Build Multi-Agent Systems for Data Generation
CAMEL-AI.org is the 1st LLM multi-agent framework and an open-source community dedicated to finding the scaling law of agents.
CAMEL-AI.org is the 1st LLM multi-agent framework and an open-source community dedicated to finding the scaling law of agents.
We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks.
Rigorous 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
Enhance your AI with models like Whisper, GPT-4, GPT-4o & more
Enable real-time communication and collaboration within your team effortlessly
Manage code repositories and streamline development processes seamlessly
Automate social media engagement and optimize online interactions
Efficiently handle large-scale data storage and retrieval
Implement advanced vector similarity search and recommendation systems
Boost productivity with simplified communication and project collaboration
Utilize Google services for enhanced productivity and efficiency
+ plus many more.
“The thing that I find really interesting with this is that it’s an unbelievably good way to make synthetic data. If you’re 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.”
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"The CAMEL AI “Domain Expert” dataset, comprising 25,000 conversations between two GPT 3.5 Turbo agents was used as part of the training data for Teknium’s 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."
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“The 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.”
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."
