SJ
0711ec346f
* fix: resolve MCP tools race condition causing 'completion 无法解析' error - Wait for MCP client initialization to complete before accepting requests - Add Future-based synchronization in init_mcp_clients() - Prevent tool_calls from being rejected due to empty func_list - Improve error logging for MCP initialization failures Fixes race condition where AI attempts to call MCP tools before they are registered, resulting in 'API 返回的 completion 无法解析' exceptions. The issue occurred because: 1. MCP clients were initialized asynchronously without waiting 2. System accepted user requests immediately after startup 3. AI received empty tool list and attempted to call non-existent tools 4. Tool matching failed, causing parsing errors This fix ensures all MCP tools are loaded before the system processes any requests that might use them. * perf: add timeout and better error handling for MCP initialization - Add 20-second total timeout to prevent slow MCP servers from blocking startup - Show detailed configuration info when MCP initialization fails - List all failed services in a summary warning - Gracefully handle timeout by using already-completed services This ensures that even if some MCP servers are slow or unreachable, the system will start within a reasonable time and provide clear feedback about which services failed and why. * refactor: simplify MCP init orchestration and improve log security - Replace Future-based sync with asyncio.wait + name→task mapping - Explicitly cancel timed-out tasks after 20s timeout - Downgrade sensitive config details (command/args/URL) to debug level - Move urllib.parse import to top-level * fix: prevent initialized MCP clients from being cleaned up on timeout - Do not cancel pending tasks on timeout; let them continue running in the background waiting for the termination signal (event.set()), so successfully initialized services remain available - Track initialization state with a flag to distinguish init failures from post-init cancellations in _init_mcp_client_task_wrapper * fix: restore task cancellation on timeout per review feedback Pending tasks in asyncio.wait are tasks that have NOT completed initialization within 20s, so cancelling them is safe and correct. * fix: separate init signal from client lifetime in MCP task wrapper The previous design awaited task completion, but tasks only finish on shutdown (after event.wait()), causing asyncio.wait to always hit the 20s timeout and cancel all clients. Fix: introduce a dedicated ready_event that is set immediately after _init_mcp_client completes. init_mcp_clients now waits only for ready_event (with 20s timeout), while the long-lived client task continues running in the background until shutdown_event is set. This ensures startup returns promptly once clients are ready. * security: redact sensitive MCP config from debug logs Only log executable name and argument count instead of full command/args to avoid leaking tokens or credentials even at debug level. * refactor: use McpClientInfo dataclass and MCP_INIT_TIMEOUT constant - Extract MCP_INIT_TIMEOUT = 20.0 as a named module-level constant - Replace tuple-based client_info with _McpClientInfo dataclass to eliminate index-based access and improve readability - Remove _wait_ready helper; use asyncio.create_task(event.wait()) directly - Await cancelled tasks after timeout to prevent lingering background tasks and unobserved exceptions * fix: handle CancelledError and clean up wait_tasks on timeout - Catch asyncio.CancelledError separately in _init_mcp_client_task_wrapper so ready_event.set() is always called (Python 3.8+ CancelledError inherits BaseException, not Exception) - Cancel and await lingering wait_tasks after timeout to prevent them from hanging indefinitely when ready_event is never set * fix: align enable_mcp_server with new wrapper API and fix security/config issues - Fix enable_mcp_server to pass shutdown_event + ready_event instead of ready_future, matching _init_mcp_client_task_wrapper's current signature - Cancel and await init_task on timeout; clean up mcp_client_event on failure - Read MCP_INIT_TIMEOUT from env var ASTRBOT_MCP_INIT_TIMEOUT (default 20s) so operators can tune it without code changes - Strip userinfo from URL in debug log (use hostname+port only, not netloc) to avoid leaking credentials embedded in URLs * refactor: register mcp_client_event only after successful init in enable_mcp_server Move self.mcp_client_event[name] assignment to after initialization succeeds, so callers never observe a stale event for a failed client. * fix: harden MCP init state handling and timeout parsing * fix: improve MCP timeout and post-init error observability * refactor: simplify MCP init lifecycle orchestration * refactor: simplify MCP init flow and cap timeout values * fix: refine mcp timeout handling and lifecycle task tracking * fix: harden mcp shutdown and timeout source logging * refactor: simplify mcp runtime registry and timeout flow * fix: keep mcp init summary return contract * refactor: streamline mcp lifecycle and init errors * refactor: unify mcp lifecycle wait handling * refactor: simplify mcp runtime ownership and timeout resolution * fix: harden mcp shutdown waiting and startup signaling * refactor: streamline mcp lifecycle and shutdown errors * refactor: harden mcp runtime access and shutdown * fix: ensure mcp client cleanup and clarify views * refactor: cache mcp client view and guard startup * refactor: simplify mcp init cleanup and runtime lock * refactor: reduce mcp runtime duplication * refactor: reuse mcp cleanup and client view --------- Co-authored-by: idiotsj <idiotsj@users.noreply.github.com> Co-authored-by: 邹永赫 <1259085392@qq.com>
AstrBot is an open-source all-in-one Agent chatbot platform that integrates with mainstream instant messaging apps. It provides reliable and scalable conversational AI infrastructure for individuals, developers, and teams. Whether you're building a personal AI companion, intelligent customer service, automation assistant, or enterprise knowledge base, AstrBot enables you to quickly build production-ready AI applications within your IM platform workflows.
Key Features
- 💯 Free & Open Source.
- ✨ AI LLM Conversations, Multimodal, Agent, MCP, Skills, Knowledge Base, Persona Settings, Auto Context Compression.
- 🤖 Supports integration with Dify, Alibaba Cloud Bailian, Coze, and other agent platforms.
- 🌐 Multi-Platform: QQ, WeChat Work, Feishu, DingTalk, WeChat Official Accounts, Telegram, Slack, and more.
- 📦 Plugin Extensions with 1000+ plugins available for one-click installation.
- 🛡️ Agent Sandbox for isolated, safe execution of code, shell calls, and session-level resource reuse.
- 💻 WebUI Support.
- 🌈 Web ChatUI Support with built-in agent sandbox and web search.
- 🌐 Internationalization (i18n) Support.
| 💙 Role-playing & Emotional Companionship | ✨ Proactive Agent | 🚀 General Agentic Capabilities | 🧩 1000+ Community Plugins |
|---|---|---|---|
Quick Start
One-Click Deployment
For users who want to quickly experience AstrBot, we recommend using the one-click deployment method with uv ⚡️:
uv tool install astrbot
astrbot init # Only execute this command for the first time to initialize the environment
astrbot
Requires uv to be installed.
Docker Deployment
For users who want a more stable and production-ready deployment, we recommend using Docker / Docker Compose to deploy AstrBot.
Please refer to the official documentation: Deploy AstrBot with Docker.
Deploy on RainYun
For users who want to deploy AstrBot with one-click and don't want to manage the server, we recommend using RainYun's one-click cloud deployment service ☁️:
Desktop Application (Tauri)
For users who want to deploy AstrBot on their desktop, primarily using AstrBot ChatUI, rarely use AstrBot plugins, we recommend using the AstrBot App:
Desktop repository: AstrBot-desktop.
Supports multiple system architectures, direct package installation, and out-of-the-box usage. A convenient one-click desktop deployment option for beginners.
One-Click Launcher Deployment (AstrBot Launcher)
For users who want a quick deployment and multi-instance solution with environment isolation, we recommend using the AstrBot Launcher:
Visit the AstrBot Launcher repository and install the package for your OS from the latest release.
A quick deployment and multi-instance solution with environment isolation.
Deploy on Replit
Community-contributed deployment method.
AUR
yay -S astrbot-git
More deployment methods: BT-Panel Deployment | 1Panel Deployment | CasaOS Deployment | Manual Deployment
Supported Messaging Platforms
Connect AstrBot to your favorite chat platform.
| Platform | Maintainer |
|---|---|
| Official | |
| OneBot v11 protocol implementation | Official |
| Telegram | Official |
| Wecom & Wecom AI Bot | Official |
| WeChat Official Accounts | Official |
| Feishu (Lark) | Official |
| DingTalk | Official |
| Slack | Official |
| Discord | Official |
| LINE | Official |
| Satori | Official |
| Misskey | Official |
| WhatsApp (Coming Soon) | Official |
| Matrix | Community |
| KOOK | Community |
| VoceChat | Community |
Supported Model Services
| Service | Type |
|---|---|
| OpenAI and Compatible Services | LLM Services |
| Anthropic | LLM Services |
| Google Gemini | LLM Services |
| Moonshot AI | LLM Services |
| Zhipu AI | LLM Services |
| DeepSeek | LLM Services |
| Ollama (Self-hosted) | LLM Services |
| LM Studio (Self-hosted) | LLM Services |
| AIHubMix | LLM Services (API Gateway, supports all models) |
| CompShare | LLM Services |
| 302.AI | LLM Services |
| TokenPony | LLM Services |
| SiliconFlow | LLM Services |
| PPIO Cloud | LLM Services |
| ModelScope | LLM Services |
| OneAPI | LLM Services |
| Dify | LLMOps Platforms |
| Alibaba Cloud Bailian Applications | LLMOps Platforms |
| Coze | LLMOps Platforms |
| OpenAI Whisper | Speech-to-Text Services |
| SenseVoice | Speech-to-Text Services |
| OpenAI TTS | Text-to-Speech Services |
| Gemini TTS | Text-to-Speech Services |
| GPT-Sovits-Inference | Text-to-Speech Services |
| GPT-Sovits | Text-to-Speech Services |
| FishAudio | Text-to-Speech Services |
| Edge TTS | Text-to-Speech Services |
| Alibaba Cloud Bailian TTS | Text-to-Speech Services |
| Azure TTS | Text-to-Speech Services |
| Minimax TTS | Text-to-Speech Services |
| Volcano Engine TTS | Text-to-Speech Services |
❤️ Contributing
Issues and Pull Requests are always welcome! Feel free to submit your changes to this project :)
How to Contribute
You can contribute by reviewing issues or helping with pull request reviews. Any issues or PRs are welcome to encourage community participation. Of course, these are just suggestions—you can contribute in any way you like. For adding new features, please discuss through an Issue first.
Development Environment
AstrBot uses ruff for code formatting and linting.
git clone https://github.com/AstrBotDevs/AstrBot
pip install pre-commit
pre-commit install
🌍 Community
QQ Groups
- Group 1: 322154837
- Group 3: 630166526
- Group 5: 822130018
- Group 6: 753075035
- Group 7: 743746109
- Group 8: 1030353265
- Developer Group: 975206796
Discord Server
❤️ Special Thanks
Special thanks to all Contributors and plugin developers for their contributions to AstrBot ❤️
Additionally, the birth of this project would not have been possible without the help of the following open-source projects:
- NapNeko/NapCatQQ - The amazing cat framework
⭐ Star History
Tip
If this project has helped you in your life or work, or if you're interested in its future development, please give the project a Star. It's the driving force behind maintaining this open-source project <3
Companionship and capability should never be at odds. What we aim to create is a robot that can understand emotions, provide genuine companionship, and reliably accomplish tasks.
私は、高性能ですから!
