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![](readme_assets/framework_logic.jpg)
# AWorld: Advancing Agentic AI

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## News
- 🦩 [2025/06/19] We have updated our score to 72.43 on the GAIA test. Additionally, we have introduced a new local running mode. See `./README-local.md` for detailed instructions.
- 🐳 [2025/05/22] For quick GAIA evaluation, MCP tools, AWorld, and models are now available in a single Docker image. See <code>./README-docker.md</code> for instructions and [youtube video](https://www.youtube.com/watch?v=kkYWeVvJKrg) for demo.
- 🥳 [2025/05/13] AWorld has updated its state management for browser use and enhanced the video processing MCP server, achieving a score of 77.58 on GAIA validation (Pass@1 = 61.8) and maintaining its position as the top-ranked open-source framework. Learn more: [GAIA leaderboard](https://huggingface.co/spaces/gaia-benchmark/leaderboard)
- ✨ [2025/04/23] AWorld ranks 3rd on GAIA benchmark (69.7 avg) with impressive Pass@1 = 58.8, 1st among open-source frameworks. Reproduce with <code>python examples/gaia/run.py</code>


## Introduction
For self-improving, AWorld (Agent World) is designed to achieve two primary objectives: (1) provide the effiecent forward process, and (2) facilitate diverse backward processes, including but not limit foundation model training and system design meta-learning.
![](readme_assets/aworld_overview.jpg)

### Forward process
| 1. Agent Construction | 2. Topology Orchestration | 3. Environments |
|-------------------|------------------------|--------------|
| • ✅ Support different model services <br> • ✅ Support MCP tools <br> • ✅ Support custom tools | • ✅ Encapsulate protocol between models and tools <br> • ✅ Encapsulate protocol among agents | • ✅ Encapsulate runtime state management <br> • ✅ Support state tracing <br> • ✅ Support distributed high-concurrency envs |

Follow the instructions in `./README-local.md` to run a forward process on the GAIA benchmark. Watch the demo on [Youtube](https://www.youtube.com/watch?v=Z_B8D9CsAFI)

### Incubated backward methods
| Method Category | Description | Key Information |
|----------------|-------------|--------------|
| Foundation Model Training | Improving Function call ability of large language models | [![Dataset](https://img.shields.io/badge/Dataset-Coming%20Soon-007ACC?style=for-the-badge&logo=dataset&logoColor=white)]() <br> [![Model](https://img.shields.io/badge/Model-Hugging%20Face-FF6B6B?style=for-the-badge&logo=huggingface&logoColor=white)](https://huggingface.co/Bingguang/FunReason) <br> [![Paper](https://img.shields.io/badge/Paper-arXiv-B31B1B?style=for-the-badge&logo=arxiv&logoColor=white)](https://arxiv.org/pdf/2505.20192) <br> [![Blog](https://img.shields.io/badge/Blog-Coming%20Soon-FF5722?style=for-the-badge&logo=blogger&logoColor=white)]() <br> [![Code](https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/BingguangHao/FunReason)|

> **Want to build your own multi-agent system? Check out the detailed tutorials below to get started! ⬇️⬇️⬇️** 

## Installation
 Python>=3.11:
```bash
git clone https://github.com/inclusionAI/AWorld
cd AWorld
python setup.py install
```

## Usage
> Here’s a quick start guide to: (1) create your first agent; (2) equip it with a MCP tool; (3) assign a teammate; and (4) answer a user query through teamwork.

```python
from aworld.config.conf import AgentConfig
from aworld.agents.llm_agent import Agent
from aworld.runner import Runners
from aworld.core.agent.swarm import Swarm

if __name__ == '__main__':
    agent_config = AgentConfig(
        llm_provider="openai",
        llm_model_name="gpt-4o",

        # Set via environment variable or direct configuration
        # llm_api_key="YOUR_API_KEY", 
        # llm_base_url="https://api.openai.com/v1"
    )

    # Register the MCP tool here, or create a separate configuration file.
    mcp_config = {
        "mcpServers": {
            "amap-amap-sse": {
                "type": "sse",
                "url": "https://mcp.amap.com/sse?key=YOUR_API_KEY",
                "timeout": 5,
                "sse_read_timeout": 300
            }
        }
    }

    # Create your first agent equipped with an MCP tool
    search = Agent(
        conf=agent_config,
        name="search_agent",
        system_prompt="You are a helpful agent.",
        mcp_servers=["amap-amap-sse"], # MCP server name for agent to use
        mcp_config=mcp_config
    )

    # Add a new teammate to the agent
    summary = Agent(
        conf=agent_config,
        name="summary_agent",
        system_prompt="You are a helpful summary agent."
    )

    # Collaborate as a team; the default is a static workflow
    swarm = Swarm(search, summary)

    # Run agent team
    res = Runners.sync_run(input="Hotels within 1 kilometer of West Lake in Hangzhou",
                     swarm=swarm)
    print(res)
```

## Demo
> Running Pre-defined Agents (e.g., see [demo code](examples/browsers/run.py)). Below are demonstration videos showcasing AWorld's capabilities across various agent configurations and environments.


<table>
  <tr>
    <th>Mode</th>
    <th>Type</th>
    <th>Demo</th>
  </tr>
  <tr>
    <td rowspan="2">Single Agent</td>
    <td>Browser use</td>
    <td>
      <a href="https://www.youtube.com/watch?v=R7keOLrRDoM" target="_blank">
        <img src="https://img.youtube.com/vi/R7keOLrRDoM/0.jpg" alt="AWorld Browser Demo on YouTube" width="95%">
        <br>
        <p align="center">▶️ Watch Browser Demo on YouTube</p>
      </a>
    </td>
  </tr>
  <tr>
    <td>Phone use</td>
    <td>
      <a href="https://www.youtube.com/watch?v=TYh3iqDeIoQ" target="_blank">
        <img src="https://img.youtube.com/vi/TYh3iqDeIoQ/0.jpg" alt="AWorld Mobile Demo on YouTube" width="95%">
        <br>
        <p align="center">▶️ Watch Mobile Demo on YouTube</p>
      </a>
    </td>
  </tr>
  <tr>
    <td rowspan="3">Multi Agent</td>
    <td>Cooperative Teams</td>
    <td>
       <a href="https://www.youtube.com/watch?v=sEsgasRrlTs" target="_blank">
        <img src="https://img.youtube.com/vi/sEsgasRrlTs/0.jpg" alt="AWorld Travel Demo on YouTube" width="95%">
        <br>
        <p align="center">▶️ Watch Travel Demo on YouTube</p>
      </a>
    </td>
  </tr>
  <tr>
    <td>Competitive Teams</td>
     <td>
       <a href="https://www.youtube.com/watch?v=_CPdhoP4YTg" target="_blank">
        <img src="https://img.youtube.com/vi/_CPdhoP4YTg/0.jpg" alt="AWorld Debate Demo on YouTube" width="95%">
        <br>
        <p align="center">▶️ Watch Debate Arena on YouTube</p>
      </a>
    </td>
  </tr>
  <tr>
    <td>Mixed of both Teams</td>
    <td align="center"><i>Coming Soon</i> 🚀</td>
  </tr>
</table>


## Contributing
We warmly welcome developers to join us in building and improving AWorld! Whether you're interested in enhancing the framework, fixing bugs, or adding new features, your contributions are valuable to us.

For academic citations or wish to contact us, please use the following BibTeX entry:

```bibtex
@software{aworld2025,
  author = {Agent Team at InclusionAI},
  title = {AWorld: A Framework for Agent Learning of Complex Tasks via Action-Observation-Reward Experience},
  year = {2025},
  url = {https://github.com/inclusionAI/AWorld},
  version = {0.1.0},
  publisher = {GitHub},
  email = {chenyi.zcy at antgroup.com}
}
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Star History
![](https://api.star-history.com/svg?repos=inclusionAI/AWorld&type=Date)