<div align="center"> </div> <h1 align="center"> 🦉 OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation </h1> <div align="center">

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中文阅读 | Community | Installation | Examples | Paper | Citation | Contributing | CAMEL-AI |

</h4> <div align="center" style="background-color: #f0f7ff; padding: 10px; border-radius: 5px; margin: 15px 0;"> <h3 style="color: #1e88e5; margin: 0;"> 🏆 OWL achieves <span style="color: #d81b60; font-weight: bold; font-size: 1.2em;">69.09</span> average score on GAIA benchmark and ranks <span style="color: #d81b60; font-weight: bold; font-size: 1.2em;">🏅️ #1</span> among open-source frameworks! 🏆 </h3> </div> <div align="center">

🦉 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.

If you find this repo useful, please consider citing our work (citation).

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<br> </div> <!-- # Key Features -->

📋 Table of Contents

• 📋 Table of Contents
• 🔥 News
• 🎬 Demo Video
• ✨️ Core Features
• 🛠️ Installation
  • Prerequisites
    • Install Python
  • Installation Options
    • Option 1: Using uv (Recommended)
    • Option 2: Using venv and pip
    • Option 3: Using conda
    • Option 4: Using Docker
      • Using Pre-built Image (Recommended)
      • Building Image Locally
      • Using Convenience Scripts
  • Setup Environment Variables
    • Setting Environment Variables Directly
    • Alternative: Using a .env File
    • MCP Desktop Commander Setup
• 🚀 Quick Start
  • Basic Usage
  • Running with Different Models
    • Model Requirements
      • Supported Models
    • Example Tasks
• 🧰 Toolkits and Capabilities
  • Model Context Protocol (MCP)
    • Install Node.js
    • Windows
    • Linux
    • Mac
    • Install Playwright MCP Service
  • Available Toolkits
  • Available Toolkits
    • Multimodal Toolkits (Require multimodal model capabilities)
    • Text-Based Toolkits
  • Customizing Your Configuration
• 🌐 Web Interface
  • Starting the Web UI
  • Features
• 🧪 Experiments
• 📄 License
• 🤝 Contributing
• 🔥 Community
• ❓ FAQ
  • General Questions
  • Experiment Questions
• 📚 Exploring CAMEL Dependency
  • Accessing CAMEL Source Code
• 🖊️ Cite
• ⭐ Star History

🔥 News

<div align="center" style="background-color: #e8f5e9; padding: 15px; border-radius: 10px; border: 2px solid #4caf50; margin: 20px 0;"> <h3 style="color: #2e7d32; margin: 0; font-size: 1.3em;"> 🧩 <b>NEW: COMMUNITY AGENT CHALLENGES!</b> 🧩 </h3> <p style="font-size: 1.1em; margin: 10px 0;"> Showcase your creativity by designing unique challenges for AI agents! <br> Join our community and see your innovative ideas tackled by cutting-edge AI. </p> <p> <a href="https://github.com/camel-ai/owl/blob/main/community_challenges.md" style="background-color: #2e7d32; color: white; padding: 8px 15px; text-decoration: none; border-radius: 5px; font-weight: bold;">View & Submit Challenges</a> </p> </div> <!-- <div style="background-color: #e3f2fd; padding: 12px; border-radius: 8px; border-left: 4px solid #1e88e5; margin: 10px 0;"> <h4 style="color: #1e88e5; margin: 0 0 8px 0;"> 🎉 Latest Major Update - March 15, 2025 </h4> <p style="margin: 0;"> <b>Significant Improvements:</b> <ul style="margin: 5px 0 0 0; padding-left: 20px;"> <li>Restructured web-based UI architecture for enhanced stability 🏗️</li> <li>Optimized OWL Agent execution mechanisms for better performance 🚀</li> </ul> <i>Try it now and experience the improved performance in your automation tasks!</i> </p> </div> -->

• [2025.09.22]: Exicited to announce that OWL has been accepted by NeurIPS 2025!🚀 Check the latest paper here.
• [2025.07.21]: We open-sourced the training dataset and model checkpoints of OWL project. Training code coming soon. huggingface link.
• [2025.05.27]: We released the technical report of OWL, including more details on the workforce (framework) and optimized workforce learning (training methodology). paper.
• [2025.05.18]: We open-sourced an initial version for replicating workforce experiment on GAIA here.
• [2025.04.18]: We uploaded OWL's new GAIA benchmark score of 69.09%, ranking #1 among open-source frameworks. Check the technical report here.
• [2025.03.27]: Integrate SearxNGToolkit performing web searches using SearxNG search engine.
• [2025.03.26]: Enhanced Browser Toolkit with multi-browser support for "chrome", "msedge", and "chromium" channels.
• [2025.03.25]: Supported Gemini 2.5 Pro, added example run code
• [2025.03.21]: Integrated OpenRouter model platform, fix bug with Gemini tool calling.
• [2025.03.20]: Accept header in MCP Toolkit, support automatic playwright installation.
• [2025.03.16]: Support Bing search, Baidu search.
• [2025.03.12]: Added Bocha search in SearchToolkit, integrated Volcano Engine model platform, and enhanced Azure and OpenAI Compatible models with structured output and tool calling.
• [2025.03.11]: We added MCPToolkit, FileWriteToolkit, and TerminalToolkit to enhance OWL agents with MCP tool calling, file writing capabilities, and terminal command execution.
• [2025.03.09]: We added a web-based user interface that makes it easier to interact with the system.
• [2025.03.07]: We open-sourced the codebase of the 🦉 OWL project.
• [2025.03.03]: OWL achieved the #1 position among open-source frameworks on the GAIA benchmark with a score of 58.18.

🎬 Demo Video

https://github.com/user-attachments/assets/2a2a825d-39ea-45c5-9ba1-f9d58efbc372

https://private-user-images.githubusercontent.com/55657767/420212194-e813fc05-136a-485f-8df3-f10d9b4e63ec.mp4

This video demonstrates how to install OWL locally and showcases its capabilities as a cutting-edge framework for multi-agent collaboration: https://www.youtube.com/watch?v=8XlqVyAZOr8

✨️ Core Features

• Online Search: Support for multiple search engines (including Wikipedia, Google, DuckDuckGo, Baidu, Bocha, etc.) for real-time information retrieval and knowledge acquisition.
• 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.
• Built-in Toolkits: Access to a comprehensive set of built-in toolkits including:
  • Model Context Protocol (MCP): A universal protocol layer that standardizes AI model interactions with various tools and data sources
  • Core Toolkits: ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPy