An open-source framework for building and operating large-scale multi-agent systems and simulated environments. It supports agent collaboration up to one million agents and includes tools for synthetic data generation, RAG, and task automation via Python code configurations.
Project overview
The framework is designed to study the scaling laws of agents and can support systems with up to one million agents, addressing multi-agent behaviors, capabilities, and potential risks.
Project type
AI Agent
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Researchers studying multi-agent behaviors and scaling laws.
Developers building multi-agent systems requiring role-playing, workforce management, and stateful memory.
Key capabilities
Components for building and managing multi-agent systems and collaboration, supporting up to 1M agents.
Tools and methods for synthetic data creation and augmentation.
Core agent architectures and behaviors for autonomous operation.
Memory storage and retrieval mechanisms that equip agents to retain and leverage historical context.
Knowledge retrieval and retrieval-augmented generation components.
Model architectures and customization options for agent intelligence, allowing various LLMs as backends.
Automating tasks using role-playing and workforce management.
Limitations and risks
General operation requires configuring an external or local LLM provider. Basic quickstart functionality requires an OpenAI API key, meaning inference may depend on a paid external service.
Getting started
Install the package via pip from PyPI, set your API key environment variable, and run a Python script to instantiate a ChatAgent.
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