camel

Why we included this project

CAMEL is a multi-agent framework for teams that need several LLM-driven agents to collaborate instead of one chatbot answering questions. It provides agents that plan and act, a coordinator layer that assigns roles across a society of agents, code interpreters for live execution, and persistent memory so context survives across turns. The framework also ships pipelines for generating synthetic training data with verifier loops and for running large-scale social simulations, which is useful if you need realistic agent-behavior studies rather than just a demo. OWL, the automation layer bundled with the project, drives browsers and code interpreters for real-world tasks, so you can move from prototype to actual workflow automation. Researchers and engineers who want a modular, actively maintained foundation for agent-based applications will find a lot to work with here.