In the field of generative AI, multi-agent frameworks are developing rapidly, making it a challenge to make the right choice among numerous frameworks. This article will compare five major multi-agent AI frameworks to help readers make the best choice based on their own needs.
**I. AutoGen (Microsoft)**
1. Key Features
– Consists of two core roles: user agent and assistant agent. The user agent提出 programming needs or writes prompts, and the assistant agent generates and executes code and feeds back the results to the user agent or other agents.
– Excels in multi-agent orchestration for code tasks and can also handle other types of tasks.
– Allows for human guidance during the interaction between agents and has strong community support from Microsoft.
2. Limitations
– Not intuitive enough for users without a programming background.
– Configuring local deployment of large language models is cumbersome and requires additional configuration of proxy servers.
– May not perform as well as specialized tools in non-software development fields.
**II. CrewAI**
1. Functional Features
– Intuitive operation interface, mainly relying on writing prompts.
– Creating new agents and integrating them into the system is simple, and hundreds of agents can be generated in a few minutes.
– Non-technical users can also easily get started, and thanks to integration with LangChain, it can work with most LLM service providers and local LLMs.
2. Shortcomings
– Limited in flexibility and customization.
– More suitable for handling basic scenarios and not ideal for complex programming tasks.
– Interactions between agents occasionally malfunction, and technical community support is relatively weak.
**III. Langraph**
1. Functional Features
– Developed based on LangChain, with the core idea of a “directed cyclic graph”.
– Its functionality far exceeds that of general Multi-AI agent frameworks, highly flexible and customizable, and can meet almost all multi-agent collaboration application needs.
– As an extension of LangChain, it receives strong technical community support and can seamlessly collaborate with open-source large language models and various APIs.
2. Shortcomings
– Documentation is not detailed enough, and it is difficult for users with less programming experience to get started.
– Requires a certain programming ability, especially in understanding graphs and logical processes.
**IV. OpenAI Swarm**
1. Functional Features
– Very suitable for beginners in the Multi-AI Agent field.
– Mainly dedicated to simplifying the “agent creation” process and context switching operations between agents.
– Extremely simple to create short demo applications.
2. Shortcomings
– Only supports the OpenAI API and does not support other large language models.
– Not suitable for deployment in production environments. The system flexibility needs to be improved, and technical community support is weak. You can’t even submit issue feedback on GitHub.
**V. Magentic-One (Microsoft)**
1. Functional Features
– Similar to Swarm, suitable for users with less programming experience, easy and quick to operate.
– The system presets five agents, including a management agent and four dedicated agents (WebSurfer is responsible for web browsing and interaction, FileSurfer is responsible for local file management and navigation, Coder focuses on code writing and analysis, and ComputerTerminal provides console access rights to run programs and install library files).
– Built on AutoGen, it is a general framework and comes with the AutoGenBench tool for evaluating agent performance.
2. Shortcomings
– Support for open-source large language models is more complex and not easy to implement.
– Flexibility needs to be improved. It is more like an application than a framework. Currently, documentation and technical community support are almost non-existent and need to be strengthened.
**VI. Which framework is the best?**
1. In software development: AutoGen is the most suitable for handling code generation and complex multi-agent coding workflow tasks.
2. For beginners: OpenAI Swarm and CrewAI are easy to operate and suitable for newcomers who are just接触 multi-agent AI and have no complex configuration requirements.
3. The top choice for handling complex tasks: LangGraph offers extremely high flexibility and is designed for advanced users, supporting custom logic and agent orchestration.
4. In terms of compatibility with open-source large language models: LangGraph has excellent compatibility with open-source large language models and supports multiple API interfaces. CrewAI also performs well in this regard.
5. The most powerful technical community support: AutoGen has good technical community support to help users solve problems.
6. The plug-and-play choice: CrewAI is quick to configure and has an intuitive operation interface, suitable for demonstrations or tasks that require rapid creation of agents. Swarm and Magentic-One also perform well, but community support is relatively weak.
