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.

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OUR MISSION

We are 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.

Research with us

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.

Join our open source community

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.

Read our paper
components

Create powerful agents with our components

Models

These power your agents

Agents

The core entities that perform tasks

Tools

Enhance your agents' capabilities

Memory

Enable agents to retain and utilize information

Prompts

Instructions that guide your agents

Integrations

Seamless integration with popular platforms

Open AI Logo
OpenAI

Enhance your AI with models like Whisper, GPT-4, GPT-4o & more

Discord

Enable real-time communication and collaboration within your team effortlessly

Github

Manage code repositories and streamline development processes seamlessly

Twitter

Automate social media engagement and optimize online interactions

Milvus

Efficiently handle large-scale data storage and retrieval

Qdrant

Implement advanced vector similarity search and recommendation systems

Slack

Boost productivity with simplified communication and project collaboration

Google

Utilize Google services for enhanced productivity and efficiency

+ plus many more.

Testimonials

Success stories with CAMEL

“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.”

Sam Witteveen
Co-founder @ Red Dragon AI

"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"

Valory
Open-source framework

"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."

The Economist
Newspaper

“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.”

Sophia Yang, Ph.D.
Head of Developer Relations @ Mistral AI

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

Databricks
The Data and AI Company

"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."

Yogesh Haribhau Kulkarni
AI Advisor
Camel Blog

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