camel-ai/camel

Project overview

An open-source framework for building, operating, and studying multi-agent systems at large scale, supporting up to one million agents. It provides role-playing task automation, synthetic data generation, and retrieval-augmented generation components, requiring Python code configuration and a local or external LLM provider to operate.

Project type

AI Agent · RAG · Model Development · Data Processing

Use cases

Knowledge Q&A · Automation

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• Researchers, AI engineers, and developers building multi-agent systems or generating synthetic datasets who are comfortable using Python libraries and configuring LLM backends.

Key capabilities

• Components for building and managing multi-agent systems and collaboration, supporting up to 1M agents.
• Tools and methods for synthetic data creation and augmentation.
• Core agent architectures and behaviors for autonomous operation.
• Memory storage and retrieval mechanisms that equip agents to retain and leverage historical context.
• Knowledge retrieval and retrieval-augmented generation components.
• Model architectures and customization options for agent intelligence, allowing various LLMs as backends.
• Automating tasks using role-playing and workforce management.

Limitations and risks

• The quickstart functionality requires an OpenAI API key, meaning a paid external service may be needed unless substituted with a local model.
• Interacting with the framework requires writing Python code.

Getting started

• Install the package via pip from PyPI, set an LLM API key environment variable, and execute a Python script to create a ChatAgent.

Evidence and sources

• GitHub project description: 🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents.
• README: we implement and support various types of agents, tasks, prompts, models, and simulated environments.
• GitHub repository metadata: Python
• README: We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks.
• README: We are a community-driven research collective comprising over 100 researchers dedicated to advancing frontier research in Multi-Agent Systems.

Reviewed at: 2026-07-17T13:20:40Z