* feat(box): bidirectional attachment transfer for sandbox Materialize inbound attachments into the sandbox workspace so agents can process user-sent files, and collect agent-produced files from the outbox to attach them back to the reply. - box(service): add materialize_inbound_attachments / collect_outbound attachments. Prefer direct host-filesystem read/write on the bind-mounted workspace (no size limit), falling back to chunked exec only for non-shared backends (e2b/remote). Clear per-query inbox/outbox dirs at turn start to avoid query_id-reuse collisions. - provider(localagent): inject inbound attachment descriptors into the sandbox and append a system note telling the agent the inbox/outbox paths. - pipeline(wrapper): collect outbox files on the final stream chunk and append them as attachment components to the response chain. - web(debug-dialog): render File components with a download link when base64/url is present; add base64/path fields to the File entity. - tests: cover inbound/outbound, large-file transfer without truncation, and stale-dir clearing (86 passing). * feat(box): support voice/file attachment round-trip end-to-end Extends the bidirectional attachment transfer to audio and arbitrary files through the real webchat UI, and fixes the model-payload errors that non-image attachments triggered. - platform(websocket_adapter): resolve Voice/File component storage keys to base64 (previously only Image), so audio/documents reach the sandbox inbox. - web(debug-dialog): accept audio/* and any file in the uploader (was image-only), classify by mimetype, upload Voice/File via the documents endpoint, and render non-image staged attachments as a chip. - provider(litellmchat): drop non-image file parts (file_base64 / file_url) when building the OpenAI/LiteLLM payload. These come from Voice/File attachments — including ones replayed from conversation history — and the agent reads their bytes from the sandbox, not the model. Without this the provider rejects the request: 'invalid content type=file_base64'. - provider(localagent): also strip those parts from the current user message alongside the sandbox-path note (model-facing clarity; the requester is the real safety net for history). - tests: cover the requester strip/keep behavior (file dropped, image kept and reshaped to image_url, mixed history, plain-string content). * test(box): cover inbound/outbound attachment helpers; fix ruff format - ruff format localagent.py (CI ruff format --check was failing) - add unit tests for ResponseWrapper outbound-attachment helpers (wrapper.py 78%->98%) - add unit tests for LocalAgentRunner._inject_inbound_attachments - add unit tests for WebSocketAdapter._process_image_components (0%->covered) Lifts PR patch coverage from 68.97% to ~88% (>75% target).
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 |
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: