🚀 Mirobody

Your Data, Your AI — Health, Finance & More. Open Source, Privacy-First.

Self-hosted data platform that bridges your personal data with the latest AI capabilities

AI Engine:

• 🌐 HTTP Remote MCP Server - Deploy and access MCP tools over HTTPS
• 🎯 Claude Agent Skills Support - Develop tools using standard Skills format (SKILL.md)
• 🔄 Universal Tool Adapter - Works with ChatGPT, Claude, Cursor, and more
• 🔌 Pluggable Data Providers - Connect any data source via Providers API
• 🤖 Custom Agents - Create your own conversational agents via Agents API

Health Data:

• 🏥 FHIR & Health Standards - 400+ health indicators, LOINC / SNOMED CT / RxNorm cross-vocabulary search
• 📊 Health Data Pipeline - Ingest from 300+ wearables, Apple Health, EHR; normalize to StandardPulseData

📖 Table of Contents

• Why Mirobody?
• Architecture — AI & Agent Engine, FHIR & Health Standards, Health Data Pipeline, Infrastructure
• Theta Wellness: Our Health Intelligence App
• Quick Start
• Access & Authentication
• API Reference
• Documentation

✨ Why Mirobody?

🔄 Write Tools Once, Run Everywhere

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:

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, OpenRouter, and any OpenAI-compatible endpoint
• File workspace: A PostgreSQL-backed virtual filesystem (/uploads, /library, /memories, /charts) lets the agent read uploaded documents (PDF, image, Excel, …) across any provider
• Middleware Stack: Includes prompt caching, tool call patching, and message summarization
• Charts (two paths, routed by agent):
  • DeepAgent / MixAgent draw charts by emitting fenced ```vis-chart data blocks (pure-data JSON, no styling) that the frontend renders — no tool call, no PNG round-trip.
  • BaseAgent draws charts via the ChartService MCP tools (generate_*_chart), the standard "LLM + MCP tool" path: the model calls a tool, ChartService renders a PNG (Node @antv/gpt-vis-ssr), stores it under /charts, and the stream emits an image event. This is filtered out of DeepAgent/MixAgent on purpose (see _CHART_SERVICE_TOOLS in mirobody/pub/agents/deep_agent.py).
  • The /charts virtual-FS scope and its static-served storage volume (mirobody_charts → /charts) back the PNG path; a fork can also point its own chart-image tool there.

MixAgent

A two-phase model fusion architecture that separates tool orchestration from response generation:

• Phase 1 (Orchestrator): A capable model handles tool calls and data collection
• Phase 2 (Responder): A cost-effective model generates the final response with collected context

This architecture optimizes for both cost and quality by using expensive models only where necessary. MixAgent is currently experimental.

BaseAgent

A lightweight agent for direct LLM conversations, driven by the provider's own tool-calling loop. 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.

🏗️ Architecture

AI & Agent Engine

FHIR & Health Standards