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🐫 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.
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:
FreshRSS pip install camel-ai
This example demonstrates how to create a ChatAgent using the CAMEL framework and perform a search query using DuckDuckGo.
bash pip install 'camel-ai[web_tools]'
bash export OPENAI_API_KEY='your_openai_api_key'
```python 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. ```
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.
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.
| 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:
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)
| 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. |
| Dynamic Knowledge Graph Role-Playing: Multi-Agent System with dynamic, temporally-aware knowledge graphs | Builds dynamic, temporally-aware knowledge graphs for financial applications using a multi-agent system. It processes financial reports, news articles, and research papers to help traders analyze data, identify relationships, and uncover market insights. The system also utilizes diverse and optional element node deduplication techniques to ensure data integrity and optimize graph structure for financial decision-making. |
| 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 camel-ai@eigent.ai
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, TaskPrioritizationAgent and BabyAGI from Nakajima et al.: Task-Driven Autonomous Agent. [Example]
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.
A GPT-empowered penetration testing tool.
resources where we use it to solve HackTheBox challenge TEMPLATED (web challenge).Before installation, we recommend you to take a look at this installation video if you want to use cookie setup.
requirements.txt with pip install -r requirements.txt
config. You may follow a sample by cp config/chatgpt_config_sample.py config/chatgpt_config.py. Inspect - Network, find the connections to the ChatGPT session page.https://chat.openai.com/api/auth/session and paste it into the cookie field of config/chatgpt_config.py. (You may use Inspect->Network, find session and copy the cookie field in request_headers to https://chat.openai.com/api/auth/session)userAgent with your user agent.chatgpt_config.py.python3 test_connection.py. You should see some sample conversation with ChatGPT. 1. You're connected with ChatGPT Plus cookie.
To start PentestGPT, please use <python3 main.py --reasoning_model=gpt-4>
## Test connection for OpenAI api (GPT-4)
2. You're connected with OpenAI API. You have GPT-4 access. To start PentestGPT, please use <python3 main.py --reasoning_model=gpt-4 --useAPI>
## Test connection for OpenAI api (GPT-3.5)
3. You're connected with OpenAI API. You have GPT-3.5 access. To start PentestGPT, please use <python3 main.py --reasoning_model=gpt-3.5-turbo --useAPI>
https://chat.openai.com/backend-api/conversations. Please submit an issue if you encounter any problem.python3 main.py --args. --reasoning_model is the reasoning model you want to use.--useAPI is whether you want to use OpenAI API.test_connection.py, which are: python3 main.py --reasoning_model=gpt-4python3 main.py --reasoning_model=gpt-4 --useAPIpython3 main.py --reasoning_model=gpt-3.5-turbo --useAPIhelp: show the help message.next: key in the test execution result and get the next step.more: let PentestGPT to explain more details of the current step. Also, a new sub-task solver will be created to guide the tester.todo: show the todo list.discuss: discuss with the PentestGPT.google: search on Google. This function is still under development.quit: exit the tool and save the output as log file (see the reporting section below).TAB to autocomplete the commands.ENTER to select the item. Similarly, use <SHIFT + right arrow> to confirm selection.more, users can execute more commands to investigate into a specific problem: help: show the help message.brainstorm: let PentestGPT brainstorm on the local task for all the possible solutions.discuss: discuss with PentestGPT about this local task.google: search on Google. This function is still under development.continue: exit the subtask and continue the main testing session.logs folder (if you quit with quit command).python3 utils/report_generator.py <log file>. A sample report sample_pentestGPT_log.txt is also uploaded.Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
git checkout -b feature/AmazingFeature)git commit -m 'Add some AmazingFeature')git push origin feature/AmazingFeature)Distributed under the MIT License. See LICENSE.txt for more information.
Gelei Deng - gelei.deng@ntu.edu.sg
