MiroFlow
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MiroMind Research Agent: Fully Open-Source Deep Research Agent with Reproducible State-of-the-Art Performance on FutureX, GAIA, HLE, BrowserComp and xBench.
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π Try Demo ο½ δΈζ ο½ ζ₯ζ¬θͺ
This repo is the official implementation of the MiroMind Research Agent Project. It is a leading-performance, fully open-source system designed to perform multi-step internet research for addressing complex challenges such as future event prediction. The project currently comprises four key components:
- π€ MiroFlow: an open-source research agent framework that offers reproducible state-of-the-art performance on representative benchmarks (e.g., FutureX, GAIA, HLE, xBench-DeepSearch, and BrowserComp benchmarks), included in this repo. See [Get Started in Under 5 Minutes] for a quick start.
- π€ MiroThinker: an open-source agent foundation model that natively supports tool-assisted reasoning. See MiroThinker.
- π MiroVerse: 147k premium open-source training data supporting research agent training. See MiroVerse.
- π§ MiroTrain / MiroRL: The training infra that supports stable and efficient training for the research agent models. See MiroTrain / MiroRL
π Table of Contents
- π° News & Updates
- π Get Started in Under 5 Minutes
- π€ What is MiroFlow?
- π Highlights
- π Performance on Benchmarks
- π§ Supported Models & Tools
- β FAQ
- π€ Contributing
- π License
- π Acknowledgments
π° News & Updates
- [2025-09-15]: ππ MiroFlow v0.3: Enhanced codebase architecture and significantly improved benchmark performance, boosting GPT-5's prediction accuracy for future events by 11%. MiroFlow now ranks #1 in the future prediction benchmark. See FutureX.
- [2025-08-27]: MiroFlow v0.2: Achieves state-of-the-art performance across multiple agentic benchmarks, including HLE (27.2%), HLE-Text-Only (29.5%), BrowserComp-EN (33.2%), BrowserComp-ZH (47.1%), and xBench-DeepSearch (72.0%).
- [2025-08-26]: Released GAIA Validation Trace (73.94% pass@1) and Gradio Demo for local deployment.
- [2025-08-08]: MiroFlow v0.1: Complete open-source release of the research agent framework.
π Get Started in Under 5 Minutes
π Prerequisites
- Python: 3.12 or higher
- Package Manager:
uv - Operating System: Linux, macOS
β‘ Quick Setup
Example: Intelligent document analysis with file processing capabilities.
# 1. Clone and setup
git clone https://github.com/MiroMindAI/MiroFlow && cd MiroFlow
uv sync
# 2. Configure API key
cp .env.template .env
# Edit .env and add your OPENROUTER_API_KEY
# 3. Run your first agent
uv run main.py trace --config_file_name=agent_quickstart_reading --task="What is the first country listed in the XLSX file that have names starting with Co?" --task_file_name="data/FSI-2023-DOWNLOAD.xlsx"
π Expected Output: Your agent should return \boxed{Congo Democratic Republic} π
π‘ Tip: If you encounter issues, check that your API key is correctly set in the
.envfile and that all dependencies are installed.
π€ What is MiroFlow?
MiroFlow is a high-performance, modular framework for building intelligent AI agents that deliver state-of-the-art results on complex reasoning tasks like future event prediction. The framework features advanced multi-turn conversation capabilities, extensive tool ecosystem integration, and hierarchical sub-agent orchestration for optimal task completion. Learn more about our agent framework.
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Research Assistant Demo -
Read CVPR 2025 Best Paper and Provide Research Advice
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π Highlights
- Reproducible State-of-the-Art Performance: #1 ranking across multiple representative agentic benchmarks, including FutureX, GAIA, HLE, xBench-DeepSearch, and BrowserComp benchmarks)
- High Concurrency & Reliability: Built with robust concurrency management and fault-tolerant design, MiroFlow efficiently handles rate-limited APIs and unstable networks, ensuring seamless trajectory collection and reliable execution of complex tasks.
- Cost-Effective Deployment: Powered by the open-source MiroThinker model, MiroFlow can run a research agent service on a single RTX 4090. The entire stack relies on free, open-source tools, making it simple to deploy, scale, and reproduce. See MiroThinker.
π§ Supported Models & Tools
- Models: GPT, Claude, Gemini, Qwen, MiroThinker, etc.
- Tools: Audio Transcription, Python, File Reading, Reasoning, Google Search, VQA, E2B, etc.
π Performance on Benchmarks
We achieved the #1 ranking on the FutureX Benchmark Leaderboard as of September 10, 2025, boosting GPT-5's prediction accuracy for future events by 11%.
We benchmark MiroFlow on a series of benchmarks, including GAIA, HLE, BrowseComp, and xBench-DeepSearch, and achieved SOTA results.
| Model/Framework | GAIA Val | HLE | HLE-Text | BrowserComp-EN | BrowserComp-ZH | xBench-DeepSearch |
|---|---|---|---|---|---|---|
| MiroFlow | 82.4% | 27.2% | 29.5% | 33.2% | 47.1% | 72.0% |
| OpenAI Deep Research | 67.4% | 26.6% | - | 51.5% | 42.9% | - |
| Gemini Deep Research | - | 26.9% | - | - | - | 50+% |
| Kimi Researcher | - | - | 26.9% | - | - | 69.0% |
| WebSailor-72B | 55.4% | - | - | - | 30.1% | 55.0% |
| Manus | 73.3% | - | - | - | - | - |
| DeepSeek v3.1 | - | - | 29.8% | - | - | 71.2% |
Follow our detailed guides to reproduce benchmark results in our Benchmarks Documentation
β FAQ
What API keys do I need?
You only need an OpenRouter API key to get started. OpenRouter provides access to multiple language models through a single API.
Can I use other language models besides OpenRouter?
Yes, MiroFlow supports various language models. Check our documentation for configuration details.
How do I reproduce the benchmark results?
Follow our detailed Benchmarks Documentation for step-by-step reproduction guides.
Is there commercial support available?
For commercial inquiries and enterprise support, please contact us through our website.
π€ Contributing
We welcome contributions from the community! Whether you're fixing bugs, adding features, or improving documentation, your help is appreciated.
- π Issues: Report bugs or request features via GitHub Issues.
- π Pull Requests: Submit improvements via pull requests.
- π¬ Discussions: Join our Discord community for questions and discussions.
π License
This project is licensed under the Apache License 2.0.
π Acknowledgments
Benchmark Contributors for the comprehensive evaluation datasets.
Open Source Community for the tools and libraries that make this possible.
We thank all contributors who have helped make MiroFlow better:
Join our community and help us build the future of AI agents!
References
The technical report is coming soon!
@misc{2025mirothinker,
title={MiroFlow: A High-Performance Open-Source Research Agent Framework},
author={MiroMind AI Team},
howpublished={\url{https://github.com/MiroMindAI/MiroFlow}},
year={2025}
}