multi agent framework - chunhualiao/public-docs GitHub Wiki

Based on the search results, I've created a comparison table of key features for the multi-agent frameworks mentioned:

Feature LangChain/LangGraph AutoGen CrewAI MetaGPT Semantic Kernel
Multi-Agent Support ✅ ✅ ✅ ✅ ✅
Tool Coverage Extensive Modular Extensive Not specified Extensive
Memory Support Comprehensive Conversation-driven Comprehensive Not specified Not specified
Ease of Use Moderate High High High High
Structured Output Strong Not specified Strong ✅ ✅
Documentation Extensive Not specified Extensive Not specified Extensive
Code Execution ✅ Strong ✅ ✅ ✅
Human-in-the-Loop ✅ ✅ ✅ Not specified Not specified
Customization High High High High High
Open-source LLM Support ✅ ✅ ✅ ✅ ✅
Unique Feature Graph-based approach Docker support Role-based design SOP integration Planner function

This table provides a high-level comparison of the key features across the mentioned frameworks. It's important to note that the information is based on the available search results, and some features may not be explicitly mentioned for all frameworks[1][4][8].

Citations:

Several popular frameworks have emerged to help developers create multi-agent solutions using large language models (LLMs). These frameworks enable the creation of sophisticated AI systems that can tackle complex problems through collaboration between multiple specialized agents. Here are some of the most notable options:

Agents with human characters

we rely on the Sotopia platform (Zhou et al., 2024), which supports the creation of simulated human characters with LLMs. Each simulated colleague is equipped with a detailed profile that includes their name, role, responsibilities, and project affiliations.

LangChain

LangChain is a widely-used open-source framework for building LLM-powered applications. It offers:

  • An extensive library of pre-built components
  • Support for multiple LLM providers
  • Memory management and context handling capabilities

LangChain is known for its flexibility in designing complex agent behaviors and easy integration with various data sources and external tools[1].

AutoGen or AG2

AutoGen

https://github.com/orgs/ag2ai/repositories

Developed by Microsoft, AutoGen is a framework for building multi-agent conversational systems. Key features include:

  • Support for multi-agent conversations and workflows
  • Integration with LLMs and external tools
  • Customizable agent roles and behaviors

AutoGen is highly flexible and scalable, making it suitable for both simple chatbots and complex multi-agent applications[1][4].

CrewAI

CrewAI is an open-source framework designed for multi-agent orchestration. It enables:

  • Role-based agent definitions
  • Intelligent collaboration between agents
  • Integration of custom tools and APIs
  • Automated workflow management

CrewAI is particularly well-suited for scenarios requiring intelligent teamwork among agents[10].

MetaGPT

MetaGPT is an open-source framework that aims to simplify the development of multi-agent systems. It offers:

  • A user-friendly interface
  • Tools suitable for both beginners and experienced programmers
  • Easy integration of multiple agents
  • Seamless integration with existing frameworks and LLMs

MetaGPT is ideal for developers looking to quickly experiment with complex agent interactions[7].

Microsoft Semantic Kernel

While not as prominently mentioned in the search results, Microsoft Semantic Kernel is another framework worth noting. It provides a flexible architecture for integrating AI services and building intelligent applications[1].

These frameworks offer developers powerful tools to create sophisticated multi-agent systems that can collaborate, reason, and solve complex problems across various domains. By leveraging the strengths of multiple specialized agents, these solutions can often outperform single-agent systems in tackling real-world challenges.

Citations: