MiroFlow is a modular AI agent framework designed for building, managing, and scaling complex multi-agent systems with a focus on reproducible performance and robust tool integration. It targets developers and researchers building sophisticated AI applications that require reliable execution of multi-step, tool-using tasks, offering a battle-tested solution that achieves state-of-the-art results on benchmarks like GAIA.

How It Works

MiroFlow employs a multi-stage, agentic process starting with intent recognition and query augmentation. A main orchestrator agent then plans and executes tasks, delegating to specialized sub-agents for domain-specific operations. Agents interact with external capabilities via MCP (Model Context Protocol) servers, which provide implementations for various tools like code execution, web search, and visual perception. The framework emphasizes modularity, with core components for pipeline management, LLM interaction, and tool integration, supported by a configuration system and observability features.

Quick Start & Requirements

• Install: Clone the repository and use uv sync within apps/run-agent to set up the Python environment.
• Prerequisites: Requires API keys for various services including Hugging Face (HF_TOKEN), OpenRouter, Anthropic, OpenAI, Gemini, Serper, Juna, and E2B. Python 3.12+ is recommended.
• Setup: Detailed setup involves cloning, environment preparation, and API key configuration via .env files. Optional E2B sandbox setup requires Docker and npm.
• Links: MiroThinker Demo, E2B Docker Documentation

Highlighted Details

• Achieves 72.2% pass@1 (avg@3) on the GAIA validation set with Claude Sonnet 3.7, claiming state-of-the-art performance among open-source frameworks.
• Features high concurrency and fault tolerance for efficient data collection and handling of API rate limits/network instability.
• Includes baked-in observability with a web UI for visualizing and debugging agent traces, plus benchmarking scripts.
• Supports a wide range of LLM providers and custom tool integration via MCP servers.

Maintenance & Community

The project emphasizes continuous updates with monthly releases and community co-creation, welcoming pull requests and feature proposals. Community channels include X (@MiroMindAI), RedNote, and Discord.

Licensing & Compatibility

The repository's license is not explicitly stated in the README, which may impact commercial use or closed-source linking.

Limitations & Caveats

The README does not specify the project's license, which is a critical factor for adoption. While claiming reproducibility, users must set up numerous API keys and potentially complex E2B sandbox environments.