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camel-ai/camel

A multi-agent framework for studying emergent behaviors and scaling laws in AI systems through simulation and interaction.

Core Features

Evolvability: Enables multi-agent systems to continuously evolve through data generation and environment interaction.

Scalability: Designed to support systems with millions of agents for efficient coordination and resource management.

Statefulness: Agents maintain stateful memory, enabling multi-step interactions and sophisticated task tackling.

Code-as-Prompt: Code and comments serve as prompts, ensuring interpretability for both humans and agents.

Large-Scale Agent System Simulation: Facilitates the simulation of up to 1M agents to study emergent behaviors.

Detailed Introduction

CAMEL is an open-source community and framework dedicated to advancing frontier research in multi-agent systems. It provides a robust infrastructure designed to study the scaling laws of AI agents, exploring their emergent behaviors, capabilities, and potential risks when operating at scale. By implementing and supporting various types of agents, tasks, prompts, models, and simulated environments, CAMEL empowers researchers and developers to generate synthetic data, automate complex tasks, and simulate intricate worlds. This comprehensive approach fosters the development, experimentation, and understanding of sophisticated multi-agent applications, pushing the boundaries of AI research.