7.9 KiB
🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
中文阅读 | Community | Installation | Examples | Paper | Citation | Contributing | CAMEL-AI
🏆 OWL achieves 58.18 average score on GAIA benchmark and ranks 🏅️ #1 among open-source frameworks! 🏆
🦉 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.
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.
📋 Table of Contents
- 📋 Table of Contents
- 🔥 News
- 🎬 Demo Video
- ✨️ Core Features
- 🛠️ Installation
- 🚀 Quick Start
- 🧪 Experiments
- ⏱️ Future Plans
- 📄 License
- 🖊️ Cite
- 🔥 Community
- ⭐ Star History
🔥 News
- [2025.03.07]: We open-source the codebase of 🦉 OWL project.
🎬 Demo Video
✨️ 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
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:
- Copy and Rename: Duplicate the
.env_examplefile and rename the copy to.env. - Fill in Your Keys: Open the
.envfile and insert your API keys in the corresponding fields. - 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.
🚀 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
📄 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!
