README.md

---
title: owl
app_file: run_app.py
sdk: gradio
sdk_version: 5.23.1
---
<h1 align="center">
	🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
</h1>


<div align="center">

[![Documentation][docs-image]][docs-url]
[![Discord][discord-image]][discord-url]
[![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]


</div>


<hr>

<div align="center">
<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>

<div align="center" style="background-color: #f0f7ff; padding: 10px; border-radius: 5px; margin: 15px 0;">
  <h3 style="color: #1e88e5; margin: 0;">
    🏆 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)

<br>


</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)
- [🌐 Web Interface](#-web-interface)
- [🧪 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.
- **Built-in Toolkits**: Access to a comprehensive set of built-in toolkits including ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, WebToolkit, and many more for specialized tasks.

# 🛠️ Installation

## Option 1: Using uv (Recommended)

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

# Change directory into project directory
cd owl

# Install uv if you don't have it already
pip install uv

# Create a virtual environment and install dependencies
# We support using Python 3.10, 3.11, 3.12
uv venv .venv --python=3.10

# Activate the virtual environment
# For macOS/Linux
source .venv/bin/activate
# For Windows
.venv\Scripts\activate

# Install CAMEL with all dependencies
uv pip install -e .

# Exit the virtual environment when done
deactivate
```

## Option 2: Using venv and pip

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

# Change directory into project directory
cd owl

# Create a virtual environment
# For Python 3.10 (also works with 3.11, 3.12)
python3.10 -m venv .venv

# Activate the virtual environment
# For macOS/Linux
source .venv/bin/activate
# For Windows
.venv\Scripts\activate

# Install from requirements.txt
pip install -r requirements.txt
```

## Option 3: Using conda

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

# Change directory into project directory
cd owl

# Create a conda environment
conda create -n owl python=3.10

# Activate the conda environment
conda activate owl

# Option 1: Install as a package (recommended)
pip install -e .

# Option 2: Install from requirements.txt
pip install -r requirements.txt

# Exit the conda environment when done
conda deactivate
```

## **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_template` 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**

```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


# Option 1: Using docker-compose directly
cd .container
docker-compose up -d
# Run OWL inside the container
docker-compose exec owl bash -c "xvfb-python run.py"

# Option 2: Build and run using the provided scripts
cd .container
chmod +x build_docker.sh
./build_docker.sh
# Run OWL inside the container
./run_in_docker.sh "your question"
```

For more detailed Docker usage instructions, including cross-platform support, optimized configurations, and troubleshooting, please refer to [DOCKER_README.md](.container/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"\033[94mAnswer: {answer}\033[0m")
```

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"\033[94mAnswer: {answer}\033[0m")
```

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]"

# 🌐 Web Interface

OWL now includes a web-based user interface that makes it easier to interact with the system. To start the web interface, run:

```bash
python run_app.py
```

The web interface provides the following features:

- **Easy Model Selection**: Choose between different models (OpenAI, Qwen, DeepSeek, etc.)
- **Environment Variable Management**: Configure your API keys and other settings directly from the UI
- **Interactive Chat Interface**: Communicate with OWL agents through a user-friendly interface
- **Task History**: View the history and results of your interactions

The web interface is built using Gradio and runs locally on your machine. No data is sent to external servers beyond what's required for the model API calls you configure.

# 🧪 Experiments

To reproduce OWL's GAIA benchmark score of 58.18:

1. Switch to the `gaia58.18` branch:
```bash
git checkout gaia58.18
```

1. Run the evaluation script:
```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_6.png)
<!-- ![](./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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