MiroThinker: The World-Leading Open-Source Search Agent That's Revolutionizing AI Research with 4.3k+ GitHub Stars

Introduction: The Future of AI Research Agents

In the rapidly evolving landscape of AI research agents, a groundbreaking project has emerged that's setting new standards for open-source search capabilities. MiroThinker, released on January 5, 2026, by MiroMindAI, represents a paradigm shift in how we approach tool-augmented reasoning and real-world information seeking.

With over 4.3k GitHub stars and 271 forks in just days since its release, MiroThinker is rapidly becoming the go-to solution for developers and researchers who need sophisticated search agent capabilities that rival commercial offerings like OpenAI Deep Research and Gemini Deep Research.

What Makes MiroThinker Revolutionary?

🚀 Interactive Scaling: The Third Dimension of AI Performance

Unlike traditional AI models that scale only through model size or context length, MiroThinker introduces interactive scaling as a third dimension of performance improvement. This innovative approach trains the model to handle deeper and more frequent agent-environment interactions, enabling:

• 256K context window for long-horizon reasoning
• Up to 400 tool calls per task (v1.5) or 600 tool calls (v1.0)
• Deep multi-step analysis with environment feedback
• Error correction and trajectory refinement through external information acquisition

📊 World-Leading Benchmark Performance

MiroThinker v1.5 demonstrates exceptional performance across multiple benchmarks:

• 39.2% on HLE-Text
• 69.8% on BrowseComp
• 71.5% on BrowseComp-ZH
• 80.8% on GAIA-Val-165

These results surpass previous open-source agents and set new world-leading performance standards, particularly on BrowseComp benchmarks.

The MiroThinker Ecosystem: Four Powerful Components

1. 💡 MiroThinker Models

The core search models available in multiple scales:

2. 🤖 MiroFlow Framework

An open-source research agent framework offering reproducible state-of-the-art performance across multiple benchmarks with comprehensive tool integration.

3. 📚 MiroVerse Dataset

A premium open-source training dataset with 147k samples specifically designed for research agent training, available on HuggingFace.

4. 🔧 MiroTrain & MiroRL

Training infrastructure supporting stable and efficient training for research agent models.

Getting Started: Complete Setup Guide

Prerequisites

Before diving into MiroThinker, ensure you have:

• 🐍 Python 3.10+
• 📦 uv package manager (Installation guide)
• 🔑 Required API keys (detailed below)

Step 1: Installation

# Clone the repository
git clone https://github.com/MiroMindAI/MiroThinker
cd MiroThinker

# Setup environment
cd apps/miroflow-agent
uv sync

# Configure API keys
cp .env.example .env
# Edit .env with your API keys

Step 2: Minimal Configuration

For MiroThinker v1.5, you need only 3 MCP servers for core functionality:

Step 3: Environment Configuration

Create your .env file with minimal required configuration:

# Required for MiroThinker v1.5 (minimal setup)
SERPER_API_KEY=your_serper_key
SERPER_BASE_URL="https://google.serper.dev"
JINA_API_KEY=your_jina_key
JINA_BASE_URL="https://r.jina.ai"
E2B_API_KEY=your_e2b_key

# Summary LLM (can be small model like Qwen3-14B or GPT-5-Nano)
SUMMARY_LLM_BASE_URL="https://your_summary_llm_base_url/v1/chat/completions"
SUMMARY_LLM_MODEL_NAME=your_llm_model_name
SUMMARY_LLM_API_KEY=your_llm_api_key

# For benchmark evaluation (optional)
OPENAI_API_KEY=your_openai_key
OPENAI_BASE_URL="https://api.openai.com/v1"

Serving MiroThinker Models

Option 1: SGLang (Recommended)

NUM_GPUS=4
PORT=61002
MODEL_PATH=miromind-ai/MiroThinker-v1.5-30B

python3 -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --tp $NUM_GPUS \
    --dp 1 \
    --host 0.0.0.0 \
    --port $PORT \
    --trust-remote-code

Option 2: vLLM Alternative

# Similar setup with vLLM
vllm serve miromind-ai/MiroThinker-v1.5-30B \
    --tensor-parallel-size 4 \
    --host 0.0.0.0 \
    --port 61002

Running Your First Research Task

Once your environment is configured and model server is running:

cd apps/miroflow-agent

# Using MiroThinker v1.5 (recommended)
uv run python main.py llm=qwen-3 agent=mirothinker_v1.5_keep5_max200 llm.base_url=http://localhost:61002/v1

# For BrowseComp tasks (more tool calls)
uv run python main.py llm=qwen-3 agent=mirothinker_v1.5_keep5_max400 llm.base_url=http://localhost:61002/v1

Customizing Your Research Query

Edit main.py line 32 to customize your research question:

task_description = "What are the latest breakthroughs in quantum computing research published in 2026?"

Advanced Configuration Options

Pre-configured Agent Settings

Context Retention Strategy

MiroThinker implements an innovative recency-based context retention strategy:

• ✅ Preserves reasoning and action trace
• ✅ Focuses on contextually relevant observations
• ✅ Frees context space for extended reasoning
• ✅ Enables deeper tool-use trajectories

Benchmark Evaluation and Testing

Supported Benchmarks

MiroThinker supports evaluation across multiple research benchmarks:

• GAIA Validation: General AI Assistants benchmark
• HLE: Humanity's Last Exam
• BrowseComp-EN/ZH: Web browsing and comprehension
• XBench-DeepSearch: Deep research agents
• FutureX: Predicting unknown future events
• Frames: Factuality, Retrieval, And reasoning

Running Benchmark Evaluations

# Download benchmark data
wget https://huggingface.co/datasets/miromind-ai/MiroFlow-Benchmarks/resolve/main/data_20251115_password_protected.zip
unzip data_20251115_password_protected.zip
# Password: pf4*

# Run GAIA evaluation
NUM_RUNS=8 LLM_MODEL="MiroThinker-v1.5-30B" BASE_URL="https://your-api.com/v1" bash scripts/run_evaluate_multiple_runs_gaia-validation-text-103.sh

Creating Custom Tool Configurations

You can create custom YAML configurations to combine different MCP servers:

# conf/agent/my_custom_config.yaml
defaults:
  - default
  - _self_

main_agent:
  tools:
    - tool-python                    # Execution environment
    - search_and_scrape_webpage      # Google search
    - jina_scrape_llm_summary        # Web scraping with LLM
    - tool-vqa                       # Vision processing (optional)
    - tool-transcribe                # Audio processing (optional)
  max_turns: 400

keep_tool_result: 5  # Keep only 5 most recent tool responses

Performance Optimization Tips

1. Model Selection Strategy

• MiroThinker-v1.5-30B: Best balance of performance and efficiency
• MiroThinker-v1.5-235B: Maximum performance for critical tasks
• Context management: Use keep5 configurations for better efficiency

2. Hardware Requirements

• 30B model: 4x A100 GPUs (recommended)
• 235B model: 8x A100 GPUs or equivalent
• Memory: 80GB+ VRAM for 30B, 200GB+ for 235B

3. Deployment Considerations

• Use SGLang for optimal inference performance
• Enable tensor parallelism for multi-GPU setups
• Consider quantization for resource-constrained environments

Real-World Use Cases and Applications

🔬 Academic Research

• Literature reviews: Comprehensive paper analysis and synthesis
• Data collection: Automated research data gathering
• Fact verification: Cross-referencing multiple sources

💼 Business Intelligence

• Market research: Competitive analysis and trend identification
• Due diligence: Company and investment research
• Regulatory compliance: Policy and regulation monitoring

📰 Journalism and Content Creation

• Investigative reporting: Multi-source fact-checking
• Background research: Comprehensive topic exploration
• Real-time analysis: Breaking news verification

Troubleshooting Common Issues

API Key Configuration

# Verify your .env file
cat .env | grep -E "(SERPER|JINA|E2B)_API_KEY"

# Test API connectivity
curl -H "Authorization: Bearer $SERPER_API_KEY" https://google.serper.dev/search

Model Loading Issues

• Memory errors: Reduce batch size or use quantization
• CUDA errors: Check GPU compatibility and driver versions
• Network timeouts: Increase timeout values in configuration

Tool Integration Problems

• E2B sandbox: Verify API key and quota limits
• Search failures: Check Serper API rate limits
• Scraping issues: Ensure Jina API access and proper headers

Future Developments and Roadmap

The MiroThinker project continues to evolve rapidly:

• Enhanced multimodal capabilities: Better image and video processing
• Improved Chinese language support: Expanded training data
• Additional benchmark support: More evaluation frameworks
• Optimization improvements: Better inference efficiency

Community and Support

Getting Help

• 🐛 GitHub Issues: Report bugs and request features
• 💬 Discord Community: Join discussions
• 📖 Documentation: Official website
• 📄 Research Paper: Technical details

Contributing

MiroThinker welcomes contributions from the community:

• Code contributions: Submit pull requests for improvements
• Bug reports: Help identify and fix issues
• Documentation: Improve guides and examples
• Benchmarking: Add new evaluation datasets

Conclusion: The Future of Open-Source AI Research

MiroThinker represents a significant leap forward in open-source AI research capabilities. By introducing interactive scaling and achieving world-leading benchmark performance, it democratizes access to sophisticated research agent technology that was previously available only through commercial services.

Whether you're conducting academic research, performing business intelligence, or building the next generation of AI applications, MiroThinker provides the tools and performance you need to succeed. Its comprehensive ecosystem of models, frameworks, datasets, and training infrastructure makes it an ideal choice for both researchers and practitioners.

The project's rapid adoption (4.3k+ stars in just days) and active development community suggest that MiroThinker will continue to evolve and improve, potentially setting new standards for what's possible with open-source AI research agents.

Ready to get started? Clone the repository, follow the setup guide, and experience the future of AI research agents today. The combination of world-class performance, comprehensive documentation, and active community support makes MiroThinker an excellent choice for your next AI research project.

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