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

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πŸ¦‰ 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 achieves 58.18 average score on GAIA benchmark and ranks πŸ…οΈ #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.


πŸ“‹ Table of Contents

πŸ”₯ News

  • [2025.03.07]: We open-source the codebase of πŸ¦‰ OWL project.

🎬 Demo Video

371254613005d51d73c82424e56a1d22.mp4
d106cfbff2c7b75978ee9d5631ebeb75.mp4

πŸ› οΈ 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 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. 3.For using more other models: please refer to our camel 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.

πŸš€ Quick Start

Run the following minimal example:

python owl/run.py

πŸ§ͺ 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

πŸ“„ License

The source code is licensed under Apache 2.0.

πŸ–ŠοΈ 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} } 

πŸ”₯ Community

Join us for further discussions!

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