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

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

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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
• 🧰 Toolkits and Capabilities
  • Model Context Protocol (MCP)
• Web Interface
• 🧪 Experiments
• ️ Future Plans
• License
• ️ Cite
• Contributing
• Community
• FAQ
• Exploring CAMEL Dependency
• Star History

News

COMMUNITY CALL FOR USE CASES!

We're inviting the community to contribute innovative use cases for OWL!
The top ten submissions will receive special community gifts and recognition.

Learn More & Submit

Submission deadline: March 31, 2025

Latest Major Update - March 15, 2025

Significant Improvements:

• Restructured web-based UI architecture for enhanced stability ️
• Optimized OWL Agent execution mechanisms for better performance

Try it now and experience the improved performance in your automation tasks!

• [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

OWL.main.mp4 d106cfbff2c7b75978ee9d5631ebeb75.mp4

️ 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, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, BrowserToolkit, and many more for specialized tasks

️ Installation

OWL supports multiple installation methods to fit your workflow preferences. Choose the option that works best for you.

Option 1: Using uv (Recommended)

# Clone github repo
git clone https://github.com/camel-ai/owl.git

# Change directory into project directory
cd owl

# Install uv if you don't have it already
pip install uv

# Create a virtual environment and install dependencies
# We support using Python 3.10, 3.11, 3.12
uv venv .venv --python=3.10

# Activate the virtual environment
# For macOS/Linux
source .venv/bin/activate
# For Windows
.venv\Scripts\activate

# Install CAMEL with all dependencies
uv pip install -e .

# Exit the virtual environment when done
deactivate

Option 2: Using venv and pip

# Clone github repo
git clone https://github.com/camel-ai/owl.git

# Change directory into project directory
cd owl

# Create a virtual environment
# For Python 3.10 (also works with 3.11, 3.12)
python3.10 -m venv .venv

# Activate the virtual environment
# For macOS/Linux
source .venv/bin/activate
# For Windows
.venv\Scripts\activate

# Install from requirements.txt
pip install -r requirements.txt --use-pep517

Option 3: Using conda

# Clone github repo
git clone https://github.com/camel-ai/owl.git

# Change directory into project directory
cd owl

# Create a conda environment
conda create -n owl python=3.10

# Activate the conda environment
conda activate owl

# Option 1: Install as a package (recommended)
pip install -e .

# Option 2: Install from requirements.txt
pip install -r requirements.txt --use-pep517

# Exit the conda environment when done
conda deactivate

Setup Environment Variables

OWL requires various API keys to interact with different services. The owl/.env_template file contains placeholders for all necessary API keys along with links to the services where you can register for them.

Option 1: Using a .env File (Recommended)

1. Copy and Rename the Template:

   cd owl
   cp .env_template .env
   

2. Configure Your API Keys: Open the .env file in your preferred text editor and insert your API keys in the corresponding fields.

   Note: For the minimal example (examples/run_mini.py), you only need to configure the LLM API key (e.g., OPENAI_API_KEY).

Option 2: Setting Environment Variables Directly

Alternatively, you can set environment variables directly in your terminal:

• macOS/Linux (Bash/Zsh):

  export OPENAI_API_KEY="your-openai-api-key-here"
  

• Windows (Command Prompt):

  set OPENAI_API_KEY="your-openai-api-key-here"
  

• Windows (PowerShell):

  $env:OPENAI_API_KEY = "your-openai-api-key-here"
  

Note: Environment variables set directly in the terminal will only persist for the current session.

Running with Docker

OWL can be easily deployed using Docker, which provides a consistent environment across different platforms.

Setup Instructions

# Clone the repository
git clone https://github.com/camel-ai/owl.git
cd owl

# Configure environment variables
cp owl/.env_template owl/.env
# Edit the .env file and fill in your API keys

Deployment Options

Option 1: Using Pre-built Image (Recommended)

# This option downloads a ready-to-use image from Docker Hub
# Fastest and recommended for most users
docker-compose up -d

# Run OWL inside the container
docker-compose exec owl bash
cd .. && source .venv/bin/activate
playwright install-deps
xvfb-python examples/run.py

Option 2: Building Image Locally

# For users who need to customize the Docker image or cannot access Docker Hub:
# 1. Open docker-compose.yml
# 2. Comment out the "image: mugglejinx/owl:latest" line
# 3. Uncomment the "build:" section and its nested properties
# 4. Then run:
docker-compose up -d --build

# Run OWL inside the container
docker-compose exec owl bash
cd .. && source .venv/bin/activate
playwright install-deps
xvfb-python examples/run.py

Option 3: Using Convenience Scripts

# Navigate to container directory
cd .container

# Make the script executable and build the Docker image
chmod +x build_docker.sh
./build_docker.sh

# Run OWL with your question
./run_in_docker.sh "your question"

MCP Desktop Commander Setup

If using MCP Desktop Commander within Docker, run:

npx -y @wonderwhy-er/desktop-commander setup --force-file-protocol

For more detailed Docker usage instructions, including cross-platform support, optimized configurations, and troubleshooting, please refer to DOCKER_README.md.