CAMEL

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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
- Why Use CAMEL for Your Research?
- What Can You Build With CAMEL?
- Data Generation
- Task Automation
- World Simulation
- Quick Start
- Starting with ChatAgent
- Seeking Help
- Tech Stack
- Research
- Synthetic Datasets
- Cookbooks (Usecases)
- Basic Concepts
- Advanced Features
- Model Training & Data Generation
- Multi-Agent Systems & Applications
- Data Processing
- 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
🧬 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:
Starting with ChatAgent
This example demonstrates how to create a ChatAgent using the CAMEL framework and perform a search query using DuckDuckGo.
1. **Install the tools package:**
2. **Set up your OpenAI API key:**
Alternatively, use a .env file:
3. **Run the following Python code:**
4. **(Optional) Enable model request/response logs:**
- CAMEL_MODEL_LOG_ENABLED: Enables request/response JSON logs.
- CAMEL_MODEL_LOG_MODEL_CONFIG_ENABLED: Controls whether