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Dongchuan Fu
0755beebcd
* feat: Implement workflow form handling for paused workflows - Added module-level storage for pending forms to manage state across sessions. - Introduced functions to set, get, and clear pending forms with expiration handling. - Enhanced DifyServiceAPIRunner to support resuming paused workflows via form actions. - Implemented logic to yield human input requests and display appropriate messages. - Updated workflow submission methods to handle paused states and resume actions. - Ensured proper merging of pending form actions with user inputs for seamless interaction. * feat: Add '_routed_by_rule' variable to form action in Lark and Telegram adapters * feat: Enhance Lark and Telegram adapters with new form handling for paused workflows * feat: Enhance TelegramAdapter to handle form action buttons and message threading * feat: Improve TelegramAdapter message handling with enhanced error management and draft message support * feat: Add the function for formatting human input text to support adapters without rich UI. * feat(dingtalk): implement human input card support and card action handling - Add a new module `card_callback.py` to handle card action button clicks from DingTalk. - Introduce `DingTalkCardActionHandler` to process card action callbacks and extract parameters. - Update `DingTalkAdapter` to manage card state and handle form input through a single card template. - Add configuration for `human_input_card_template_id` in `dingtalk.yaml` to specify the template for human input. - Create a new card template `dingtalk_human_input_card.json` for rendering human input prompts and buttons. * feat(dingtalk): enhance human input card functionality with streaming support and active turn management - Updated the DingTalk card template to enable streaming mode and multi-update configuration. - Removed the obsolete delete_card method from DingTalkClient to streamline card management. - Enhanced DingTalkAdapter to manage active turn cards and accumulated streaming text, ensuring a seamless user experience during human input prompts. - Modified the create_message_card method to utilize existing active cards for resumed workflows, preventing duplication. - Improved the _paint_form_on_card method to update existing cards with human input prompts and buttons dynamically. - Updated the dingtalk_human_input_card.json template to reflect the new streaming capabilities and configuration options. * feat(wecom): implement Dify human input pause handling with button interaction support * feat(qqofficial): implement Dify human input button interaction handling and markdown keyboard support * feat(qqofficial): implement one-click QR binding and enhance localization support * feat(discord): implement Discord form view with button interactions for Dify actions * fix(telegram): correct group chat type check and handle oversized callback data for Telegram actions fix(difysvapi): ensure safe access to remove-think configuration in pipeline settings * feat(dify): add support for chatflow app type and enhance human input handling * feat(telegram): add action title feedback for user selections in Telegram messages * feat(lark): enhance LarkAdapter to store form content for resume notices * feat(dingtalk): update display formatting for card content with HTML line breaks * feat(dingtalk): add feedback functionality to cards with 👍/👎 buttons - Implemented feedback state management for cards, allowing users to provide feedback via thumbs up/down buttons. - Enhanced card rendering to include feedback buttons when appropriate. - Registered feedback listeners to handle feedback events and update card states accordingly. - Updated the card template to support dynamic button rendering for feedback actions. - Improved error handling and logging for feedback actions and card updates. * fix: add Avatar component to dingtalk_human_input_card.json for enhanced user interaction * feat(wecom): add optional source block to interactive template cards for enhanced branding * feat(wecom): add functions for template card action extraction and update, enhance button interaction handling * feat(qqofficial): synchronize passive-reply counter with inbound message sequence * feat(qqofficial): add method to identify invisible form placeholder chunks in messages * feat(dingtalk): add download link for human input card template and enhance dynamic form configuration * feat(telegram): enhance message handling with group stream deletion and form placeholder detection * Add unit tests for DingTalk, Lark, WeComBot, and Dify service API runners - Implement tests for DingTalk adapter helper functions including form content cleaning, input extraction, and completed input lines. - Create unit tests for Lark adapter helper functions focusing on input extraction and completed input lines. - Add tests for WeComBot template card functionalities, including event extraction and payload building for human input. - Enhance Dify service API runner tests to cover human input forms, including input collection, action handling, and form snapshot extraction. * feat: Enhance Telegram and QQ Official adapters with select field handling and form action processing - Added support for select fields in Telegram adapter, including option extraction and callback handling. - Implemented form action processing for Telegram callbacks, improving user interaction feedback. - Introduced new helper functions for building keyboards and resolving select button actions in QQ Official adapter. - Enhanced DifyServiceAPIRunner to handle cumulative streaming responses and improve error handling during workflow resumes. - Added unit tests for new functionalities in Telegram and QQ Official adapters, ensuring robust behavior for select fields and form actions. * feat(lark): add functions for current input definitions and visible form content handling feat(qqofficial): update fallback text handling for non-streaming scenarios feat(difysvapi): enhance form content processing for interactive fields and actions test: add unit tests for Lark and QQ Official adapter functionalities * Add tests for DingTalk adapter content processing and markdown formatting - Updated the assertion in `test_dingtalk_completed_input_lines_include_text_and_select_values` to remove unnecessary markdown formatting. - Added new tests to verify that `_dingtalk_clean_form_content` maintains the order of prompts and completed values in various scenarios. - Introduced `test_dingtalk_card_markdown_preserves_internal_line_breaks` to ensure internal line breaks are correctly converted to HTML line breaks. * feat: Refactor input handling and feedback messages across multiple adapters * feat: Update the human-computer interaction template cards, and optimize the prompt information and content display. * feat: Refactor pending form handling to isolate by bot and pipeline * feat: Enhance error handling and caching for Dify and WeCom interactions * feat: Enhance select input handling and validation in Dify API runner and Telegram adapter * feat: Add missing completed input lines handling in DingTalk adapter * feat: Add pipeline_uuid handling across multiple adapters and update related tests
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.
😎 Stay Updated
Click the Star and Watch buttons in the top-right corner of the repository to get the latest updates.
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 --profile all 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: