Top 5 AI Agent Frameworks To Explore in 2025

Hello everyone, I am Xuan Jie.

Before we start the main content, let me promote myself a bit. The Spring Festival is approaching, to give back to the support of my fans, the “3-Day AI Agent Project Practical Live Training Camp” originally priced at 199 yuan, is now directly reduced to 19 yuan, and today we are opening registration privileges for one more day, limited to 99 people.

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Top 5 AI Agent Multi-Agent Development Frameworks

In the new era of AI large models, AI Agent multi-agent system (Multi-Agent) technology is increasingly attracting the attention of many tech giants. With the launch of frameworks such as OpenAI’s Swarm and Microsoft’s Magentic-One, the development in this field has become more intricate. Faced with numerous options, choosing the Multi-Agent framework that best matches one’s needs has become a key issue for many developers and enterprises.

In this issue, we will deeply analyze the five most popular AI Agent multi-agent frameworks on the market, including Microsoft’s AutoGen, CrewAI, LangChain’s LangGraph, OpenAI’s Swarm, and Microsoft’s Magentic-One, aiming to provide guidance and reference for your framework selection.

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AutoGen: Microsoft’s Pioneering Work, Designed for Software Engineering

As one of Microsoft’s early and popular frameworks in the multi-agent field, AutoGen aims to provide solutions for software development. In this framework, the core consists of two types of agents: User-Agent and Assistant-Agent. The User-Agent is responsible for conveying instructions and needs, while the Assistant-Agent is responsible for generating and executing code, and feeding back results to the user or other agents.

One of the highlights of AutoGen is its excellent multi-agent coordination capability, especially in handling programming tasks. It also allows for human intervention during agent interactions, increasing the flexibility and controllability of the development process.

However, AutoGen is not without its flaws. Its user interface may not be intuitive enough, and it may require some learning time for non-technical users. Additionally, the configuration process of AutoGen is relatively cumbersome, especially when integrating local large language models (LLM), which also requires setting up a proxy server. Therefore, AutoGen is more suitable for developers who have a certain understanding of software development and are willing to invest time and energy to master its usage.

Github Address:

https://github.com/microsoft/autogen

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CrewAI: The Preferred Tool for Demonstrations, Easy and Efficient to Operate

Compared to AutoGen, CrewAI emphasizes its ease of use and the ability to quickly set up demonstrations. The platform is intuitive and easy to operate, mainly generating and configuring agents through writing prompts. On the CrewAI platform, the creation and integration process of agents is extremely simple, allowing users to easily build hundreds of agents in a short time, making it the preferred tool for developers pursuing quick production of Multi-Agent demonstrations or prototypes.

However, CrewAI lacks flexibility and customization, making it more suitable for handling simple use cases rather than complex programming tasks. Additionally, there may be some bugs in the interactions between agents, which could affect the stability and reliability of the project. Despite these limitations, for developers who only need to quickly build demonstrations or prototypes and have low requirements for system flexibility, CrewAI is still a suitable choice.

Github Address：

https://github.com/crewAIInc/crewAI

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LangGraph: Highly Flexible, Suitable for Complex Tasks

LangGraph is a Multi-Agent framework based on LangChain, which introduces the concept of directed cyclic graphs to create a highly flexible and customizable solution. LangGraph is not only suitable for various Multi-Agent tasks but also supports almost all multi-agent orchestration applications, making it the preferred tool for developers facing complex tasks and pursuing high flexibility and customization capabilities.

Nevertheless, LangGraph’s documentation is relatively sparse, which may make it difficult for beginners or users with insufficient programming experience to get started. Additionally, using LangGraph requires users to have a certain level of programming ability, especially in understanding graphical structures and logical processes. Therefore, LangGraph is more suitable for advanced developers with rich programming backgrounds who are willing to invest time to learn in-depth.

Github Address：

https://github.com/langchain-ai/langgraph

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OpenAI Swarm: Beginner-Friendly, But Limited in Functionality

OpenAI Swarm is OpenAI’s latest multi-agent framework, aimed at simplifying the process of building agents and the handoff operations between agents. The Swarm framework is particularly suitable for beginners, allowing them to easily get started with multi-agent technology and quickly build demonstration projects.

However, the functionality of Swarm is somewhat limited, only supporting the OpenAI API and not being compatible with APIs from other language model providers, which may pose restrictions in actual production deployments. Additionally, Swarm lacks flexibility and may struggle to meet the needs of users seeking high customization and flexible configurations. Furthermore, the community support for Swarm is relatively weak, and users may encounter difficulties when submitting issues or seeking help on GitHub.

Github Address：

https://github.com/openai/swarm

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Magentic-One: Another Masterpiece from Microsoft, Simplifying AutoGen

Magnetic-One is a new multi-agent framework launched by Microsoft after AutoGen. Similar to OpenAI’s Swarm, Magnetic-One also focuses on reducing the complexity of building and operating agents. This framework comes pre-installed with five basic agents, including one management agent and four agents with different functions (WebSurfer, FileSurfer, Coder, and ComputerTerminal), making Magnetic-One a versatile platform suitable for users without a programming background and those who need to quickly master its usage.

Nevertheless, Magnetic-One faces certain difficulties in supporting open-source language models (LLM), which may pose challenges for users looking to leverage open-source LLMs. Additionally, Magnetic-One lacks flexibility, leaning more towards being an application rather than a fully open framework. Currently, the documentation and community support for Magnetic-One are also relatively limited, which may negatively impact user experience and problem-solving efficiency.

Github Address：

https://github.com/microsoft/autogen/tree/main/python/packages/autogen-magentic-one

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How to Choose AI Agent Development Frameworks?

First, Code Generation and Multi-Agent Workflow

In tasks involving code generation and complex multi-agent coding, AutoGen demonstrates its outstanding capabilities. Its powerful code handling and multi-agent coordination features can tackle various challenges in software development. Despite its complex configuration process, AutoGen can maximize its utility in professional development environments.

Second, Beginner-Friendly Frameworks

For newcomers in the multi-agent field, OpenAI Swarm and CrewAI are ideal choices. The easy creation process of OpenAI Swarm and the high usability and intuitiveness of CrewAI allow beginners to quickly get started and easily build basic multi-agent applications, avoiding the troubles of technical difficulties.

Third, Tackling Complex Challenges

LangGraph has a leading advantage in handling complex tasks. Its high flexibility and customization capabilities, combined with its innovative architecture based on directed cyclic graphs, enable it to support advanced users in managing complex logic and variable agent orchestration requirements.

Fourth, Integration of Open-Source Language Models

LangGraph showcases excellent compatibility in integrating open-source language models, capable of seamless collaboration with various open-source LLMs and APIs. CrewAI also offers decent compatibility, and choices can be weighed based on specific needs.

Fifth, The Importance of Community Support

If you value community support, AutoGen will be a wise choice, as its strong community resources can provide timely assistance and solutions for developers. For those who do not heavily rely on community support, CrewAI, OpenAI Swarm, and Magentic-One also have their own advantages.

Sixth, Cost-Effectiveness Analysis

Magnetic-One’s pre-configured settings and general methods may have certain advantages in terms of cost. OpenAI Swarm and CrewAI can also provide good cost-effectiveness under specific circumstances, depending on the scale, needs, and budget of the project.

Each multi-agent framework has its unique characteristics. When choosing, please conduct a comprehensive assessment based on your specific needs, technical capabilities, and budget. I hope these suggestions can help you select the most suitable multi-agent framework.

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Why is AI Agent so important?

First, This is the trend, we are experiencing a major technological revolution, unlike the rise of the Internet, this is a disruptive change. Falling behind means elimination, as all future applications will be rewritten by AI Agents;

Second, We are currently in a period of dividends, those who enter the field first will enjoy at least 4-5 years of dividends, earn high salaries, and have the initiative and career choices in technology.

Third, there is a strong demand from enterprises, more and more companies have already implemented projects in the Agent field, providing us with rich job opportunities and broad development space.

Fourth, major companies are strategically laying out, whether it’s foreign companies like Microsoft and Google or domestic companies like Baidu, they are all strategically laying out, and 2025 will definitely be the year of commercialization for AI Agents.

My team and I have been researching large model application technology for the past two years. I want to say: the value of large models is immense, and the potential of AI Agents is tremendous! “All future applications will be rewritten by AI Agents!” This is also the most frequently heard statement this year. My team and I, especially this year, have helped over 60 companies implement nearly 100 AI Agent projects. I have personally felt that more and more companies are indeed starting to implement AI Agent projects.

Therefore, AI Agents are important enough, but also complex enough. My conclusion from two years of practice is that developing a reliable and stable AI Agent application is quite difficult. The complexity of large model technology, the uncertainty of large model inference, performance issues, etc., directly lead many people to shy away from it or have no idea how to start when encountering problems. It is indeed not easy for regular technical personnel to master AI Agents!

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Reference:

https://mp.weixin.qq.com/s/Ws46zAh-hwUZQmDxsSO6Eg

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