Editor’s Note: The current development of AI technology is advancing rapidly, and multi-agent frameworks are emerging like mushrooms after rain. How to make the right choice among numerous frameworks like AutoGen, LangGraph, CrewAI, etc., and find out which multi-agent framework truly fits your needs?

The author of this article presents a key point through the comparison of five major multi-agent AI frameworks: different AI frameworks are suitable for different scenarios and needs, and the key to selection lies in precisely matching project characteristics and technology routes.

• The interface is intuitive, primarily relying on writing prompts.

• Creating new agents and integrating them into the system is very simple, allowing for the generation of hundreds of agents within minutes.

• Even users without a technical background can easily get started.

• Thanks to integration with LangChain, it can work with most LLM service providers and local LLMs.

• There are limitations in flexibility and customization.

• More suitable for handling basic scenarios; not ideal for complex programming tasks.

• Interactions between agents may occasionally experience some faults.

• The support from the technical community is relatively weak.

• LangGraph is developed based on LangChain, with the core idea being a “Directed Cyclic Graph”.

• It is not just a Multi-AI agent framework; its functionality goes far beyond that.

• Highly flexible and customizable, it can meet almost all multi-agent collaboration application needs.

• As an extension of LangChain, it has strong support from the technical community.

• It can seamlessly collaborate with open-source LLMs and various APIs.

• The documentation is not detailed enough. For users with less programming experience, the learning curve can be steep.

• Using it requires a certain level of programming ability, especially in understanding graphs and logical flows.

• Very suitable for beginners in the Multi-AI Agent field.

• It primarily focuses on simplifying the “agent creation” process and the context-switching operations between agents (which we call Handoffs).

• Creating a short demo application is extremely simple.

• Only supports the OpenAI API and does not support other LLMs.

• Not suitable for deployment in production environments.

• The system’s flexibility needs improvement.

• Technical community support is weak, with no ability to submit issues on GitHub.

• Similar to Swarm, Magentic-One is also suitable for users with less programming experience, making it easy and quick to operate.

• The system is preset with five agents, including one management agent and four specialized agents: WebSurfer, responsible for browsing web pages and interacting with them; FileSurfer, responsible for managing and navigating local files; Coder, focused on code writing and analysis; and ComputerTerminal, which provides console access to run programs and install libraries.

• This framework is built on AutoGen and is a general-purpose framework.

• It comes with the AutoGenBench tool, specifically designed to evaluate agent performance.

• Support for open-source LLMs is complex and not easy to implement.

• Flexibility needs improvement; in some ways, it resembles an application rather than a framework.

• The current documentation and technical community support are almost non-existent and need to be strengthened.

• In software development: AutoGen (launched by Microsoft) —— it is best suited for handling code generation and complex multi-agent coding workflows.

• For beginners: OpenAI Swarm and CrewAI —— these two frameworks are easy to operate and very suitable for novices who are just getting into multi-agent AI and have no complex configuration needs.

• Best for handling complex tasks: LangGraph —— this framework offers extremely high flexibility and is designed for advanced users, supporting custom logic and agent orchestration.

• In terms of compatibility with open-source LLMs: LangGraph —— it has excellent compatibility with open-source LLMs and supports various API interfaces, which is not available in some other frameworks. CrewAI also performs well in this regard.

• Best technical community support: AutoGen has quite good technical community support, which can help users solve some problems.

• Ready-to-use choice: CrewAI —— its configuration is quick and operation is intuitive, making it very suitable for demonstrations or tasks that require rapid creation of agents. Swarm and Magentic-One also perform well, but community support is relatively weak.

• Best cost-performance ratio: Magentic-One —— it provides a set of pre-configured solutions and adopts a general framework design approach, which may save costs in the early stages. Swarm and CrewAI are also worth paying attention to in terms of cost-effectiveness.