CAMEL-AI is a open-source community for finding the scaling laws of agents for data generation, world simulation, task automation.
CAMELâs multi-agent approach streamlines large-scale synthetic data creation and labeling. By assigning different specialized roles to each agent, it encourages dynamic, chain-of-thought collaborations that yield high-quality outputs. The orchestrated interaction ensures comprehensive coverage of data variations and consistency across domains. This makes it ideal for generating training sets, question-answer pairs, or other structured content.
CAMEL powers automated workflows by breaking down complex tasks among coordinated agents. Each agent assumes a specific role, collaborating through an iterative conversational framework. This reduces manual intervention, minimizes errors, and accelerates solution deliveryâespecially useful for repetitive or logic-intensive processes.
CAMEL enables simulations of dynamic, interactive worlds. Agents act as entities with distinct personas, communicating and responding in real time. This setup can be used for modeling scenarios, building interactive storylines, or testing multi-layered strategies. By capturing the nuances of diverse viewpoints, CAMEL creates immersive simulations for experimentation, training, and creative exploration.
We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks.
Join Research MeetingRigorous research takes time and resources. With advanced GPU access and a dedicated team, we explore the frontier research topics by balancing urgency and patience. Join our ongoing projects or test new ideas with us, reach out via email for more information.
We value every contribution, from new features to bug fixes. Projects at CAMEL evolves around enhancing infrastructure, improving documentation, and implementing research ideas. Check out our Contributing Guidelines on GitHub.
These power your agents
The core entities that perform tasks
Enhance your agents' capabilities
Enable agents to retain and utilize information
Instructions that guide your agents
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âThe thing that I find really interesting with this is that itâs an unbelievably good way to make synthetic data. If youâre trying to create any sort of customer service or chatbot agent that communicates with the public, this allows you to make synthetic data for training and fine-tuning.â
"The CAMEL AI âDomain Expertâ dataset, comprising 25,000 conversations between two GPT 3.5 Turbo agents was used as part of the training data for Tekniumâs OpenHermes model and the Microsoft Phi model"
"Guohao Li, who designed Camel, highlights the potential of multi-agent systems to bypass traditional AI limitations, enabling tasks like phishing email generation and cyber bug development."
âThe essence of Camel lies in its prompt engineering, i.e., inception prompting. The prompts are actually carefully defined to assign roles, prevent flipping roles, prohibit harm and false information, and encourage consistent conversation.â
MPT-30B-Chat is a chatbot-like model for dialogue generation. It was built by finetuning MPT-30B and trained on 19.54% Camel-AI sourced data
"This innovative concept is set to redefine the way AI agents interact with each other and, in doing so, revolutionize the realm of conversational AI."