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4b8a8c5e31
* feat(skills): add Agent Skills management system
Implement comprehensive skills management feature inspired by agentskills spec:
Backend:
- Add Skill and SkillPipelineBinding database entities
- Add database migration (dbm018) for skills tables
- Implement SkillManager for skill loading, matching, and resolution
- Implement SkillService for CRUD operations
- Add skills API endpoints for skill and pipeline binding management
- Integrate skill index injection into pipeline preprocessor
- Add skill activation detection in LocalAgentRunner
Frontend:
- Add Skills page with listing, search, and type filter
- Add SkillDetailDialog for create/edit with preview
- Add SkillCard and SkillForm components
- Add skills API methods to BackendClient
- Add skills entry to sidebar navigation
- Add i18n translations (en-US, zh-Hans)
Features:
- Support skill and workflow types
- Sub-skill composition via {{INVOKE_SKILL: name}} syntax
- Progressive disclosure (index in prompt, full instructions on activation)
- Pipeline-specific skill bindings with priority
* fix: resolve cherry-pick conflicts for agentskills onto sandbox
- Remove non-existent external_kb service import
- Add skill_mgr mock to localagent sandbox_exec tests
- Keep database version at 24 (sandbox branch's latest)
* feat(skills): upgrade to package-backed skills with sandbox execution
Evolve the skills system from pure prompt-based to package-backed with
sandbox tool execution support:
- Add source_type/package_root/entry_file/skill_tools fields to Skill entity
- SkillManager loads SKILL.md from local package directories
- SkillToolLoader as 4th dispatch layer in ToolManager (query-scoped)
- LocalAgent injects skill tools into use_funcs on skill activation
- BoxService.execute_skill_tool() runs scripts in sandbox (ro mount, env params)
- Skill tool names auto-namespaced as skill__{skill}__{tool}
- API validation for package_root allowlist and entry path traversal
- Frontend source_type toggle, package_root input, skill_tools editor
- Migration renumbered to 025 with ALTER TABLE fallback for existing DBs
- Fix unclosed limitation section in i18n files
- Fix skills API methods misplaced outside BackendClient class
* fix: test info
* feat(skills): switch skills to package-backed storage and add import tooling
- skills 从 inline/package 双轨收敛成 package-first
- instructions 改为写入并读取 SKILL.md
- 新增本地目录扫描和 GitHub 安装 skill
- 前端把 skills 整合进 plugins 页，新增 SkillsComponent 和 GitHub 导入弹窗
- skill form 去掉 source_type / type 筛选，改成目录扫描驱动
- Box skill tool 挂载模式从 ro 改成 rw
- 测试和中英文文案同步更新
* feat: simplify langbot skill create and import
* refactor(skills): clean up legacy skill API and harden activation flow
* refactor(skills): remove skill dependency expansion and add skill_get
* fix: lint
* fix: delete
* fix(skills): align tool manager loader initialization
* refactor: remove sandbox execute skill
* fix(skills): hide activation markers and isolate skill activation flow
* refactor(skills): switch skill model to filesystem-backed packages
* refactor(skills): switch skill model to filesystem-backed packages
* refactor(skills): unify runtime skill access around filesystem paths
* refactor(skills): unify runtime skill access around filesystem paths
* feat(skills): align rw package design and fix skill activation, visibility, and lint issues
* refactor(skills): replace rich authoring API with import/reload flow and update
Box design doc
* feat(box): add sandbox_exec tool loop for local-agent calculations
* feat(box): add host workspace mounting and sandbox_exec guidance
* feat(box): add BoxProfile with resource limits and improved output truncation
- Implement head+tail output truncation (60/40 split) so LLM sees both
beginning and final results; add streaming byte-limited reads in backend
to prevent unbounded memory usage (_MAX_RAW_OUTPUT_BYTES = 1MB)
- Define BoxProfile model with locked fields and max_timeout_sec clamping
- Add four built-in profiles: default, offline_readonly, network_basic,
network_extended with differentiated resource and security constraints
- Add resource limit fields to BoxSpec (cpus, memory_mb, pids_limit,
read_only_rootfs) and pass corresponding container CLI flags
(--cpus, --memory, --pids-limit, --read-only, --tmpfs)
- Profile loaded from config (box.profile), applied in service layer
before BoxSpec validation; locked fields cannot be overridden by
tool-call parameters
* feat(box): add obs
* refactor(box): unify box service lifecycle and local runtime
management
* refactor(box): remove legacy in-process runtime code and clean up smells
After the architecture settled on always using an independent Box Runtime
service, several pieces of compatibility code and design shortcuts were
left behind. This commit cleans them up:
- Remove `LocalBoxRuntimeClient` and `create_box_runtime_client` from
production code (moved to test-only helper).
- Remove unused `_clip_bytes` method from backend.
- Remove `__langbot_session_placeholder__` hack by making `BoxSpec.cmd`
default to empty and validating non-empty only in `runtime.execute()`.
- Extract `get_box_config()` helper to eliminate 5× duplicated config
access boilerplate.
- Remove `session_id`/`host_path`/`host_path_mode` from the LLM-facing
tool schema to enforce request-scoped session isolation.
- Fix dual shutdown path: `NativeToolLoader.shutdown()` no longer calls
`box_service.shutdown()` (handled by `Application.dispose()`).
- Simplify `_assert_session_compatible` with a loop.
- Inline client creation in `BoxRuntimeConnector`.
- Remove redundant `BOX__RUNTIME_URL` env var from docker-compose
(auto-detected by code).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* feat(box/mcp): integrate MCP stdio with Box sandbox — auto-isolation, dep install, security
## Summary
When Podman/Docker is available, all stdio-mode MCP servers now automatically
run inside Box containers with dependency installation, path rewriting, and
lifecycle management. When no container runtime exists, LangBot starts normally
and stdio MCP falls back to host-direct execution.
## What changed
### MCP stdio → Box integration (mcp.py)
- Add `MCPServerBoxConfig` pydantic model for structured box configuration
with validation and defaults (network, host_path_mode, timeouts, resources)
- Auto-infer `host_path` from command/args with venv detection: recognizes
`.venv/bin/python` patterns and walks up to the project root
- Rewrite host paths to container `/workspace` paths transparently
- Replace venv python commands with container-native `python`
- Auto-detect `pyproject.toml`/`setup.py`/`requirements.txt` and run
`pip install` inside the container before starting the MCP server
- Copy project to `/tmp` before install to handle read-only mounts
- Add retry with exponential backoff (3 retries, 2s/4s/8s delays)
- Add Box managed process health monitoring (poll every 5s)
- Fix session leak: `_cleanup_box_stdio_session()` now runs in `finally`
block of `_lifecycle_loop`, covering all exit paths
- Fix retry logic: `_ready_event` is only set after all retries exhaust
or on success, not on first failure
- Enhance `get_runtime_info_dict()` with `box_session_id` and `box_enabled`
### Box security (security.py — new)
- `validate_sandbox_security()` blocks dangerous host paths:
`/etc`, `/proc`, `/sys`, `/dev`, `/root`, `/boot`, `/run`,
docker.sock, podman socket
- Called at the start of `CLISandboxBackend.start_session()`
### Box models (models.py)
- Add `BoxHostMountMode.NONE` — skips volume mount entirely
- Adjust `validate_host_mount_consistency` to allow arbitrary workdir
when `host_path_mode=NONE`
### Box backend (backend.py)
- Add `validate_sandbox_security()` call in `start_session()`
- Add `langbot.box.config_hash` label on containers for drift detection
- Handle `BoxHostMountMode.NONE` — skip `-v` mount arg
- Add `cleanup_orphaned_containers()` to base class (no-op default) and
CLI implementation (single batched `rm -f` command)
### Box runtime (runtime.py)
- Call `cleanup_orphaned_containers()` during `initialize()` to remove
lingering containers from previous runs
### Box service (service.py)
- Graceful degradation: `initialize()` catches runtime errors and sets
`available=False` instead of crashing LangBot startup
- Add `available` property and guard on `execute_sandbox_tool()`
- Add `skip_host_mount_validation` parameter to `build_spec()` and
`create_session()` — MCP paths are admin-configured and trusted,
bypassing `allowed_host_mount_roots` restrictions meant for
LLM-generated sandbox_exec commands
### Default behavior
- stdio MCP servers automatically use Box when `box_service.available`
is True (Podman/Docker detected); no explicit `box` config needed
- When no container runtime exists, falls back to host-direct stdio
- MCP Box defaults: `network=on` (for pip install), `read_only_rootfs=false`
(for site-packages), `host_path_mode=ro`, `startup_timeout=120s`
### Tests
- `test_box_security.py`: blocked paths, safe paths, subpath rejection
- `test_mcp_box_integration.py`: config model, path rewriting, venv
unwrap, host_path inference, payload building, runtime info, box
availability check
- `test_box_service.py`: `BoxHostMountMode.NONE` validation tests
* feat(box/mcp): instance-based orphan cleanup, error classification, session API, and integration tests
## Changes
### Precise orphan container cleanup
- Runtime generates a unique instance_id on startup
- Every container gets a `langbot.box.instance_id` label
- `cleanup_orphaned_containers()` only removes containers from
previous instances, preserving containers owned by the current one
- Containers from older versions (no label) are also cleaned up
- `cleanup_orphaned_containers` added to `BaseSandboxBackend` as
a no-op default method, removing hasattr duck-typing
### Fine-grained MCP error classification
- New `MCPSessionErrorPhase` enum with 7 phases: session_create,
dep_install, process_start, relay_connect, mcp_init, runtime,
tool_call
- Each phase in `_init_box_stdio_server()` sets the error phase
before re-raising, enabling precise failure diagnosis
- `retry_count` tracked across retry attempts
- `get_runtime_info_dict()` exposes `error_phase` and `retry_count`
### GET /v1/sessions/{id} API
- `BoxRuntime.get_session()` returns session details including
managed process info when present
- `handle_get_session` HTTP handler + route in server.py
- `BoxRuntimeClient.get_session()` abstract method + remote impl
### stdio defaults to Box when runtime is available
- `_uses_box_stdio()` checks `box_service.available` instead of
requiring explicit `box` key in server_config
- `BoxService.initialize()` catches runtime errors gracefully,
sets `available=False` instead of crashing LangBot startup
- When no container runtime exists, stdio MCP falls back to
host-direct execution
### Code quality (from /simplify review)
- Extracted `_VENV_DIRS` / `_VENV_BIN_DIRS` module-level constants
- Removed dead `_box_network_mode()` method and unused `bc` variable
- Fixed broken import `from ....box.models` → `from ...box.models`
- Cached `_resolve_host_path()` result — computed once, passed through
- Config hash now includes `host_path` field
- Batched orphan cleanup into single `rm -f` command
### Session leak fix
- `_cleanup_box_stdio_session()` now runs in `_lifecycle_loop`'s
finally block, covering all exit paths (normal shutdown, error,
retry, final failure)
### Integration tests
- 6 end-to-end tests covering managed process lifecycle, WebSocket
stdio bidirectional IO, session cleanup verification, single
session query, process exit detection, and orphan cleanup safety
* refactor: use rpc
* fix: import
* refactor(box): clean up sandbox subsystem code quality and efficiency
- Fix O(n²) stderr trimming in runtime.py with running length tracker
- Remove dead code: RESERVED_CONTAINER_PATHS, _subprocess_wait_task,
unused config_hash computation, unused imports
- Deduplicate connection callback in BoxRuntimeConnector, parse URL once
- Use enum comparison instead of stringly-typed spec.network.value check
- Replace manual _result_to_dict/_session_to_dict with model_dump()
- Cache NativeToolLoader tool definition and sandbox system guidance
- Extract _is_path_under() helper to eliminate duplicated path checks
- Import SANDBOX_EXEC_TOOL_NAME from native.py instead of redefining
- Add JSON startswith guard in logging_utils to skip futile json.loads
- Fix ruff lint errors (F401 unused imports, F841 unused variables)
* fix: ruff
* refactor(sandbox): keep box logic out of pipeline and localagent
- Move sandbox system-prompt guidance from LocalAgentRunner into
BoxService.get_system_guidance() so all box domain knowledge stays
in the box module.
- Remove standalone logging_utils.py; merge format_result_log() into
MessageHandler base class alongside cut_str().
- Strip sandbox-specific JSON parsing from log formatting; tool
results now use generic truncation.
- Revert TYPE_CHECKING changes in stage.py and runner.py that were
unrelated to this feature.
- Skip two test files affected by a pre-existing circular import
(runner ↔ app) until the import cycle is resolved in a separate PR.
* refactor(box): move box runtime to langbot-plugin-sdk
Extract self-contained box runtime modules (actions, backend, client,
errors, models, runtime, security, server) to langbot-plugin-sdk and
update all imports to use `langbot_plugin.box.*`. Keep only service
and
connector in LangBot core as they depend on the Application context.
- Update docker-compose to use `langbot_plugin.box.server` entry
point
- Update pyproject.toml to use local SDK via `tool.uv.sources`
- Remove migrated source files and their unit/integration tests
- Update remaining test imports to match new module paths
* fix: ruff
* fix(box): tighten sandbox exposure and restore box integration coverage
* refactor(types): remove quoted annotations under postponed evaluation
* chore(sandbox): move MCP loader changes to follow-up branch
* refactor(plugins): simplify GitHub install flow to default master archive
* revert(api): restore plugin GitHub import flow in plugins controller
* Improve data-root handling and skill install previews
* Add managed skill authoring tools for local agents
* Refactor the skills UI around sidebar detail pages
* Document why managed skill authoring tools bypass box
* fix: lint
* feat(web): refactor plugin/skill install flows and fix skills page
- Fix sidebar skill icon
- Add skills route and error page component
- Refactor plugin GitHub install from dialog modal to inline card
- Add skill install dropdown menu (create/upload/github) in sidebar
- Wire sidebar → skills page communication via pendingSkillInstallAction context
- Add i18n keys for error page and skill install actions
* fix(web): persist sidebar collapsible section open state on navigation
Sections opened via sub-item navigation now retain their expanded state
when the user switches to a different section, instead of collapsing
because the isActive fallback becomes false.
---------
Co-authored-by: youhuanghe <1051233107@qq.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: Junyan Qin <rockchinq@gmail.com>
Production-grade platform for building agentic IM bots.
Quickly build, debug, and ship AI bots to Slack, Discord, Telegram, WeChat, and more.
English / 简体中文 / 繁體中文 / 日本語 / Español / Français / 한국어 / Русский / Tiếng Việt
Website ｜ Features ｜ Docs ｜ API ｜ Cloud ｜ Plugin Market ｜ Roadmap
What is LangBot?
LangBot is an open-source, production-grade platform for building AI-powered instant messaging bots. It connects Large Language Models (LLMs) to any chat platform, enabling you to create intelligent agents that can converse, execute tasks, and integrate with your existing workflows.
Key Capabilities
- AI Conversations & Agents — Multi-turn dialogues, tool calling, multi-modal support, streaming output. Built-in RAG (knowledge base) with deep integration to Dify, Coze, n8n, Langflow.
- Universal IM Platform Support — One codebase for Discord, Telegram, Slack, LINE, QQ, WeChat, WeCom, Lark, DingTalk, KOOK.
- Production-Ready — Access control, rate limiting, sensitive word filtering, comprehensive monitoring, and exception handling. Trusted by enterprises.
- Plugin Ecosystem — Hundreds of plugins, event-driven architecture, component extensions, and MCP protocol support.
- Web Management Panel — Configure, manage, and monitor your bots through an intuitive browser interface. No YAML editing required.
- Multi-Pipeline Architecture — Different bots for different scenarios, with comprehensive monitoring and exception handling.
→ Learn more about all features
Quick Start
☁️ LangBot Cloud (Recommended)
LangBot Cloud — Zero deployment, ready to use.
One-Line Launch
uvx langbot
Requires uv. Visit http://localhost:5300 — done.
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git clone https://github.com/langbot-app/LangBot
cd LangBot/docker
docker compose up -d
One-Click Cloud Deploy
More options: Docker · Manual · BTPanel · Kubernetes
Supported Platforms
| Platform | Status | Notes |
|---|---|---|
| Discord | ✅ | |
| Telegram | ✅ | |
| Slack | ✅ | |
| LINE | ✅ | |
| ✅ | Personal & Official API | |
| WeCom | ✅ | Enterprise WeChat, External CS, AI Bot |
| ✅ | Personal & Official Account | |
| Lark | ✅ | |
| DingTalk | ✅ | |
| KOOK | ✅ | |
| Satori | ✅ |
Supported LLMs & Integrations
| Provider | Type | Status |
|---|---|---|
| OpenAI | LLM | ✅ |
| Anthropic | LLM | ✅ |
| DeepSeek | LLM | ✅ |
| Google Gemini | LLM | ✅ |
| xAI | LLM | ✅ |
| Moonshot | LLM | ✅ |
| Zhipu AI | LLM | ✅ |
| Ollama | Local LLM | ✅ |
| LM Studio | Local LLM | ✅ |
| Dify | LLMOps | ✅ |
| MCP | Protocol | ✅ |
| SiliconFlow | Gateway | ✅ |
| Aliyun Bailian | Gateway | ✅ |
| Volc Engine Ark | Gateway | ✅ |
| ModelScope | Gateway | ✅ |
| GiteeAI | Gateway | ✅ |
| CompShare | GPU Platform | ✅ |
| PPIO | GPU Platform | ✅ |
| ShengSuanYun | GPU Platform | ✅ |
| 接口 AI | Gateway | ✅ |
| 302.AI | Gateway | ✅ |
Why LangBot?
| Use Case | How LangBot Helps |
|---|---|
| Customer Support | Deploy AI agents to Slack/Discord/Telegram that answer questions using your knowledge base |
| Internal Tools | Connect n8n/Dify workflows to WeCom/DingTalk for automated business processes |
| Community Management | Moderate QQ/Discord groups with AI-powered content filtering and interaction |
| Multi-Platform Presence | One bot, all platforms. Manage from a single dashboard |
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Try it now: https://demo.langbot.dev/
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demo@langbot.app - Password:
langbot123456
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Thanks to all contributors who have helped make LangBot better: