Introduction
CAMEL is a community-driven open-source multi-agent framework designed to study the scaling effects and collaborative behaviors of agents. It supports simulation, data generation, and evaluation workflows from single agents to millions of agents. The project provides extensive examples, toolchains, and cookbooks to help researchers and engineers quickly build multi-agent systems.
Key Features
- Large-scale simulation: Scalable to a massive number of agents for emergent behavior research.
- Rich toolset: Includes data generation, evaluation benchmarks, RAG pipelines, and integration plugins.
- Composable agent architecture: Supports multi-role agents, stateful memory, and society mechanisms.
- Open community and documentation: Offers detailed documentation, example code, and community channels (Discord, documentation site).
Use Cases
- Multi-agent research: Explore the impact of agent scale and collaboration strategies.
- Data generation and annotation: Use built-in pipelines to produce training and evaluation data.
- Task automation and workflow orchestration: Build collaborative task agents to automate complex processes.
- RAG and knowledge retrieval: Integrate retrieval modules for enhanced retrieval-augmented multi-agent dialogue systems.
Technical Highlights
- Modular design: Decoupled modules for Agents, Societies, Memory, Tools, and RAG.
- Stateful memory: Supports long-term context and multi-step interaction memory mechanisms.
- Multi-backend model support: Compatible with various LLM backends for agent behavior evaluation and training.
- Research-friendly: Includes benchmarks, reproducible experiment configurations, and visualization tools.