CAMEL-AI

CAMEL-AI is an Apache-2.0 open-source multi-agent framework for building role-playing agents, generating synthetic data, running world simulations, and automating tasks, maintained by a large research community.

CAMEL-AI (Communicative Agents for Mind exploration of Large Language model society) is one of the earliest and most-cited open-source multi-agent frameworks. It provides a modular architecture for building systems where multiple LLM-powered agents communicate, take on roles, and collaborate to accomplish tasks. Its research framing centers on discovering the scaling laws of agents across data generation, world simulation, and automation. Beyond the core framework, the CAMEL-AI ecosystem includes specialized projects: synthetic data engines for generating training data, world-simulation platforms such as Oasis for large-scale multi-agent social simulations, and task-automation benchmarks like CRAB for real-world multi-step software workflows. This makes CAMEL-AI attractive to researchers and engineers who want both a practical agent-building toolkit and a research substrate for studying agent behavior at scale. The project is community-driven, with a research collective of over 100 contributors, and remains actively maintained into 2026. The source code is licensed under Apache 2.0, so it is free to use, including commercially, while some associated datasets carry non-commercial (CC BY-NC) terms. Teams typically pair CAMEL-AI with their own model provider and infrastructure.