🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation

🦉 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. OWL ranks #1 among open-source frameworks on GAIA benchmark.

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.

🔥 News

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

🛠️ Installation

Clone the Github repository

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

Set up Environment

Using Conda (recommended):

conda create -n owl python=3.11
conda activate owl

Using venv (alternative):

python -m venv owl_env
# On Windows
owl_env\Scripts\activate
# On Unix or MacOS
source owl_env/bin/activate

Install Dependencies

python -m pip install -r requirements.txt

Setup Environment Variables

In the .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:

Copy and Rename: Duplicate the .env.example file and rename the copy to .env.
Fill in Your Keys: Open the .env file and insert your API keys in the corresponding fields.

🚀 Quick Start

Run the following minimal example:

python owl/run.py

🧪 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:

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

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