🐫 CAMEL is an open-source community dedicated to finding the scaling laws of agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.
Join us (Discord or WeChat) in pushing the boundaries of finding the scaling laws of agents.
🌟 Star CAMEL on GitHub and be instantly notified of new releases.
The framework enables multi-agent systems to continuously evolve by generating data and interacting with environments. This evolution can be driven by reinforcement learning with verifiable rewards or supervised learning.
The framework is designed to support systems with millions of agents, ensuring efficient coordination, communication, and resource management at scale.
Agents maintain stateful memory, enabling them to perform multi-step interactions with environments and efficiently tackle sophisticated tasks.
Every line of code and comment serves as a prompt for agents. Code should be written clearly and readably, ensuring both humans and agents can interpret it effectively.
We are a community-driven research collective comprising over 100 researchers dedicated to advancing frontier research in Multi-Agent Systems. Researchers worldwide choose CAMEL for their studies based on the following reasons.
| ✅ | Large-Scale Agent System | Simulate up to 1M agents to study emergent behaviors and scaling laws in complex, multi-agent environments. |
| ✅ | Dynamic Communication | Enable real-time interactions among agents, fostering seamless collaboration for tackling intricate tasks. |
| ✅ | Stateful Memory | Equip agents with the ability to retain and leverage historical context, improving decision-making over extended interactions. |
| ✅ | Support for Multiple Benchmarks | Utilize standardized benchmarks to rigorously evaluate agent performance, ensuring reproducibility and reliable comparisons. |
| ✅ | Support for Different Agent Types | Work with a variety of agent roles, tasks, models, and environments, supporting interdisciplinary experiments and diverse research applications. |
| ✅ | Data Generation and Tool Integration | Automate the creation of large-scale, structured datasets while seamlessly integrating with multiple tools, streamlining synthetic data generation and research workflows. |
Installing CAMEL is a breeze thanks to its availability on PyPI. Simply open your terminal and run:
pip install camel-aiFor more detailed instructions and additional configuration options, check out the installation section.
After running, you can explore our CAMEL Tech Stack and Cookbooks at www.docs.camel-ai.org to build powerful multi-agent systems.
We provide a 
Explore different types of agents, their roles, and their applications.
Please reachout to us on CAMEL discord if you encounter any issue set up CAMEL.
Core components and utilities to build, operate, and enhance CAMEL-AI agents and societies.
| Module | Description |
|---|---|
| Agents | Core agent architectures and behaviors for autonomous operation. |
| Agent Societies | Components for building and managing multi-agent systems and collaboration. |
| Data Generation | Tools and methods for synthetic data creation and augmentation. |
| Models | Model architectures and customization options for agent intelligence. |
| Tools | Tools integration for specialized agent tasks. |
| Memory | Memory storage and retrieval mechanisms for agent state management. |
| Storage | Persistent storage solutions for agent data and states. |
| Benchmarks | Performance evaluation and testing frameworks. |
| Interpreters | Code and command interpretation capabilities. |
| Data Loaders | Data ingestion and preprocessing tools. |
| Retrievers | Knowledge retrieval and RAG components. |
| Runtime | Execution environment and process management. |
| Human-in-the-Loop | Interactive components for human oversight and intervention. |
We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks.
Explore our research projects:
We warmly invite you to use CAMEL for your impactful research.
Rigorous research takes time and resources. We are a community-driven research collective with 100+ researchers exploring the frontier research of Multi-agent Systems. Join our ongoing projects or test new ideas with us, reach out via email for more information.
For more details, please see our Models Documentation.
Data (Hosted on Hugging Face)
| Dataset | Chat format | Instruction format | Chat format (translated) |
|---|---|---|---|
| AI Society | Chat format | Instruction format | Chat format (translated) |
| Code | Chat format | Instruction format | x |
| Math | Chat format | x | x |
| Physics | Chat format | x | x |
| Chemistry | Chat format | x | x |
| Biology | Chat format | x | x |
| Dataset | Instructions | Tasks |
|---|---|---|
| AI Society | Instructions | Tasks |
| Code | Instructions | Tasks |
| Misalignment | Instructions | Tasks |
Practical guides and tutorials for implementing specific functionalities in CAMEL-AI agents and societies.
| Cookbook | Description |
|---|---|
| Creating Your First Agent | A step-by-step guide to building your first agent. |
| Creating Your First Agent Society | Learn to build a collaborative society of agents. |
| Message Cookbook | Best practices for message handling in agents. |
| Cookbook | Description |
|---|---|
| Tools Cookbook | Integrating tools for enhanced functionality. |
| Memory Cookbook | Implementing memory systems in agents. |
| RAG Cookbook | Recipes for Retrieval-Augmented Generation. |
| Graph RAG Cookbook | Leveraging knowledge graphs with RAG. |
| Track CAMEL Agents with AgentOps | Tools for tracking and managing agents in operations. |
| Cookbook | Description |
|---|---|
| Data Generation with CAMEL and Finetuning with Unsloth | Learn how to generate data with CAMEL and fine-tune models effectively with Unsloth. |
| Data Gen with Real Function Calls and Hermes Format | Explore how to generate data with real function calls and the Hermes format. |
| CoT Data Generation and Upload Data to Huggingface | Uncover how to generate CoT data with CAMEL and seamlessly upload it to Huggingface. |
| CoT Data Generation and SFT Qwen with Unsolth | Discover how to generate CoT data using CAMEL and SFT Qwen with Unsolth, and seamlessly upload your data and model to Huggingface. |
| Cookbook | Description |
|---|---|
| Role-Playing Scraper for Report & Knowledge Graph Generation | Create role-playing agents for data scraping and reporting. |
| Create A Hackathon Judge Committee with Workforce | Building a team of agents for collaborative judging. |
| Customer Service Discord Bot with Agentic RAG | Learn how to build a robust customer service bot for Discord using Agentic RAG. |
| Customer Service Discord Bot with Local Model | Learn how to build a robust customer service bot for Discord using Agentic RAG which supports local deployment. |
| Cookbook | Description |
|---|---|
| Video Analysis | Techniques for agents in video data analysis. |
| 3 Ways to Ingest Data from Websites with Firecrawl | Explore three methods for extracting and processing data from websites using Firecrawl. |
| Create AI Agents that work with your PDFs | Learn how to create AI agents that work with your PDFs using Chunkr and Mistral AI. |
For those who'd like to contribute code, we appreciate your interest in contributing to our open-source initiative. Please take a moment to review our contributing guidelines to get started on a smooth collaboration journey.🚀
We also welcome you to help CAMEL grow by sharing it on social media, at events, or during conferences. Your support makes a big difference!
For more information please contact [email protected]
-
GitHub Issues: Report bugs, request features, and track development. Submit an issue
-
Discord: Get real-time support, chat with the community, and stay updated. Join us
-
X (Twitter): Follow for updates, AI insights, and key announcements. Follow us
-
Ambassador Project: Advocate for CAMEL-AI, host events, and contribute content. Learn more
@inproceedings{li2023camel,
title={CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society},
author={Li, Guohao and Hammoud, Hasan Abed Al Kader and Itani, Hani and Khizbullin, Dmitrii and Ghanem, Bernard},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}
Special thanks to Nomic AI for giving us extended access to their data set exploration tool (Atlas).
We would also like to thank Haya Hammoud for designing the initial logo of our project.
We implemented amazing research ideas from other works for you to build, compare and customize your agents. If you use any of these modules, please kindly cite the original works:
-
TaskCreationAgent,TaskPrioritizationAgentandBabyAGIfrom Nakajima et al.: Task-Driven Autonomous Agent. [Example] -
PersonaHubfrom Tao Ge et al.: Scaling Synthetic Data Creation with 1,000,000,000 Personas. [Example] -
Self-Instructfrom Yizhong Wang et al.: SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions. [Example]
The source code is licensed under Apache 2.0.