

OWL
Overview :
OWL is a cutting-edge multi-agent collaboration framework developed based on the CAMEL-AI framework, aiming to achieve more natural, efficient, and robust task automation through dynamic agent interaction. The framework ranked first among open-source frameworks in the GAIA benchmark test with an average score of 58.18, demonstrating its powerful capabilities in the field of multi-agent collaboration. OWL supports various functions, including real-time information retrieval, multimodal processing, browser automation, document parsing, and code execution, and provides a rich set of built-in tools suitable for various complex tasks and benchmark tests. Its main advantage is its ability to flexibly handle diverse task requirements and improve task execution efficiency.
Target Users :
This product is suitable for developers, researchers, and enterprise users who need efficient task automation and multi-agent collaboration. It is especially suitable for scenarios that require handling complex tasks, multimodal data, and real-time information retrieval, significantly improving work efficiency and task execution capabilities.
Use Cases
Query Apple's latest stock price using OWL.
Use OWL to analyze the latest opinions on climate change on social media.
Use OWL to help debug Python code.
Use OWL to summarize the main points of a research paper.
Features
Real-time information retrieval: Obtain the latest information from online resources such as Wikipedia and Google Search.
Multimodal processing: Supports processing video, image, and audio data from the internet or local sources.
Browser automation: Simulates browser interaction through the Playwright framework, including scrolling, clicking, and input handling.
Document parsing: Extracts content from Word, Excel, PDF, and PowerPoint files, converting them into text or Markdown format.
Code execution: Write and execute code using the Python code interpreter.
Built-in toolset: Provides a rich toolset including ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, etc., supporting various professional tasks.
Support for multiple model backends: Compatible with various models such as OpenAI, Qwen, and DeepSeek, allowing users to choose according to their needs.
How to Use
1. Clone the OWL GitHub repository: `git clone https://github.com/camel-ai/owl.git`
2. Enter the project directory: `cd owl`
3. Install dependencies: `pip install uv`, then create a virtual environment and install all dependencies.
4. Configure environment variables: Copy and rename `.env_template` to `.env`, and fill in the API key.
5. Start OWL: Run `python owl/run.py` to begin.
6. Use the web interface: Run `python run_app.py` to start the web interface, select the model and configure the API key through the interface.
7. Run experiments: Switch to a specific branch (e.g., `gaia58.18`), run `python run_gaia_roleplaying.py` to reproduce benchmark results.
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