Top 5 Frameworks for AI Agent Development in 2025

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Top 5 AI Agent Multi-Agent Development Frameworks

In the new era of AI large models, AI Agent multi-agent systems (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 a Multi-Agent framework that best matches one’s needs has become a key issue for many developers and enterprises.

This time 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 everyone’s framework selection.

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

AutoGen is one of the early and popular frameworks launched by Microsoft in the multi-agent field, aimed at providing solutions for software development. In this framework, the core consists of two types of agents: the User-Agent and the Assistant-Agent. The User-Agent is responsible for conveying instructions and requirements, while the Assistant-Agent is responsible for generating and executing code, and providing feedback to the user or other agents.

A major highlight of AutoGen is its excellent multi-agent coordination capabilities, especially prominent when tackling programming tasks. It also allows for manual 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 non-technical users may require some learning time. Additionally, the configuration process for AutoGen can be cumbersome, especially when integrating local large language models (LLM), as it also requires setting up a proxy server. Therefore, AutoGen is better suited for developers who have some understanding of software development and are willing to invest time and effort 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 mainly generates and configures agents through writing prompts. On the CrewAI platform, the process of creating and integrating agents is extremely simple, allowing users to easily build hundreds of agents in a short time, making it the preferred tool for developers aiming for rapid 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 agent interactions, which could affect the stability and reliability of projects. Despite these limitations, for developers who only need to quickly build demonstrations or prototypes and do not require high system flexibility, CrewAI remains 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 built on LangChain, which introduces the concept of directed cyclic graphs to create a highly flexible and customizable solution. LangGraph is not only applicable to various Multi-Agent tasks but also supports almost all multi-agent orchestration applications, making it the preferred tool for developers facing complex tasks and seeking high flexibility and customization capabilities.

However, LangGraph has relatively limited documentation, which may pose difficulties for beginners or users with insufficient programming experience when getting started. Additionally, using LangGraph requires users to have a certain level of programming ability, particularly in understanding graphical structures and logical flows. Therefore, LangGraph is more suitable for advanced developers with a rich programming background who are willing to invest time in in-depth learning.

Github Address:

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

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OpenAI Swarm: Beginner Friendly, But Limited Features

OpenAI Swarm is the latest multi-agent framework launched by OpenAI, aimed at simplifying the construction process of 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, Swarm has a relatively narrow scope of functionality, supporting only the OpenAI API and not being compatible with APIs from other language model providers, which may pose limitations in actual production deployments. Additionally, Swarm lacks flexibility, making it difficult to meet the needs of users seeking high customization and flexible configurations. Furthermore, the community support for Swarm is 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 following AutoGen. Similar to OpenAI’s Swarm, Magnetic-One also focuses on reducing the complexity of constructing and operating agents. This framework comes pre-installed with five basic agents, including one responsible for management and four that perform different functions (WebSurfer, FileSurfer, Coder, and ComputerTerminal), making Magnetic-One a versatile platform suitable for non-programming background users and those who need to quickly master its usage.

However, Magnetic-One faces certain difficulties in supporting open-source language models (LLM), which may pose challenges for users looking to utilize open-source LLMs. Additionally, Magnetic-One is somewhat lacking in 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 adversely affect 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 an AI Agent Development Framework?

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 functions can tackle various challenges in software development. Despite its complex configuration process, in a professional development environment, AutoGen can maximize its utility.

Second, Beginner-Friendly Frameworks

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

Third, Addressing 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 dealing with complex logic and variable agent orchestration requirements.

Fourth, Integration of Open-Source Language Models

In integrating open-source language models, LangGraph shows excellent compatibility, able to seamlessly collaborate with various open-source LLMs and APIs. CrewAI also provides good compatibility, allowing for trade-offs 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 developers with timely help and solutions. For users who do not rely heavily on community support, CrewAI, OpenAI Swarm, and Magentic-One also have their respective advantages.

Sixth, Cost-Effectiveness Analysis

Magnetic-One’s pre-configured settings and general methods may offer certain advantages in cost. OpenAI Swarm and CrewAI can also provide good cost-effectiveness in specific cases, 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 situation. I hope these suggestions can help you select the most suitable multi-agent framework.

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Why is AI Agent so important?

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Reference:

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