๐ฆ OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
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[Community](https://github.com/camel-ai/owl#community) |
[Installation](https://github.com/camel-ai/owl#installation) |
[Examples](https://github.com/camel-ai/camel/tree/HEAD/examples) |
[Paper](https://arxiv.org/abs/2303.17760) |
[Citation](https://github.com/camel-ai/owl#citation) |
[Contributing](https://github.com/camel-ai/camel#contributing-to-camel-) |
[CAMEL-AI](https://www.camel-ai.org/)
๐ฆ 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 benchmark and ranks ๐
๏ธ #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.
# ๐ Table of Contents
- [๐ Table of Contents](#-table-of-contents)
- [๐ฅ News](#-news)
- [๐ฌ Demo Video](#-demo-video)
- [๐ ๏ธ Installation](#๏ธ-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)
- [๐ Quick Start](#-quick-start)
- [๐งช Experiments](#-experiments)
- [โฑ๏ธ Future Plans](#๏ธ-future-plans)
- [๐ License](#-license)
- [๐๏ธ Cite](#๏ธ-cite)
- [๐ฅ Community](#-community)
# ๐ฅ News
- **[2025.03.07]**: We open-source the codebase of ๐ฆ OWL project.
# ๐ฌ 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
# ๐ ๏ธ 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_example` 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`.
2. *Fill in Your Keys*: Open the `.env` file and insert your API keys in the corresponding fields.
# ๐ Quick Start
Run the following minimal example:
```bash
python owl/run.py
```
# ๐งช 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
```
# โฑ๏ธ 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
# ๐ License
The source code is licensed under Apache 2.0.
# ๐๏ธ 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}
}
```
# ๐ฅ Community
Join us for further discussions!
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