OWL (Optimized Workforce Learning) is a cutting-edge multi-agent collaboration framework developed by CAMEL-AI, focusing on general multi-agent assistance in real-world task automation. OWL achieves a 58.18 average score on the GAIA benchmark, ranking #1 among open-source frameworks.
Product Screenshot
Features
Optimized Workforce Learning
Enables more natural, efficient, and robust task automation through dynamic agent interactions
Advanced Benchmark Performance
Excels in the GAIA benchmark, demonstrating its capabilities in handling complex tasks
Multi-Agent Collaboration
Built on top of the CAMEL-AI Framework, supporting multiple agents working together to solve complex tasks
Pricing
Open Source Version
Free
- Apache 2.0 license
- Requires self-configuration of LLM API
- Community support
Use Cases
Real-World Task Automation
Automatically executes various real-world tasks, improving efficiency and reducing manual intervention
Research & Experimentation
Provides a platform for AI researchers to study multi-agent systems collaboration and interaction methods