CAMEL-AI
Role-play multi-agent framework for data generation, simulation, and scaling research
View on GitHubWhat it solves
Generating diverse synthetic training data at scale by running structured conversations between specialized agent personas: not just one-off completions, but whole multi-turn interaction datasets.
Overview
CAMEL (Communicative Agents for Mind Exploration of Large Language Model Society) was the first paper to use structured role-playing between LLM agents to complete tasks. The library has grown into a full framework for multi-agent simulation, synthetic data generation at scale, and studying emergent agent behaviors. The OWL project, built on CAMEL, achieved #1 open-source score on the GAIA benchmark.
Key facts
- Language
- Python
- License
- Apache 2.0
- Maturity
- Fast-moving
- Maintainer
- camel-ai
- Reviewed
- 2026-06-11
Where to start
ChatAgent is the atomic unit of every CAMEL interaction, understanding its message-passing model is the foundation for building anything more complex.
camel/agents/chat_agent.pyTechnologies
More in this domain
LangGraph
Stateful multi-agent orchestration for LLM applications
The state management problem in multi-agent systems: how you build agents that pass context across turns without losing the execution thread.
AutoGen
Multi-agent conversation framework from Microsoft Research
How to coordinate multiple specialized agents toward a shared goal without writing low-level orchestration logic by hand.
CrewAI
Role-based multi-agent orchestration for collaborative task execution
The boilerplate problem in multi-agent setup: defining roles, delegation rules, and task routing without writing a custom orchestration graph.