mirror of
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Junyan Chin
e9dd584792
* feat(api): support global API key from config.yaml (api.global_api_key) Accept a config-defined global API key anywhere a web-UI key is accepted (X-API-Key / Bearer), with no login session and no DB record. Useful for automated deployments and AI agents (HTTP API + MCP). Defaults to empty (disabled); does not require the lbk_ prefix. - templates/config.yaml: add api.global_api_key with security notes - service/apikey.py: verify_api_key checks global key first (constant-time) - docs/API_KEY_AUTH.md: document the global key + security guidance - tests: cover global-key match, prefix-free, fallback-to-db, disabled * feat(mcp): expose LangBot management as an MCP server at /mcp Add an MCP (Model Context Protocol) server so external AI agents can manage a LangBot instance. Reuses the same API-key auth as the HTTP API (including the config.yaml global API key). - pkg/api/mcp/server.py: FastMCP server wrapping the service layer; 21 curated tools across system/bots/pipelines/models/knowledge/mcp-servers/skills - pkg/api/mcp/mount.py: ASGI dispatcher fronting Quart; authenticates /mcp requests with an API key, runs the streamable-HTTP session manager lifespan - controller/main.py: serve the wrapped ASGI app via hypercorn (was run_task) - web: new 'MCP' tab in the API integration dialog showing endpoint, auth, and client config; i18n for 8 locales - tests/manual/mcp_smoke.py: e2e check (401 unauth, list tools, call tools) Tool surface is intentionally curated (not all ~25 route groups) to keep the agent surface small, safe, and maintainable. Extend deliberately. * feat(skills): add in-repo skills/ as the single source of truth Migrate the agent skills + QA/e2e test harness from the (now archived) langbot-app/langbot-skills repo into LangBot/skills/, and add four new skills. Migrated: - langbot-plugin-dev, langbot-testing (e2e), langbot-env-setup, langbot-skills-maintenance, langbot-eba-adapter-dev - the bin/lbs CLI (src/, test/, scripts/, schemas/, qa-agent-docs/) New: - langbot-dev core backend + web development - langbot-deploy Docker/K8s deployment + config.yaml + global API key - langbot-mcp-ops operating the LangBot MCP server (/mcp) - langbot-space-ops operating the Space marketplace MCP server - src/cli.ts repoRoot(): recognize the skills assets root (skills.index.json + bin/lbs) so the CLI works when nested inside the LangBot repo - README.md: unified skill catalog; skills.index.json regenerated Parity with source verified: bin/lbs validate + node test suite match the source repo (only the uncommitted .lbpkg build-artifact fixture differs). * docs(agents): document agent-facing surfaces + API/MCP/skills sync rule * docs(readme): add 'Built for AI Agents' section across all locales Highlight MCP server, in-repo skills (single source of truth), AGENTS.md sync rule, and llms.txt. Cross-link LangBot Space MCP marketplace. * style(mcp): fix ruff format + prettier lint in MCP server and API panel * style(web): prettier format MCP i18n locale entries * docs(skills): note MCP instance control in dev/testing skills All development-guidance skills now point to the LangBot instance MCP server (/mcp) and the Space marketplace MCP server, reusing API keys.
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, Deerflow, Weknora.
- 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
📍 Practical guides: deploy a multi-platform AI bot in 5 minutes, connect DeepSeek to WeChat, Discord, and Telegram, run a Dify Agent in Discord, Telegram, and Slack, and build an n8n-powered chatbot.
Quick Start
☁️ LangBot Cloud (Recommended)
LangBot Cloud — Zero deployment, ready to use.
One-Line Launch
uvx langbot
Requires uv. Visit http://localhost:5300 — done.
Docker Compose
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 | ✅ | Official |
| Telegram | ✅ | Official |
| Slack | ✅ | Official |
| LINE | ✅ | Official |
| ✅ | Personal & Official API (Channel, DM, Group) | |
| WeCom | ✅ | Enterprise WeChat, External CS, AI Bot |
| ✅ | Personal & Official Account | |
| Lark | ✅ | Official |
| DingTalk | ✅ | Official |
| KOOK | ✅ | Official |
| Satori | ✅ | |
| ✅ | Matrix, Satori | |
| Matrix | ✅ | Supports multiple bridged platforms such as Signal, WhatsApp, Messenger, iMessage, Mattermost, Google Chat, IRC, XMPP, Zulip, and more |
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 | ✅ |
| Qiniu | 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 |
Built for AI Agents 🤖
LangBot is agent-friendly by design — your coding agents (Claude Code, Codex, Copilot, Cursor, …) can operate, extend, and deploy LangBot with first-class support:
- MCP Server — LangBot exposes a built-in Model Context Protocol endpoint at
/mcp, mirroring the HTTP API so an agent can manage bots, pipelines, plugins, and models programmatically. Authenticate with the same API key (set a global key inconfig.yamlor use a per-user key) — no login flow required. Configure it in the Web panel's API & MCP tab. - In-repo Skills — The
skills/directory is the single source of truth for working with LangBot: plugin development, core development, end-to-end testing, deployment, and operating the LangBot / LangBot Space MCP servers. Point your agent at this directory and it knows how to build. - AGENTS.md — Every repo ships an
AGENTS.md(symlinked toCLAUDE.md) describing architecture, conventions, and the rule that API changes must keep the MCP server and skills in sync. llms.txt— Machine-readable project context for LLMs is published on the website.
Cloud / Marketplace: LangBot Space also exposes an MCP server so agents can search and inspect the plugin / MCP / skill marketplace, authenticated with a Personal Access Token.
Live Demo
Try it now: https://demo.langbot.dev/
- Email:
demo@langbot.app - Password:
langbot123456
Note: Public demo environment. Do not enter sensitive information.
Community
Star History
Contributors
Thanks to all contributors who have helped make LangBot better: