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</h4> <p style="line-height: 1.5; text-align: center;"> 🐫 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.</p> <br>

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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<details> <summary><kbd>Table of contents</kbd></summary> <br/>

CAMEL Framework Design Principles
Why Use CAMEL for Your Research?
What Can You Build With CAMEL?
Quick Start
- Starting with ChatAgent
- Seeking Help
Tech Stack
Research
Synthetic Datasets
Cookbooks (Usecases)
Real-World Usecases
🧱 Built with CAMEL (Real-world Producs & Research)
- Research Projects
- Product Projects
🗓️ Events
Contributing to CAMEL
Community & Contact
Citation
Acknowledgment
License

CAMEL Framework Design Principles

<h3>🧬 Evolvability</h3 >

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.

<h3>📈 Scalability</h3>

The framework is designed to support systems with millions of agents, ensuring efficient coordination, communication, and resource management at scale.

<h3>💾 Statefulness</h3>

Agents maintain stateful memory, enabling them to perform multi-step interactions with environments and efficiently tackle sophisticated tasks.

<h3>📖 Code-as-Prompt</h3>

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.

<br>

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.

<table style="width: 100%;"> <tr> <td align="left"></td> <td align="left"></td> <td align="left"></td> </tr> <tr> <td align="left">✅</td> <td align="left" style="font-weight: bold;">Large-Scale Agent System</td> <td align="left">Simulate up to 1M agents to study emergent behaviors and scaling laws in complex, multi-agent environments.</td> </tr> <tr> <td align="left">✅</td> <td align="left" style="font-weight: bold;">Dynamic Communication</td> <td align="left">Enable real-time interactions among agents, fostering seamless collaboration for tackling intricate tasks.</td> </tr> <tr> <td align="left">✅</td> <td align="left" style="font-weight: bold;">Stateful Memory</td> <td align="left">Equip agents with the ability to retain and leverage historical context, improving decision-making over extended interactions.</td> </tr> <tr> <td align="left">✅</td> <td align="left" style="font-weight: bold;">Support for Multiple Benchmarks</td> <td align="left">Utilize standardized benchmarks to rigorously evaluate agent performance, ensuring reproducibility and reliable comparisons.</td> </tr> <tr> <td align="left">✅</td> <td align="left" style="font-weight: bold;">Support for Different Agent Types</td> <td align="left">Work with a variety of agent roles, tasks, models, and environments, supporting interdisciplinary experiments and diverse research applications.</td> </tr> <tr> <td align="left">✅</td> <td align="left" style="font-weight: bold;">Data Generation and Tool Integration</td> <td align="left">Automate the creation of large-scale, structured datasets while seamlessly integrating with multiple tools, streamlining synthetic data generation and research workflows.</td> </tr> </table> <br>

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: C

Camel

Install / Use

README