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CAMEL-AI
🐫 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.
Join us (Discord or WeChat) in pushing the boundaries of finding the scaling laws of agents.
🌟 Star CAMEL on GitHub and be instantly notified of new releases.
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Table of contents
CAMEL Framework Design Principles
🧬 Evolvability
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.
📈 Scalability
The framework is designed to support systems with millions of agents, ensuring efficient coordination, communication, and resource management at scale.
💾 Statefulness
Agents maintain stateful memory, enabling them to perform multi-step interactions with environments and efficiently tackle sophisticated tasks.
📖 Code-as-Prompt
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.
Why Use CAMEL for Your Research?
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. |
What Can You Build With CAMEL?
1. Data Generation
2. Task Automation
3. World Simulation
Quick Start
Installing CAMEL is a breeze thanks to its availability on PyPI. Simply open your terminal and run:
pip install camel-ai
Starting with ChatAgent
This example demonstrates how to create a ChatAgent using the CAMEL framework and perform a search query using DuckDuckGo.
- Install the tools package:
pip install 'camel-ai[web_tools]'
- 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
- 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).
- (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.
Seeking Help
Please reach out to us on CAMEL discord if you encounter any issue set up CAMEL.
Tech Stack
Key Modules
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. |
Research
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.
Synthetic Datasets
1. Utilize Various LLMs as Backends
For more details, please see our Models Documentation.
Data (Hosted on Hugging Face)
2. Visualizations of Instructions and Tasks
Cookbooks (Usecases)
Practical guides and tutorials for implementing specific functionalities in CAMEL-AI agents and societies.
1. Basic Concepts
2.
CAMEL 是一个开源的多智能体框架，用于构建、模拟和研究由大语言模型驱动的智能体，规模可以很大，目标是探索“智能体的缩放定律”。