mirror of
https://github.com/camel-ai/owl.git
synced 2026-03-22 14:07:17 +08:00
83 lines
2.2 KiB
Markdown
83 lines
2.2 KiB
Markdown
<h1 align="center">
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🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
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</h1>
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🦉 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).
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OWL ranks #1 among open-source frameworks on GAIA benchmark.
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Our vision is to revolutionize how AI agents collaborate to solve real-world tasks. By leveraging role-playing mechanisms and dynamic agent interactions, OWL enables more natural, efficient, and robust task automation across diverse domains.
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<!-- # Key Features -->
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# 🔥 News
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- **[2025.03.06]**: We open-source the codebase of 🦉 OWL project.
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## 🛠️ Installation
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### **Clone the Github repository**
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```bash
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git clone https://github.com/camel-ai/owl.git
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cd owl
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```
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### **Set up Environment**
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Using Conda (recommended):
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```bash
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conda create -n owl python=3.11
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conda activate owl
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```
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Using venv (alternative):
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```bash
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python -m venv owl_env
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# On Windows
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owl_env\Scripts\activate
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# On Unix or MacOS
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source owl_env/bin/activate
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```
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### **Install Dependencies**
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```bash
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python -m pip install -r requirements.txt
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```
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### **Setup Environment Variables**
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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:
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1. *Copy and Rename*: Duplicate the `.env.example` file and rename the copy to `.env`.
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2. *Fill in Your Keys*: Open the `.env` file and insert your API keys in the corresponding fields.
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## 🚀 Quick Start
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Run the following minimal example:
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```bash
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python owl/run.py
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```
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# 🧪 Experiments
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We provided a script to reproduce the results on GAIA.
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You can check the `run_gaia_roleplaying.py` file and run the following command:
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```bash
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python run_gaia_roleplaying.py
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```
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# ⏱️ Future Plans
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- [ ] Write a technical blog post detailing our exploration and insights in multi-agent collaboration in real-world tasks.
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- [ ] Enhance the toolkit ecosystem with more specialized tools for domain-specific tasks.
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- [ ] Develop more sophisticated agent interaction patterns and communication protocols
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# Architecture
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# Cite
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