camel-ai/camel

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.

Installing CAMEL is a breeze thanks to its availability on PyPI. Simply open your terminal and run:

pip install camel-ai

This example demonstrates how to create a ChatAgent using the CAMEL framework and perform a search query using DuckDuckGo.

1. Install the tools package:

pip install 'camel-ai[web_tools]'

1. Set up your OpenAI API key:

export OPENAI_API_KEY='your_openai_api_key'

Alternatively, use a .env file:

cp .env.example .env
# then edit .env and add your keys

1. Run the following Python code:

from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType
from camel.agents import ChatAgent
from camel.toolkits import SearchToolkit

model = ModelFactory.create(
  model_platform=ModelPlatformType.OPENAI,
  model_type=ModelType.GPT_4O,
  model_config_dict={"temperature": 0.0},
)

search_tool = SearchToolkit().search_duckduckgo

agent = ChatAgent(model=model, tools=[search_tool])

response_1 = agent.step("What is CAMEL-AI?")
print(response_1.msgs[0].content)
# CAMEL-AI is the first LLM (Large Language Model) multi-agent framework
# and an open-source community focused on finding the scaling laws of agents.
# ...

response_2 = agent.step("What is the Github link to CAMEL framework?")
print(response_2.msgs[0].content)
# The GitHub link to the CAMEL framework is
# [https://github.com/camel-ai/camel](https://github.com/camel-ai/camel).

1. (Optional) Enable model request/response logs:

export CAMEL_MODEL_LOG_ENABLED=true
export CAMEL_MODEL_LOG_MODEL_CONFIG_ENABLED=true
export CAMEL_LOG_DIR=camel_logs

• CAMEL_MODEL_LOG_ENABLED: Enables request/response JSON logs.
• CAMEL_MODEL_LOG_MODEL_CONFIG_ENABLED: Controls whether model_config_dict is logged under request.model_config_dict. When unset, it defaults to the same value as CAMEL_MODEL_LOG_ENABLED.
• CAMEL_LOG_DIR: Directory for generated log files (default: camel_logs).
• Logs are written as UTF-8 JSON with multilingual text preserved (for example Chinese, Japanese, Arabic) without Unicode escape noise.

For more detailed instructions and additional configuration options, check out the installation section.

After running, you can explore our CAMEL Tech Stack and Cookbooks at docs.camel-ai.org to build powerful multi-agent systems.

We provide a demo showcasing a conversation between two ChatGPT agents playing roles as a python programmer and a stock trader collaborating on developing a trading bot for stock market.

Explore different types of agents, their roles, and their applications.

• Creating Your First Agent
• Creating Your First Agent Society
• Critic Agents

Please reach out 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.

We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks.

Explore our research projects:

Research with US

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)

Practical guides and tutorials for implementing specific functionalities in CAMEL-AI agents and societies.

Real-world usecases demonstrating how CAMEL’s multi-agent framework enables real business value across infrastructure automation, productivity workflows, retrieval-augmented conversations, intelligent document/video analysis, and collaborative research.

We are actively involved in community events including:

• 🎙️ Community Meetings — Weekly virtual syncs with the CAMEL team
• 🏆 Competitions — Hackathons, Bounty Tasks and coding challenges hosted by CAMEL
• 🤝 Volunteer Activities — Contributions, documentation drives, and mentorship
• 🌍 Ambassador Programs — Represent CAMEL in your university or local tech groups

Want to host or participate in a CAMEL event? Join our Discord or want to be part of Ambassador Program.

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!

Made with contrib.rocks.

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 and TaskPrioritizationAgent from Nakajima et al.: Task-Driven Autonomous Agent.

• PersonaHub from Tao Ge et al.: Scaling Synthetic Data Creation with 1,000,000,000 Personas. [Example]

• Self-Instruct from Yizhong Wang et al.: SELF-INSTRUCT: Aligning Language Models with Self-Generated Instructions. [Example]

The source code is licensed under Apache 2.0.

@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}
}

Here is an example of how to cite our work:

We use the CAMEL framework \cite{li2023camel} to develop the agents used in our experiments.

For more information please contact camel-ai@eigent.ai

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