Forget about complex JSON schemas, manual bindings, or router configurations. In Mirobody, your Python code is the only definition required.
Tools built here instantly work in ChatGPT (via Apps-SDK) and the entire MCP Ecosystem (Claude, Cursor, IDEs).
Mirobody works simultaneously as an MCP Client (to use tools) and an OAuth-enabled MCP Server (to provide data), creating a complete data loop.
🌐 HTTP Remote MCP Support: Mirobody supports HTTP-based remote MCP servers, enabling cloud deployments and cross-network tool access. Configure MCP_PUBLIC_URL to expose your MCP server over HTTPS for ChatGPT Apps and other remote integrations.
💎 Your Data Is an Asset, Not a Payload
Mirobody is built for Personal Intelligence, not just local storage. We believe the next frontier of AI is not knowing more about the world, but knowing more about you.
General AI creates generic answers. Mirobody uses your data to create a Personal Knowledge Base, enabling AI to give answers that are truly relevant to your life.
You can run the entire engine locally on your machine. We provide the architecture to unlock your data's value without ever compromising ownership.
🤖 Native Agent Engine
Powered by a self-developed agent engine that fully reproduces Claude Code's autonomous capabilities locally.
🎯 Skills-Based Tool Development: Mirobody supports developing tools using Claude Agent Skills format (SKILL.md files). You can create reusable tools that work seamlessly across the MCP ecosystem. Simply structure your tools as Skills and drop them into the skills/ directory - Mirobody will automatically discover and expose them.
Designed to load Claude Agent Skills SKILL.md files, turning your private data into an actionable knowledge base.
🧠 Agent Architecture
Mirobody provides three agent types for different use cases:
| Agent | Description | Use Case |
| ----------------------- | ------------------------------- | ------------------------------------------------------ |
| DeepAgent | Single-model tool orchestration | Complex queries requiring data retrieval and analysis |
| MixAgent | Two-phase model fusion | Optimized cost/quality balance with specialized models |
| BaselineAgent | Direct LLM conversation | Simple Q&A without tool calls |
DeepAgent
Inspired by LangChain DeepAgents, DeepAgent is our primary agent for tool-assisted conversations. Key features:
Full MCP Tool Support: All tools are MCP-compliant and configurable via ALLOWED_TOOLS / DISALLOWED_TOOLS
Multi-Provider: Supports Google GenAI, OpenAI, Anthropic, and OpenRouter
Middleware Stack: Includes prompt caching, tool call patching, and message summarization
MixAgent
A two-phase model fusion architecture that separates tool orchestration from response generation:
Phase 1 (Orchestrator): A capable model (e.g., Claude Sonnet) handles tool calls and data collection
Phase 2 (Responder): A cost-effective model (e.g., Gemini Flash) generates the final response with collected context
This architecture optimizes for both cost and quality by using expensive models only where necessary.
BaselineAgent
A lightweight agent for direct LLM conversations without tool access. Ideal for:
Simple Q&A scenarios
Testing and development
Low-latency responses
📁 Secure File Operations: File tools (ls, read_file, write_file, edit_file, glob, grep) are backed by PostgreSQL for data persistence and auditability. See mirobody/pub/tools/file_read_service.py and file_write_service.py for implementation details.
🧪 Sandbox Code Execution: The execute tool runs shell commands in isolated E2B cloud sandboxes for data analysis and computation. Requires E2B_API_KEY configuration. See CONFIG for setup details.
👉 See CONFIG for detailed agent configuration guide.
⭐AI-driven public opinion & trend monitor with multi-platform aggregation, RSS, and smart alerts.🎯 告别信息过载，你的 AI 舆情监控助手与热点筛选工具！聚合多平台热点 + RSS 订阅，支持关键词精准筛选。AI 智能筛选新闻 + AI 翻译 + AI 分析简报直推手机，也支持接入 MCP 架构，赋能 AI 自然语言对话分析、情感洞察与趋势预测等。支持 Docker ，数据本地/云端自持。集成微信/飞书/钉钉/Telegram/邮件/ntfy/bark/slack 等渠道智能推送。
