CAMEL is an open-source framework and community for researching the scaling laws of large language model (LLM) agents. It enables the simulation of up to one million agents, facilitating the study of emergent behaviors and complex interactions in multi-agent systems. The framework is designed for researchers and developers focused on advancing the field of AI agents.

How It Works

CAMEL employs a "Code-as-Prompt" philosophy, where code and comments serve as interpretable prompts for agents. Its design prioritizes evolvability, scalability, and statefulness. Agents maintain stateful memory for multi-step interactions, and the framework supports dynamic communication and integration with various tools, enabling large-scale data generation and task automation.

Quick Start & Requirements

• Install via pip: pip install camel-ai
• For tool integration: pip install 'camel-ai[web_tools]'
• Requires an OpenAI API key set as an environment variable: export OPENAI_API_KEY='your_openai_api_key'
• Example code provided for creating a ChatAgent with tool integration (DuckDuckGo search).
• Documentation and examples are available at docs.camel-ai.org.

Highlighted Details

• Supports simulation of up to 1 million agents for large-scale studies.
• Enables dynamic, real-time agent communication and collaboration.
• Provides stateful memory for agents to retain and leverage historical context.
• Offers support for multiple benchmarks, agent types, and tool integrations.
• Facilitates automated data generation and seamless integration with research workflows.

Maintenance & Community

CAMEL is a community-driven initiative with over 100 researchers. Active community channels include Discord, X (Twitter), and WeChat. Contact is available via email at camel-ai@eigent.ai.

Licensing & Compatibility

The source code is licensed under Apache 2.0, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The framework relies heavily on OpenAI models for its core functionality, requiring API access and associated costs. While designed for scalability, managing and analyzing simulations with millions of agents may require significant computational resources.