Editor’s Note: The current development of AI technology is advancing rapidly, and multi-agent frameworks are emerging one after another. How to make the right choice among numerous frameworks such as AutoGen, LangGraph, CrewAI, etc. and find the one that truly suits your needs?
The author of this article compares five major multi-agent AI frameworks and presents a key point: different AI frameworks are suitable for different scenarios and needs, and the key to selection lies in accurately matching project characteristics and technical routes.
Author | Mehul Gupta
Compiled by | Yue Yang
In the field of generative AI, the topic of Multi-AI Agent is becoming increasingly popular. Many tech giants have launched related frameworks, making it overwhelming.
However, facing numerous Multi-AI Agent frameworks, making a choice is indeed a challenge.
The options on the market are plentiful, making it hard to decide!
Especially after OpenAI launched Swarm and Microsoft introduced Magentic-One, this field has become crowded. To help everyone clarify their thoughts, I will analyze the core features, advantages, and potential shortcomings of these frameworks in detail so that everyone can make the best choice based on their needs. Next, we will discuss these frameworks one by one:
AutoGen (Microsoft)
LangGraph (LangChain)
CrewAI
OpenAI Swarm (OpenAI)
Magentic-One (Microsoft)
AutoGen
The AutoGen framework is a pioneer in this field, launched by Microsoft, and has been widely used in software development.
Main features include:
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AutoGen includes two core roles: user agents and assistant agents.
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User agents are responsible for proposing programming requirements or writing prompts, while assistant agents are responsible for generating and executing code.
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Assistant agents not only handle code generation but also include the code execution process and provide feedback to user agents or other agents.
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This framework excels in multi-agent orchestration of coding tasks, while also being capable of handling other types of tasks.
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During interactions between agents, human guidance is allowed.
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Strong and solid community support from Microsoft.
However, AutoGen also has the following limitations:
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Not intuitive enough for users without a programming background.
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The configuration process is cumbersome when deploying large language models (LLMs) locally, requiring additional configuration of proxy servers.
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Its performance may not be as excellent as specialized tools in non-software development fields.
CrewAI
CrewAI is often the preferred tool for quickly setting up Multi-AI Agent task demonstrations due to its intuitive operation and easy configuration.
Feature highlights:
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The interface is intuitive, primarily relying on prompt writing.
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Creating new agents and integrating them into the system is very simple, allowing for the generation of hundreds of agents in just a few minutes.
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Even users without a technical background can easily get started.
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Thanks to its integration with LangChain, it can work with most LLM service providers and local LLMs.
Drawbacks:
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There are limitations in flexibility and customization.
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More suitable for handling basic scenarios; not ideal for complex programming tasks.
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Interactions between agents may occasionally experience some failures.
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The support from the technical community is relatively weak.
LangGraph
I personally highly recommend LangGraph, as this tool can be applied to various Multi-AI Agent tasks and offers great flexibility.
Feature highlights:
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LangGraph is developed based on LangChain, with the core idea being a “Directed Cyclic Graph”.
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It is not just a Multi-AI agent framework; its functionality goes far beyond that.
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Highly flexible and customizable, it can meet the needs of almost all multi-agent collaboration applications.
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As an extension of LangChain, it has strong support from the technical community.
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It can seamlessly collaborate with open-source LLMs and various APIs.
Drawbacks:
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Documentation is not detailed enough. It may be challenging for users with less programming experience to get started.
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Using it requires a certain level of programming ability, especially in understanding graphs and logical flows.
OpenAI Swarm
OpenAI recently released Swarm, and I must say, for newcomers looking to enter the Multi-AI agent framework, this might be the easiest option available.
Feature highlights:
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Very suitable for beginners in the Multi-AI Agent field.
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Mainly focuses on simplifying the “agent creation” process and the context-switching operations between agents (which we call Handoffs).
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Creating a short demonstration application is extremely simple.
Drawbacks:
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Only supports OpenAI API and does not support other LLMs.
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Not suitable for deployment in production environments.
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The system’s flexibility needs improvement.
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Technical community support is weak, with no ability to submit issues on GitHub.
Magentic-One
The latest to debut is Microsoft’s Magnetic-One (this is Microsoft’s second framework), which aims to simplify the existing AutoGen framework.
Feature highlights:
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Similar to Swarm, Magnetic-One is also suitable for users with little programming experience, making it easy and quick to operate.
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The system is preset with five agents, including one management agent and four specialized agents: WebSurfer for browsing and interacting with web pages in a browser, FileSurfer for managing and navigating local files, Coder focusing on code writing and analysis, and ComputerTerminal providing console access to run programs and install libraries.
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This framework is built on AutoGen and is a general-purpose framework.
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It comes with the AutoGenBench tool, specifically designed to evaluate the performance of agents.
Drawbacks:
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Support for open-source LLMs is complex and not easy to implement.
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Flexibility needs improvement; to some extent, it feels more like an application than a framework.
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Current documentation and community support are almost non-existent and need to be strengthened.
So, which Multi-AI Agent framework is the best?
Here are my personal insights (based on my experience with these agent frameworks):
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For software development: AutoGen (by Microsoft) — it is best suited for handling code generation and complex multi-agent coding workflows.
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For beginners: OpenAI Swarm and CrewAI — these two frameworks are easy to operate, making them very suitable for newcomers who are just getting into multi-agent AI without complex configuration needs.
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The first choice for handling complex tasks: LangGraph — this framework offers great flexibility and is designed for advanced users, supporting custom logic and agent orchestration.
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In terms of compatibility with open-source LLMs: LangGraph — it has excellent compatibility with open-source LLMs and supports various APIs, which is not available in some other frameworks. CrewAI also performs well in this regard.
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The strongest technical community support: AutoGen has quite good technical community support, helping users solve some problems.
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An out-of-the-box choice: CrewAI — its configuration is quick, and the operation is intuitive, making it very suitable for demonstrations or tasks that require rapid creation of agents. Swarm and Magentic-One also perform quite well, but community support is relatively weak.
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The king of cost-effectiveness: Magentic-One — it offers a pre-configured solution using a general framework design approach, which may save costs in the early stages. Swarm and CrewAI are also worth noting for their cost-effectiveness.
Thanks for reading!
Hope you have enjoyed and learned new things from this blog!
About the authors
Mehul Gupta
GenAI Courses & Projects :
https://datasciencepocket.gumroad.com/
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