owl-main/README.md

<h1 align="center">
	🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
</h1>


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[![Documentation][docs-image]][docs-url]
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[![X][x-image]][x-url]
[![Reddit][reddit-image]][reddit-url]
[![Wechat][wechat-image]][wechat-url]
[![Wechat][owl-image]][owl-url]
[![Hugging Face][huggingface-image]][huggingface-url]
[![Star][star-image]][star-url]
[![Package License][package-license-image]][package-license-url]


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<h4 align="center">

[中文阅读](https://github.com/camel-ai/owl/tree/main/README_zh.md) |
[Community](https://github.com/camel-ai/owl#community) |
[Installation](#️-installation) |
[Examples](https://github.com/camel-ai/owl/tree/main/owl) |
[Paper](https://arxiv.org/abs/2303.17760) |
[Citation](https://github.com/camel-ai/owl#citation) |
[Contributing](https://github.com/camel-ai/owl/graphs/contributors) |
[CAMEL-AI](https://www.camel-ai.org/)

</h4>

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    🏆 OWL achieves <span style="color: #d81b60; font-weight: bold; font-size: 1.2em;">58.18</span> average score on GAIA benchmark and ranks <span style="color: #d81b60; font-weight: bold; font-size: 1.2em;">🏅️ #1</span> among open-source frameworks! 🏆
  </h3>
</div>

<div align="center">

🦉 OWL is a cutting-edge framework for multi-agent collaboration that pushes the boundaries of task automation, built on top of the [CAMEL-AI Framework](https://github.com/camel-ai/camel).

<!-- OWL achieves **58.18** average score on [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) benchmark and ranks 🏅️ #1 among open-source frameworks. -->

Our vision is to revolutionize how AI agents collaborate to solve real-world tasks. By leveraging dynamic agent interactions, OWL enables more natural, efficient, and robust task automation across diverse domains.

</div>

![](./assets/owl_architecture.png)

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</div>

<!-- # Key Features -->
# 📋 Table of Contents

- [📋 Table of Contents](#-table-of-contents)
- [🔥 News](#-news)
- [🎬 Demo Video](#-demo-video)
- [✨️ Core Features](#-core-features)
- [🛠️ Installation](#️-installation)
  - [**Clone the Github repository**](#clone-the-github-repository)
  - [**Set up Environment**](#set-up-environment)
  - [**Install Dependencies**](#install-dependencies)
  - [**Setup Environment Variables**](#setup-environment-variables)
  - [**Running with Docker**](#running-with-docker)
  
- [🚀 Quick Start](#-quick-start)
- [🧪 Experiments](#-experiments)
- [⏱️ Future Plans](#️-future-plans)
- [📄 License](#-license)
- [🖊️ Cite](#️-cite)
- [🔥 Community](#-community)
- [❓ FAQ](#-faq)
- [⭐ Star History](#-star-history)


# 🔥 News

- **[2025.03.07]**: We open-source the codebase of 🦉 OWL project.

# 🎬 Demo Video

https://private-user-images.githubusercontent.com/55657767/420211368-f29f477d-7eef-46da-8d7a-8f3bcf506da2.mp4

https://private-user-images.githubusercontent.com/55657767/420212194-e813fc05-136a-485f-8df3-f10d9b4e63ec.mp4

# ✨️ Core Features

- **Real-time Information Retrieval**: Leverage Wikipedia, Google Search, and other online sources for up-to-date information.
- **Multimodal Processing**: Support for handling internet or local videos, images, and audio data.
- **Browser Automation**: Utilize the Playwright framework for simulating browser interactions, including scrolling, clicking, input handling, downloading, navigation, and more.
- **Document Parsing**: Extract content from Word, Excel, PDF, and PowerPoint files, converting them into text or Markdown format.
- **Code Execution**: Write and execute Python code using interpreter.

# 🛠️ Installation

## **Clone the Github repository**

```bash
git clone https://github.com/camel-ai/owl.git
cd owl
```

## **Set up Environment**

Using Conda (recommended):
```bash
conda create -n owl python=3.11
conda activate owl
```

Using venv (alternative):
```bash
python -m venv owl_env
# On Windows
owl_env\Scripts\activate
# On Unix or MacOS
source owl_env/bin/activate
```


## **Install Dependencies**

```bash
python -m pip install -r requirements.txt
playwright install
```

## **Setup Environment Variables** 

In the `owl/.env_template` file, you will find all the necessary API keys along with the websites where you can register for each service. To use these API services, follow these steps:

1. *Copy and Rename*: Duplicate the `.env_example` file and rename the copy to `.env`.
```bash
cp owl/.env_template .env
```
2. *Fill in Your Keys*: Open the `.env` file and insert your API keys in the corresponding fields.  (For the minimal example (`run_mini.py`), you only need to configure the LLM API key (e.g., OPENAI_API_KEY).)
3. *For using more other models*: please refer to our CAMEL models docs:https://docs.camel-ai.org/key_modules/models.html#supported-model-platforms-in-camel


> **Note**: For optimal performance, we strongly recommend using OpenAI models. Our experiments show that other models may result in significantly lower performance on complex tasks and benchmarks.

## **Running with Docker**

If you prefer to run the OWL project using Docker, we provide full Docker support:

```bash
# Clone the repository
git clone https://github.com/camel-ai/owl.git
cd owl

# Configure environment variables
cp owl/.env_template owl/.env
# Edit the .env file and fill in your API keys

# Build and run the Docker container
docker-compose up -d

# Run OWL inside the container
docker-compose exec owl bash -c "xvfb-python run.py"
```

For more detailed Docker usage instructions, including cross-platform support, optimized configurations, and troubleshooting, please refer to [DOCKER_README.md](DOCKER_README_en.md).

# 🚀 Quick Start


   
Run the following demo case:

```bash
python owl/run.py
```

## Running with Different Models

OWL supports various LLM backends. You can use the following scripts to run with different models:

```bash
# Run with Qwen model
python owl/run_qwen.py

# Run with Deepseek model
python owl/run_deepseek.py

# Run with other OpenAI-compatible models
python owl/run_openai_compatiable_model.py
```

For a simpler version that only requires an LLM API key, you can try our minimal example:

```bash
python owl/run_mini.py
```

You can run OWL agent with your own task by modifying the `run.py` script:

```python
# Define your own task
question = "Task description here."

society = construct_society(question)
answer, chat_history, token_count = run_society(society)

print(f"Answer: {answer}")
```

For uploading files, simply provide the file path along with your question:

```python
# Task with a local file (e.g., file path: `tmp/example.docx`)
question = "What is in the given DOCX file? Here is the file path: tmp/example.docx"

society = construct_society(question)
answer, chat_history, token_count = run_society(society)
print(f"Answer: {answer}")
```

OWL will then automatically invoke document-related tools to process the file and extract the answer.


Example tasks you can try:
- "Find the latest stock price for Apple Inc."
- "Analyze the sentiment of recent tweets about climate change"
- "Help me debug this Python code: [your code here]"
- "Summarize the main points from this research paper: [paper URL]"

# 🧪 Experiments

We provided a script to reproduce the results on GAIA. 
You can check the `run_gaia_roleplaying.py` file and run the following command:

```bash
python run_gaia_roleplaying.py
```

# ⏱️ Future Plans

- [ ] Write a technical blog post detailing our exploration and insights in multi-agent collaboration in real-world tasks.
- [ ] Enhance the toolkit ecosystem with more specialized tools for domain-specific tasks.
- [ ] Develop more sophisticated agent interaction patterns and communication protocols


# 📄 License

The source code is licensed under Apache 2.0.

# 🖊️ Cite

If you find this repo useful, please cite:


```
@misc{owl2025,
  title        = {OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation},
  author       = {{CAMEL-AI.org}},
  howpublished = {\url{https://github.com/camel-ai/owl}},
  note         = {Accessed: 2025-03-07},
  year         = {2025}
}
```

# 🔥 Community
Join us for further discussions!
<!-- ![](./assets/community.png) -->
![](./assets/community_5.jpg)
<!-- ![](./assets/meetup.jpg) -->

# ❓ FAQ

**Q: Why don't I see Chrome running locally after starting the example script?**

A: If OWL determines that a task can be completed using non-browser tools (such as search or code execution), the browser will not be launched. The browser window will only appear when OWL determines that browser-based interaction is necessary.

# ⭐ Star History

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



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