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Top 5 Frameworks for Building Multi-Agents and Their Usage

How to Build a Universal LLM Agent

1. AutoGen (Microsoft)1.1 Features1.2 Limitations2. CrewAI2.1 Features2.2 Limitations3. LangGraph (LangChain)3.1 Features3.2 Limitations4. OpenAI Swarm4.1 Features4.2 Limitations5. Magentic-One (Microsoft)5.1 Features5.2 Limitations6. Framework Selection Recommendations

This article provides a summary of the features and limitations of different Multi-Agent frameworks to assist in selecting the appropriate framework.

1. AutoGen (Microsoft)

1.1 Features

• As one of the earliest and most popular frameworks in the field, it mainly involves two types of agents: User and Assistant. In its user-assistant agent model, the user agent provides prompts or requests, while the assistant agent is responsible for generating and executing code, and can provide results back to the user or other agents.

• Focuses on orchestrating multi-agents for coding tasks but can also handle other tasks. Human guidance can be introduced during interactions, supported by Microsoft’s strong community.

1.2 Limitations

• Not intuitive enough and unfriendly to non-programmers.

• Setting up local large language models (LLM) is complex, often requiring a proxy server. Performance is mediocre if not related to software development tasks.

2. CrewAI

2.1 Features

• Due to its extreme intuitiveness and ease of setup, it has become the preferred choice for quickly building multi-AI agent task demonstrations.

• Primarily relies on prompt writing, making it very convenient to create new agents and integrate them into the ecosystem. Non-technical users can easily get started, and it collaborates well with most LLM providers and local LLMs via LangChain integration.

2.2 Limitations

• Limited flexibility and customization, only suitable for basic use cases, not suitable for complex programming tasks.

• There are some vulnerabilities in the agent interaction process, and community support is lacking.

3. LangGraph (LangChain)

3.1 Features

• Built on LangChain, adopting a directed cyclic graph concept, it is not just a simple multi-AI agent framework.

• Highly flexible and customizable, it supports almost any multi-agent orchestration application. As an extension of LangChain, it has strong community support and works well with open-source LLMs and various APIs.

3.2 Limitations

• Documentation is not detailed enough.

• Unfriendly to non-programmers or beginners, requiring certain programming skills, especially in understanding graphs and logical flows.

4. OpenAI Swarm

4.1 Features

• An ideal choice for beginners entering the multi-AI agent space, focusing on simplifying agent creation and context switching between agents, making it very easy to create short demonstrations.

4.2 Limitations

• Only supports LLMs via the OpenAI API, not suitable for production deployment, lacks flexibility, and has weak community support, with no means to report issues on GitHub.

5. Magentic-One (Microsoft)

5.1 Features

• A newly launched framework by Microsoft, similar to Swarm, friendly to non-programmers and easy to run.

• Comes with 5 default agents, including management agent, web browsing (WebSurfer), file management (FileSurfer), coding and analysis (Coder), and console access (ComputerTerminal) agents.

• Built on AutoGen, it is more versatile and includes the AutoGenBench tool for analyzing agent performance.

5.2 Limitations

• Complex support for open-source LLMs.

• Not flexible enough, more like an application than a framework, and currently lacks documentation and community support.

6. Framework Selection Recommendations

• Software Development: AutoGen is the best choice, excelling in code generation and complex multi-agent coding workflows.

• Beginner Friendly: OpenAI Swarm and CrewAI are more suitable as they are easy to operate and require no complex setup, allowing beginners to get started quickly.

• Complex Task Handling: LangGraph, with its high flexibility and customization, is designed for advanced users and can handle complex logical orchestration.

• Open-Source LLM Support: LangGraph performs outstandingly, seamlessly integrating with open-source LLMs and supporting various APIs, while CrewAI also meets basic needs.

• Community Support: The AutoGen community can effectively resolve difficult issues.

• Quick Demonstration Setup: CrewAI is quick and intuitive for setting up agent tasks, while OpenAI Swarm and Magentic-One are also good but have slightly less community support.

• Cost-Effectiveness: Magentic-One’s pre-packaged setup and general design may save initial costs, while OpenAI Swarm and CrewAI are also under consideration.

The author has limited capabilities and welcomes criticism or discussion in the comments section.

Original link:https://medium.com/data-science-in-your-pocket/magentic-one-autogen-langgraph-crewai-or-openai-swarm-which-multi-ai-agent-framework-is-best-6629d8bd…