**, best-effort. Retries on timeout/5xx with
exponential backoff up to `callback_max_retries`. Callbacks for one
`session_id` are delivered **in `sequence` order** (serialised per session);
across sessions they may interleave.
- **No persistence in v1**: if LangBot restarts mid-turn, in-flight callbacks
may be lost. Durable replay is deferred (see §13). Callers needing exactly-once
should dedup on `(session_id, reply_to, sequence)`.
- **Backpressure**: the adapter must not block the pipeline on slow callbacks —
outbound POSTs run on a per-session ordered queue with the configured timeout.
---
## 9. Optional: synchronous convenience mode (v1.1, behind a flag)
Some simple callers genuinely want "POST a message, get the reply in the HTTP
response" and don't care about streaming/multi-part. We can offer an **opt-in**
sync endpoint that internally waits for `is_final` and **collapses** all 1→M
parts into one array:
```
POST /bots/{bot_uuid}/sync → 200 { session_id, message: [ ...all parts concatenated... ] }
```
Implemented by attaching a per-request future that resolves on the final reply,
with a hard timeout. This is a **convenience wrapper** over the same machinery,
explicitly documented as lossy for streaming/ordering. Not in v1 core.
---
## 10. Adapter implementation sketch (`platform/sources/http_bot.py`)
Implements `AbstractMessagePlatformAdapter`. Key methods:
```python
class HttpBotAdapter(AbstractMessagePlatformAdapter):
listeners: dict = pydantic.Field(default_factory=dict, exclude=True)
# --- inbound -------------------------------------------------------
async def handle_unified_webhook(self, bot_uuid, path, request):
body = await request.get_body()
if self.config.get("signature_required", True):
if not self._verify(request, body):
return jsonify({"code": 40101, "msg": "invalid signature"}), 401
data = json.loads(body)
session_id = data["session_id"] # caller-defined identity
session_type = data.get("session_type", self.config.get("default_session_type", "person"))
chain = MessageChain.model_validate(data["message"])
event = self._build_event(session_type, session_id, data.get("sender"), chain)
# remember where to send replies for this session
self._callback_for[session_id] = data.get("callback_url") or self.config.get("callback_url")
# fire the registered listener → botmgr → msg_aggregator (N→1) → pipeline
if type(event) in self.listeners:
asyncio.create_task(self.listeners[type(event)](event, self))
return jsonify({"code": 0, "msg": "accepted",
"data": {"session_id": session_id, "accepted_message_id": event.message_id}}), 202
# --- outbound (called 1..M times per turn by the pipeline) ---------
async def reply_message(self, message_source, message, quote_origin=False):
return await self._post_callback(message_source, message, is_final=True, stream=False)
async def reply_message_chunk(self, message_source, bot_message, message,
quote_origin=False, is_final=False):
return await self._post_callback(message_source, message, is_final=is_final, stream=True)
async def is_stream_output_supported(self) -> bool:
return True
def register_listener(self, event_type, func): self.listeners[event_type] = func
def unregister_listener(self, event_type, func): self.listeners.pop(event_type, None)
async def run_async(self): pass # nothing to poll; purely webhook-driven
async def kill(self): pass
```
`_post_callback` resolves the session's callback URL, assigns the next
`sequence`, signs the body, and enqueues an ordered, retrying POST.
Session→callback mapping is kept in a small in-memory dict keyed by
`session_id` (acceptable for v1; a turn's callback URL is captured at inbound
time so replies always have a destination even if config later changes).
---
## 11. Security considerations
- **Inbound route is `AuthType.NONE`** at the framework level (same as all
webhook adapters) — the adapter **must** enforce HMAC itself. Default
`signature_required: true`.
- **Timestamp window** (±300s) + idempotency key blunt replay.
- **SSRF on callback_url**: validate scheme (`https` in prod), and consider an
allow-list / block of private CIDRs since LangBot initiates the POST. Document
this; enforce in code where feasible.
- **Secret storage**: secrets live in the bot's `adapter_config` like every
other adapter credential; surfaced as `type: string`/secret in the dashboard.
- **One secret per bot** in v1. Per-caller key rotation / multiple keys is a
future enhancement (§13).
---
## 12. Developer Experience (explicit deliverables)
The whole point of a standalone adapter is that **integrating is pleasant**. v1
ships:
1. **`docs/platforms/http-bot.md`** — task-oriented integration guide:
create the bot → copy inbound URL → set secret → stand up a callback
endpoint → send first message → handle 1→M.
2. **Copy-paste curl** for the first message (with a working signing one-liner).
3. **Reference clients** (≤50 LOC each) in `examples/http-bot/`:
`client.py` (push + a Flask/Quart callback receiver) and `client.ts`.
4. **OpenAPI fragment** `docs/http-bot-openapi.json` describing inbound +
callback shapes, so integrators can codegen.
5. **Local echo recipe**: a one-command callback server that prints every
reply, so a developer sees N→1 and 1→M working in under five minutes.
6. **Postman/Hoppscotch collection** (nice-to-have).
DX acceptance check: *a developer who has never seen LangBot can, from the docs
alone, push a message and observe a multi-part reply on their callback within
10 minutes.*
### Quickstart (curl)
```bash
BOT=https://your-langbot/bots/2f1c....
SECRET=supersecret
BODY='{"session_id":"ticket-10293","message":[{"type":"Plain","text":"hello"}]}'
TS=$(date +%s)
SIG="sha256=$(printf '%s.%s' "$TS" "$BODY" | openssl dgst -sha256 -hmac "$SECRET" -r | cut -d' ' -f1)"
curl -sS -X POST "$BOT" \
-H "Content-Type: application/json" \
-H "X-LB-Timestamp: $TS" \
-H "X-LB-Signature: $SIG" \
-d "$BODY"
```
---
## 13. Future work
- **Durable outbound queue** (persist + replay across restarts; exactly-once).
- **Per-caller API keys** with rotation and scopes (multi-tenant Space usage).
- **Sync convenience endpoint** (§9) once core is stable.
- **Server-Sent Events outbound option** for callers that *do* want a stream but
not a full duplex socket — single GET, server pushes chunks.
- **Dashboard "test console"** for `http_bot` (send a message, watch callbacks)
mirroring the existing WebSocket debug panel.
---
## 14. Rollout / task breakdown
| # | Task | Touches |
|---|---|---|
| 1 | `http_bot.yaml` manifest + icon | `platform/sources/` |
| 2 | `HttpBotAdapter` (inbound verify, event build, outbound queue) | `platform/sources/http_bot.py` |
| 3 | Signing helper module (shared) | `platform/sources/` or `utils/` |
| 4 | i18n strings (en/zh/ja) | adapter yaml + web locale |
| 5 | Integration docs `docs/platforms/http-bot.md` | `docs/` |
| 6 | OpenAPI fragment + reference clients | `docs/`, `examples/http-bot/` |
| 7 | Tests: signature verify, N→1 aggregation, 1→M ordering, retry | `tests/` |
| 8 | (opt) SSRF guard for callback_url | adapter |
No changes required to: the unified webhook router, the aggregator, the query
pool, or the pipeline. That is the design's main payoff.
---
## 15. Resolved decisions
1. **Callback URL trust****config-only.** The inbound message may not carry a
`callback_url`; replies always go to the bot-config URL. Closes the SSRF
vector where a leaked inbound secret could redirect replies.
2. **Session lifecycle****`POST /bots/<uuid>/reset`** (body `{session_id,
session_type?}`) drops the matching session from the session manager; the
next message starts a fresh conversation. Implemented via sub-path routing in
`handle_unified_webhook`.
3. **Group semantics** — for `session_type: group`, `session_id` is the group/
launcher id; `sender.id` (and optional `sender.group_name`) identify the
member. A Space ticket maps to one `session_id`.
4. **Backpressure** — bounded per-session outbound queue (maxlen 1000); on
overflow the oldest reply is dropped and a warning logged, so a persistently
down callback can never exhaust memory.
### Still open / deferred (see §13)
- Durable outbound queue (persist + replay across restarts).
- Per-caller API keys with rotation/scopes for multi-tenant Space usage.
- SSE outbound option and a dashboard test console.
+713
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# Workflow 系统开发者文档
本文档面向 LangBot 开发者,详细介绍 Workflow 系统的技术架构、核心组件和扩展方法。
## 目录
- [系统架构概述](#系统架构概述)
- [目录结构](#目录结构)
- [核心组件](#核心组件)
- [后端模块](#后端模块)
- [前端组件](#前端组件)
- [数据库表结构](#数据库表结构)
- [API 接口文档](#api-接口文档)
- [如何添加新节点类型](#如何添加新节点类型)
- [调试功能实现](#调试功能实现)
---
## 系统架构概述
Workflow 系统采用前后端分离架构,主要包含以下层次:
```
┌─────────────────────────────────────────────────────────────┐
│ 前端层 (React) │
│ ┌─────────────┬──────────────┬──────────────┬───────────┐ │
│ │ 可视化编辑器 │ 节点面板 │ 属性面板 │ 调试器 │ │
│ │ ReactFlow │ NodePalette │ PropertyPanel│ Debugger │ │
│ └─────────────┴──────────────┴──────────────┴───────────┘ │
├─────────────────────────────────────────────────────────────┤
│ API 层 (Quart) │
│ ┌─────────────┬──────────────┬──────────────────────────┐ │
│ │ Workflow API│ Debug API │ Node Types API │ │
│ └─────────────┴──────────────┴──────────────────────────┘ │
├─────────────────────────────────────────────────────────────┤
│ 核心引擎层 (Python) │
│ ┌─────────────┬──────────────┬──────────────┬───────────┐ │
│ │ Executor │ Registry │ Node │ Entities │ │
│ │ 执行引擎 │ 节点注册表 │ 节点基类 │ 数据结构 │ │
│ └─────────────┴──────────────┴──────────────┴───────────┘ │
├─────────────────────────────────────────────────────────────┤
│ 存储层 (SQLAlchemy) │
│ ┌─────────────┬──────────────┬──────────────────────────┐ │
│ │ Workflow │ Executions │ Triggers │ │
│ └─────────────┴──────────────┴──────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
---
## 目录结构
### 后端代码结构
```
LangBot/src/langbot/pkg/
├── workflow/ # Workflow 核心模块
│ ├── __init__.py # 模块初始化,导出公共接口
│ ├── entities.py # 数据实体定义
│ ├── executor.py # 执行引擎
│ ├── node.py # 节点基类和装饰器
│ ├── registry.py # 节点类型注册表
│ └── nodes/ # 内置节点实现
│ ├── __init__.py # 注册所有内置节点
│ ├── trigger.py # 触发节点
│ ├── process.py # 处理节点
│ ├── control.py # 控制节点
│ └── action.py # 动作节点
├── entity/persistence/
│ └── workflow.py # 数据库模型
├── api/http/
│ ├── controller/groups/workflows/
│ │ └── workflows.py # API 路由控制器
│ └── service/
│ └── workflow.py # 业务逻辑服务
└── persistence/migrations/
└── dbm026_workflow_tables.py # 数据库迁移
```
### 前端代码结构
```
LangBot/web/src/app/home/workflows/
├── page.tsx # Workflow 列表页
├── WorkflowDetailContent.tsx # 详情页内容
├── store/
│ └── useWorkflowStore.ts # Zustand 状态管理
└── components/
├── workflow-editor/ # 可视化编辑器
│ ├── index.ts # 导出
│ ├── WorkflowEditorComponent.tsx # 主编辑器组件
│ ├── WorkflowNodeComponent.tsx # 自定义节点组件
│ ├── NodePalette.tsx # 节点面板
│ ├── PropertyPanel.tsx # 属性面板
│ └── node-configs/ # 节点配置元数据
│ ├── types.ts # 配置类型定义
│ ├── trigger-configs.ts
│ ├── ai-configs.ts
│ ├── process-configs.ts
│ ├── control-configs.ts
│ ├── action-configs.ts
│ ├── integration-configs.ts
│ └── index.ts # 配置汇总
├── workflow-debugger/ # 调试器组件
│ ├── index.ts
│ └── WorkflowDebugger.tsx
├── workflow-form/ # 表单组件
│ └── WorkflowFormComponent.tsx
└── workflow-executions/ # 执行历史组件
└── WorkflowExecutionsTab.tsx
```
---
## 核心组件
### 后端模块
#### 1. 执行引擎 (WorkflowExecutor)
位置:[`executor.py`](../../src/langbot/pkg/workflow/executor.py)
执行引擎负责工作流的实际执行,包括:
- **拓扑排序**:确定节点执行顺序
- **节点执行**:调用各节点的 execute 方法
- **控制流处理**:处理条件分支、循环、并行执行
- **错误处理**:支持重试机制
```python
class WorkflowExecutor:
async def execute(
self,
workflow: WorkflowDefinition,
context: ExecutionContext,
start_node_id: Optional[str] = None
) -> ExecutionContext:
"""执行工作流"""
# 1. 构建执行图
# 2. 初始化节点状态
# 3. 找到起始节点
# 4. 按拓扑顺序执行
```
**调试执行器 (DebugWorkflowExecutor)**
继承自 WorkflowExecutor,增加了调试支持:
- 断点支持
- 单步执行
- 暂停/继续
- 实时日志
```python
class DebugWorkflowExecutor(WorkflowExecutor):
async def execute_debug(
self,
workflow: WorkflowDefinition,
context: ExecutionContext,
debug_state: DebugExecutionState,
) -> ExecutionContext:
"""调试模式执行"""
```
#### 2. 节点注册表 (NodeTypeRegistry)
位置:[`registry.py`](../../src/langbot/pkg/workflow/registry.py)
单例模式管理所有节点类型:
```python
class NodeTypeRegistry:
_instance: Optional['NodeTypeRegistry'] = None
def register(self, node_type: str, node_class: type[WorkflowNode]):
"""注册节点类型"""
def create_instance(self, node_type: str, node_id: str, config: dict) -> WorkflowNode:
"""创建节点实例"""
def list_all(self) -> list[dict]:
"""获取所有节点类型的 Schema"""
```
#### 3. 节点基类 (WorkflowNode)
位置:[`node.py`](../../src/langbot/pkg/workflow/node.py)
所有节点必须继承此基类:
```python
class WorkflowNode(abc.ABC):
# 节点元数据
type_name: str = ""
name: str = ""
description: str = ""
category: str = "misc"
icon: str = ""
# 端口定义
inputs: list[NodePort] = []
outputs: list[NodePort] = []
# 配置 Schema
config_schema: list[NodeConfig] = []
@abc.abstractmethod
async def execute(
self,
inputs: dict[str, Any],
context: ExecutionContext
) -> dict[str, Any]:
"""执行节点逻辑"""
pass
```
#### 4. 数据实体 (entities.py)
主要数据结构:
```python
class WorkflowDefinition:
"""工作流定义"""
uuid: str
name: str
nodes: list[NodeDefinition]
edges: list[EdgeDefinition]
settings: WorkflowSettings
class ExecutionContext:
"""执行上下文"""
execution_id: str
workflow_id: str
status: ExecutionStatus
variables: dict
node_states: dict[str, NodeState]
history: list[ExecutionStep]
```
### 前端组件
#### 1. WorkflowEditorComponent
主编辑器组件,基于 React Flow 实现:
- **画布交互**:拖拽、缩放、平移
- **节点连接**:自动验证端口类型
- **撤销/重做**:基于历史记录栈
- **复制/粘贴**:支持多选复制
关键功能:
```tsx
function WorkflowEditorInner() {
const { nodes, edges, onNodesChange, onEdgesChange, onConnect } = useWorkflowStore();
// 拖放添加节点
const onDrop = useCallback((event: React.DragEvent) => {
const type = event.dataTransfer.getData('application/reactflow');
const position = screenToFlowPosition({ x: event.clientX, y: event.clientY });
addNode(type, position);
}, []);
// 复制粘贴
const handleCopy = useCallback(() => { ... }, []);
const handlePaste = useCallback(() => { ... }, []);
}
```
#### 2. NodePalette
节点面板组件,展示可用节点类型:
```tsx
function NodePalette() {
// 按类别组织节点
const categories = [
{ id: 'trigger', name: '触发节点', icon: Zap },
{ id: 'ai', name: 'AI 节点', icon: Brain },
{ id: 'process', name: '处理节点', icon: Cpu },
{ id: 'control', name: '控制节点', icon: GitBranch },
{ id: 'action', name: '动作节点', icon: Send },
{ id: 'integration', name: '集成节点', icon: Plug },
];
// 拖拽开始
const onDragStart = (event: React.DragEvent, nodeType: string) => {
event.dataTransfer.setData('application/reactflow', nodeType);
};
}
```
#### 3. PropertyPanel
属性面板组件,动态渲染节点配置表单:
```tsx
function PropertyPanel() {
const { selectedNodeId, nodes, updateNodeData } = useWorkflowStore();
// 根据节点类型获取配置元数据
const selectedNode = nodes.find(n => n.id === selectedNodeId);
const nodeConfig = getNodeConfig(selectedNode?.data?.nodeType);
// 动态渲染配置字段
return (
<div>
{nodeConfig?.fields.map(field => (
<ConfigField key={field.name} field={field} />
))}
</div>
);
}
```
#### 4. WorkflowDebugger
调试器组件,支持实时调试:
```tsx
function WorkflowDebugger({ workflowUuid, workflow }) {
const [debugState, setDebugState] = useState<DebugState>('idle');
const [executionId, setExecutionId] = useState<string>('');
const [logs, setLogs] = useState<ExecutionLog[]>([]);
// 启动调试
const startDebug = async () => {
const result = await backendClient.post(
`/api/v1/workflows/${workflowUuid}/debug/start`,
{ context, variables, breakpoints }
);
setExecutionId(result.execution_id);
};
// 轮询状态
useEffect(() => {
if (debugState === 'running') {
const interval = setInterval(fetchState, 500);
return () => clearInterval(interval);
}
}, [debugState]);
}
```
#### 5. useWorkflowStore
Zustand 状态管理:
```typescript
interface WorkflowState {
nodes: WorkflowNode[];
edges: WorkflowEdge[];
selectedNodeId: string | null;
history: HistoryEntry[];
historyIndex: number;
isDirty: boolean;
// Actions
addNode: (type: string, position: XYPosition) => void;
updateNodeData: (nodeId: string, data: Partial<NodeData>) => void;
deleteNode: (nodeId: string) => void;
undo: () => void;
redo: () => void;
}
export const useWorkflowStore = create<WorkflowState>((set, get) => ({
// ... state and actions
}));
```
---
## 数据库表结构
### workflows 表
```sql
CREATE TABLE workflows (
uuid VARCHAR(255) PRIMARY KEY,
name VARCHAR(255) NOT NULL,
description TEXT,
emoji VARCHAR(10) DEFAULT '🔄',
version INTEGER DEFAULT 1,
is_enabled BOOLEAN DEFAULT TRUE,
definition JSON NOT NULL, -- 节点和边定义
global_config JSON DEFAULT '{}', -- 全局配置
extensions_preferences JSON, -- 插件和 MCP 配置
created_at TIMESTAMP,
updated_at TIMESTAMP
);
```
### workflow_versions 表
```sql
CREATE TABLE workflow_versions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
workflow_uuid VARCHAR(255) NOT NULL,
version INTEGER NOT NULL,
definition JSON NOT NULL,
global_config JSON DEFAULT '{}',
created_at TIMESTAMP,
created_by VARCHAR(255),
UNIQUE(workflow_uuid, version)
);
```
### workflow_executions 表
```sql
CREATE TABLE workflow_executions (
uuid VARCHAR(255) PRIMARY KEY,
workflow_uuid VARCHAR(255) NOT NULL,
workflow_version INTEGER NOT NULL,
status VARCHAR(20) NOT NULL, -- pending/running/completed/failed/cancelled
trigger_type VARCHAR(50),
trigger_data JSON,
variables JSON,
start_time TIMESTAMP,
end_time TIMESTAMP,
error TEXT,
created_at TIMESTAMP
);
```
### workflow_node_executions 表
```sql
CREATE TABLE workflow_node_executions (
id INTEGER PRIMARY KEY AUTOINCREMENT,
execution_uuid VARCHAR(255) NOT NULL,
node_id VARCHAR(100) NOT NULL,
node_type VARCHAR(50) NOT NULL,
status VARCHAR(20) NOT NULL,
inputs JSON,
outputs JSON,
start_time TIMESTAMP,
end_time TIMESTAMP,
error TEXT,
retry_count INTEGER DEFAULT 0
);
```
### workflow_triggers 表
```sql
CREATE TABLE workflow_triggers (
uuid VARCHAR(255) PRIMARY KEY,
workflow_uuid VARCHAR(255) NOT NULL,
type VARCHAR(50) NOT NULL, -- message/cron/event/webhook
config JSON NOT NULL,
is_enabled BOOLEAN DEFAULT TRUE,
priority INTEGER DEFAULT 0,
created_at TIMESTAMP,
updated_at TIMESTAMP
);
```
---
## API 接口文档
### Workflow CRUD
| 方法 | 路径 | 描述 |
|-----|------|------|
| GET | `/api/v1/workflows` | 获取工作流列表 |
| POST | `/api/v1/workflows` | 创建工作流 |
| GET | `/api/v1/workflows/:uuid` | 获取单个工作流 |
| PUT | `/api/v1/workflows/:uuid` | 更新工作流 |
| DELETE | `/api/v1/workflows/:uuid` | 删除工作流 |
| POST | `/api/v1/workflows/:uuid/copy` | 复制工作流 |
### 执行相关
| 方法 | 路径 | 描述 |
|-----|------|------|
| POST | `/api/v1/workflows/:uuid/execute` | 手动执行工作流 |
| GET | `/api/v1/workflows/:uuid/executions` | 获取执行记录 |
### 版本管理
| 方法 | 路径 | 描述 |
|-----|------|------|
| GET | `/api/v1/workflows/:uuid/versions` | 获取版本列表 |
| POST | `/api/v1/workflows/:uuid/rollback/:version` | 回滚到指定版本 |
### 调试 API
| 方法 | 路径 | 描述 |
|-----|------|------|
| POST | `/api/v1/workflows/:uuid/debug/start` | 启动调试 |
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/pause` | 暂停执行 |
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/resume` | 继续执行 |
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/stop` | 停止执行 |
| POST | `/api/v1/workflows/:uuid/debug/:exec_id/step` | 单步执行 |
| GET | `/api/v1/workflows/:uuid/debug/:exec_id/state` | 获取调试状态 |
### 节点类型
| 方法 | 路径 | 描述 |
|-----|------|------|
| GET | `/api/v1/workflows/_/node-types` | 获取所有节点类型 |
| GET | `/api/v1/workflows/_/node-types/categories` | 按类别获取节点类型 |
---
## 如何添加新节点类型
### 步骤 1:创建节点类
`LangBot/src/langbot/pkg/workflow/nodes/` 下创建或修改文件:
```python
from ..node import WorkflowNode, NodePort, NodeConfig, workflow_node
from ..entities import ExecutionContext
@workflow_node('my_custom_node')
class MyCustomNode(WorkflowNode):
"""自定义节点"""
# 元数据
type_name = 'my_custom_node'
name = '我的自定义节点'
description = '这是一个自定义节点'
category = 'process' # trigger/process/control/action/integration
icon = '🔧'
# 输入端口
inputs = [
NodePort(name='input', type='string', description='输入数据', required=True),
]
# 输出端口
outputs = [
NodePort(name='output', type='string', description='输出数据'),
]
# 配置字段
config_schema = [
NodeConfig(
name='option',
type='select',
required=True,
options=['选项A', '选项B'],
description='选择一个选项'
),
NodeConfig(
name='value',
type='string',
required=False,
default='默认值',
description='配置值'
),
]
async def execute(
self,
inputs: dict[str, Any],
context: ExecutionContext
) -> dict[str, Any]:
"""执行节点逻辑"""
input_data = inputs.get('input', '')
option = self.get_config('option')
value = self.get_config('value', '')
# 处理逻辑
result = f"处理: {input_data} with {option} and {value}"
return {'output': result}
```
### 步骤 2:注册节点
`LangBot/src/langbot/pkg/workflow/nodes/__init__.py` 中导入:
```python
from .process import (
CodeExecutorNode,
HttpRequestNode,
DataTransformNode,
MyCustomNode, # 添加新节点
)
```
### 步骤 3:添加前端配置
`LangBot/web/src/app/home/workflows/components/workflow-editor/node-configs/` 目录下添加配置:
```typescript
// process-configs.ts
export const processNodeConfigs: NodeConfigMap = {
// ... 其他配置
my_custom_node: {
type: 'my_custom_node',
label: 'workflows.nodes.myCustomNode',
description: 'workflows.nodes.myCustomNodeDesc',
icon: 'Wrench',
category: 'process',
fields: [
{
name: 'option',
type: 'select',
label: 'workflows.fields.option',
required: true,
options: [
{ value: '选项A', label: '选项 A' },
{ value: '选项B', label: '选项 B' },
],
},
{
name: 'value',
type: 'string',
label: 'workflows.fields.value',
required: false,
defaultValue: '默认值',
},
],
},
};
```
### 步骤 4:添加国际化
`LangBot/web/src/i18n/locales/` 中添加翻译:
```typescript
// zh-Hans.ts
workflows: {
nodes: {
myCustomNode: '我的自定义节点',
myCustomNodeDesc: '这是一个自定义节点',
},
fields: {
option: '选项',
value: '值',
},
}
```
---
## 调试功能实现
### 后端调试状态管理
```python
class DebugExecutionState:
"""调试执行状态"""
def __init__(self, execution_id: str, breakpoints: list[str] = None):
self.execution_id = execution_id
self.status: str = 'running'
self.is_paused: bool = False
self.is_stopped: bool = False
self.breakpoints: set[str] = set(breakpoints or [])
self.logs: list[ExecutionLog] = []
self._pause_event = asyncio.Event()
def pause(self):
"""暂停执行"""
self.is_paused = True
self._pause_event.clear()
def resume(self):
"""继续执行"""
self.is_paused = False
self._pause_event.set()
async def wait_if_paused(self):
"""如果暂停则等待"""
if self.is_paused:
await self._pause_event.wait()
```
### 前端调试流程
1. **设置断点**:点击节点设置断点
2. **启动调试**:调用 `/debug/start` 启动调试执行
3. **轮询状态**:定期调用 `/debug/:id/state` 获取状态
4. **控制执行**:调用 pause/resume/step/stop 控制执行
5. **查看日志**:实时显示执行日志和节点状态
```typescript
// 调试状态轮询
const fetchDebugState = async () => {
const state = await backendClient.get(
`/api/v1/workflows/${workflowUuid}/debug/${executionId}/state`
);
// 更新节点状态
setNodeStates(state.node_states);
// 追加新日志
if (state.new_logs.length > 0) {
setLogs(prev => [...prev, ...state.new_logs]);
}
// 检查完成状态
if (state.status === 'completed' || state.status === 'error') {
setDebugState('idle');
}
};
```
---
## 扩展阅读
- [Workflow 功能设计文档](../../../plans/langbot-workflow-design.md)
- [用户使用指南](../user-guide/workflow-guide.md)
- [API 认证文档](../API_KEY_AUTH.md)
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{
"openapi": "3.0.3",
"info": {
"title": "LangBot HTTP Bot Adapter",
"version": "1.0.0",
"description": "Server-to-server HTTP integration for a LangBot pipeline. Inbound messages are POSTed to the unified webhook route; replies are delivered to a configured callback URL (one POST per reply part). All requests are HMAC-SHA256 signed. See docs/platforms/http-bot.md."
},
"paths": {
"/bots/{bot_uuid}": {
"post": {
"summary": "Push a message into the pipeline (fire-and-collect)",
"description": "Returns 202 immediately. Replies arrive asynchronously on the configured callback URL. Reuse the same session_id within the aggregation window to merge multiple messages into one turn (N->1).",
"parameters": [
{ "$ref": "#/components/parameters/BotUuid" },
{ "$ref": "#/components/parameters/Timestamp" },
{ "$ref": "#/components/parameters/Signature" },
{ "$ref": "#/components/parameters/Idempotency" }
],
"requestBody": {
"required": true,
"content": { "application/json": { "schema": { "$ref": "#/components/schemas/InboundMessage" } } }
},
"responses": {
"202": {
"description": "Accepted (queued for the pipeline)",
"content": { "application/json": { "schema": { "$ref": "#/components/schemas/AcceptedResponse" } } }
},
"400": { "$ref": "#/components/responses/Error" },
"401": { "$ref": "#/components/responses/Error" },
"409": { "$ref": "#/components/responses/Error" },
"413": { "$ref": "#/components/responses/Error" }
}
}
},
"/bots/{bot_uuid}/sync": {
"post": {
"summary": "Push a message and wait for the collapsed reply",
"description": "Blocking convenience mode. Waits for is_final and returns all reply parts collapsed into one array. Lossy (no sequence/streaming). One in-flight sync per session_id.",
"parameters": [
{ "$ref": "#/components/parameters/BotUuid" },
{ "$ref": "#/components/parameters/Timestamp" },
{ "$ref": "#/components/parameters/Signature" }
],
"requestBody": {
"required": true,
"content": { "application/json": { "schema": { "$ref": "#/components/schemas/InboundMessage" } } }
},
"responses": {
"200": {
"description": "The collapsed reply",
"content": { "application/json": { "schema": { "$ref": "#/components/schemas/SyncResponse" } } }
},
"400": { "$ref": "#/components/responses/Error" },
"401": { "$ref": "#/components/responses/Error" }
}
}
},
"/bots/{bot_uuid}/reset": {
"post": {
"summary": "Reset a session's conversation",
"parameters": [
{ "$ref": "#/components/parameters/BotUuid" },
{ "$ref": "#/components/parameters/Timestamp" },
{ "$ref": "#/components/parameters/Signature" }
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["session_id"],
"properties": {
"session_id": { "type": "string" },
"session_type": { "type": "string", "enum": ["person", "group"] }
}
}
}
}
},
"responses": {
"200": { "description": "Reset done" },
"400": { "$ref": "#/components/responses/Error" },
"401": { "$ref": "#/components/responses/Error" }
}
}
}
},
"components": {
"parameters": {
"BotUuid": {
"name": "bot_uuid", "in": "path", "required": true,
"schema": { "type": "string", "format": "uuid" }
},
"Timestamp": {
"name": "X-LB-Timestamp", "in": "header", "required": true,
"description": "Unix seconds; rejected if more than +/-300s from server time.",
"schema": { "type": "string" }
},
"Signature": {
"name": "X-LB-Signature", "in": "header", "required": true,
"description": "sha256=<hex> of HMAC-SHA256(secret, \"{timestamp}.\" + raw_body).",
"schema": { "type": "string" }
},
"Idempotency": {
"name": "X-LB-Idempotency-Key", "in": "header", "required": false,
"description": "Dedup key; a repeat within the dedup window returns 409.",
"schema": { "type": "string" }
}
},
"schemas": {
"Segment": {
"type": "object",
"required": ["type"],
"properties": {
"type": { "type": "string", "enum": ["Plain", "Image", "Voice", "File", "At", "Quote"] },
"text": { "type": "string", "description": "For type=Plain." },
"url": { "type": "string", "description": "For media types." },
"base64": { "type": "string", "description": "For media types (data URI or raw base64)." }
}
},
"InboundMessage": {
"type": "object",
"required": ["session_id", "message"],
"properties": {
"session_id": { "type": "string", "description": "Caller-defined; maps 1:1 to a LangBot session." },
"session_type": { "type": "string", "enum": ["person", "group"], "default": "person" },
"sender": {
"type": "object",
"properties": {
"id": { "type": "string" },
"name": { "type": "string" },
"group_name": { "type": "string", "description": "For session_type=group." }
}
},
"message": { "type": "array", "items": { "$ref": "#/components/schemas/Segment" } }
}
},
"AcceptedResponse": {
"type": "object",
"properties": {
"code": { "type": "integer", "example": 0 },
"msg": { "type": "string", "example": "accepted" },
"data": {
"type": "object",
"properties": {
"session_id": { "type": "string" },
"accepted_message_id": { "type": "string", "example": "in_01H..." },
"aggregating": { "type": "boolean" }
}
}
}
},
"SyncResponse": {
"type": "object",
"properties": {
"code": { "type": "integer", "example": 0 },
"msg": { "type": "string", "example": "ok" },
"data": {
"type": "object",
"properties": {
"session_id": { "type": "string" },
"reply_to": { "type": "string" },
"message": { "type": "array", "items": { "$ref": "#/components/schemas/Segment" } }
}
}
}
},
"Callback": {
"type": "object",
"description": "Delivered by LangBot to your callback_url, one POST per reply part. Signed with the outbound secret.",
"properties": {
"session_id": { "type": "string" },
"reply_to": { "type": "string", "description": "The accepted_message_id this answers." },
"sequence": { "type": "integer", "description": "1-based ordinal within the turn." },
"is_final": { "type": "boolean", "description": "True on the last part of the turn." },
"stream": { "type": "boolean" },
"message": { "type": "array", "items": { "$ref": "#/components/schemas/Segment" } },
"timestamp": { "type": "string", "format": "date-time" }
}
},
"ErrorEnvelope": {
"type": "object",
"properties": {
"code": { "type": "integer", "example": 40101 },
"msg": { "type": "string", "example": "invalid signature: signature_mismatch" },
"data": { "nullable": true }
}
}
},
"responses": {
"Error": {
"description": "Error envelope",
"content": { "application/json": { "schema": { "$ref": "#/components/schemas/ErrorEnvelope" } } }
}
}
}
}
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# HTTP Bot Adapter — Integration Guide
Integrate **any backend system** with a LangBot pipeline over plain HTTP. Push
messages in via a signed webhook; receive replies on a callback URL. No
long-lived connection, full support for message **aggregation** (many inbound
messages merged into one turn) and **multi-part replies** (one turn → many
outbound messages).
This is the right adapter for **server-to-server** integrations — ticketing
systems, CRMs, internal tools, custom web backends. (For an in-browser,
real-time chat widget, use the embeddable Web Page Bot instead.)
> **5-minute goal:** stand up a callback receiver, send a message, and watch a
> multi-part reply arrive — using the reference client in
> [`examples/http-bot/`](../../examples/http-bot/).
---
## 1. Mental model
```
Your backend ──(1) POST signed message──► LangBot /bots/<bot_uuid>
(pipeline runs: aggregate → think → reply)
Your callback ◄─(2) POST signed reply(s)── LangBot one POST per reply part
```
- **(1) Inbound** is *fire-and-collect*: LangBot answers `202 Accepted`
immediately and does **not** return the pipeline result on that response.
- **(2) Outbound** replies arrive later as separate signed POSTs to your
`callback_url`. A single turn may produce **several** callbacks (e.g. a tool
call narration followed by the final answer).
- Everything is keyed by a **`session_id` you choose** (e.g. a ticket number).
Each `session_id` maps to one isolated LangBot conversation.
---
## 2. Create the bot
1. In the LangBot dashboard, add a bot and choose the **HTTP Bot** platform.
2. Fill in the config:
| Field | Required | Notes |
|---|---|---|
| **Inbound Signing Secret** | yes | Your backend signs inbound requests with this. |
| **Outbound Callback URL** | yes | Where LangBot POSTs replies. **Config-only** — cannot be overridden per message (SSRF protection). |
| **Outbound Signing Secret** | no | LangBot signs callbacks with this; defaults to the inbound secret. |
| **Default Session Type** | no | `person` (default) or `group`. |
| **Require Inbound Signature** | no | Keep `true` in production. |
| **Callback Timeout / Max Retries** | no | Defaults: 15s, 3 retries. |
3. Bind the bot to a **pipeline** and **enable** it.
4. Copy the **Inbound Webhook URL** shown in the config — it looks like
`https://your-langbot/bots/<bot_uuid>`.
---
## 3. The signature scheme
Both directions use the same dependency-free HMAC-SHA256 scheme:
```
signing_string = "{timestamp}." + raw_body_bytes
signature = "sha256=" + hex(HMAC_SHA256(secret, signing_string))
```
Sent as headers:
| Header | Meaning |
|---|---|
| `X-LB-Timestamp` | Unix seconds. Rejected if more than **±300s** from server time. |
| `X-LB-Signature` | `sha256=<hex>` over `"{timestamp}." + body`. |
| `X-LB-Idempotency-Key` | *(optional, inbound)* dedup key; retries with the same key return `409`. |
Verify outbound callbacks the same way, using the **outbound** secret (or the
inbound secret if you left it blank).
A six-line reference implementation is in `examples/http-bot/client.py`
(`sign()` / `verify()`); a Node/TS version is in `client.ts`.
---
## 4. Send your first message (curl)
```bash
BOT="https://your-langbot/bots/<bot_uuid>"
SECRET="your-inbound-secret"
BODY='{"session_id":"ticket-10293","message":[{"type":"Plain","text":"Export keeps failing on the dashboard."}]}'
TS=$(date +%s)
SIG="sha256=$(printf '%s.%s' "$TS" "$BODY" | openssl dgst -sha256 -hmac "$SECRET" -r | cut -d' ' -f1)"
curl -sS -X POST "$BOT" \
-H "Content-Type: application/json" \
-H "X-LB-Timestamp: $TS" \
-H "X-LB-Signature: $SIG" \
-d "$BODY"
# -> 202 {"code":0,"msg":"accepted","data":{"session_id":"ticket-10293","accepted_message_id":"in_...","aggregating":true}}
```
The reply(s) will be POSTed to your configured callback URL shortly after.
---
## 5. Inbound request format
`POST /bots/{bot_uuid}`
```jsonc
{
"session_id": "ticket-10293", // REQUIRED. Your stable id. Maps 1:1 to a LangBot session.
"session_type": "person", // optional: "person" | "group"; default from config
"sender": { // optional metadata, surfaced to the pipeline/plugins
"id": "user-5567",
"name": "Alice"
},
"message": [ // REQUIRED. A LangBot MessageChain (array of segments).
{ "type": "Plain", "text": "Export keeps failing on the dashboard." },
{ "type": "Image", "url": "https://example.com/screenshot.png" }
]
}
```
**Message segments.** Text uses `{"type":"Plain","text":"..."}`. Images use
`{"type":"Image","url":"..."}` (or `base64`). Other supported types: `Voice`,
`File`, `At`, `Quote`.
> Note: the callback URL is **not** accepted in the body — it is taken only from
> bot config. This is deliberate (prevents an attacker who obtains the inbound
> secret from redirecting replies to an arbitrary host).
### Aggregation (N → 1)
If your pipeline has **message aggregation** enabled, send several messages with
the **same `session_id`** within the aggregation window and they are merged into
**one** pipeline turn. No special flag — just reuse the `session_id`.
---
## 6. Outbound callback format
LangBot POSTs each reply part to your `callback_url`:
```jsonc
{
"session_id": "ticket-10293", // echoes the inbound session
"reply_to": "in_01H...", // the accepted_message_id this answers
"sequence": 1, // 1-based ordinal within this turn
"is_final": false, // true on the last part of the turn
"stream": false, // true for streamed chunks
"message": [ { "type": "Plain", "text": "Looking into it…" } ],
"timestamp": "2026-06-22T09:00:01Z"
}
```
Your endpoint should return `2xx` quickly. Non-2xx / timeout → LangBot retries
with exponential backoff (up to `callback_max_retries`).
### Multi-part replies (1 → M)
One turn may emit multiple callbacks, delivered **in `sequence` order** for a
given session:
```
seq=1 is_final=false "Checking your export logs…"
seq=2 is_final=false "Found 2 failed exports."
seq=3 is_final=true "Fixed — please try again."
```
Stitch by `session_id` + `sequence`; the turn is complete when
`is_final: true` arrives.
---
## 7. Reset a session
Start a fresh conversation for a `session_id` (drops history):
```
POST /bots/{bot_uuid}/reset
{ "session_id": "ticket-10293", "session_type": "person" }
→ 200 { "code":0, "msg":"reset", "data": { "session_id":"ticket-10293", "removed": true } }
```
Signed exactly like an inbound message.
---
## 8. Synchronous convenience mode
If you don't need streaming/multi-part and just want one reply back on the same
HTTP call, POST to `/sync`. LangBot waits for the turn to finish and returns all
parts **collapsed** into one array:
```
POST /bots/{bot_uuid}/sync
{ "session_id": "ticket-10293", "message": [ { "type":"Plain", "text":"hi" } ] }
→ 200 { "code":0, "msg":"ok",
"data": { "session_id":"ticket-10293", "reply_to":"in_...",
"message": [ {"type":"Plain","text":"..."}, ... ] } }
```
This is **lossy** (you lose `sequence` / streaming boundaries) and blocks up to
`callback_timeout × 4` seconds. Prefer the callback model for anything
real-time or multi-part. Only one in-flight `/sync` per `session_id`.
---
## 9. Error envelope
```jsonc
{ "code": 40101, "msg": "invalid signature: signature_mismatch", "data": null }
```
| HTTP | code | meaning |
|---|---|---|
| 202 | 0 | accepted |
| 400 | 40001 | malformed body / missing `session_id` or `message` |
| 401 | 40101 | bad/expired signature |
| 409 | 40901 | duplicate idempotency key |
| 413 | 41301 | message too large (>1 MiB) |
| 500 | 50001 | internal error |
---
## 10. Try it end-to-end in 5 minutes
```bash
cd examples/http-bot
pip install flask requests
# Terminal 1 — your callback receiver (point the bot's callback_url here, e.g. via a tunnel):
python client.py serve --port 8900 --secret SHARED_SECRET
# Terminal 2 — push a message:
python client.py push \
--url https://your-langbot/bots/<bot_uuid> \
--secret SHARED_SECRET \
--session ticket-1 \
--text "hello"
```
Watch Terminal 1 print each reply part (`[part ]` / `[FINAL]`) with its
sequence number — that's 1→M working, signatures verified.
A machine-readable contract is in
[`docs/http-bot-openapi.json`](../http-bot-openapi.json).
---
## 11. Security checklist
- Keep **Require Inbound Signature** on in production.
- Use **HTTPS** callback URLs; the URL is config-only (no per-message override).
- Treat the secrets like passwords; rotate via the dashboard.
- The inbound route is unauthenticated at the framework level **by design**
security comes entirely from the HMAC signature, so never disable it on a
public deployment.
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# MCP Resources PR #2215 Review
> 更新日期: 2026-06-29
> 分支: `mcp_resources`
> PR: langbot-app/LangBot#2215
> 主题: MCP Resources 在 LangBot 中的产品价值、AgentRunner 集成方式与后续架构方向
## 结论
PR #2215 对 LangBot 有明确价值:它补齐了 MCP 协议中 Resources 这一重要能力,让 MCP server 不再只暴露 tools,也可以暴露文档、代码片段、配置、日志、图片等上下文资源。管理端可以发现和预览资源,Agent 也可以通过当前实现按需列出和读取资源。
但当前 AgentRunner 层的接入方式更接近一个可用的第一阶段方案,而不是最终架构。现在 MCP Resources 被包装成两个 synthetic tools
- `langbot_mcp_list_resources`
- `langbot_mcp_read_resource`
这让模型可以通过 function calling 主动探索资源,落地成本低,也复用了已有 `ToolManager` / `LocalAgentRunner` 的工具调用链路。不过从 MCP 规范和主流实现来看,Resources 更适合作为一种一等上下文来源,而不是长期隐藏在工具列表里。
建议保留当前 synthetic tools 作为探索能力,同时把后续主线设计调整为:MCP Resources 是 pipeline / conversation / message 级别可选择、可固定、可审计的上下文输入。
## 当前实现判断
当前 AgentRunner 集成路径如下:
```text
Pipeline 绑定 MCP server
-> query.variables['_pipeline_bound_mcp_servers']
-> Preproc 为 local-agent 加载工具
-> ToolManager.get_all_tools()
-> MCPLoader 注入 synthetic resource tools
-> LocalAgentRunner 将工具 schema 传给模型
-> 模型发起 list/read tool call
-> ToolManager.execute_func_call()
-> MCPLoader 调 MCP session.list_resources/read_resource
-> tool result 回灌给模型
```
这个路径的优点是:
- 复用现有工具调用机制,改动范围小。
- Agent 可以按需探索资源,不需要每轮预先读取所有资源。
- 可以沿用 pipeline 绑定的 MCP server 范围,避免越权读取未绑定 server。
- 对已有 MCP tools 行为影响较小。
主要问题是:
- Resources 在语义上被降级成 tools,和 MCP 规范里的 resource primitive 不完全一致。
- 模型必须先理解并主动调用 `list/read`,资源不会自然成为上下文。
- pipeline 不能配置“默认携带某些资源”或“本轮附加某些资源”。
- UI 资源 tab 目前是管理端预览能力,和 Agent 上下文选择没有打通。
- 对 blob、图片、大文件、结构化资源的处理还比较粗糙。
- 缺少 resource templates、订阅更新、缓存、chunk、token budget、trace 与审计策略。
## 主流项目做法
### MCP 官方规范
MCP Resources 是 server 暴露上下文数据的协议能力。规范没有要求 resources 必须以 tool call 形式给模型使用,而是把如何选择、过滤、读取和纳入上下文交给 Host application。
这意味着比较正统的集成方式是:LangBot 作为 Host,在 pipeline、会话或消息层决定哪些 resources 进入模型上下文。
参考: https://modelcontextprotocol.io/specification/2025-06-18/server/resources
### VS Code Copilot
VS Code 把 MCP Resources 做成 chat context 的一部分。用户可以通过 `Add Context > MCP Resources` 或命令浏览 MCP resources,并把选中的资源附加到一次 chat request。
这是目前最值得 LangBot 参考的产品形态:资源不是模型工具,而是用户和 Host 可控的上下文附件。
参考: https://code.visualstudio.com/docs/agent-customization/mcp-servers
### Anthropic SDK
Anthropic 的 client-side MCP helpers 提供资源读取和转换能力,例如把 MCP resource 转为 Claude message content 或 file。也就是说,应用先读取 resource,再显式放进模型消息。
这同样是 application-owned context injection,而不是把 resource 伪装成模型工具。
参考: https://platform.claude.com/docs/en/agents-and-tools/mcp-connector
### LangChain MCP Adapters
LangChain 把 MCP Resources 更像 data loader / document input 来处理,可以把资源加载成 `Blob`,再进入 LangChain 的文档、检索或上下文处理链路。
这说明 Resources 很适合作为知识源、文档源或上下文源,而不只是即时工具调用。
参考: https://docs.langchain.com/oss/python/langchain/mcp
### OpenAI Agents SDK
OpenAI Agents SDK 主路径仍偏向 MCP tools,但底层 MCP server API 已经有 `list_resources``list_resource_templates``read_resource` 等能力。当前形态说明 resources 是 client 能力,但并未默认变成 agent-visible tools。
参考: https://openai.github.io/openai-agents-python/mcp/
### Cline
Cline 会拉取 MCP tools、resources、resourceTemplates、prompts,并通过类似 `access_mcp_resource` 的内置访问方式让模型读取资源。这个方向和 LangBot 当前 synthetic tools 比较接近。
这种模式适合让 Agent 自主探索,但更像 Host 自定义的模型访问协议,不应成为唯一集成路径。
参考: https://github.com/cline/cline/blob/main/src/services/mcp/McpHub.ts
## 建议架构方向
### 1. 保留探索型工具
保留当前两个 synthetic tools
- `langbot_mcp_list_resources`
- `langbot_mcp_read_resource`
它们适合处理“用户没有显式选择资源,但 Agent 判断需要探索 MCP server 上下文”的场景。后续可以优化工具描述、返回格式、资源大小限制和错误信息。
### 2. 增加一等 Resource Context
新增一个 Host 层资源上下文概念,例如:
```text
PipelineResourceBinding
ConversationResourceAttachment
MessageResourceAttachment
```
Preproc 或独立的 `ResourceContextProvider` 在模型调用前读取这些资源,按 MIME 类型、大小、token budget 转为模型可消费的上下文。
### 3. 打通 UI 与 Agent 上下文
当前 MCP 详情页的 Resources tab 可以继续作为资源发现和预览入口。建议增加操作:
- 添加到本轮上下文
- 固定到当前 pipeline
- 固定到当前 bot / conversation
- 查看资源读取历史和错误
这样 UI 资源管理能力才能真正影响 Agent 行为。
### 4. 支持 resource templates
MCP resource templates 允许 server 暴露参数化资源,例如:
```text
repo://{owner}/{repo}/file/{path}
log://{service}/{date}
```
LangBot 后续应支持模板发现、参数填写、实例化和绑定。否则只能使用静态 resources,覆盖面会受限。
### 5. 增加资源处理策略
建议补齐:
- 文本资源 token budget 与截断策略。
- 大文件 chunk 与摘要策略。
- 图片/blob 的模型能力判断与 fallback。
- MIME 类型白名单与安全限制。
- 缓存与过期策略。
- `resources/listChanged` 或订阅更新。
- resource read trace,便于审计 Agent 读取了什么上下文。
## 推荐落地顺序
### Phase 1: 完成当前 PR 可用性
- 保留 synthetic tools。
- 明确文档说明当前 Agent 集成是 tool-mediated。
- 完善资源工具描述,降低模型误用概率。
- 给 read/list 增加大小限制和更清晰的 MIME 处理。
- 前端 Resources tab 与 Tools tab 分离,保持管理端清晰。
### Phase 2: 做 Host-owned context attachments
- 在 pipeline 或 conversation 层新增 resource attachment 配置。
- Preproc 读取已绑定 resources,注入模型上下文。
- UI 支持“添加到上下文 / 固定到 pipeline”。
- 记录每轮实际注入的 resource URI 和 token 消耗。
### Phase 3: 做完整 MCP Resources 能力
- 支持 resource templates。
- 支持资源订阅更新。
- 支持 chunk、summary、RAG 化接入。
- 为 DifyAgentRunner、LocalAgentRunner 等不同 runner 定义统一资源上下文接口。
## 最终建议
PR #2215 可以作为 MCP Resources 的第一阶段实现继续推进。它让 LangBot 快速拥有“资源发现、预览、按需读取”的闭环,也给 Agent 探索资源提供了可运行路径。
但在正式设计上,不建议把 “Resources == Tools” 固化为长期抽象。LangBot 更应该把 MCP Resources 定位为上下文来源,与 tools、prompts、knowledge base 并列:
```text
Tools -> Agent 可以执行的动作
Resources -> Host/用户/Agent 可以选择的上下文数据
Prompts -> 可复用的任务模板
Knowledge -> 可检索、可索引的长期知识
```
这样既尊重 MCP 协议语义,也能让 LangBot 在 Agent 工作流、企业知识接入和多 MCP server 管理上走得更稳。
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# Workflow 用户指南
本文档帮助您了解和使用 LangBot 的 Workflow(工作流)功能,通过可视化方式构建自动化的对话处理流程。
## 目录
- [功能介绍](#功能介绍)
- [快速入门](#快速入门)
- [节点类型说明](#节点类型说明)
- [编辑器使用指南](#编辑器使用指南)
- [调试功能](#调试功能)
- [常见问题解答](#常见问题解答)
---
## 功能介绍
### 什么是 Workflow
Workflow(工作流)是 LangBot 提供的可视化自动化编排系统。通过拖拽节点、连接边的方式,您可以:
- 📝 **构建复杂的对话流程**:使用条件分支、循环等控制节点
- 🤖 **调用 AI 能力**:集成 LLM、知识库检索、参数提取
- 🔗 **连接外部服务**:集成 Dify、n8n、Coze 等平台
- ⚡ **自动化任务执行**:消息触发、定时触发、Webhook 触发
### Workflow vs Pipeline
| 对比项 | Pipeline | Workflow |
|-------|----------|----------|
| 配置方式 | 表单配置 | 可视化拖拽 |
| 流程控制 | 线性执行 | 支持分支、循环、并行 |
| 适用场景 | 简单对话 | 复杂流程 |
| 学习曲线 | 低 | 中等 |
---
## 快速入门
### 第一步:创建 Workflow
1. 在侧边栏点击 **Workflow** 进入工作流列表
2. 点击右上角 **创建工作流** 按钮
3. 填写基本信息:
- **名称**:给工作流起一个描述性的名字
- **描述**:可选,说明工作流的用途
- **图标**:选择一个 emoji 作为标识
### 第二步:添加节点
进入编辑器后,左侧是节点面板,中间是画布区域,右侧是属性面板。
1. **添加触发节点**:从左侧面板拖拽一个"消息触发"节点到画布
2. **添加 AI 节点**:拖拽一个"LLM 调用"节点
3. **添加回复节点**:拖拽一个"回复消息"节点
### 第三步:连接节点
1. 将鼠标悬停在触发节点的输出端口(右侧小圆点)
2. 按住鼠标拖拽到 LLM 节点的输入端口(左侧小圆点)
3. 同样方式连接 LLM 节点和回复节点
```
[消息触发] ──▶ [LLM 调用] ──▶ [回复消息]
```
### 第四步:配置节点
点击 LLM 调用节点,在右侧属性面板配置:
- **运行方式**:选择"本地 Agent"
- **系统提示词**:描述 AI 的角色和行为
- **模型**:选择要使用的 LLM 模型
点击回复消息节点配置:
- **消息内容**:设置为 `{{nodes.llm_call.outputs.response}}`(引用 LLM 输出)
### 第五步:保存并绑定
1. 点击工具栏的 **保存** 按钮
2. 返回 Bot 配置页面
3. 在 Bot 的绑定设置中选择 **Workflow**,然后选择刚创建的工作流
恭喜!您已经创建了第一个 Workflow。
---
## 节点类型说明
### 触发节点 (Trigger)
触发节点是工作流的入口,定义何时启动执行。
| 节点 | 说明 | 输出 |
|-----|------|------|
| 消息触发 | 收到消息时触发 | message, sender_id, platform |
| 定时触发 | 按 Cron 表达式定时触发 | timestamp |
| Webhook 触发 | 收到 HTTP 请求时触发 | request_body, headers |
| 事件触发 | 系统事件触发 | event_type, event_data |
**消息触发配置示例**
```yaml
触发条件:
- 关键词匹配: ["帮助", "help"]
- 平台: ["wechat", "qq"]
```
### AI 节点
AI 节点用于调用各种 AI 能力。
| 节点 | 说明 | 典型用途 |
|-----|------|---------|
| LLM 调用 | 调用大语言模型 | 生成回复、理解意图 |
| 问题分类器 | 对用户问题分类 | 路由到不同处理分支 |
| 参数提取器 | 从文本提取结构化数据 | 提取订单号、日期等 |
| 知识库检索 | 查询知识库 | RAG 增强回复 |
**LLM 调用配置示例**
```yaml
运行方式: 本地 Agent
模型: gpt-4
系统提示词: |
你是一个友好的客服助手。
请根据用户的问题提供帮助。
温度: 0.7
最大 Token 数: 2000
```
### 处理节点 (Process)
处理节点用于数据处理和外部调用。
| 节点 | 说明 | 典型用途 |
|-----|------|---------|
| 代码执行 | 执行 Python/JavaScript 代码 | 数据处理、格式转换 |
| HTTP 请求 | 发送 HTTP 请求 | 调用外部 API |
| 数据转换 | JSON/模板转换 | 数据格式化 |
**HTTP 请求配置示例**
```yaml
URL: https://api.example.com/data
方法: POST
请求头:
Content-Type: application/json
Authorization: Bearer {{variables.api_key}}
请求体: |
{"query": "{{message.content}}"}
```
### 控制节点 (Control)
控制节点用于流程控制。
| 节点 | 说明 | 用途 |
|-----|------|------|
| 条件分支 | 二选一分支 | if-else 逻辑 |
| 多路分支 | 多选一分支 | switch-case 逻辑 |
| 循环 | 遍历数组 | 批量处理 |
| 并行 | 同时执行多分支 | 并发处理 |
| 等待 | 暂停执行 | 延时处理 |
| 合并 | 合并多个分支 | 汇总结果 |
**条件分支配置示例**
```yaml
条件表达式: "{{nodes.classifier.outputs.category}}" == "complaint"
真分支: 投诉处理
假分支: 普通咨询
```
### 动作节点 (Action)
动作节点执行具体操作。
| 节点 | 说明 | 用途 |
|-----|------|------|
| 发送消息 | 主动发送消息 | 通知、推送 |
| 回复消息 | 回复当前消息 | 对话回复 |
| 存储数据 | 保存数据到存储 | 持久化 |
| 调用 Pipeline | 调用现有 Pipeline | 复用现有流程 |
**回复消息配置示例**
```yaml
消息内容: |
感谢您的咨询!
{{nodes.llm_call.outputs.response}}
如有其他问题,随时联系我。
```
### 集成节点 (Integration)
集成节点连接外部平台。
| 节点 | 说明 | 平台 |
|-----|------|------|
| Dify 工作流 | 调用 Dify 应用 | Dify |
| Dify 知识库 | 查询 Dify 知识库 | Dify |
| n8n 工作流 | 调用 n8n 流程 | n8n |
| Langflow | 调用 Langflow 流程 | Langflow |
| Coze Bot | 调用扣子 Bot | Coze |
**Dify 工作流配置示例**
```yaml
API 地址: https://api.dify.ai/v1
API Key: sk-xxxxx
应用类型: workflow
同步对话历史: true
```
---
## 编辑器使用指南
### 画布操作
| 操作 | 方式 |
|-----|------|
| 平移画布 | 按住鼠标中键/空格+左键 拖拽 |
| 缩放画布 | 鼠标滚轮 / 工具栏按钮 |
| 框选多个节点 | 按住 Shift + 拖拽框选 |
| 适应视图 | 点击工具栏"适应"按钮 |
### 节点操作
| 操作 | 方式 |
|-----|------|
| 添加节点 | 从左侧面板拖拽到画布 |
| 移动节点 | 点击节点拖拽 |
| 删除节点 | 选中后按 Delete / 点击工具栏删除 |
| 复制节点 | 选中后 Ctrl+C / 工具栏复制 |
| 粘贴节点 | Ctrl+V / 工具栏粘贴 |
### 连接操作
| 操作 | 方式 |
|-----|------|
| 创建连接 | 从输出端口拖拽到输入端口 |
| 删除连接 | 点击连接线后按 Delete |
| 选中连接 | 点击连接线 |
### 快捷键
| 快捷键 | 功能 |
|-------|------|
| Ctrl + Z | 撤销 |
| Ctrl + Shift + Z | 重做 |
| Ctrl + C | 复制 |
| Ctrl + V | 粘贴 |
| Delete | 删除选中 |
| Ctrl + S | 保存 |
### 工具栏功能
```
[撤销] [重做] | [放大] [缩小] [适应] | [复制] [粘贴] [删除] | [保存] [调试]
```
---
## 调试功能
### 启动调试
1. 点击工具栏的 **调试** 按钮
2. 在调试面板中配置初始数据:
- **输入消息**:模拟用户发送的消息
- **会话 ID**:可选,用于测试会话变量
- **变量**:设置初始变量值
3. 点击 **开始调试** 按钮
### 调试控制
| 按钮 | 功能 |
|-----|------|
| ▶️ 开始/继续 | 开始或继续执行 |
| ⏸️ 暂停 | 暂停执行 |
| ⏹️ 停止 | 停止执行 |
| ⏭️ 单步 | 执行下一个节点 |
### 断点
- **设置断点**:点击节点上的断点图标
- **断点触发**:执行到断点时自动暂停
- **查看状态**:在暂停时查看节点的输入输出
### 执行日志
调试面板下方显示实时日志:
```
[INFO] 2024-01-15 10:30:00 - Starting debug execution
[INFO] 2024-01-15 10:30:00 - Executing node: message_trigger
[DEBUG] 2024-01-15 10:30:00 - Node inputs: {"message": "你好"}
[INFO] 2024-01-15 10:30:01 - Node completed in 50ms
[INFO] 2024-01-15 10:30:01 - Executing node: llm_call
...
```
### 节点状态颜色
| 颜色 | 状态 |
|-----|------|
| 灰色 | 待执行 |
| 蓝色 | 执行中 |
| 绿色 | 已完成 |
| 红色 | 失败 |
| 黄色 | 已跳过 |
---
## 常见问题解答
### Q1:如何在节点间传递数据?
使用表达式语法引用其他节点的输出:
```
{{nodes.节点ID.outputs.输出名称}}
```
例如:
- `{{nodes.llm_call.outputs.response}}` - 引用 LLM 节点的响应
- `{{nodes.http_request.outputs.body}}` - 引用 HTTP 请求的响应体
### Q2:如何使用变量?
Workflow 支持三种变量类型:
1. **工作流变量**`{{variables.变量名}}`
2. **会话变量**`{{conversation_variables.变量名}}`
3. **消息上下文**`{{message.content}}``{{message.sender_id}}`
### Q3:条件分支如何写条件表达式?
支持以下运算符:
- 比较:`==`, `!=`, `>`, `<`, `>=`, `<=`
- 逻辑:`and`, `or`, `not`
- 包含:`in`
示例:
```python
# 字符串比较
"{{nodes.classifier.outputs.intent}}" == "purchase"
# 数值比较
{{nodes.extractor.outputs.amount}} > 1000
# 包含检查
"退款" in "{{message.content}}"
```
### Q4:如何处理错误?
1. **节点级重试**:在节点配置中设置重试次数
2. **全局错误处理**:在 Workflow 设置中配置错误处理策略
3. **条件分支**:使用条件节点检查上一节点的状态
### Q5:如何查看执行历史?
1. 进入 Workflow 详情页
2. 点击 **执行历史** 标签
3. 查看每次执行的状态、耗时、输入输出
### Q6Workflow 可以被多个 Bot 使用吗?
是的。一个 Workflow 可以被多个 Bot 绑定使用,但每个 Bot 只能绑定一个处理单元(Pipeline 或 Workflow)。
### Q7:如何复制现有的 Workflow
在 Workflow 列表页,点击工作流卡片右上角的菜单,选择"复制"即可创建副本。
### Q8:支持版本回滚吗?
支持。每次保存都会创建新版本。在 Workflow 详情页可以查看版本历史并回滚到指定版本。
---
## 最佳实践
### 1. 合理命名
- 为节点和 Workflow 使用描述性名称
- 使用统一的命名规范
### 2. 模块化设计
- 将复杂流程拆分为多个小 Workflow
- 使用"调用 Pipeline"节点复用现有流程
### 3. 错误处理
- 为关键节点设置重试机制
- 使用条件分支处理异常情况
- 添加日志记录便于排查问题
### 4. 测试先行
- 使用调试功能充分测试
- 准备多种测试场景
- 检查边界情况
### 5. 性能优化
- 避免不必要的节点
- 使用并行节点提高效率
- 合理设置超时时间
---
## 更多资源
- [开发者文档](../development/workflow-system.md)
- [设计文档](../../../plans/langbot-workflow-design.md)
- [API 文档](../service-api-openapi.json)
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# HTTP Bot Adapter — Reference Clients
> English | [中文](./README.zh.md)
Minimal, dependency-light clients for the LangBot **HTTP Bot** platform adapter.
They show the whole loop: signing a request, pushing a message, and receiving
multi-part replies on a callback endpoint.
Full guide: [docs.langbot.app — HTTP Bot](https://docs.langbot.app/en/usage/platforms/http-bot).
Machine-readable contract: [`docs/http-bot-openapi.json`](../../docs/http-bot-openapi.json).
## Files
| File | What it is |
|---|---|
| `playground.py` | **Interactive browser debug console** — a single-file web app you open in a browser to chat with a running `http_bot` bot and watch signing / 202 / callbacks live. Zero extra deps. |
| `client.py` | Python client + Flask callback receiver (`pip install flask requests`). |
| `client.ts` | TypeScript/Node 18+ client + callback receiver, **zero deps** (`npx tsx client.ts`). |
All three implement the identical HMAC-SHA256 scheme
(`sha256=hex(HMAC(secret, "{timestamp}." + body))`) — verified byte-for-byte
against the adapter.
## Interactive playground (recommended first run)
A self-contained web console: type a message in your browser, it is signed and
POSTed to a **running** `http_bot` bot, and the bot's replies stream back into
the page — with a debug panel showing the signature, the `202` ack, and each
callback's `sequence` / signature-verification.
```bash
# From the LangBot repo root, with the backend already running:
PUBLIC_IP=<your-host-ip> ./.venv/bin/python examples/http-bot/playground.py
# then open http://<your-host-ip>:8920/
```
On startup it reads the LangBot API key + the `http_bot` bot from
`data/langbot.db`, and configures that bot (inbound/outbound secret +
`callback_url`) to point back at itself via the LangBot API — the bot reloads
live, no restart needed. Requirements: an enabled `http_bot` bot bound to a
working pipeline, and port `8920` reachable from your browser.
Env knobs: `PUBLIC_IP` (default `127.0.0.1`), `PLAYGROUND_PORT` (default `8920`).
## Headless clients
```bash
# Python — Terminal 1: callback receiver (your callback_url target)
python client.py serve --port 8900 --secret SHARED_SECRET
# Python — Terminal 2: push a message
python client.py push --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-1 --text "hello"
# blocking sync mode
python client.py sync --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-1 --text "hello"
# reset a session
python client.py reset --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-1
```
```bash
# TypeScript (Node 18+)
npx tsx client.ts serve 8900 SHARED_SECRET
npx tsx client.ts push https://your-langbot/bots/<BOT_UUID> SHARED_SECRET ticket-1 "hello"
```
When the bot replies, the receiver prints each part with its `sequence` and an
`[FINAL]` marker on the last one — that's the 1→M multi-reply model in action.
> The bot's `callback_url` must be reachable from LangBot. For local testing,
> expose your receiver with a tunnel (cloudflared / ngrok) and set that URL in
> the bot config.
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# HTTP Bot 适配器 —— 参考客户端
> [English](./README.md) | 中文
面向 LangBot **HTTP Bot** 平台适配器的极简、低依赖客户端示例。
它们完整展示了整条链路:对请求签名、推送一条消息、在回调端点接收
1→M 的多段回复。
完整指南:[docs.langbot.app —— HTTP Bot](https://docs.langbot.app/zh/usage/platforms/http-bot)。
机器可读的接口契约:[`docs/http-bot-openapi.json`](../../docs/http-bot-openapi.json)。
## 文件清单
| 文件 | 是什么 |
|---|---|
| `playground.py` | **浏览器交互式调试台** —— 单文件 Web 应用,在浏览器里和一个运行中的 `http_bot` bot 对话,实时观察签名 / 202 / 回调。零额外依赖。 |
| `client.py` | Python 客户端 + Flask 回调接收端(`pip install flask requests`)。 |
| `client.ts` | TypeScript/Node 18+ 客户端 + 回调接收端,**零依赖**(`npx tsx client.ts`)。 |
三者实现完全一致的 HMAC-SHA256 签名方案
(`sha256=hex(HMAC(secret, "{timestamp}." + body))`)—— 已与适配器逐字节比对验证。
## 交互式 playground(推荐先跑这个)
一个自包含的 Web 控制台:在浏览器里输入消息,它会被签名并 POST 给一个
**运行中**的 `http_bot` bot,bot 的回复会流式回到页面上 —— 调试面板会显示
签名、`202` 确认,以及每条回调的 `sequence` / 签名验证结果。
```bash
# 在 LangBot 仓库根目录、后端已启动的前提下:
PUBLIC_IP=<你的主机IP> ./.venv/bin/python examples/http-bot/playground.py
# 然后打开 http://<你的主机IP>:8920/
```
启动时它会从 `data/langbot.db` 读取 LangBot API key 和 `http_bot` bot,
并通过 LangBot API 把该 bot 配好(入站/出站密钥 + `callback_url`)指回自己 ——
bot 会热加载,无需重启。前提:有一个已启用、绑定了可用 pipeline 的
`http_bot` bot,且端口 `8920` 能从你的浏览器访问到。
可调环境变量:`PUBLIC_IP`(默认 `127.0.0.1`)、`PLAYGROUND_PORT`(默认 `8920`)。
## 无头客户端
```bash
# Python —— 终端 1:回调接收端(你的 callback_url 指向它)
python client.py serve --port 8900 --secret SHARED_SECRET
# Python —— 终端 2:推送一条消息
python client.py push --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-1 --text "hello"
# 阻塞式同步模式
python client.py sync --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-1 --text "hello"
# 重置一个会话
python client.py reset --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-1
```
```bash
# TypeScript(Node 18+)
npx tsx client.ts serve 8900 SHARED_SECRET
npx tsx client.ts push https://your-langbot/bots/<BOT_UUID> SHARED_SECRET ticket-1 "hello"
```
当 bot 回复时,接收端会逐条打印,带上各自的 `sequence`,并在最后一条标记
`[FINAL]` —— 这就是 1→M 多段回复模型的实际效果。
> bot 的 `callback_url` 必须能从 LangBot 访问到。本地测试时,可用隧道
> (cloudflared / ngrok)把你的接收端暴露出去,并把那个 URL 填进 bot 配置。
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#!/usr/bin/env python3
"""LangBot HTTP Bot adapter — reference client (Python).
Two things in one file:
1. ``push()`` / ``push_sync()`` send a message into a LangBot ``http_bot`` bot.
2. A tiny Flask callback receiver that verifies signatures and prints replies,
so you can watch N->1 aggregation and 1->M multi-reply working live.
Usage
-----
pip install flask requests
# Terminal 1 — start the callback receiver (this is your callback_url):
python client.py serve --port 8900 --secret SHARED_SECRET
# Terminal 2 — push a message (async; reply lands on the receiver):
python client.py push \
--url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET \
--session ticket-10293 \
--text "Export keeps failing on the dashboard."
# Or push and block for the collapsed reply (sync convenience mode):
python client.py sync --url https://your-langbot/bots/<BOT_UUID> \
--secret SHARED_SECRET --session ticket-10293 --text "hi"
The signing scheme is HMAC-SHA256 over ``"{timestamp}." + raw_body``; see
``sign()`` below it is intentionally tiny and easy to port.
"""
from __future__ import annotations
import argparse
import hashlib
import hmac
import json
import sys
import time
import uuid
HEADER_TIMESTAMP = 'X-LB-Timestamp'
HEADER_SIGNATURE = 'X-LB-Signature'
HEADER_IDEMPOTENCY = 'X-LB-Idempotency-Key'
REPLAY_WINDOW = 300
def sign(secret: str, body: bytes, timestamp: int | None = None) -> tuple[str, str]:
"""Return (timestamp, signature) for *body*."""
ts = str(timestamp if timestamp is not None else int(time.time()))
mac = hmac.new(secret.encode(), f'{ts}.'.encode() + body, hashlib.sha256)
return ts, 'sha256=' + mac.hexdigest()
def verify(secret: str, body: bytes, timestamp: str | None, signature: str | None) -> bool:
"""Verify an inbound signature (used by the callback receiver)."""
if not timestamp or not signature:
return False
try:
if abs(int(time.time()) - int(float(timestamp))) > REPLAY_WINDOW:
return False
except ValueError:
return False
_, expected = sign(secret, body, int(float(timestamp)))
return hmac.compare_digest(expected, signature)
def _post(url: str, secret: str, payload: dict, idempotency: bool = True):
import requests
body = json.dumps(payload, ensure_ascii=False).encode()
ts, sig = sign(secret, body)
headers = {
'Content-Type': 'application/json',
HEADER_TIMESTAMP: ts,
HEADER_SIGNATURE: sig,
}
if idempotency:
headers[HEADER_IDEMPOTENCY] = uuid.uuid4().hex
resp = requests.post(url, data=body, headers=headers, timeout=30)
print(f'-> {resp.status_code} {resp.text}')
return resp
def push(url: str, secret: str, session: str, text: str, session_type: str = 'person'):
"""Fire-and-collect: returns 202 immediately; reply arrives on your callback."""
payload = {
'session_id': session,
'session_type': session_type,
'message': [{'type': 'Plain', 'text': text}],
}
return _post(url.rstrip('/'), secret, payload)
def push_sync(url: str, secret: str, session: str, text: str, session_type: str = 'person'):
"""Blocking convenience: POST to /sync and get the collapsed reply back."""
payload = {
'session_id': session,
'session_type': session_type,
'message': [{'type': 'Plain', 'text': text}],
}
resp = _post(url.rstrip('/') + '/sync', secret, payload, idempotency=False)
return resp
def reset(url: str, secret: str, session: str, session_type: str = 'person'):
"""Reset a session's conversation (next message starts fresh)."""
payload = {'session_id': session, 'session_type': session_type}
return _post(url.rstrip('/') + '/reset', secret, payload, idempotency=False)
def serve(port: int, secret: str):
"""Run a callback receiver that verifies signatures and prints replies."""
from flask import Flask, request
app = Flask(__name__)
@app.route('/', methods=['POST'])
def recv():
raw = request.get_data()
ok = verify(secret, raw, request.headers.get(HEADER_TIMESTAMP), request.headers.get(HEADER_SIGNATURE))
if not ok:
print('!! signature verification FAILED — rejecting')
return {'error': 'bad signature'}, 401
data = json.loads(raw)
text_parts = [c.get('text', '') for c in data.get('message', []) if c.get('type') == 'Plain']
marker = 'FINAL' if data.get('is_final') else 'part '
print(
f'[{marker}] session={data["session_id"]} seq={data["sequence"]} '
f'reply_to={data.get("reply_to")}: {" ".join(text_parts)}'
)
return {'ok': True}
print(f'callback receiver listening on http://0.0.0.0:{port}/ (Ctrl-C to stop)')
app.run(host='0.0.0.0', port=port)
def main(argv=None):
p = argparse.ArgumentParser(description='LangBot HTTP Bot reference client')
sub = p.add_subparsers(dest='cmd', required=True)
sp = sub.add_parser('serve', help='run the callback receiver')
sp.add_argument('--port', type=int, default=8900)
sp.add_argument('--secret', required=True)
for name in ('push', 'sync', 'reset'):
c = sub.add_parser(name)
c.add_argument('--url', required=True, help='https://host/bots/<BOT_UUID>')
c.add_argument('--secret', required=True)
c.add_argument('--session', required=True)
c.add_argument('--session-type', default='person', choices=['person', 'group'])
if name != 'reset':
c.add_argument('--text', required=True)
args = p.parse_args(argv)
if args.cmd == 'serve':
serve(args.port, args.secret)
elif args.cmd == 'push':
push(args.url, args.secret, args.session, args.text, args.session_type)
elif args.cmd == 'sync':
push_sync(args.url, args.secret, args.session, args.text, args.session_type)
elif args.cmd == 'reset':
reset(args.url, args.secret, args.session, args.session_type)
if __name__ == '__main__':
sys.exit(main())
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/**
* LangBot HTTP Bot adapter reference client (TypeScript / Node 18+).
*
* Zero runtime dependencies (uses global `fetch`, `crypto`, and `http`).
*
* - `push()` : fire-and-collect; reply lands on your callback URL.
* - `pushSync()` : POST /sync and await the collapsed reply.
* - `reset()` : reset a session's conversation.
* - `startReceiver()` : a callback server that verifies signatures and logs
* replies, so you can watch N->1 and 1->M live.
*
* Run the demos:
* npx tsx client.ts serve 8900 SHARED_SECRET
* npx tsx client.ts push https://host/bots/<UUID> SHARED_SECRET ticket-1 "hello"
* npx tsx client.ts sync https://host/bots/<UUID> SHARED_SECRET ticket-1 "hello"
* npx tsx client.ts reset https://host/bots/<UUID> SHARED_SECRET ticket-1
*/
import { createHmac, randomUUID, timingSafeEqual } from 'node:crypto';
import { createServer } from 'node:http';
const HEADER_TIMESTAMP = 'X-LB-Timestamp';
const HEADER_SIGNATURE = 'X-LB-Signature';
const HEADER_IDEMPOTENCY = 'X-LB-Idempotency-Key';
const REPLAY_WINDOW = 300;
/** Compute the `sha256=<hex>` signature over `"{ts}." + body`. */
export function sign(secret: string, body: Buffer | string, timestamp?: number): [string, string] {
const ts = String(timestamp ?? Math.floor(Date.now() / 1000));
const buf = typeof body === 'string' ? Buffer.from(body) : body;
const mac = createHmac('sha256', secret).update(Buffer.concat([Buffer.from(`${ts}.`), buf])).digest('hex');
return [ts, `sha256=${mac}`];
}
/** Verify an inbound signature (used by the callback receiver). */
export function verify(secret: string, body: Buffer, timestamp?: string, signature?: string): boolean {
if (!timestamp || !signature) return false;
if (Math.abs(Math.floor(Date.now() / 1000) - Number(timestamp)) > REPLAY_WINDOW) return false;
const [, expected] = sign(secret, body, Number(timestamp));
const a = Buffer.from(expected);
const b = Buffer.from(signature);
return a.length === b.length && timingSafeEqual(a, b);
}
interface Segment { type: string; text?: string; url?: string; [k: string]: unknown }
async function post(url: string, secret: string, payload: object, idempotency = true) {
const body = Buffer.from(JSON.stringify(payload));
const [ts, sig] = sign(secret, body);
const headers: Record<string, string> = {
'Content-Type': 'application/json',
[HEADER_TIMESTAMP]: ts,
[HEADER_SIGNATURE]: sig,
};
if (idempotency) headers[HEADER_IDEMPOTENCY] = randomUUID();
const resp = await fetch(url, { method: 'POST', headers, body });
const text = await resp.text();
console.log(`-> ${resp.status} ${text}`);
return { status: resp.status, text };
}
/** Fire-and-collect: 202 now, reply later on your callback URL. */
export function push(url: string, secret: string, session: string, text: string, sessionType = 'person') {
return post(url.replace(/\/$/, ''), secret, {
session_id: session,
session_type: sessionType,
message: [{ type: 'Plain', text }] as Segment[],
});
}
/** Blocking convenience: POST /sync, get the collapsed reply. */
export function pushSync(url: string, secret: string, session: string, text: string, sessionType = 'person') {
return post(`${url.replace(/\/$/, '')}/sync`, secret, {
session_id: session,
session_type: sessionType,
message: [{ type: 'Plain', text }] as Segment[],
}, false);
}
/** Reset a session's conversation. */
export function reset(url: string, secret: string, session: string, sessionType = 'person') {
return post(`${url.replace(/\/$/, '')}/reset`, secret, { session_id: session, session_type: sessionType }, false);
}
/** Run a callback receiver that verifies signatures and prints replies. */
export function startReceiver(port: number, secret: string) {
const server = createServer((req, res) => {
if (req.method !== 'POST') { res.writeHead(405).end(); return; }
const chunks: Buffer[] = [];
req.on('data', (c) => chunks.push(c));
req.on('end', () => {
const raw = Buffer.concat(chunks);
const ok = verify(secret, raw, req.headers[HEADER_TIMESTAMP.toLowerCase()] as string,
req.headers[HEADER_SIGNATURE.toLowerCase()] as string);
if (!ok) {
console.log('!! signature verification FAILED — rejecting');
res.writeHead(401, { 'Content-Type': 'application/json' }).end(JSON.stringify({ error: 'bad signature' }));
return;
}
const data = JSON.parse(raw.toString());
const parts = (data.message as Segment[]).filter((c) => c.type === 'Plain').map((c) => c.text).join(' ');
const marker = data.is_final ? 'FINAL' : 'part ';
console.log(`[${marker}] session=${data.session_id} seq=${data.sequence} reply_to=${data.reply_to}: ${parts}`);
res.writeHead(200, { 'Content-Type': 'application/json' }).end(JSON.stringify({ ok: true }));
});
});
server.listen(port, () => console.log(`callback receiver listening on http://0.0.0.0:${port}/ (Ctrl-C to stop)`));
}
// --- CLI ---
const [cmd, ...rest] = process.argv.slice(2);
if (cmd === 'serve') {
startReceiver(Number(rest[0] ?? 8900), rest[1] ?? 'SHARED_SECRET');
} else if (cmd === 'push') {
push(rest[0], rest[1], rest[2], rest[3]);
} else if (cmd === 'sync') {
pushSync(rest[0], rest[1], rest[2], rest[3]);
} else if (cmd === 'reset') {
reset(rest[0], rest[1], rest[2]);
} else if (cmd) {
console.error(`unknown command: ${cmd}`);
process.exit(1);
}
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#!/usr/bin/env python3
"""LangBot HTTP Bot — interactive playground (public, browser-based).
This is a REAL end-to-end demo against the RUNNING LangBot instance on this
host. It is NOT a mock and NOT an in-process import: every message you type in
the browser is signed and POSTed to the live `http_bot` bot at
http://127.0.0.1:5300/bots/<uuid>, and the bot's replies come back to this
server's /callback endpoint over real HTTP, then stream to your browser via SSE.
What it does on startup:
1. Reads the LangBot API key + the http_bot bot from data/langbot.db.
2. Configures the bot via the LangBot API (PUT /api/v1/platform/bots/<uuid>):
sets inbound_secret + outbound_secret + callback_url to point back here.
(LangBot reloads the bot live no server restart needed.)
3. Serves a chat page on 0.0.0.0:<PORT> so you can open it from the internet.
Run: ./.venv/bin/python examples/http-bot/playground.py
Then open: http://<this-host-public-ip>:<PORT>/
"""
from __future__ import annotations
import asyncio
import json
import os
import sqlite3
import sys
REPO = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
sys.path.insert(0, os.path.join(REPO, 'src'))
from aiohttp import web # noqa: E402
import aiohttp # noqa: E402
from langbot.pkg.platform.sources import http_bot_signing as sg # noqa: E402
# ---- config -----------------------------------------------------------------
LANGBOT_BASE = 'http://127.0.0.1:5300'
DB_PATH = os.path.join(REPO, 'data', 'langbot.db')
PUBLIC_IP = os.environ.get('PUBLIC_IP', '127.0.0.1')
PORT = int(os.environ.get('PLAYGROUND_PORT', '8920'))
SECRET = 'playground-shared-secret'
# SSE subscribers: list of asyncio.Queue
subscribers: list[asyncio.Queue] = []
def db_lookup() -> tuple[str, str]:
"""Return (api_key, http_bot_uuid) from the LangBot DB."""
db = sqlite3.connect(DB_PATH)
db.row_factory = sqlite3.Row
api_key = db.execute('SELECT key FROM api_keys LIMIT 1').fetchone()['key']
bot = db.execute("SELECT uuid FROM bots WHERE adapter='http_bot' LIMIT 1").fetchone()
if not bot:
raise SystemExit('No http_bot bot found. Create one in the WebUI first.')
return api_key, bot['uuid']
async def configure_bot(api_key: str, bot_uuid: str, callback_url: str):
"""Point the live bot at this playground via the LangBot API.
update_bot() runs a raw SQL UPDATE with whatever keys we send, so we send a
MINIMAL payload: only adapter_config (built from scratch, not read back
the GET masks secrets). LangBot reloads + reruns the bot live.
"""
cfg = {
'inbound_secret': SECRET,
'outbound_secret': SECRET,
'callback_url': callback_url,
'signature_required': True,
'default_session_type': 'person',
'callback_timeout': 15,
'callback_max_retries': 3,
}
async with aiohttp.ClientSession() as s:
async with s.put(
f'{LANGBOT_BASE}/api/v1/platform/bots/{bot_uuid}',
headers={'Authorization': f'Bearer {api_key}', 'Content-Type': 'application/json'},
json={'adapter_config': cfg},
) as r:
txt = await r.text()
print(f'[configure] PUT adapter_config -> {r.status} {txt[:200]}')
return r.status < 400
async def broadcast(event: dict):
for q in list(subscribers):
try:
q.put_nowait(event)
except Exception:
pass
# ---- HTTP handlers ----------------------------------------------------------
async def index(request: web.Request):
return web.Response(text=PAGE, content_type='text/html')
async def send(request: web.Request):
"""Browser -> here -> signed POST -> live LangBot bot."""
body_in = await request.json()
session_id = body_in.get('session_id') or 'playground-1'
text = body_in.get('text', '')
bot_uuid = request.app['bot_uuid']
payload = {
'session_id': session_id,
'sender': {'id': 'browser-user', 'name': 'You'},
'message': [{'type': 'Plain', 'text': text}],
}
raw = json.dumps(payload, ensure_ascii=False).encode()
ts, sig = sg.sign(SECRET, raw)
url = f'{LANGBOT_BASE}/bots/{bot_uuid}'
# echo what we send to the browser timeline
await broadcast(
{'dir': 'out', 'kind': 'request', 'session_id': session_id, 'text': text, 'url': url, 'sig': sig[:24] + ''}
)
async with aiohttp.ClientSession() as s:
async with s.post(
url,
data=raw,
headers={
'Content-Type': 'application/json',
sg.HEADER_TIMESTAMP: ts,
sg.HEADER_SIGNATURE: sig,
},
) as r:
status = r.status
try:
jr = await r.json()
except Exception:
jr = {'raw': await r.text()}
await broadcast({'dir': 'in', 'kind': 'ack', 'status': status, 'data': jr})
return web.json_response({'status': status, 'data': jr})
async def callback(request: web.Request):
"""Live LangBot bot -> here. Verify signature, stream to browser."""
raw = await request.read()
ok, why = sg.verify(SECRET, raw, request.headers.get(sg.HEADER_TIMESTAMP), request.headers.get(sg.HEADER_SIGNATURE))
data = json.loads(raw)
text = ' '.join(c.get('text', '') for c in data.get('message', []) if c.get('type') == 'Plain')
await broadcast(
{
'dir': 'in',
'kind': 'reply',
'session_id': data.get('session_id'),
'sequence': data.get('sequence'),
'is_final': data.get('is_final'),
'sig_ok': ok,
'sig_why': why,
'text': text,
}
)
return web.json_response({'ok': True})
async def events(request: web.Request):
"""SSE stream to the browser."""
resp = web.StreamResponse(
headers={
'Content-Type': 'text/event-stream',
'Cache-Control': 'no-cache',
'Connection': 'keep-alive',
'Access-Control-Allow-Origin': '*',
}
)
await resp.prepare(request)
q: asyncio.Queue = asyncio.Queue()
subscribers.append(q)
try:
await resp.write(b': connected\n\n')
while True:
try:
ev = await asyncio.wait_for(q.get(), timeout=15)
await resp.write(f'data: {json.dumps(ev, ensure_ascii=False)}\n\n'.encode())
except asyncio.TimeoutError:
await resp.write(b': ping\n\n')
except (asyncio.CancelledError, ConnectionResetError):
pass
finally:
if q in subscribers:
subscribers.remove(q)
return resp
PAGE = r"""<!doctype html>
<html lang="zh"><head><meta charset="utf-8"/>
<meta name="viewport" content="width=device-width,initial-scale=1"/>
<title>LangBot HTTP Bot · 调试台</title>
<style>
:root{
--bg:#f7f8fa; --panel:#ffffff; --line:#e8eaed; --ink:#1f2329; --mut:#8a909a;
--brand:#2563eb; --brand-soft:#eef3ff; --ok:#16a34a; --bad:#dc2626; --code:#f3f4f6;
}
*{box-sizing:border-box}
html,body{height:100%}
body{margin:0;background:var(--bg);color:var(--ink);
font:14px/1.6 -apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"PingFang SC","Microsoft YaHei",sans-serif}
.top{height:52px;background:var(--panel);border-bottom:1px solid var(--line);
display:flex;align-items:center;gap:10px;padding:0 18px}
.logo{width:26px;height:26px;border-radius:7px;background:var(--brand);display:grid;place-items:center;color:#fff;font-weight:700;font-size:14px}
.top b{font-size:15px} .top .ver{font-size:12px;color:var(--mut)}
.dot{width:8px;height:8px;border-radius:50%;background:#cbd2dc;display:inline-block;margin-right:5px;vertical-align:middle}
.dot.on{background:var(--ok)} .dot.off{background:var(--bad)}
.conn{margin-left:auto;font-size:12px;color:var(--mut)}
.wrap{max-width:1080px;margin:0 auto;padding:18px;display:grid;grid-template-columns:1fr 360px;gap:16px}
@media(max-width:880px){.wrap{grid-template-columns:1fr}}
.card{background:var(--panel);border:1px solid var(--line);border-radius:12px;display:flex;flex-direction:column;min-height:0}
.card h3{margin:0;padding:12px 16px;font-size:13px;font-weight:600;color:#4b5563;border-bottom:1px solid var(--line);display:flex;align-items:center;gap:8px}
.chat{height:62vh}
.msgs{flex:1;overflow:auto;padding:16px;display:flex;flex-direction:column;gap:12px}
.row{display:flex;flex-direction:column;gap:4px;max-width:82%}
.row.me{align-self:flex-end;align-items:flex-end}
.row.bot{align-self:flex-start}
.bub{padding:9px 13px;border-radius:12px;white-space:pre-wrap;word-break:break-word}
.me .bub{background:var(--brand);color:#fff;border-bottom-right-radius:3px}
.bot .bub{background:#f1f3f6;color:var(--ink);border-bottom-left-radius:3px}
.meta{font-size:11px;color:var(--mut)}
.meta .ok{color:var(--ok)} .meta .bad{color:var(--bad)}
.sys{align-self:center;font-size:12px;color:var(--mut);background:#f1f3f6;border-radius:8px;padding:4px 12px}
.bar{display:flex;gap:8px;padding:12px;border-top:1px solid var(--line)}
.bar input{flex:1;border:1px solid var(--line);border-radius:9px;padding:10px 12px;font-size:14px;outline:none}
.bar input:focus{border-color:var(--brand);box-shadow:0 0 0 3px var(--brand-soft)}
.bar button{background:var(--brand);color:#fff;border:0;border-radius:9px;padding:0 18px;font-size:14px;font-weight:500;cursor:pointer}
.bar button:disabled{opacity:.5;cursor:default}
.side{height:62vh}
.kv{padding:12px 16px;border-bottom:1px solid var(--line);font-size:12px}
.kv .k{color:var(--mut)} .kv .v{color:var(--ink);word-break:break-all}
.kv code{background:var(--code);border-radius:5px;padding:1px 5px;font-size:11px}
.sessrow{display:flex;align-items:center;gap:8px;padding:10px 16px;border-bottom:1px solid var(--line);font-size:12px}
.sessrow input{flex:1;border:1px solid var(--line);border-radius:7px;padding:5px 8px;font-size:12px}
.sessrow button{border:1px solid var(--line);background:#fff;border-radius:7px;padding:5px 9px;font-size:12px;cursor:pointer;color:#4b5563}
.trace{flex:1;overflow:auto;padding:10px 12px;font:11px/1.55 ui-monospace,SFMono-Regular,Menlo,monospace}
.ev{padding:6px 8px;border-radius:7px;margin-bottom:6px;border:1px solid var(--line)}
.ev .t{font-weight:600;font-size:10px;letter-spacing:.3px;text-transform:uppercase}
.ev.out{background:#f5f8ff;border-color:#dbe6ff}.ev.out .t{color:var(--brand)}
.ev.ack{background:#f4f6f8}.ev.ack .t{color:#6b7280}
.ev.reply{background:#f1faf3;border-color:#cdeed6}.ev.reply .t{color:var(--ok)}
.ev pre{margin:3px 0 0;white-space:pre-wrap;word-break:break-all;color:#374151}
</style></head>
<body>
<div class="top">
<div class="logo">L</div>
<b>HTTP Bot 调试台</b><span class="ver">examples/http-bot</span>
<span class="conn"><span class="dot off" id="cdot"></span><span id="conn">连接中</span></span>
</div>
<div class="wrap">
<!-- chat -->
<div class="card chat">
<h3>对话 · 真实发往运行中的 http_bot</h3>
<div class="msgs" id="msgs"></div>
<div class="bar">
<input id="msg" placeholder="输入消息,回车发送…" autofocus/>
<button id="send">发送</button>
</div>
</div>
<!-- debug -->
<div class="card side">
<h3>调试信息</h3>
<div class="kv"><span class="k">入站地址</span><br><span class="v"><code id="endpoint">/bots/&lt;uuid&gt;</code></span></div>
<div class="kv"><span class="k">签名</span> <span class="v">HMAC-SHA256 · <code>X-LB-Signature</code></span></div>
<div class="sessrow">
<span class="k">会话</span>
<input id="sid" value="playground-1"/>
<button id="reset">新会话</button>
</div>
<div class="trace" id="trace"></div>
</div>
</div>
<script>
const $=s=>document.querySelector(s);
const msgs=$('#msgs'),trace=$('#trace'),inp=$('#msg'),btn=$('#send'),
conn=$('#conn'),cdot=$('#cdot'),sidIn=$('#sid');
function el(c){const d=document.createElement('div');d.className=c;return d}
function atBottom(n){n.scrollTop=n.scrollHeight}
function bubble(side,text,metaHtml){
const r=el('row '+side),b=el('bub');b.textContent=text;r.appendChild(b);
if(metaHtml){const m=el('meta');m.innerHTML=metaHtml;r.appendChild(m)}
msgs.appendChild(r);atBottom(msgs)}
function sys(t){const d=el('sys');d.textContent=t;msgs.appendChild(d);atBottom(msgs)}
function logEv(kind,title,obj){
const e=el('ev '+kind),t=el('t');t.textContent=title;e.appendChild(t);
if(obj!==undefined){const p=document.createElement('pre');
p.textContent=typeof obj==='string'?obj:JSON.stringify(obj,null,2);e.appendChild(p)}
trace.appendChild(e);atBottom(trace)}
const es=new EventSource('/events');
es.onopen=()=>{conn.textContent='SSE 已连接';cdot.className='dot on'};
es.onerror=()=>{conn.textContent='SSE 断开,重连…';cdot.className='dot off'};
es.onmessage=e=>{const ev=JSON.parse(e.data);
if(ev.kind==='request'){
if(ev.endpoint)$('#endpoint').textContent=ev.url||ev.endpoint;
logEv('out','出站 · 已签名 POST',{url:ev.url,session_id:ev.session_id,'X-LB-Signature':ev.sig});
}else if(ev.kind==='ack'){
const id=ev.data&&ev.data.data&&ev.data.data.accepted_message_id;
sys(`LangBot 已接收 · HTTP ${ev.status}`);
logEv('ack','入站确认 202',{status:ev.status,accepted_message_id:id||'-'});
}else if(ev.kind==='reply'){
const sig=ev.sig_ok?'<span class=ok>验签通过</span>':'<span class=bad>验签失败</span>';
bubble('bot',ev.text,`seq=${ev.sequence} · ${ev.is_final?'<b>FINAL</b>':'中间段'} · ${sig}`);
logEv('reply',`回调 · seq ${ev.sequence}${ev.is_final?' · FINAL':''}`,
{session_id:ev.session_id,sequence:ev.sequence,is_final:ev.is_final,sig_ok:ev.sig_ok,text:ev.text});
}};
async function send(){
const t=inp.value.trim();if(!t)return;inp.value='';btn.disabled=true;
bubble('me',t,'已签名 → POST /bots/&lt;uuid&gt;');
try{await fetch('/send',{method:'POST',headers:{'Content-Type':'application/json'},
body:JSON.stringify({session_id:sidIn.value.trim()||'playground-1',text:t})});}
catch(e){sys('发送失败:'+e)}
btn.disabled=false;inp.focus();}
btn.onclick=send;inp.addEventListener('keydown',e=>{if(e.key==='Enter')send()});
$('#reset').onclick=()=>{sidIn.value='playground-'+Math.random().toString(36).slice(2,7);
sys('已切换到新会话 '+sidIn.value);};
sys('调试台就绪 · 每条消息都会真实发往运行中的 http_bot,右侧可观察签名 / 202 / 回调全过程。');
</script>
</body></html>"""
async def main():
api_key, bot_uuid = db_lookup()
callback_url = f'http://{PUBLIC_IP}:{PORT}/callback'
print(f'[init] http_bot uuid = {bot_uuid}')
print(f'[init] callback_url = {callback_url}')
ok = await configure_bot(api_key, bot_uuid, callback_url)
if not ok:
print('[warn] bot config update failed; check the API key / payload shape')
app = web.Application()
app['bot_uuid'] = bot_uuid
app.router.add_get('/', index)
app.router.add_post('/send', send)
app.router.add_post('/callback', callback)
app.router.add_get('/events', events)
runner = web.AppRunner(app)
await runner.setup()
site = web.TCPSite(runner, '0.0.0.0', PORT)
await site.start()
print(f'\n ▶ 打开: http://{PUBLIC_IP}:{PORT}/\n')
while True:
await asyncio.sleep(3600)
if __name__ == '__main__':
asyncio.run(main())
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# Page Bot Adapter — Embed Demo
> English | [中文](./README.zh.md)
A single self-contained HTML page that demos the LangBot **Page Bot**
(`web_page_bot`) embeddable chat widget — the one you drop onto any website with
a single `<script>` tag.
Full guide: [docs.langbot.app — Page Bot](https://docs.langbot.app/en/usage/platforms/webpage).
## Files
| File | What it is |
|---|---|
| `index.html` | **Browser demo** — open it, point it at a running LangBot instance + a Page Bot you created, and it loads the live embed widget so you can chat with the bot exactly as a site visitor would. Zero deps, no build step. |
## How to use
1. In the LangBot WebUI, create a bot with the **Page Bot** (`页面机器人`)
adapter and bind it to a working pipeline. Copy its **bot UUID** from the
generated embed code.
2. Open `index.html` in a browser. Any of these work:
- double-click the file, or
- serve the folder: `python3 -m http.server 8930` then open
`http://localhost:8930/examples/web-page-bot/`.
3. Fill in:
- **LangBot base URL** — where your instance is reachable from the browser
(e.g. `http://localhost:5300`, or your public address).
- **Page Bot UUID** — from step 1.
- **Widget title** — optional, sets the `data-title` attribute.
4. Click **Load widget**. A floating chat bubble appears in the bottom-right
corner — click it and chat.
The page also renders the exact `<script>` snippet you'd paste into your own
site (before `</body>`), and updates it live as you edit the fields.
## What it demonstrates
- The embed contract: `<script data-title="…" src="<base>/api/v1/embed/<uuid>/widget.js"></script>`.
- `widget.js` is served by LangBot pre-configured for that bot UUID — title,
bubble icon, language and optional Cloudflare Turnstile protection all come
from the bot's config, no page changes needed.
- Messages travel over a WebSocket to the bot's bound pipeline; replies stream
back into the bubble.
> The widget loads `widget.js` from your LangBot instance, so the **base URL
> must be reachable from the browser** you open this page in. If LangBot runs on
> a server, use its public address instead of `localhost`.
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# 页面机器人适配器 —— 嵌入演示
> [English](./README.md) | 中文
一个自包含的单文件 HTML 页面,用于演示 LangBot **页面机器人**
(`web_page_bot`) 的可嵌入聊天组件 —— 也就是你用一行 `<script>` 标签就能放到任意
网站上的那个组件。
完整指南:[docs.langbot.app —— 页面机器人](https://docs.langbot.app/zh/usage/platforms/webpage)。
## 文件清单
| 文件 | 是什么 |
|---|---|
| `index.html` | **浏览器演示页** —— 打开它,填上一个运行中的 LangBot 实例地址 + 你创建的页面机器人,它就会加载真实的嵌入组件,让你像网站访客一样和机器人对话。零依赖,无需构建。 |
## 使用方法
1. 在 LangBot WebUI 中,用 **页面机器人**`web_page_bot`)适配器创建一个机器人,
并绑定一个可用的流水线。从生成的嵌入代码里复制它的 **机器人 UUID**
2. 在浏览器中打开 `index.html`,以下任一方式皆可:
- 直接双击该文件;或
- 起一个静态服务:`python3 -m http.server 8930`,然后打开
`http://localhost:8930/examples/web-page-bot/`
3. 填写:
- **LangBot base URL** —— 你的实例在该浏览器中可访问的地址
(例如 `http://localhost:5300`,或你的公网地址)。
- **页面机器人 UUID** —— 第 1 步里复制的。
- **组件标题** —— 可选,对应 `data-title` 属性。
4. 点击 **Load widget**。页面右下角会出现一个浮动聊天气泡 —— 点开即可对话。
页面还会实时渲染出你需要粘贴到自己网站(放在 `</body>` 前)的那段 `<script>`
代码,并随着你编辑输入框同步更新。
## 它演示了什么
- 嵌入契约:`<script data-title="…" src="<base>/api/v1/embed/<uuid>/widget.js"></script>`
- `widget.js` 由 LangBot 针对该机器人 UUID 预配置后下发 —— 标题、气泡图标、语言
以及可选的 Cloudflare Turnstile 防护,全部来自机器人配置,无需改动页面。
- 消息通过 WebSocket 发往机器人绑定的流水线,回复流式回到气泡中。
> 组件会从你的 LangBot 实例加载 `widget.js`,因此 **base URL 必须能从你打开本页
> 的浏览器访问到**。如果 LangBot 部署在服务器上,请用它的公网地址而非
> `localhost`
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>LangBot Page Bot · Embed Demo</title>
<style>
:root {
--bg: #f7f8fa; --panel: #ffffff; --line: #e8eaed; --ink: #1f2329;
--mut: #8a909a; --brand: #2563eb; --brand-soft: #eef3ff;
--ok: #16a34a; --bad: #dc2626; --code: #f3f4f6;
}
* { box-sizing: border-box; }
html, body { height: 100%; }
body {
margin: 0; background: var(--bg); color: var(--ink);
font: 14px/1.6 -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto,
"PingFang SC", "Microsoft YaHei", sans-serif;
}
.top {
height: 52px; background: var(--panel); border-bottom: 1px solid var(--line);
display: flex; align-items: center; gap: 10px; padding: 0 18px;
}
.logo {
width: 26px; height: 26px; border-radius: 7px; background: var(--brand);
display: grid; place-items: center; color: #fff; font-weight: 700; font-size: 14px;
}
.top b { font-size: 15px; }
.top .ver { font-size: 12px; color: var(--mut); }
.wrap { max-width: 760px; margin: 0 auto; padding: 28px 18px 80px; }
.hero h1 { margin: 8px 0 6px; font-size: 22px; }
.hero p { margin: 0 0 4px; color: var(--mut); }
.card {
background: var(--panel); border: 1px solid var(--line); border-radius: 12px;
padding: 20px; margin-top: 20px;
}
.card h3 {
margin: 0 0 14px; font-size: 14px; font-weight: 600; color: #4b5563;
display: flex; align-items: center; gap: 8px;
}
.card h3 .num {
width: 20px; height: 20px; border-radius: 50%; background: var(--brand-soft);
color: var(--brand); display: grid; place-items: center; font-size: 12px; font-weight: 700;
}
.field { margin-bottom: 14px; }
.field:last-child { margin-bottom: 0; }
.field label { display: block; font-size: 12px; color: var(--mut); margin-bottom: 5px; }
.field input {
width: 100%; border: 1px solid var(--line); border-radius: 9px;
padding: 10px 12px; font-size: 14px; outline: none; font-family: inherit;
}
.field input:focus { border-color: var(--brand); box-shadow: 0 0 0 3px var(--brand-soft); }
.hint { font-size: 12px; color: var(--mut); margin-top: 5px; }
.hint code { background: var(--code); border-radius: 5px; padding: 1px 5px; font-size: 11px; }
.actions { display: flex; gap: 10px; margin-top: 18px; align-items: center; }
button {
border: 0; border-radius: 9px; padding: 10px 18px; font-size: 14px;
font-weight: 500; cursor: pointer; font-family: inherit;
}
.btn-primary { background: var(--brand); color: #fff; }
.btn-primary:disabled { opacity: .5; cursor: default; }
.btn-ghost { background: #fff; border: 1px solid var(--line); color: #4b5563; }
.status { font-size: 13px; color: var(--mut); }
.status .ok { color: var(--ok); }
.status .bad { color: var(--bad); }
pre {
background: #0f172a; color: #e2e8f0; border-radius: 10px; padding: 14px 16px;
overflow: auto; font: 12px/1.6 ui-monospace, SFMono-Regular, Menlo, monospace;
margin: 0;
}
.snippet-row { position: relative; }
.snippet-row .copy {
position: absolute; top: 10px; right: 10px; background: rgba(255,255,255,.12);
color: #fff; border: 0; border-radius: 7px; padding: 5px 10px; font-size: 12px; cursor: pointer;
}
ul.steps { margin: 0; padding-left: 18px; color: #4b5563; }
ul.steps li { margin-bottom: 6px; }
</style>
</head>
<body>
<div class="top">
<div class="logo">L</div>
<b>Page Bot · Embed Demo</b>
<span class="ver">examples/web-page-bot</span>
</div>
<div class="wrap">
<div class="hero">
<h1>Try the LangBot Page Bot widget</h1>
<p>Point this page at a running LangBot instance and a <strong>Page Bot</strong> you created,</p>
<p>then load the live embed widget below to chat with it — exactly as your site visitors would.</p>
</div>
<div class="card">
<h3><span class="num">1</span> Connect your Page Bot</h3>
<div class="field">
<label for="base">LangBot base URL</label>
<input id="base" placeholder="http://localhost:5300" value="http://localhost:5300" />
<div class="hint">The address where your LangBot instance is reachable from this browser. No trailing slash.</div>
</div>
<div class="field">
<label for="uuid">Page Bot UUID</label>
<input id="uuid" placeholder="xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx" />
<div class="hint">Create a bot with the <code>Page Bot</code> adapter in the WebUI, then copy its UUID from the embed code.</div>
</div>
<div class="field">
<label for="title">Widget title (optional)</label>
<input id="title" placeholder="LangBot" value="LangBot" />
</div>
<div class="actions">
<button id="load" class="btn-primary">Load widget</button>
<button id="unload" class="btn-ghost">Remove widget</button>
<span class="status" id="status">Not loaded.</span>
</div>
</div>
<div class="card">
<h3><span class="num">2</span> The embed snippet</h3>
<p style="margin:0 0 12px;color:var(--mut)">This is exactly what you paste into your own site (before <code>&lt;/body&gt;</code>). It updates as you edit the fields above.</p>
<div class="snippet-row">
<button class="copy" id="copy">Copy</button>
<pre id="snippet">&lt;script data-title="LangBot" src="http://localhost:5300/api/v1/embed/&lt;bot-uuid&gt;/widget.js"&gt;&lt;/script&gt;</pre>
</div>
</div>
<div class="card">
<h3><span class="num">3</span> How it works</h3>
<ul class="steps">
<li>The <code>&lt;script&gt;</code> tag pulls <code>widget.js</code> from your LangBot instance, pre-configured for that bot UUID.</li>
<li>A floating chat bubble appears in the bottom-right corner of the page.</li>
<li>Messages travel over a WebSocket to the bot's bound pipeline; replies stream back into the bubble.</li>
<li>Title, bubble icon, language and optional Cloudflare Turnstile protection are all set in the bot's config — no page changes needed.</li>
</ul>
</div>
</div>
<script>
var $ = function (s) { return document.querySelector(s); };
var baseEl = $("#base"), uuidEl = $("#uuid"), titleEl = $("#title"),
statusEl = $("#status"), snippetEl = $("#snippet");
var WIDGET_ID = "langbot-embed-demo-script";
function clean(v) { return (v || "").trim().replace(/\/+$/, ""); }
function buildSrc() {
var base = clean(baseEl.value) || "http://localhost:5300";
var uuid = uuidEl.value.trim() || "<bot-uuid>";
return base + "/api/v1/embed/" + uuid + "/widget.js";
}
function refreshSnippet() {
var title = titleEl.value.trim() || "LangBot";
var src = buildSrc();
snippetEl.textContent =
'<script data-title="' + title + '" src="' + src + '"><\/script>';
}
function setStatus(html) { statusEl.innerHTML = html; }
function removeWidget() {
var old = document.getElementById(WIDGET_ID);
if (old) old.remove();
// The widget injects its own DOM (bubble + panel). Clear the common containers it creates.
document.querySelectorAll('[id^="langbot-"]').forEach(function (n) {
if (n.id !== WIDGET_ID) n.remove();
});
}
function loadWidget() {
var uuid = uuidEl.value.trim();
var uuidRe = /^[a-f0-9]{8}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{4}-[a-f0-9]{12}$/i;
if (!uuidRe.test(uuid)) {
setStatus('<span class="bad">Enter a valid bot UUID first.</span>');
return;
}
removeWidget();
var s = document.createElement("script");
s.id = WIDGET_ID;
s.setAttribute("data-title", titleEl.value.trim() || "LangBot");
s.src = buildSrc();
s.onload = function () {
setStatus('<span class="ok">Widget loaded — look bottom-right.</span>');
};
s.onerror = function () {
setStatus('<span class="bad">Failed to load widget.js — check the base URL and that the bot is enabled.</span>');
};
document.body.appendChild(s);
setStatus("Loading…");
}
$("#load").onclick = loadWidget;
$("#unload").onclick = function () {
removeWidget();
setStatus("Widget removed.");
};
$("#copy").onclick = function () {
navigator.clipboard.writeText(snippetEl.textContent).then(function () {
var b = $("#copy"); b.textContent = "Copied"; setTimeout(function () { b.textContent = "Copy"; }, 1200);
});
};
[baseEl, uuidEl, titleEl].forEach(function (el) { el.addEventListener("input", refreshSnippet); });
refreshSnippet();
</script>
</body>
</html>
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@@ -1,6 +1,6 @@
[project]
name = "langbot"
version = "4.10.2"
version = "4.10.4"
description = "Production-grade platform for building agentic IM bots"
readme = "README.md"
license-files = ["LICENSE"]
@@ -70,7 +70,7 @@ dependencies = [
"chromadb>=1.0.0,<2.0.0",
"qdrant-client (>=1.15.1,<2.0.0)",
"pyseekdb==1.1.0.post3",
"langbot-plugin==0.4.5",
"langbot-plugin @ file:///home/qinjunyan/code/projects/langbot/langbot-plugin-sdk",
"asyncpg>=0.30.0",
"line-bot-sdk>=3.19.0",
"matrix-nio>=0.25.2",
Regular → Executable
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@@ -42,6 +42,38 @@ MyPlugin/
Each component has a `.yaml` (metadata) and `.py` (implementation).
## README & i18n convention (enforced on the marketplace)
A plugin published to LangBot Space serves a localized README on its detail page.
The resolver (`langbot-space` `PluginService.GetPluginREADME`) works like this:
- **Root `README.md` MUST be in English.** It is the default and the fallback —
when no per-language README matches the viewer's locale, the page serves the
root `README.md`. A non-English root README makes the English/default view show
the wrong language.
- **All other languages live under `readme/README_{lang}.md`** — e.g.
`readme/README_zh_Hans.md`, `readme/README_ja_JP.md`. The 8 supported locales:
`en_US, zh_Hans, zh_Hant, ja_JP, th_TH, vi_VN, es_ES, ru_RU`.
- `manifest.yaml` `metadata.label` / `metadata.description` should carry the same
8-locale i18n set (`repository` must be a real, alive URL).
```
MyPlugin/
├── manifest.yaml
├── README.md # English (default + fallback) — REQUIRED, must be English
└── readme/
├── README_zh_Hans.md
├── README_zh_Hant.md
├── README_ja_JP.md
├── README_th_TH.md
├── README_vi_VN.md
├── README_es_ES.md
└── README_ru_RU.md
```
`manifest.yaml` (incl. `repository`) is the source of truth — the marketplace
syncs from it, so edit the package and re-publish rather than patching live data.
## Critical SDK Pitfalls
### 1. MessageChain is a RootModel — iterate directly
@@ -439,7 +471,7 @@ async def on_msg(event_context: context.EventContext):
if isinstance(component, platform_message.Plain):
text_parts.append(component.text)
text = "".join(text_parts).strip()
if should_handle(text):
event_context.prevent_default()
event_context.prevent_postorder()
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@@ -1,3 +1,5 @@
"""LangBot - Production-grade platform for building agentic IM bots"""
__version__ = '4.10.2'
from importlib.metadata import version
__version__ = version('langbot')
@@ -1,6 +1,9 @@
from __future__ import annotations
from langbot.pkg.utils import constants
from .. import group
from .box_visibility import should_hide_box_runtime_status
@group.group_class('box', '/api/v1/box')
@@ -9,6 +12,7 @@ class BoxRouterGroup(group.RouterGroup):
@self.route('/status', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _() -> str:
status = await self.ap.box_service.get_status()
status['hidden'] = should_hide_box_runtime_status(constants.edition, status.get('enabled'))
return self.success(data=status)
@self.route('/sessions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
@@ -0,0 +1,5 @@
from __future__ import annotations
def should_hide_box_runtime_status(edition: str, box_enabled: bool | None) -> bool:
return edition == 'cloud' and box_enabled is False
@@ -62,16 +62,24 @@ class EmbedRouterGroup(group.RouterGroup):
"""Resolve *bot_uuid* to ``(runtime_bot, pipeline_uuid)``.
Returns ``(None, None)`` when the bot does not exist, is not a
``web_page_bot``, is disabled, or has no pipeline bound.
``web_page_bot``, is disabled, or has no pipeline/workflow bound.
"""
for bot in self.ap.platform_mgr.bots:
if (
bot.bot_entity.uuid == bot_uuid
and bot.bot_entity.adapter == 'web_page_bot'
and bot.bot_entity.enable
and bot.bot_entity.use_pipeline_uuid
):
return bot, bot.bot_entity.use_pipeline_uuid
# Check for workflow binding first
binding_type = getattr(bot.bot_entity, 'binding_type', 'pipeline') or 'pipeline'
binding_uuid = getattr(bot.bot_entity, 'binding_uuid', None)
if binding_type == 'workflow' and binding_uuid:
# For workflow binding, return workflow UUID
return bot, binding_uuid
elif bot.bot_entity.use_pipeline_uuid:
# For pipeline binding, return pipeline UUID
return bot, bot.bot_entity.use_pipeline_uuid
return None, None
def _get_bot_config(self, bot_uuid: str) -> dict:
@@ -86,6 +86,10 @@ class PipelinesRouterGroup(group.RouterGroup):
'available_plugins': plugins,
'bound_mcp_servers': extensions_prefs.get('mcp_servers', []),
'available_mcp_servers': mcp_servers,
'bound_mcp_resources': extensions_prefs.get('mcp_resources', []),
'mcp_resource_agent_read_enabled': extensions_prefs.get(
'mcp_resource_agent_read_enabled', True
),
'bound_skills': extensions_prefs.get('skills', []),
'available_skills': available_skills,
}
@@ -99,6 +103,8 @@ class PipelinesRouterGroup(group.RouterGroup):
bound_plugins = json_data.get('bound_plugins', [])
bound_mcp_servers = json_data.get('bound_mcp_servers', [])
bound_skills = json_data.get('bound_skills', [])
bound_mcp_resources = json_data.get('bound_mcp_resources')
mcp_resource_agent_read_enabled = json_data.get('mcp_resource_agent_read_enabled')
await self.ap.pipeline_service.update_pipeline_extensions(
pipeline_uuid,
@@ -108,6 +114,8 @@ class PipelinesRouterGroup(group.RouterGroup):
enable_all_mcp_servers,
bound_skills=bound_skills,
enable_all_skills=enable_all_skills,
bound_mcp_resources=bound_mcp_resources,
mcp_resource_agent_read_enabled=mcp_resource_agent_read_enabled,
)
return self.success()
@@ -18,7 +18,6 @@ class BotsRouterGroup(group.RouterGroup):
@self.route('/<bot_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(bot_uuid: str) -> str:
if quart.request.method == 'GET':
# 返回运行时信息,包括webhook地址等
bot = await self.ap.bot_service.get_runtime_bot_info(bot_uuid)
if bot is None:
return self.http_status(404, -1, 'bot not found')
@@ -37,30 +36,21 @@ class BotsRouterGroup(group.RouterGroup):
from_index = json_data.get('from_index', -1)
max_count = json_data.get('max_count', 10)
logs, total_count = await self.ap.bot_service.list_event_logs(bot_uuid, from_index, max_count)
return self.success(
data={
'logs': logs,
'total_count': total_count,
}
)
return self.success(data={'logs': logs, 'total_count': total_count})
@self.route('/<bot_uuid>/send_message', methods=['POST'], auth_type=group.AuthType.API_KEY)
async def _(bot_uuid: str) -> str:
"""Send message to a specific target via bot"""
json_data = await quart.request.json
target_type = json_data.get('target_type')
target_id = json_data.get('target_id')
message_chain_data = json_data.get('message_chain')
# Validate required fields
if not target_type:
return self.http_status(400, -1, 'target_type is required')
if not target_id:
return self.http_status(400, -1, 'target_id is required')
if not message_chain_data:
return self.http_status(400, -1, 'message_chain is required')
# Validate target_type
if target_type not in ['person', 'group']:
return self.http_status(400, -1, 'target_type must be either "person" or "group"')
@@ -72,3 +62,29 @@ class BotsRouterGroup(group.RouterGroup):
traceback.print_exc()
return self.http_status(500, -1, f'Failed to send message: {str(e)}')
# ============ Bot Admins ============
@self.route('/<bot_uuid>/admins', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(bot_uuid: str) -> str:
if quart.request.method == 'GET':
admins = await self.ap.bot_service.get_bot_admins(bot_uuid)
return self.success(data={'admins': admins})
elif quart.request.method == 'POST':
json_data = await quart.request.json
launcher_type = json_data.get('launcher_type', '').strip()
launcher_id = str(json_data.get('launcher_id', '')).strip()
if not launcher_type or not launcher_id:
return self.http_status(400, -1, 'launcher_type and launcher_id are required')
try:
admin_id = await self.ap.bot_service.add_bot_admin(bot_uuid, launcher_type, launcher_id)
return self.success(data={'id': admin_id})
except Exception as e:
return self.http_status(409, -1, str(e))
@self.route(
'/<bot_uuid>/admins/<int:admin_id>', methods=['DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
)
async def _(bot_uuid: str, admin_id: int) -> str:
await self.ap.bot_service.delete_bot_admin(bot_uuid, admin_id)
return self.success()
@@ -2,6 +2,7 @@ from __future__ import annotations
import quart
import traceback
from urllib.parse import unquote
from ... import group
@@ -66,3 +67,50 @@ class MCPRouterGroup(group.RouterGroup):
server_data = await quart.request.json
task_id = await self.ap.mcp_service.test_mcp_server(server_name=server_name, server_data=server_data)
return self.success(data={'task_id': task_id})
@self.route('/servers/<server_name>/resources', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _(server_name: str) -> str:
"""Get resources from an MCP server"""
server_name = unquote(server_name)
try:
resources = await self.ap.mcp_service.get_mcp_server_resources(server_name)
templates = await self.ap.mcp_service.get_mcp_server_resource_templates(server_name)
runtime_info = await self.ap.mcp_service.get_runtime_info(server_name)
return self.success(
data={
'resources': resources,
'resource_templates': templates,
'resource_capabilities': (runtime_info or {}).get('resource_capabilities', {}),
}
)
except Exception as e:
return self.http_status(500, -1, f'Failed to get resources: {str(e)}')
@self.route('/servers/<server_name>/resource-templates', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _(server_name: str) -> str:
"""Get resource templates from an MCP server"""
server_name = unquote(server_name)
try:
templates = await self.ap.mcp_service.get_mcp_server_resource_templates(server_name)
return self.success(data={'resource_templates': templates})
except Exception as e:
return self.http_status(500, -1, f'Failed to get resource templates: {str(e)}')
@self.route('/servers/<server_name>/resources/read', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
async def _(server_name: str) -> str:
"""Read a resource from an MCP server"""
server_name = unquote(server_name)
data = await quart.request.json
uri = data.get('uri')
if not uri:
return self.http_status(400, -1, 'URI is required')
try:
envelope = await self.ap.mcp_service.read_mcp_server_resource_envelope(
server_name,
uri,
max_bytes=data.get('max_bytes'),
include_blob=bool(data.get('include_blob', False)),
)
return self.success(data=envelope)
except Exception as e:
return self.http_status(500, -1, f'Failed to read resource: {str(e)}')
@@ -1,5 +1,7 @@
from __future__ import annotations
import quart
from ... import group
@@ -9,25 +11,41 @@ class ToolsRouterGroup(group.RouterGroup):
@self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _() -> str:
"""获取所有可用工具列表"""
tools = await self.ap.tool_mgr.get_all_tools()
pipeline_uuid = quart.request.args.get('pipeline_uuid') or quart.request.args.get('pipeline_id')
bound_plugins: list[str] | None = None
bound_mcp_servers: list[str] | None = None
tool_list = []
for tool in tools:
tool_list.append(
{
'name': tool.name,
'description': tool.description,
'human_desc': tool.human_desc,
'parameters': tool.parameters,
}
)
if pipeline_uuid:
pipeline = await self.ap.pipeline_service.get_pipeline(pipeline_uuid)
if pipeline is None:
return self.http_status(404, -1, 'pipeline not found')
return self.success(data={'tools': tool_list})
extensions_prefs = pipeline.get('extensions_preferences', {}) or {}
if not extensions_prefs.get('enable_all_plugins', True):
bound_plugins = [
f'{plugin.get("author", "")}/{plugin.get("name", "")}'
for plugin in extensions_prefs.get('plugins', [])
if isinstance(plugin, dict) and plugin.get('name')
]
if not extensions_prefs.get('enable_all_mcp_servers', True):
bound_mcp_servers = [
server for server in (extensions_prefs.get('mcp_servers', []) or []) if isinstance(server, str)
]
return self.success(
data={
'tools': await self.ap.tool_mgr.get_tool_catalog(
bound_plugins,
bound_mcp_servers,
include_skill_authoring=True,
)
}
)
@self.route('/<tool_name>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN)
async def _(tool_name: str) -> str:
"""获取特定工具详情"""
tools = await self.ap.tool_mgr.get_all_tools()
tools = await self.ap.tool_mgr.get_all_tools(include_skill_authoring=True)
for tool in tools:
if tool.name == tool_name:
@@ -1,3 +1,5 @@
import base64
import quart
from .. import group
@@ -30,6 +32,50 @@ class SurveyRouterGroup(group.RouterGroup):
return self.fail(2, 'Failed to submit response')
return self.fail(3, 'Survey not available')
@self.route('/feedback', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
async def _feedback(user_email: str) -> str:
"""Submit on-demand user feedback from the sidebar."""
json_data = await quart.request.get_json(silent=True) or {}
content = str(json_data.get('content', '')).strip()
attachments = json_data.get('attachments', [])
if not content:
return self.fail(1, 'content required')
if len(content) > 5000:
return self.fail(2, 'content too long')
if not isinstance(attachments, list):
return self.fail(3, 'attachments must be an array')
if len(attachments) > 3:
return self.fail(4, 'too many attachments')
normalized_attachments = []
for item in attachments:
if not isinstance(item, dict):
continue
data_url = str(item.get('data_url', ''))
mime_type = str(item.get('mime_type', ''))[:128]
name = str(item.get('name', ''))[:255]
if not data_url.startswith('data:image/'):
continue
try:
payload = data_url.split(',', 1)[1]
if len(base64.b64decode(payload, validate=True)) > 1024 * 1024:
return self.fail(5, 'attachment too large')
except Exception:
return self.fail(5, 'attachment too large')
normalized_attachments.append({'name': name, 'mime_type': mime_type, 'data_url': data_url})
if self.ap.survey:
ok = await self.ap.survey.submit_feedback(
content=content,
attachments=normalized_attachments,
user_email=user_email,
)
if ok:
return self.success()
return self.fail(6, 'Failed to submit feedback')
return self.fail(7, 'Survey not available')
@self.route('/dismiss', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
async def _dismiss() -> str:
"""Dismiss survey."""
@@ -195,6 +195,13 @@ class UserRouterGroup(group.RouterGroup):
@self.route('/set-password', methods=['POST'], auth_type=group.AuthType.USER_TOKEN)
async def _(user_email: str) -> str:
"""Set password for Space account (first time) or change password"""
# Check if modifying login info is allowed
allow_modify_login_info = self.ap.instance_config.data.get('system', {}).get(
'allow_modify_login_info', True
)
if not allow_modify_login_info:
return self.http_status(403, -1, 'Modifying login info is disabled')
json_data = await quart.request.json
new_password = json_data.get('new_password')
current_password = json_data.get('current_password')
@@ -0,0 +1,5 @@
# Workflow router group
from .workflows import WorkflowsRouterGroup, ExecutionsRouterGroup
from .websocket_chat import WorkflowWebSocketChatRouterGroup
__all__ = ['WorkflowsRouterGroup', 'ExecutionsRouterGroup', 'WorkflowWebSocketChatRouterGroup']
@@ -0,0 +1,260 @@
"""Workflow WebSocket聊天路由 - 支持工作流调试的双向实时通信"""
import asyncio
import datetime
import json
import logging
import quart
from ... import group
from ......platform.sources.websocket_manager import ws_connection_manager
logger = logging.getLogger(__name__)
@group.group_class('workflow_websocket_chat', '/api/v1/workflows/<workflow_uuid>/ws')
class WorkflowWebSocketChatRouterGroup(group.RouterGroup):
async def initialize(self) -> None:
@self.quart_app.websocket(self.path + '/connect')
async def workflow_websocket_connect(workflow_uuid: str):
"""
建立工作流WebSocket连接
URL参数:
- workflow_uuid: 工作流UUID
- session_type: 会话类型 (person/group)
"""
try:
session_type = quart.websocket.args.get('session_type', 'person')
logger.info(
'Workflow WebSocket connect request received',
extra={
'workflow_uuid': workflow_uuid,
'session_type': session_type,
'path': quart.websocket.path,
'query_string': quart.websocket.query_string.decode('utf-8', errors='ignore'),
'remote_addr': getattr(quart.websocket, 'remote_addr', None),
'user_agent': quart.websocket.headers.get('User-Agent', ''),
'host': quart.websocket.headers.get('Host', ''),
'origin': quart.websocket.headers.get('Origin', ''),
},
)
if session_type not in ['person', 'group']:
await quart.websocket.send(
json.dumps({'type': 'error', 'message': 'session_type must be person or group'})
)
return
websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
if not websocket_adapter:
logger.warning(
'Workflow WebSocket adapter missing',
extra={
'workflow_uuid': workflow_uuid,
'session_type': session_type,
},
)
await quart.websocket.send(json.dumps({'type': 'error', 'message': 'WebSocket adapter not found'}))
return
connection = await ws_connection_manager.add_connection(
websocket=quart.websocket._get_current_object(),
pipeline_uuid=workflow_uuid,
session_type=session_type,
metadata={'user_agent': quart.websocket.headers.get('User-Agent', ''), 'is_workflow': True},
)
await quart.websocket.send(
json.dumps(
{
'type': 'connected',
'connection_id': connection.connection_id,
'workflow_uuid': workflow_uuid,
'session_type': session_type,
'timestamp': connection.created_at.isoformat(),
}
)
)
logger.debug(
f'Workflow WebSocket connection established: {connection.connection_id} '
f'(workflow={workflow_uuid}, session_type={session_type})'
)
receive_task = asyncio.create_task(self._handle_receive(connection, websocket_adapter))
send_task = asyncio.create_task(self._handle_send(connection))
try:
await asyncio.gather(receive_task, send_task)
except Exception as e:
logger.error(f'Workflow WebSocket task execution error: {e}')
finally:
await ws_connection_manager.remove_connection(connection.connection_id)
logger.debug(f'Workflow WebSocket connection cleaned: {connection.connection_id}')
except Exception as e:
logger.error(
'Workflow WebSocket connection error',
exc_info=True,
extra={
'workflow_uuid': workflow_uuid,
'session_type': quart.websocket.args.get('session_type', 'person'),
'path': quart.websocket.path,
'query_string': quart.websocket.query_string.decode('utf-8', errors='ignore'),
'remote_addr': getattr(quart.websocket, 'remote_addr', None),
},
)
try:
await quart.websocket.send(json.dumps({'type': 'error', 'message': str(e)}))
except Exception as send_error:
logger.debug(
'Failed to send error message to workflow websocket client',
exc_info=True,
extra={
'workflow_uuid': workflow_uuid,
'send_error': str(send_error),
},
)
@self.route('/messages/<session_type>', methods=['GET'])
async def get_messages(workflow_uuid: str, session_type: str) -> str:
"""获取工作流消息历史"""
try:
if session_type not in ['person', 'group']:
return self.http_status(400, -1, 'session_type must be person or group')
websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
if not websocket_adapter:
return self.http_status(404, -1, 'WebSocket adapter not found')
messages = websocket_adapter.get_websocket_messages(workflow_uuid, session_type)
return self.success(data={'messages': messages})
except Exception as e:
return self.http_status(500, -1, f'Internal server error: {str(e)}')
@self.route('/reset/<session_type>', methods=['POST'])
async def reset_session(workflow_uuid: str, session_type: str) -> str:
"""重置工作流会话"""
try:
if session_type not in ['person', 'group']:
return self.http_status(400, -1, 'session_type must be person or group')
websocket_adapter = self.ap.platform_mgr.websocket_proxy_bot.adapter
if not websocket_adapter:
return self.http_status(404, -1, 'WebSocket adapter not found')
websocket_adapter.reset_session(workflow_uuid, session_type)
return self.success(data={'message': 'Session reset successfully'})
except Exception as e:
return self.http_status(500, -1, f'Internal server error: {str(e)}')
@self.route('/connections', methods=['GET'])
async def get_connections(workflow_uuid: str) -> str:
"""获取当前工作流连接统计"""
try:
stats = ws_connection_manager.get_stats()
connections = await ws_connection_manager.get_connections_by_pipeline(workflow_uuid)
return self.success(
data={
'stats': stats,
'connections': [
{
'connection_id': conn.connection_id,
'session_type': conn.session_type,
'created_at': conn.created_at.isoformat(),
'last_active': conn.last_active.isoformat(),
'is_active': conn.is_active,
}
for conn in connections
],
}
)
except Exception as e:
return self.http_status(500, -1, f'Internal server error: {str(e)}')
@self.route('/broadcast', methods=['POST'])
async def broadcast_message(workflow_uuid: str) -> str:
"""向所有工作流连接广播消息"""
try:
data = await quart.request.get_json()
message = data.get('message')
if not message:
return self.http_status(400, -1, 'message is required')
broadcast_data = {
'type': 'broadcast',
'message': message,
'timestamp': datetime.datetime.now().isoformat(),
}
await ws_connection_manager.broadcast_to_pipeline(workflow_uuid, broadcast_data)
return self.success(data={'message': 'Broadcast sent successfully'})
except Exception as e:
return self.http_status(500, -1, f'Internal server error: {str(e)}')
async def _handle_receive(self, connection, websocket_adapter):
"""处理接收消息的任务"""
try:
while connection.is_active:
message = await quart.websocket.receive()
await ws_connection_manager.update_activity(connection.connection_id)
try:
data = json.loads(message)
message_type = data.get('type', 'message')
if message_type == 'ping':
await connection.send_queue.put(
{'type': 'pong', 'timestamp': datetime.datetime.now().isoformat()}
)
elif message_type == 'message':
logger.debug(f'收到工作流消息: {data} from {connection.connection_id}')
await websocket_adapter.handle_websocket_message(connection, data)
elif message_type == 'disconnect':
logger.debug(f'Client disconnected: {connection.connection_id}')
break
else:
logger.warning(f'Unknown message type: {message_type}')
except json.JSONDecodeError:
logger.error(f'Invalid JSON message: {message}')
await connection.send_queue.put({'type': 'error', 'message': 'Invalid JSON format'})
except Exception as e:
logger.error(f'Receive message error: {e}', exc_info=True)
finally:
connection.is_active = False
async def _handle_send(self, connection):
"""处理发送消息的任务"""
try:
while connection.is_active:
try:
message = await asyncio.wait_for(connection.send_queue.get(), timeout=1.0)
await quart.websocket.send(json.dumps(message))
except asyncio.TimeoutError:
continue
except Exception as e:
logger.error(f'Send message error: {e}', exc_info=True)
finally:
connection.is_active = False
@@ -0,0 +1,484 @@
from __future__ import annotations
import quart
from ... import group
from ....service.workflow import WorkflowExecutionFailedError
@group.group_class('workflows', '/api/v1/workflows')
class WorkflowsRouterGroup(group.RouterGroup):
"""Workflow API router group"""
async def initialize(self) -> None:
# Workflow CRUD
@self.route('', methods=['GET', 'POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _() -> str:
if quart.request.method == 'GET':
sort_by = quart.request.args.get('sort_by', 'created_at')
sort_order = quart.request.args.get('sort_order', 'DESC')
enabled_only = quart.request.args.get('enabled_only', 'false').lower() == 'true'
return self.success(
data={'workflows': await self.ap.workflow_service.get_workflows(sort_by, sort_order, enabled_only)}
)
elif quart.request.method == 'POST':
json_data = await quart.request.json
workflow_uuid = await self.ap.workflow_service.create_workflow(json_data)
return self.success(data={'uuid': workflow_uuid})
# Get node types (available nodes for the editor)
@self.route('/_/node-types', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _() -> str:
return self.success(
data={
'node_types': await self.ap.workflow_service.get_node_types(),
'categories': await self.ap.workflow_service.get_node_types_by_category_meta(),
}
)
# Get node types by category
@self.route('/_/node-types/categories', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _() -> str:
return self.success(data={'categories': await self.ap.workflow_service.get_node_types_by_category()})
# Single workflow operations
@self.route(
'/<workflow_uuid>', methods=['GET', 'PUT', 'DELETE'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
)
async def _(workflow_uuid: str) -> str:
if quart.request.method == 'GET':
workflow = await self.ap.workflow_service.get_workflow(workflow_uuid)
if workflow is None:
return self.http_status(404, -1, 'workflow not found')
return self.success(data={'workflow': workflow})
elif quart.request.method == 'PUT':
json_data = await quart.request.json
try:
await self.ap.workflow_service.update_workflow(workflow_uuid, json_data)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
elif quart.request.method == 'DELETE':
await self.ap.workflow_service.delete_workflow(workflow_uuid)
return self.success()
return self.http_status(405, -1, 'method not allowed')
# Publish workflow (enable)
@self.route('/<workflow_uuid>/publish', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
try:
await self.ap.workflow_service.publish_workflow(workflow_uuid)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
# Unpublish workflow (disable)
@self.route('/<workflow_uuid>/unpublish', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
try:
await self.ap.workflow_service.unpublish_workflow(workflow_uuid)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
# Copy workflow
@self.route('/<workflow_uuid>/copy', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
try:
new_uuid = await self.ap.workflow_service.copy_workflow(workflow_uuid)
return self.success(data={'uuid': new_uuid})
except ValueError as e:
return self.http_status(404, -1, str(e))
# Execute workflow manually
@self.route('/<workflow_uuid>/execute', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
json_data = await quart.request.json or {}
trigger_data = json_data.get('trigger_data', {})
session_id = json_data.get('session_id')
user_id = json_data.get('user_id')
bot_id = json_data.get('bot_id')
try:
execution_id = await self.ap.workflow_service.execute_workflow(
workflow_uuid,
trigger_type='manual',
trigger_data=trigger_data,
session_id=session_id,
user_id=user_id,
bot_id=bot_id,
)
return self.success(data={'execution_id': execution_id})
except ValueError as e:
return self.http_status(404, -1, str(e))
except WorkflowExecutionFailedError as e:
return self.http_status(500, -1, e.message)
# Get workflow executions
@self.route('/<workflow_uuid>/executions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
limit = int(quart.request.args.get('limit', 50))
offset = int(quart.request.args.get('offset', 0))
executions = await self.ap.workflow_service.get_executions(
workflow_uuid=workflow_uuid, limit=limit, offset=offset
)
return self.success(data=executions)
@self.route(
'/<workflow_uuid>/executions/<execution_uuid>',
methods=['GET'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
execution = await self.ap.workflow_service.get_execution(execution_uuid)
if execution is None:
return self.http_status(404, -1, 'execution not found')
if execution.get('workflow_uuid') != workflow_uuid:
return self.http_status(404, -1, 'execution not found in workflow')
return self.success(data={'execution': execution})
# Get workflow versions
@self.route('/<workflow_uuid>/versions', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
versions = await self.ap.workflow_service.get_versions(workflow_uuid)
return self.success(data={'versions': versions})
# Rollback to a specific version
@self.route(
'/<workflow_uuid>/rollback/<int:version>', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
)
async def _(workflow_uuid: str, version: int) -> str:
try:
await self.ap.workflow_service.rollback_to_version(workflow_uuid, version)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
# Workflow extensions (plugins and MCP servers)
@self.route(
'/<workflow_uuid>/extensions', methods=['GET', 'PUT'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY
)
async def _(workflow_uuid: str) -> str:
if quart.request.method == 'GET':
workflow = await self.ap.workflow_service.get_workflow(workflow_uuid)
if workflow is None:
return self.http_status(404, -1, 'workflow not found')
# Get available plugins and MCP servers
pipeline_component_kinds = ['Command', 'EventListener', 'Tool']
plugins = await self.ap.plugin_connector.list_plugins(component_kinds=pipeline_component_kinds)
mcp_servers = await self.ap.mcp_service.get_mcp_servers(contain_runtime_info=True)
extensions_prefs = workflow.get('extensions_preferences', {})
return self.success(
data={
'enable_all_plugins': extensions_prefs.get('enable_all_plugins', True),
'enable_all_mcp_servers': extensions_prefs.get('enable_all_mcp_servers', True),
'bound_plugins': extensions_prefs.get('plugins', []),
'available_plugins': plugins,
'bound_mcp_servers': extensions_prefs.get('mcp_servers', []),
'available_mcp_servers': mcp_servers,
}
)
elif quart.request.method == 'PUT':
json_data = await quart.request.json
enable_all_plugins = json_data.get('enable_all_plugins', True)
enable_all_mcp_servers = json_data.get('enable_all_mcp_servers', True)
bound_plugins = json_data.get('bound_plugins', [])
bound_mcp_servers = json_data.get('bound_mcp_servers', [])
try:
await self.ap.workflow_service.update_workflow_extensions(
workflow_uuid, bound_plugins, bound_mcp_servers, enable_all_plugins, enable_all_mcp_servers
)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
return self.http_status(405, -1, 'method not allowed')
# Debug API - Start debug execution
@self.route('/<workflow_uuid>/debug/start', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
json_data = await quart.request.json or {}
context = json_data.get('context', {})
variables = json_data.get('variables', {})
breakpoints = json_data.get('breakpoints', [])
try:
execution_id = await self.ap.workflow_service.start_debug_execution(
workflow_uuid, context=context, variables=variables, breakpoints=breakpoints
)
return self.success(data={'execution_id': execution_id})
except ValueError as e:
return self.http_status(404, -1, str(e))
# Debug API - Pause execution
@self.route(
'/<workflow_uuid>/debug/<execution_uuid>/pause',
methods=['POST'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
try:
await self.ap.workflow_service.pause_debug_execution(workflow_uuid, execution_uuid)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
# Debug API - Resume execution
@self.route(
'/<workflow_uuid>/debug/<execution_uuid>/resume',
methods=['POST'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
try:
await self.ap.workflow_service.resume_debug_execution(workflow_uuid, execution_uuid)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
# Debug API - Step execution
@self.route(
'/<workflow_uuid>/debug/<execution_uuid>/step',
methods=['POST'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
try:
result = await self.ap.workflow_service.step_debug_execution(workflow_uuid, execution_uuid)
return self.success(data=result)
except ValueError as e:
return self.http_status(404, -1, str(e))
# Debug API - Stop execution
@self.route(
'/<workflow_uuid>/debug/<execution_uuid>/stop',
methods=['POST'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
try:
await self.ap.workflow_service.stop_debug_execution(workflow_uuid, execution_uuid)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
# Debug API - Get debug state
@self.route(
'/<workflow_uuid>/debug/<execution_uuid>/state',
methods=['GET'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
try:
state = await self.ap.workflow_service.get_debug_state(workflow_uuid, execution_uuid)
return self.success(data=state)
except ValueError as e:
return self.http_status(404, -1, str(e))
# Get execution logs
@self.route(
'/<workflow_uuid>/executions/<execution_uuid>/logs',
methods=['GET'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
limit = int(quart.request.args.get('limit', 100))
offset = int(quart.request.args.get('offset', 0))
try:
result = await self.ap.workflow_service.get_execution_logs(workflow_uuid, execution_uuid, limit, offset)
return self.success(data=result)
except ValueError as e:
return self.http_status(404, -1, str(e))
# Rerun execution
@self.route(
'/<workflow_uuid>/executions/<execution_uuid>/rerun',
methods=['POST'],
auth_type=group.AuthType.USER_TOKEN_OR_API_KEY,
)
async def _(workflow_uuid: str, execution_uuid: str) -> str:
try:
new_execution_id = await self.ap.workflow_service.rerun_execution(workflow_uuid, execution_uuid)
return self.success(data={'execution_uuid': new_execution_id})
except ValueError as e:
return self.http_status(404, -1, str(e))
# Get workflow statistics
@self.route('/<workflow_uuid>/stats', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(workflow_uuid: str) -> str:
try:
stats = await self.ap.workflow_service.get_workflow_stats(workflow_uuid)
return self.success(data=stats)
except ValueError as e:
return self.http_status(404, -1, str(e))
# LLM Node Performance Test Endpoint
# Tests each step of LLM node execution with detailed timing
@self.route('/_/test/llm-node', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _() -> str:
"""Test LLM node performance with detailed step-by-step timing.
Request body:
{
"model_uuid": "uuid-of-model",
"system_prompt": "optional system prompt",
"user_prompt": "test message",
"temperature": 0.7,
"max_tokens": 100
}
Response includes timing for each step:
- model_fetch: Time to get model from model_mgr
- prompt_build: Time to build messages
- llm_call: Time for actual LLM invocation
- total: Total time
- usage: Token usage information
"""
import time
json_data = await quart.request.json
if not json_data:
return self.http_status(400, -1, 'Request body is required')
model_uuid = json_data.get('model_uuid', '')
if not model_uuid:
return self.http_status(400, -1, 'model_uuid is required')
user_prompt = json_data.get('user_prompt', 'test')
system_prompt = json_data.get('system_prompt', '')
temperature = json_data.get('temperature')
max_tokens = json_data.get('max_tokens', 0)
timings = {}
errors = []
# Step 1: Model fetch
t_start = time.perf_counter()
try:
runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
timings['model_found'] = True
timings['model_name'] = runtime_model.model_entity.name if runtime_model else None
except Exception as e:
timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
timings['model_found'] = False
errors.append(f'Model fetch failed: {str(e)}')
return self.http_status(400, -1, {
'error': errors[0],
'timings': timings,
})
# Step 2: Build messages
t_start = time.perf_counter()
import langbot_plugin.api.entities.builtin.provider.message as provider_message
messages = []
if system_prompt:
messages.append(provider_message.Message(role='system', content=system_prompt))
messages.append(provider_message.Message(role='user', content=user_prompt))
timings['prompt_build_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
# Step 3: Build extra args
extra_args = {}
if temperature is not None:
extra_args['temperature'] = float(temperature)
if max_tokens and int(max_tokens) > 0:
extra_args['max_tokens'] = int(max_tokens)
# Step 4: LLM call
t_start = time.perf_counter()
try:
result_message = await runtime_model.provider.invoke_llm(
query=None,
model=runtime_model,
messages=messages,
funcs=None,
extra_args=extra_args,
)
timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
timings['llm_call_success'] = True
# Extract response text
response_text = ''
if isinstance(result_message.content, str):
response_text = result_message.content
elif isinstance(result_message.content, list):
for elem in result_message.content:
if hasattr(elem, 'text') and elem.text:
response_text += elem.text
elif isinstance(elem, str):
response_text += elem
timings['response_length'] = len(response_text)
timings['response_preview'] = response_text[:200]
# Extract usage
usage = {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
if hasattr(result_message, 'usage') and result_message.usage:
u = result_message.usage
usage = {
'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
'total_tokens': getattr(u, 'total_tokens', 0) or 0,
}
timings['usage'] = usage
except Exception as e:
timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
timings['llm_call_success'] = False
errors.append(f'LLM call failed: {str(e)}')
# Calculate total
timings['total_ms'] = round(sum([
timings.get('model_fetch_ms', 0),
timings.get('prompt_build_ms', 0),
timings.get('llm_call_ms', 0),
]), 2)
# Add breakdown percentage
if timings['total_ms'] > 0:
timings['breakdown'] = {
'model_fetch_pct': round(timings.get('model_fetch_ms', 0) / timings['total_ms'] * 100, 1),
'prompt_build_pct': round(timings.get('prompt_build_ms', 0) / timings['total_ms'] * 100, 1),
'llm_call_pct': round(timings.get('llm_call_ms', 0) / timings['total_ms'] * 100, 1),
}
if errors:
timings['errors'] = errors
return self.success(data={'test_result': timings})
@group.group_class('executions', '/api/v1/executions')
class ExecutionsRouterGroup(group.RouterGroup):
"""Workflow execution API router group"""
async def initialize(self) -> None:
# Get all executions (across all workflows)
@self.route('', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _() -> str:
limit = int(quart.request.args.get('limit', 50))
offset = int(quart.request.args.get('offset', 0))
status = quart.request.args.get('status')
executions = await self.ap.workflow_service.get_executions(limit=limit, offset=offset, status=status)
return self.success(data=executions)
# Get single execution
@self.route('/<execution_uuid>', methods=['GET'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(execution_uuid: str) -> str:
execution = await self.ap.workflow_service.get_execution(execution_uuid)
if execution is None:
return self.http_status(404, -1, 'execution not found')
return self.success(data={'execution': execution})
# Cancel execution
@self.route('/<execution_uuid>/cancel', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
async def _(execution_uuid: str) -> str:
try:
await self.ap.workflow_service.cancel_execution(execution_uuid)
return self.success()
except ValueError as e:
return self.http_status(404, -1, str(e))
except RuntimeError as e:
return self.http_status(400, -1, str(e))
@@ -17,6 +17,7 @@ from .groups import platform as groups_platform
from .groups import pipelines as groups_pipelines
from .groups import knowledge as groups_knowledge
from .groups import resources as groups_resources
from .groups import workflows as groups_workflows
from ...mcp.mount import MCPMount
importutil.import_modules_in_pkg(groups)
@@ -25,6 +26,7 @@ importutil.import_modules_in_pkg(groups_platform)
importutil.import_modules_in_pkg(groups_pipelines)
importutil.import_modules_in_pkg(groups_knowledge)
importutil.import_modules_in_pkg(groups_resources)
importutil.import_modules_in_pkg(groups_workflows)
class HTTPController:
+71 -13
View File
@@ -99,16 +99,23 @@ class BotService:
# TODO: 检查配置信息格式
bot_data['uuid'] = str(uuid.uuid4())
# bind the most recently updated pipeline if any exist
# Set default binding_type if not provided
if 'binding_type' not in bot_data:
bot_data['binding_type'] = 'pipeline'
# checkout the default pipeline (for backward compatibility)
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_pipeline.LegacyPipeline)
.order_by(persistence_pipeline.LegacyPipeline.updated_at.desc())
.limit(1)
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
persistence_pipeline.LegacyPipeline.is_default == True
)
)
pipeline = result.first()
if pipeline is not None:
bot_data['use_pipeline_uuid'] = pipeline.uuid
bot_data['use_pipeline_name'] = pipeline.name
# Also set binding_uuid for new unified binding model
if 'binding_uuid' not in bot_data:
bot_data['binding_uuid'] = pipeline.uuid
await self.ap.persistence_mgr.execute_async(sqlalchemy.insert(persistence_bot.Bot).values(bot_data))
@@ -120,26 +127,45 @@ class BotService:
async def update_bot(self, bot_uuid: str, bot_data: dict) -> None:
"""Update bot"""
update_data = bot_data.copy()
if 'uuid' in bot_data:
del bot_data['uuid']
if 'uuid' in update_data:
del update_data['uuid']
# Handle binding_type and binding_uuid for the new unified binding model
# If binding_type is explicitly set to 'workflow', skip pipeline validation
binding_type = bot_data.get('binding_type')
# set use_pipeline_name
if 'use_pipeline_uuid' in update_data:
# set use_pipeline_name (for backward compatibility with 'pipeline' binding_type)
# Only validate pipeline when binding_type is 'pipeline' or not set (default to pipeline)
if 'use_pipeline_uuid' in bot_data and binding_type != 'workflow':
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
persistence_pipeline.LegacyPipeline.uuid == update_data['use_pipeline_uuid']
persistence_pipeline.LegacyPipeline.uuid == bot_data['use_pipeline_uuid']
)
)
pipeline = result.first()
if pipeline is not None:
update_data['use_pipeline_name'] = pipeline.name
bot_data['use_pipeline_name'] = pipeline.name
# Also sync to binding_uuid if binding_type is 'pipeline' or not set
if binding_type is None or binding_type == 'pipeline':
bot_data['binding_uuid'] = bot_data['use_pipeline_uuid']
bot_data['binding_type'] = 'pipeline'
else:
raise Exception('Pipeline not found')
# Only raise error if binding_type is explicitly 'pipeline' or not set
if binding_type is None or binding_type == 'pipeline':
raise Exception('Pipeline not found')
# If binding_type is 'workflow', just clear the use_pipeline_uuid
bot_data['use_pipeline_uuid'] = None
bot_data['use_pipeline_name'] = None
# If binding_uuid is set directly (for workflow), clear pipeline fields
if 'binding_uuid' in bot_data and binding_type == 'workflow':
# For workflow binding, clear pipeline-related fields to avoid confusion
bot_data['binding_type'] = 'workflow'
bot_data['use_pipeline_uuid'] = None
bot_data['use_pipeline_name'] = None
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(persistence_bot.Bot).values(update_data).where(persistence_bot.Bot.uuid == bot_uuid)
sqlalchemy.update(persistence_bot.Bot).values(bot_data).where(persistence_bot.Bot.uuid == bot_uuid)
)
await self.ap.platform_mgr.remove_bot(bot_uuid)
@@ -199,3 +225,35 @@ class BotService:
# Send message via adapter
await runtime_bot.adapter.send_message(target_type, str(target_id), message_chain)
# ============ Bot Admins ============
async def get_bot_admins(self, bot_uuid: str) -> list[dict]:
from ....entity.persistence import bot as persistence_bot
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_bot.BotAdmin).where(persistence_bot.BotAdmin.bot_uuid == bot_uuid)
)
return [{'id': r.id, 'launcher_type': r.launcher_type, 'launcher_id': r.launcher_id} for r in result.all()]
async def add_bot_admin(self, bot_uuid: str, launcher_type: str, launcher_id: str) -> int:
from ....entity.persistence import bot as persistence_bot
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.insert(persistence_bot.BotAdmin).values(
bot_uuid=bot_uuid,
launcher_type=launcher_type,
launcher_id=launcher_id,
)
)
return result.inserted_primary_key[0]
async def delete_bot_admin(self, bot_uuid: str, admin_id: int) -> None:
from ....entity.persistence import bot as persistence_bot
await self.ap.persistence_mgr.execute_async(
sqlalchemy.delete(persistence_bot.BotAdmin).where(
persistence_bot.BotAdmin.bot_uuid == bot_uuid,
persistence_bot.BotAdmin.id == admin_id,
)
)
+26
View File
@@ -136,6 +136,32 @@ class MCPService:
if server_name in self.ap.tool_mgr.mcp_tool_loader.sessions:
await self.ap.tool_mgr.mcp_tool_loader.remove_mcp_server(server_name)
async def get_mcp_server_resources(self, server_name: str) -> list[dict]:
"""Get resources from a specific MCP server."""
return await self.ap.tool_mgr.mcp_tool_loader.get_resources(server_name)
async def get_mcp_server_resource_templates(self, server_name: str) -> list[dict]:
"""Get resource templates from a specific MCP server."""
return await self.ap.tool_mgr.mcp_tool_loader.get_resource_templates(server_name)
async def read_mcp_server_resource_envelope(
self,
server_name: str,
uri: str,
*,
max_bytes: int | None = None,
include_blob: bool = False,
) -> dict:
"""Read a resource from a specific MCP server with metadata."""
kwargs = {'include_blob': include_blob, 'source': 'ui_preview'}
if max_bytes is not None:
kwargs['max_bytes'] = max_bytes
return await self.ap.tool_mgr.mcp_tool_loader.read_resource_envelope(server_name, uri, **kwargs)
async def read_mcp_server_resource(self, server_name: str, uri: str) -> list[dict]:
"""Read a resource from a specific MCP server."""
return await self.ap.tool_mgr.mcp_tool_loader.read_resource(server_name, uri)
async def test_mcp_server(self, server_name: str, server_data: dict) -> int:
"""测试 MCP 服务器连接并返回任务 ID"""
@@ -73,6 +73,20 @@ class PipelineService:
return self.ap.persistence_mgr.serialize_model(persistence_pipeline.LegacyPipeline, pipeline)
async def get_pipeline_by_name(self, pipeline_name: str) -> dict | None:
result = await self.ap.persistence_mgr.execute_async(
sqlalchemy.select(persistence_pipeline.LegacyPipeline).where(
persistence_pipeline.LegacyPipeline.name == pipeline_name
)
)
pipeline = result.first()
if pipeline is None:
return None
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
@@ -100,6 +114,8 @@ class PipelineService:
'enable_all_mcp_servers': True,
'plugins': [],
'mcp_servers': [],
'mcp_resources': [],
'mcp_resource_agent_read_enabled': True,
}
await self.ap.persistence_mgr.execute_async(
@@ -193,6 +209,8 @@ class PipelineService:
'enable_all_mcp_servers': True,
'plugins': [],
'mcp_servers': [],
'mcp_resources': [],
'mcp_resource_agent_read_enabled': True,
}
),
}
@@ -217,6 +235,8 @@ class PipelineService:
enable_all_mcp_servers: bool = True,
bound_skills: list[str] = None,
enable_all_skills: bool = True,
bound_mcp_resources: list[dict] = None,
mcp_resource_agent_read_enabled: bool | None = None,
) -> None:
"""Update the bound plugins and MCP servers for a pipeline"""
# Get current pipeline
@@ -236,10 +256,14 @@ class PipelineService:
extensions_preferences['enable_all_mcp_servers'] = enable_all_mcp_servers
extensions_preferences['enable_all_skills'] = enable_all_skills
extensions_preferences['plugins'] = bound_plugins
if mcp_resource_agent_read_enabled is not None:
extensions_preferences['mcp_resource_agent_read_enabled'] = mcp_resource_agent_read_enabled
if bound_mcp_servers is not None:
extensions_preferences['mcp_servers'] = bound_mcp_servers
if bound_skills is not None:
extensions_preferences['skills'] = bound_skills
if bound_mcp_resources is not None:
extensions_preferences['mcp_resources'] = bound_mcp_resources
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(persistence_pipeline.LegacyPipeline)
+16 -16
View File
@@ -20,6 +20,15 @@ class UserService:
def __init__(self, ap: app.Application) -> None:
self.ap = ap
self._create_user_lock = asyncio.Lock()
self._password_hash_lock = asyncio.Semaphore(1)
async def _hash_password(self, password: str) -> str:
async with self._password_hash_lock:
return await asyncio.to_thread(argon2.PasswordHasher().hash, password)
async def _verify_password(self, hashed_password: str, password: str) -> None:
async with self._password_hash_lock:
await asyncio.to_thread(argon2.PasswordHasher().verify, hashed_password, password)
async def is_initialized(self) -> bool:
result = await self.ap.persistence_mgr.execute_async(sqlalchemy.select(user.User).limit(1))
@@ -28,9 +37,7 @@ class UserService:
return result_list is not None and len(result_list) > 0
async def create_user(self, user_email: str, password: str) -> None:
ph = argon2.PasswordHasher()
hashed_password = ph.hash(password)
hashed_password = await self._hash_password(password)
await self.ap.persistence_mgr.execute_async(
sqlalchemy.insert(user.User).values(user=user_email, password=hashed_password, account_type='local')
@@ -69,9 +76,7 @@ class UserService:
if not user_obj.password:
raise ValueError('请使用 Space 账户登录')
ph = argon2.PasswordHasher()
ph.verify(user_obj.password, password)
await self._verify_password(user_obj.password, password)
return await self.generate_jwt_token(user_email)
@@ -93,17 +98,13 @@ class UserService:
return jwt.decode(token, jwt_secret, algorithms=['HS256'])['user']
async def reset_password(self, user_email: str, new_password: str) -> None:
ph = argon2.PasswordHasher()
hashed_password = ph.hash(new_password)
hashed_password = await self._hash_password(new_password)
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
)
async def change_password(self, user_email: str, current_password: str, new_password: str) -> None:
ph = argon2.PasswordHasher()
user_obj = await self.get_user_by_email(user_email)
if user_obj is None:
raise ValueError('User not found')
@@ -111,9 +112,9 @@ class UserService:
if not user_obj.password:
raise ValueError('No local password set, please set a password first')
ph.verify(user_obj.password, current_password)
await self._verify_password(user_obj.password, current_password)
hashed_password = ph.hash(new_password)
hashed_password = await self._hash_password(new_password)
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
@@ -232,7 +233,6 @@ class UserService:
async def set_password(self, user_email: str, new_password: str, current_password: str | None = None) -> None:
"""Set or change password for a user"""
ph = argon2.PasswordHasher()
user_obj = await self.get_user_by_email(user_email)
if user_obj is None:
@@ -243,9 +243,9 @@ class UserService:
if has_password:
if not current_password:
raise ValueError('Current password is required')
ph.verify(user_obj.password, current_password)
await self._verify_password(user_obj.password, current_password)
hashed_password = ph.hash(new_password)
hashed_password = await self._hash_password(new_password)
await self.ap.persistence_mgr.execute_async(
sqlalchemy.update(user.User).where(user.User.user == user_email).values(password=hashed_password)
)
File diff suppressed because it is too large Load Diff
+5 -2
View File
@@ -82,7 +82,6 @@ class BoxService:
return self._enabled
async def initialize(self):
self._ensure_default_workspace()
if not self._enabled:
# Disabled by config: do NOT connect to a remote runtime, do NOT
# fork a stdio subprocess. Every consumer of box_service should
@@ -99,6 +98,7 @@ class BoxService:
await self._runtime_connector.initialize()
else:
await self.client.initialize()
self._ensure_default_workspace()
self._available = True
self._connector_error = ''
self.ap.logger.info(
@@ -1152,6 +1152,9 @@ class BoxService:
if self.default_workspace is None:
return
if not self.shares_filesystem_with_box:
return
if os.path.isdir(self.default_workspace):
return
@@ -1176,7 +1179,7 @@ class BoxService:
return
host_path = os.path.realpath(spec.host_path)
if not os.path.isdir(host_path):
if self.shares_filesystem_with_box and not os.path.isdir(host_path):
raise BoxValidationError('host_path must point to an existing directory on the host')
if not self.allowed_mount_roots:
+11 -1
View File
@@ -84,7 +84,17 @@ class CommandManager:
privilege = 1
if f'{query.launcher_type.value}_{query.launcher_id}' in self.ap.instance_config.data['admins']:
import sqlalchemy as _sa
from ..entity.persistence.bot import BotAdmin as _BotAdmin
_admins = await self.ap.persistence_mgr.execute_async(
_sa.select(_BotAdmin).where(
_BotAdmin.bot_uuid == (query.bot_uuid or ''),
_BotAdmin.launcher_type == query.launcher_type.value,
_BotAdmin.launcher_id == str(query.launcher_id),
)
)
if _admins.first() is not None:
privilege = 2
ctx = command_context.ExecuteContext(
+19
View File
@@ -32,6 +32,7 @@ from ..api.http.service import mcp as mcp_service
from ..api.http.service import apikey as apikey_service
from ..api.http.service import webhook as webhook_service
from ..api.http.service import monitoring as monitoring_service
from ..api.http.service import workflow as workflow_service
from ..api.http.service import skill as skill_service
from ..api.http.service import maintenance as maintenance_service
from ..discover import engine as discover_engine
@@ -153,6 +154,8 @@ class Application:
webhook_service: webhook_service.WebhookService = None
workflow_service: workflow_service.WorkflowService = None
telemetry: telemetry_module.TelemetryManager = None
survey: survey_module.SurveyManager = None
@@ -255,6 +258,22 @@ class Application:
scopes=[core_entities.LifecycleControlScope.APPLICATION],
)
async def workflow_execution_cleanup_loop():
check_interval_seconds = 60
while True:
try:
cancelled = await self.workflow_service.cleanup_stale_executions()
if cancelled > 0:
self.logger.info(f'Workflow execution auto-cleanup: cancelled {cancelled} stale executions')
except Exception as e:
self.logger.warning(f'Workflow execution auto-cleanup error: {e}')
await asyncio.sleep(check_interval_seconds)
self.task_mgr.create_task(
workflow_execution_cleanup_loop(),
name='workflow-execution-cleanup',
scopes=[core_entities.LifecycleControlScope.APPLICATION],
)
# Start storage/log maintenance task if enabled
storage_cleanup_cfg = self.instance_config.data.get('storage', {}).get('cleanup', {})
if storage_cleanup_cfg.get('enabled', True) and self.maintenance_service is not None:
+4
View File
@@ -29,6 +29,7 @@ from ...api.http.service import mcp as mcp_service
from ...api.http.service import apikey as apikey_service
from ...api.http.service import webhook as webhook_service
from ...api.http.service import monitoring as monitoring_service
from ...api.http.service import workflow as workflow_service
from ...api.http.service import skill as skill_service
from ...skill import manager as skill_mgr
from ...api.http.service import maintenance as maintenance_service
@@ -89,6 +90,9 @@ class BuildAppStage(stage.BootingStage):
webhook_service_inst = webhook_service.WebhookService(ap)
ap.webhook_service = webhook_service_inst
workflow_service_inst = workflow_service.WorkflowService(ap)
ap.workflow_service = workflow_service_inst
skill_service_inst = skill_service.SkillService(ap)
ap.skill_service = skill_service_inst
@@ -231,3 +231,34 @@ class LoadConfigStage(stage.BootingStage):
ap.pipeline_config_meta_safety = await load_resource_yaml_template_data('metadata/pipeline/safety.yaml')
ap.pipeline_config_meta_ai = await load_resource_yaml_template_data('metadata/pipeline/ai.yaml')
ap.pipeline_config_meta_output = await load_resource_yaml_template_data('metadata/pipeline/output.yaml')
# Load workflow node metadata from YAML files. YAML is the source of
# truth for workflow editor metadata; Python classes provide execution
# logic and are bound through the registry.
from langbot.pkg.workflow.metadata import NodeMetadataLoader
from langbot.pkg.workflow.registry import NodeTypeRegistry
workflow_metadata_loader = NodeMetadataLoader()
workflow_node_count = await workflow_metadata_loader.load_core_metadata()
ap.workflow_node_configs = workflow_metadata_loader.get_all_metadata()
ap.workflow_node_metadata_loader = workflow_metadata_loader
workflow_registry = NodeTypeRegistry.instance()
for node_config in ap.workflow_node_configs.values():
workflow_registry.register_metadata(node_config, source=node_config.get('_source', 'core'))
# Auto-discover and register workflow nodes using discovery engine
if hasattr(ap, 'discover') and ap.discover is not None:
workflow_registry.discover_nodes(ap.discover)
workflow_load_errors = workflow_metadata_loader.get_load_errors()
if workflow_load_errors:
print(f'Workflow node metadata load errors: {len(workflow_load_errors)}')
for error in workflow_load_errors:
print(f" - {error.get('file')}: {error.get('error')}")
print(
f'Loaded {workflow_node_count} workflow node metadata files; '
f'registered {workflow_registry.metadata_count()} metadata definitions, '
f'{workflow_registry.count()} node types'
)
+62
View File
@@ -304,3 +304,65 @@ class ComponentDiscoveryEngine:
if component.kind == kind:
result.append(component)
return result
def discover_workflow_nodes(self, nodes_dir: str) -> typing.List[typing.Type]:
"""Discover workflow node classes from a directory of Python modules.
Scans all .py files in the given directory, imports them, and collects
classes that are subclasses of WorkflowNode.
Args:
nodes_dir: Directory path like 'pkg/workflow/nodes/'
Returns:
List of WorkflowNode subclasses found
"""
from langbot.pkg.workflow.node import WorkflowNode
node_classes: typing.List[typing.Type[WorkflowNode]] = []
# Normalize path
if nodes_dir.endswith('/'):
nodes_dir = nodes_dir[:-1]
# Import the nodes package to trigger all module imports
module_path = nodes_dir.replace('/', '.').replace('\\', '.')
package_path = module_path
try:
# Import the package __init__ to trigger submodule imports
importlib.import_module(f'langbot.{package_path}')
except ImportError:
self.ap.logger.warning(f'Failed to import workflow nodes package: langbot.{package_path}')
# Since workflow/__init__.py is empty, explicitly import all .py files in the nodes directory
import os
# engine.py is in langbot/pkg/discover/, nodes are in langbot/pkg/workflow/nodes/
nodes_abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'workflow', 'nodes'))
if os.path.isdir(nodes_abs_path):
for filename in os.listdir(nodes_abs_path):
if filename.endswith('.py') and not filename.startswith('_'):
module_name = filename[:-3]
try:
importlib.import_module(f'langbot.{package_path}.{module_name}')
except ImportError as e:
self.ap.logger.warning(f'Failed to import workflow node module: {module_name}: {e}')
# Now collect all WorkflowNode subclasses from sys.modules
import sys
prefix = f'langbot.{package_path}.'
for mod_name, mod in sys.modules.items():
if mod_name.startswith(prefix) and mod is not None:
for attr_name in dir(mod):
attr = getattr(mod, attr_name)
if (
isinstance(attr, type)
and issubclass(attr, WorkflowNode)
and attr is not WorkflowNode
and hasattr(attr, 'type_name')
and attr.type_name
):
if attr not in node_classes:
node_classes.append(attr)
return node_classes
+21
View File
@@ -3,6 +3,20 @@ import sqlalchemy
from .base import Base
class BotAdmin(Base):
"""Bot admin — a launcher that has admin privilege for a specific bot's commands"""
__tablename__ = 'bot_admins'
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
bot_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
launcher_type = sqlalchemy.Column(sqlalchemy.String(64), nullable=False)
launcher_id = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
__table_args__ = (sqlalchemy.UniqueConstraint('bot_uuid', 'launcher_type', 'launcher_id', name='uq_bot_admin'),)
class Bot(Base):
"""Bot"""
@@ -17,6 +31,13 @@ class Bot(Base):
use_pipeline_name = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
use_pipeline_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
pipeline_routing_rules = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, server_default='[]')
# New unified binding fields
# binding_type: 'pipeline' or 'workflow'
binding_type = sqlalchemy.Column(sqlalchemy.String(32), nullable=False, server_default='pipeline')
# binding_uuid: UUID of the bound Pipeline or Workflow
binding_uuid = sqlalchemy.Column(sqlalchemy.String(64), nullable=True)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
updated_at = sqlalchemy.Column(
sqlalchemy.DateTime,
@@ -26,7 +26,14 @@ class LegacyPipeline(Base):
extensions_preferences = sqlalchemy.Column(
sqlalchemy.JSON,
nullable=False,
default={'enable_all_plugins': True, 'enable_all_mcp_servers': True, 'plugins': [], 'mcp_servers': []},
default={
'enable_all_plugins': True,
'enable_all_mcp_servers': True,
'plugins': [],
'mcp_servers': [],
'mcp_resources': [],
'mcp_resource_agent_read_enabled': True,
},
)
@@ -0,0 +1,126 @@
"""Workflow persistence entities"""
import sqlalchemy
from .base import Base
class Workflow(Base):
"""Workflow definition"""
__tablename__ = 'workflows'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
name = sqlalchemy.Column(sqlalchemy.String(255), nullable=False)
description = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
emoji = sqlalchemy.Column(sqlalchemy.String(10), nullable=True, default='🔄')
version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=1)
is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
# Workflow definition stored as JSON
# Contains: nodes, edges, variables, settings
definition = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
# Global config (inherited from Pipeline capabilities)
# Contains: safety, output configs
global_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
# Extensions preferences (same as Pipeline)
extensions_preferences = sqlalchemy.Column(
sqlalchemy.JSON,
nullable=False,
default={'enable_all_plugins': True, 'enable_all_mcp_servers': True, 'plugins': [], 'mcp_servers': []},
)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
updated_at = sqlalchemy.Column(
sqlalchemy.DateTime,
nullable=False,
server_default=sqlalchemy.func.now(),
onupdate=sqlalchemy.func.now(),
)
class WorkflowVersion(Base):
"""Workflow version history"""
__tablename__ = 'workflow_versions'
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
definition = sqlalchemy.Column(sqlalchemy.JSON, nullable=False)
global_config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
created_by = sqlalchemy.Column(sqlalchemy.String(255), nullable=True)
__table_args__ = (sqlalchemy.UniqueConstraint('workflow_uuid', 'version', name='uq_workflow_version'),)
class WorkflowTrigger(Base):
"""Workflow trigger configuration"""
__tablename__ = 'workflow_triggers'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False) # message, cron, event, webhook
config = sqlalchemy.Column(sqlalchemy.JSON, nullable=False, default={})
is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
priority = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
updated_at = sqlalchemy.Column(
sqlalchemy.DateTime,
nullable=False,
server_default=sqlalchemy.func.now(),
onupdate=sqlalchemy.func.now(),
)
class WorkflowExecution(Base):
"""Workflow execution record"""
__tablename__ = 'workflow_executions'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
workflow_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
workflow_version = sqlalchemy.Column(sqlalchemy.Integer, nullable=False)
status = sqlalchemy.Column(sqlalchemy.String(20), nullable=False) # pending, running, completed, failed, cancelled
trigger_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=True)
trigger_data = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
variables = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
end_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
error = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
created_at = sqlalchemy.Column(sqlalchemy.DateTime, nullable=False, server_default=sqlalchemy.func.now())
class WorkflowNodeExecution(Base):
"""Workflow node execution record"""
__tablename__ = 'workflow_node_executions'
id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True, autoincrement=True)
execution_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
node_id = sqlalchemy.Column(sqlalchemy.String(100), nullable=False)
node_type = sqlalchemy.Column(sqlalchemy.String(50), nullable=False)
status = sqlalchemy.Column(sqlalchemy.String(20), nullable=False) # pending, running, completed, failed, skipped
inputs = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
outputs = sqlalchemy.Column(sqlalchemy.JSON, nullable=True)
start_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
end_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
error = sqlalchemy.Column(sqlalchemy.Text, nullable=True)
retry_count = sqlalchemy.Column(sqlalchemy.Integer, nullable=False, default=0)
class ScheduledJob(Base):
"""Scheduled job for cron triggers"""
__tablename__ = 'workflow_scheduled_jobs'
uuid = sqlalchemy.Column(sqlalchemy.String(255), primary_key=True, unique=True)
trigger_uuid = sqlalchemy.Column(sqlalchemy.String(255), nullable=False, index=True)
cron_expression = sqlalchemy.Column(sqlalchemy.String(100), nullable=True)
next_run_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
last_run_time = sqlalchemy.Column(sqlalchemy.DateTime, nullable=True)
is_enabled = sqlalchemy.Column(sqlalchemy.Boolean, nullable=False, default=True)
@@ -0,0 +1,84 @@
"""add bot_admins table and migrate config admins
Revision ID: 0007_add_bot_admins
Revises: 0006_normalize_mcp_remote_mode
Create Date: 2026-06-26
"""
import sqlalchemy as sa
from alembic import op
revision = '0007_add_bot_admins'
down_revision = '0006_normalize_mcp_remote_mode'
branch_labels = None
depends_on = None
def upgrade() -> None:
conn = op.get_bind()
if 'bot_admins' in sa.inspect(conn).get_table_names():
return
op.create_table(
'bot_admins',
sa.Column('id', sa.Integer, primary_key=True, autoincrement=True),
sa.Column('bot_uuid', sa.String(255), nullable=False),
sa.Column('launcher_type', sa.String(64), nullable=False),
sa.Column('launcher_id', sa.String(255), nullable=False),
sa.Column('created_at', sa.DateTime, nullable=False, server_default=sa.func.now()),
sa.UniqueConstraint('bot_uuid', 'launcher_type', 'launcher_id', name='uq_bot_admin'),
)
# Migrate old config-based admins into the first bot (best-effort)
inspector = sa.inspect(conn)
tables = inspector.get_table_names()
if 'bots' not in tables:
return
# Read the first bot uuid
row = conn.execute(sa.text('SELECT uuid FROM bots ORDER BY created_at LIMIT 1')).first()
if row is None:
return
first_bot_uuid = row[0]
# Read instance_config metadata key that holds the admins list
if 'metadata' not in tables:
return
meta_row = conn.execute(sa.text("SELECT value FROM metadata WHERE key = 'instance_config'")).first()
if meta_row is None:
return
import json
try:
cfg = json.loads(meta_row[0])
except Exception:
return
admins = cfg.get('admins', [])
for entry in admins:
parts = entry.split('_', 1)
if len(parts) != 2:
continue
launcher_type, launcher_id = parts
try:
conn.execute(
sa.text(
'INSERT OR IGNORE INTO bot_admins (bot_uuid, launcher_type, launcher_id) VALUES (:bu, :lt, :li)'
),
{'bu': first_bot_uuid, 'lt': launcher_type, 'li': launcher_id},
)
except Exception:
pass
# Remove admins key from stored config
if 'admins' in cfg:
del cfg['admins']
conn.execute(
sa.text("UPDATE metadata SET value = :v WHERE key = 'instance_config'"),
{'v': json.dumps(cfg)},
)
def downgrade() -> None:
op.drop_table('bot_admins')
@@ -0,0 +1,95 @@
"""add mcp resource preferences to pipelines
Revision ID: 0008_mcp_resource_prefs
Revises: 0007_add_bot_admins
Create Date: 2026-06-30
"""
from __future__ import annotations
import json
from typing import Any
import sqlalchemy as sa
from alembic import op
revision = '0008_mcp_resource_prefs'
down_revision = '0007_add_bot_admins'
branch_labels = None
depends_on = None
_PIPELINE_TABLE = sa.table(
'legacy_pipelines',
sa.column('uuid', sa.String(255)),
sa.column('extensions_preferences', sa.JSON()),
)
def _has_extensions_preferences_table(conn: sa.Connection) -> bool:
inspector = sa.inspect(conn)
if 'legacy_pipelines' not in inspector.get_table_names():
return False
columns = {column['name'] for column in inspector.get_columns('legacy_pipelines')}
return 'extensions_preferences' in columns
def _decode_preferences(value: Any) -> dict[str, Any]:
if value is None:
return {}
if isinstance(value, dict):
return dict(value)
if isinstance(value, str):
try:
decoded = json.loads(value)
except json.JSONDecodeError:
return {}
if isinstance(decoded, dict):
return decoded
return {}
def _update_preferences(conn: sa.Connection, uuid: str, preferences: dict[str, Any]) -> None:
conn.execute(
_PIPELINE_TABLE.update().where(_PIPELINE_TABLE.c.uuid == uuid).values(extensions_preferences=preferences)
)
def upgrade() -> None:
conn = op.get_bind()
if not _has_extensions_preferences_table(conn):
return
rows = conn.execute(sa.select(_PIPELINE_TABLE.c.uuid, _PIPELINE_TABLE.c.extensions_preferences)).all()
for uuid, raw_preferences in rows:
preferences = _decode_preferences(raw_preferences)
changed = False
if 'mcp_resources' not in preferences:
preferences['mcp_resources'] = []
changed = True
if 'mcp_resource_agent_read_enabled' not in preferences:
preferences['mcp_resource_agent_read_enabled'] = True
changed = True
if changed:
_update_preferences(conn, uuid, preferences)
def downgrade() -> None:
conn = op.get_bind()
if not _has_extensions_preferences_table(conn):
return
rows = conn.execute(sa.select(_PIPELINE_TABLE.c.uuid, _PIPELINE_TABLE.c.extensions_preferences)).all()
for uuid, raw_preferences in rows:
preferences = _decode_preferences(raw_preferences)
changed = False
for key in ('mcp_resources', 'mcp_resource_agent_read_enabled'):
if key in preferences:
preferences.pop(key)
changed = True
if changed:
_update_preferences(conn, uuid, preferences)
@@ -0,0 +1,207 @@
"""add workflow tables and bot binding fields
Revision ID: 0009_add_workflow_tables
Revises: 0008_mcp_resource_prefs
Create Date: 2026-07-01
"""
from __future__ import annotations
import sqlalchemy as sa
from alembic import op
revision = '0009_add_workflow_tables'
down_revision = '0008_mcp_resource_prefs'
branch_labels = None
depends_on = None
def _table_exists(conn: sa.Connection, table_name: str) -> bool:
return table_name in sa.inspect(conn).get_table_names()
def _has_column(conn: sa.Connection, table_name: str, column_name: str) -> bool:
if not _table_exists(conn, table_name):
return False
return column_name in {column['name'] for column in sa.inspect(conn).get_columns(table_name)}
def _has_index_for_columns(conn: sa.Connection, table_name: str, columns: tuple[str, ...]) -> bool:
if not _table_exists(conn, table_name):
return False
for index in sa.inspect(conn).get_indexes(table_name):
if tuple(index.get('column_names') or ()) == columns:
return True
return False
def _ensure_index(conn: sa.Connection, table_name: str, index_name: str, columns: list[str]) -> None:
if _has_index_for_columns(conn, table_name, tuple(columns)):
return
op.create_index(index_name, table_name, columns)
def _create_workflow_tables(conn: sa.Connection) -> None:
if not _table_exists(conn, 'workflows'):
op.create_table(
'workflows',
sa.Column('uuid', sa.String(255), primary_key=True),
sa.Column('name', sa.String(255), nullable=False),
sa.Column('description', sa.Text(), nullable=True),
sa.Column('emoji', sa.String(10), nullable=True),
sa.Column('version', sa.Integer(), nullable=False, server_default='1'),
sa.Column('is_enabled', sa.Boolean(), nullable=False, server_default=sa.true()),
sa.Column('definition', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
sa.Column('global_config', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
sa.Column(
'extensions_preferences',
sa.JSON(),
nullable=False,
server_default=sa.text(
'\'{"enable_all_plugins": true, "enable_all_mcp_servers": true, "plugins": [], "mcp_servers": []}\''
),
),
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
sa.Column('updated_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
)
if not _table_exists(conn, 'workflow_versions'):
op.create_table(
'workflow_versions',
sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
sa.Column('workflow_uuid', sa.String(255), nullable=False),
sa.Column('version', sa.Integer(), nullable=False),
sa.Column('definition', sa.JSON(), nullable=False),
sa.Column('global_config', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
sa.Column('created_by', sa.String(255), nullable=True),
sa.UniqueConstraint('workflow_uuid', 'version', name='uq_workflow_version'),
)
if not _table_exists(conn, 'workflow_triggers'):
op.create_table(
'workflow_triggers',
sa.Column('uuid', sa.String(255), primary_key=True),
sa.Column('workflow_uuid', sa.String(255), nullable=False),
sa.Column('type', sa.String(50), nullable=False),
sa.Column('config', sa.JSON(), nullable=False, server_default=sa.text("'{}'")),
sa.Column('is_enabled', sa.Boolean(), nullable=False, server_default=sa.true()),
sa.Column('priority', sa.Integer(), nullable=False, server_default='0'),
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
sa.Column('updated_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
)
if not _table_exists(conn, 'workflow_executions'):
op.create_table(
'workflow_executions',
sa.Column('uuid', sa.String(255), primary_key=True),
sa.Column('workflow_uuid', sa.String(255), nullable=False),
sa.Column('workflow_version', sa.Integer(), nullable=False),
sa.Column('status', sa.String(20), nullable=False),
sa.Column('trigger_type', sa.String(50), nullable=True),
sa.Column('trigger_data', sa.JSON(), nullable=True),
sa.Column('variables', sa.JSON(), nullable=True),
sa.Column('start_time', sa.DateTime(), nullable=True),
sa.Column('end_time', sa.DateTime(), nullable=True),
sa.Column('error', sa.Text(), nullable=True),
sa.Column('created_at', sa.DateTime(), nullable=False, server_default=sa.func.now()),
)
if not _table_exists(conn, 'workflow_node_executions'):
op.create_table(
'workflow_node_executions',
sa.Column('id', sa.Integer(), primary_key=True, autoincrement=True),
sa.Column('execution_uuid', sa.String(255), nullable=False),
sa.Column('node_id', sa.String(100), nullable=False),
sa.Column('node_type', sa.String(50), nullable=False),
sa.Column('status', sa.String(20), nullable=False),
sa.Column('inputs', sa.JSON(), nullable=True),
sa.Column('outputs', sa.JSON(), nullable=True),
sa.Column('start_time', sa.DateTime(), nullable=True),
sa.Column('end_time', sa.DateTime(), nullable=True),
sa.Column('error', sa.Text(), nullable=True),
sa.Column('retry_count', sa.Integer(), nullable=False, server_default='0'),
)
if not _table_exists(conn, 'workflow_scheduled_jobs'):
op.create_table(
'workflow_scheduled_jobs',
sa.Column('uuid', sa.String(255), primary_key=True),
sa.Column('trigger_uuid', sa.String(255), nullable=False),
sa.Column('cron_expression', sa.String(100), nullable=True),
sa.Column('next_run_time', sa.DateTime(), nullable=True),
sa.Column('last_run_time', sa.DateTime(), nullable=True),
sa.Column('is_enabled', sa.Boolean(), nullable=False, server_default=sa.true()),
)
_ensure_index(conn, 'workflow_versions', 'ix_workflow_versions_workflow_uuid', ['workflow_uuid'])
_ensure_index(conn, 'workflow_triggers', 'ix_workflow_triggers_workflow_uuid', ['workflow_uuid'])
_ensure_index(conn, 'workflow_executions', 'ix_workflow_executions_workflow_uuid', ['workflow_uuid'])
_ensure_index(
conn,
'workflow_node_executions',
'ix_workflow_node_executions_execution_uuid',
['execution_uuid'],
)
_ensure_index(conn, 'workflow_scheduled_jobs', 'ix_workflow_scheduled_jobs_trigger_uuid', ['trigger_uuid'])
def _add_bot_binding_fields(conn: sa.Connection) -> None:
if not _table_exists(conn, 'bots'):
return
if not _has_column(conn, 'bots', 'binding_type'):
op.add_column(
'bots',
sa.Column('binding_type', sa.String(32), nullable=False, server_default='pipeline'),
)
if not _has_column(conn, 'bots', 'binding_uuid'):
op.add_column('bots', sa.Column('binding_uuid', sa.String(64), nullable=True))
conn.execute(
sa.text("""
UPDATE bots
SET binding_uuid = use_pipeline_uuid
WHERE use_pipeline_uuid IS NOT NULL
AND use_pipeline_uuid != ''
AND (binding_uuid IS NULL OR binding_uuid = '')
""")
)
conn.execute(
sa.text("""
UPDATE bots
SET binding_type = 'pipeline'
WHERE binding_uuid IS NOT NULL
AND binding_uuid != ''
AND (binding_type IS NULL OR binding_type = '')
""")
)
def upgrade() -> None:
conn = op.get_bind()
_create_workflow_tables(conn)
_add_bot_binding_fields(conn)
def downgrade() -> None:
conn = op.get_bind()
if _has_column(conn, 'bots', 'binding_uuid'):
with op.batch_alter_table('bots') as batch_op:
batch_op.drop_column('binding_uuid')
if _has_column(conn, 'bots', 'binding_type'):
with op.batch_alter_table('bots') as batch_op:
batch_op.drop_column('binding_type')
for table_name in (
'workflow_scheduled_jobs',
'workflow_node_executions',
'workflow_executions',
'workflow_triggers',
'workflow_versions',
'workflows',
):
if _table_exists(conn, table_name):
op.drop_table(table_name)
@@ -32,7 +32,7 @@ class MonitoringHelper:
"""Record the start of query processing, returns message_id"""
try:
# Check if session exists, if not, record session start
session_id = f'{query.launcher_type}_{query.launcher_id}'
session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
# Get sender name from message event
sender_name = None
@@ -137,7 +137,7 @@ class MonitoringHelper:
):
"""Record bot response message to monitoring"""
try:
session_id = f'{query.launcher_type}_{query.launcher_id}'
session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
# Get sender name from message event
sender_name = None
@@ -202,7 +202,7 @@ class MonitoringHelper:
) -> str:
"""Record query processing error, returns message_id"""
try:
session_id = f'{query.launcher_type}_{query.launcher_id}'
session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
# Get sender name from message event
sender_name = None
@@ -268,7 +268,7 @@ class MonitoringHelper:
):
"""Record LLM call"""
try:
session_id = f'{query.launcher_type}_{query.launcher_id}'
session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
await ap.monitoring_service.record_llm_call(
bot_id=bot_id,
+21 -9
View File
@@ -13,7 +13,7 @@ import langbot_plugin.api.entities.builtin.platform.message as platform_message
import langbot_plugin.api.entities.builtin.platform.events as platform_events
import langbot_plugin.api.entities.events as events
from ..utils import importutil
from .config_coercion import coerce_pipeline_config
from .config import coerce_pipeline_config
import langbot_plugin.api.entities.builtin.provider.session as provider_session
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
@@ -96,6 +96,15 @@ class RuntimePipeline:
extensions_prefs = pipeline_entity.extensions_preferences or {}
self.enable_all_plugins = extensions_prefs.get('enable_all_plugins', True)
self.enable_all_mcp_servers = extensions_prefs.get('enable_all_mcp_servers', True)
local_agent_config = (pipeline_entity.config or {}).get('ai', {}).get('local-agent', {})
self.mcp_resource_attachments = local_agent_config.get(
'mcp-resources',
extensions_prefs.get('mcp_resources', []),
)
self.mcp_resource_agent_read_enabled = local_agent_config.get(
'mcp-resource-agent-read-enabled',
extensions_prefs.get('mcp_resource_agent_read_enabled', True),
)
if self.enable_all_plugins:
# None indicates to use all available plugins
@@ -116,6 +125,8 @@ class RuntimePipeline:
# Store bound plugins and MCP servers in query for filtering
query.variables['_pipeline_bound_plugins'] = self.bound_plugins
query.variables['_pipeline_bound_mcp_servers'] = self.bound_mcp_servers
query.variables['_pipeline_mcp_resource_attachments'] = self.mcp_resource_attachments
query.variables['_pipeline_mcp_resource_agent_read_enabled'] = self.mcp_resource_agent_read_enabled
# Record query start for monitoring
try:
@@ -178,7 +189,7 @@ class RuntimePipeline:
bot_name = query.variables.get('_monitoring_bot_name', 'Unknown')
pipeline_name = query.variables.get('_monitoring_pipeline_name', 'Unknown')
message_id = query.variables.get('_monitoring_message_id', '')
session_id = f'{query.launcher_type}_{query.launcher_id}'
session_id = f'{query.launcher_type.value if hasattr(query.launcher_type, "value") else query.launcher_type}_{query.launcher_id}'
# Update message status to error
if message_id:
@@ -284,9 +295,9 @@ class RuntimePipeline:
# Record query start and store message_id
message_id = ''
try:
from . import monitoring_helper
from . import monitor
message_id = await monitoring_helper.MonitoringHelper.record_query_start(
message_id = await monitor.MonitoringHelper.record_query_start(
ap=self.ap,
query=query,
bot_id=query.bot_uuid or 'unknown',
@@ -338,7 +349,7 @@ class RuntimePipeline:
# Record query success only if no error occurred during processing
if not query.variables.get('_monitoring_has_error', False):
try:
await monitoring_helper.MonitoringHelper.record_query_success(
await monitor.MonitoringHelper.record_query_success(
ap=self.ap,
message_id=message_id,
query=query,
@@ -348,7 +359,7 @@ class RuntimePipeline:
# Record bot response message
try:
await monitoring_helper.MonitoringHelper.record_query_response(
await monitor.MonitoringHelper.record_query_response(
ap=self.ap,
query=query,
bot_id=query.bot_uuid or 'unknown',
@@ -367,9 +378,9 @@ class RuntimePipeline:
# Record query error
try:
from . import monitoring_helper
from . import monitor
await monitoring_helper.MonitoringHelper.record_query_error(
await monitor.MonitoringHelper.record_query_error(
ap=self.ap,
query=query,
bot_id=query.bot_uuid or 'unknown',
@@ -384,7 +395,8 @@ class RuntimePipeline:
finally:
self.ap.logger.debug(f'Query {query.query_id} processed')
del self.ap.query_pool.cached_queries[query.query_id]
# Use pop with default to avoid KeyError if query was never cached
self.ap.query_pool.cached_queries.pop(query.query_id, None)
class PipelineManager:
@@ -0,0 +1,274 @@
from __future__ import annotations
import traceback
import weakref
from dataclasses import dataclass, field
from typing import Any
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
import langbot_plugin.api.entities.builtin.platform.message as platform_message
@dataclass(frozen=True)
class PluginResponseSource:
plugin: dict[str, str]
event_name: str | None = None
is_approximate: bool = False
@dataclass
class QueryDiagnosticState:
pending_by_chain_id: dict[int, list[PluginResponseSource]] = field(default_factory=dict)
by_response_index: dict[int, list[PluginResponseSource]] = field(default_factory=dict)
finalizer: weakref.finalize | None = None
_QUERY_STATES: dict[int, QueryDiagnosticState] = {}
def record_plugin_response_source(
query: pipeline_query.Query,
response_index: int,
response_sources: list[dict[str, Any]] | None,
emitted_plugins: list[dict[str, Any]] | None = None,
event_name: str | None = None,
) -> None:
plugin_sources = _build_plugin_sources(response_sources, emitted_plugins, event_name)
if not plugin_sources:
return
state = _get_or_create_query_state(query)
state.by_response_index[response_index] = plugin_sources
def record_last_plugin_response_source(
query: pipeline_query.Query,
response_sources: list[dict[str, Any]] | None,
emitted_plugins: list[dict[str, Any]] | None = None,
event_name: str | None = None,
) -> None:
record_plugin_response_source(
query,
len(query.resp_message_chain) - 1,
response_sources,
emitted_plugins,
event_name,
)
def record_pending_plugin_response_source(
query: pipeline_query.Query,
message_chain: platform_message.MessageChain,
response_sources: list[dict[str, Any]] | None,
emitted_plugins: list[dict[str, Any]] | None = None,
event_name: str | None = None,
) -> None:
plugin_sources = _build_plugin_sources(response_sources, emitted_plugins, event_name)
if not plugin_sources:
return
state = _get_or_create_query_state(query)
state.pending_by_chain_id[id(message_chain)] = plugin_sources
def consume_pending_plugin_response_source(
query: pipeline_query.Query,
message_chain: platform_message.MessageChain,
response_index: int,
) -> None:
state = _get_query_state(query)
if state is None:
return
source = state.pending_by_chain_id.pop(id(message_chain), None)
if source is None:
return
state.by_response_index[response_index] = source
def clear_response_source(query: pipeline_query.Query, response_index: int) -> None:
state = _get_query_state(query)
if state is None:
return
state.by_response_index.pop(response_index, None)
_discard_query_state_if_empty(query)
async def notify_response_delivery_failure(
ap: Any,
query: pipeline_query.Query,
response_index: int,
message_chain: platform_message.MessageChain,
error: Exception,
) -> None:
try:
plugin_refs = _get_response_sources(query, response_index)
if not plugin_refs:
return
connector = getattr(ap, 'plugin_connector', None)
if connector is None or not hasattr(connector, 'notify_plugin_diagnostic'):
return
for source in plugin_refs:
payload = _build_delivery_failure_payload(
plugin_ref=source.plugin,
event_name=source.event_name,
is_approximate=source.is_approximate,
query=query,
response_index=response_index,
message_chain=message_chain,
error=error,
)
try:
await connector.notify_plugin_diagnostic(payload)
except Exception as diag_error:
_debug(ap, f'Plugin diagnostic forwarding failed: {diag_error}')
except Exception as diag_error:
_debug(ap, f'Plugin diagnostic forwarding skipped: {diag_error}')
def get_emitted_plugins(event_ctx: Any) -> list[dict[str, Any]]:
emitted_plugins = getattr(event_ctx, '_emitted_plugins', [])
return emitted_plugins if isinstance(emitted_plugins, list) else []
def get_response_sources(event_ctx: Any) -> list[dict[str, Any]] | None:
event_attrs = vars(event_ctx)
if '_response_sources' not in event_attrs:
return None
response_sources = event_attrs['_response_sources']
return response_sources if isinstance(response_sources, list) else []
def _get_or_create_query_state(query: pipeline_query.Query) -> QueryDiagnosticState:
query_key = id(query)
state = _QUERY_STATES.get(query_key)
if state is not None:
return state
state = QueryDiagnosticState()
try:
state.finalizer = weakref.finalize(query, _discard_query_state, query_key)
except TypeError:
state.finalizer = None
_QUERY_STATES[query_key] = state
return state
def _get_query_state(query: pipeline_query.Query) -> QueryDiagnosticState | None:
return _QUERY_STATES.get(id(query))
def _discard_query_state(query_key: int) -> None:
_QUERY_STATES.pop(query_key, None)
def _discard_query_state_if_empty(query: pipeline_query.Query) -> None:
query_key = id(query)
state = _QUERY_STATES.get(query_key)
if state is None:
return
if state.pending_by_chain_id or state.by_response_index:
return
if state.finalizer is not None:
state.finalizer.detach()
_discard_query_state(query_key)
def _get_response_sources(
query: pipeline_query.Query,
response_index: int,
) -> list[PluginResponseSource]:
state = _get_query_state(query)
if state is None:
return []
return state.by_response_index.get(response_index, [])
def _extract_plugin_ref(plugin: Any) -> dict[str, str] | None:
manifest = plugin.get('manifest') if isinstance(plugin, dict) else None
metadata = manifest.get('metadata') if isinstance(manifest, dict) else None
if not isinstance(metadata, dict):
return None
author = metadata.get('author')
name = metadata.get('name')
if not author or not name:
return None
return {'author': str(author), 'name': str(name)}
def _extract_response_source_plugin_ref(source: Any) -> dict[str, str] | None:
if not isinstance(source, dict):
return None
if source.get('kind') != 'reply_message_chain':
return None
plugin_ref = source.get('plugin')
if not isinstance(plugin_ref, dict):
return None
author = plugin_ref.get('author')
name = plugin_ref.get('name')
if not author or not name:
return None
return {'author': str(author), 'name': str(name)}
def _build_plugin_sources(
response_sources: list[dict[str, Any]] | None,
emitted_plugins: list[dict[str, Any]] | None,
event_name: str | None,
) -> list[PluginResponseSource]:
if response_sources is not None:
plugin_refs = [_extract_response_source_plugin_ref(source) for source in response_sources]
return [
PluginResponseSource(plugin=plugin, event_name=event_name) for plugin in plugin_refs if plugin is not None
]
if emitted_plugins:
plugin_refs = [_extract_plugin_ref(plugin) for plugin in emitted_plugins]
return [
PluginResponseSource(plugin=plugin, event_name=event_name, is_approximate=True)
for plugin in plugin_refs
if plugin is not None
]
return []
def _debug(ap: Any, message: str) -> None:
logger = getattr(ap, 'logger', None)
if logger is not None:
logger.debug(message)
def _build_delivery_failure_payload(
plugin_ref: dict[str, str],
event_name: str | None,
is_approximate: bool,
query: pipeline_query.Query,
response_index: int,
message_chain: platform_message.MessageChain,
error: Exception,
) -> dict[str, Any]:
details: dict[str, Any] = {
'message_component_types': [component.__class__.__name__ for component in message_chain],
'message_preview': str(message_chain)[:200],
}
if is_approximate:
details['attribution_warning'] = (
'This diagnostic was delivered to all plugins that handled the event because the '
'plugin runtime did not report the exact reply_message_chain source.'
)
return {
'level': 'ERROR',
'code': 'response_delivery_failed',
'message': 'Failed to deliver a plugin-provided response message.',
'plugin': plugin_ref,
'query': {
'query_id': query.query_id,
'event_name': event_name or query.message_event.__class__.__name__,
'stage': query.current_stage_name or 'SendResponseBackStage',
'response_index': response_index,
},
'details': details,
'delivery': {
'error_type': error.__class__.__name__,
'error_message': str(error),
'traceback': traceback.format_exception_only(type(error), error)[-1].strip(),
},
}
+25 -3
View File
@@ -25,6 +25,21 @@ class PreProcessor(stage.PipelineStage):
- use_funcs
"""
@staticmethod
def _filter_selected_tools(
tools: list,
local_agent_config: dict,
) -> list:
if local_agent_config.get('enable-all-tools', True) is not False:
return tools
selected_tools = local_agent_config.get('tools', [])
if not isinstance(selected_tools, list):
return []
selected_tool_names = {tool for tool in selected_tools if isinstance(tool, str)}
return [tool for tool in tools if tool.name in selected_tool_names]
async def process(
self,
query: pipeline_query.Query,
@@ -32,6 +47,7 @@ class PreProcessor(stage.PipelineStage):
) -> entities.StageProcessResult:
"""Process"""
selected_runner = query.pipeline_config['ai']['runner']['runner']
local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
include_skill_authoring = (
selected_runner == 'local-agent' and getattr(self.ap, 'skill_service', None) is not None
)
@@ -43,7 +59,7 @@ class PreProcessor(stage.PipelineStage):
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', {})
model_config = local_agent_config.get('model', {})
if isinstance(model_config, str):
# Legacy format: plain UUID string
primary_uuid = model_config
@@ -113,11 +129,14 @@ class PreProcessor(stage.PipelineStage):
# 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(
include_mcp_resource_tools = query.variables.get('_pipeline_mcp_resource_agent_read_enabled', True)
all_tools = await self.ap.tool_mgr.get_all_tools(
bound_plugins,
bound_mcp_servers,
include_skill_authoring=include_skill_authoring,
include_mcp_resource_tools=include_mcp_resource_tools,
)
query.use_funcs = self._filter_selected_tools(all_tools, local_agent_config)
self.ap.logger.debug(f'Bound plugins: {bound_plugins}')
self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}')
@@ -128,11 +147,14 @@ class PreProcessor(stage.PipelineStage):
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(
include_mcp_resource_tools = query.variables.get('_pipeline_mcp_resource_agent_read_enabled', True)
all_tools = await self.ap.tool_mgr.get_all_tools(
bound_plugins,
bound_mcp_servers,
include_skill_authoring=include_skill_authoring,
include_mcp_resource_tools=include_mcp_resource_tools,
)
query.use_funcs = self._filter_selected_tools(all_tools, local_agent_config)
sender_name = ''

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