Best 4 AI Agent Frameworks in 2025: How to Choose the Right One for Your Project

As we approach 2025, building AI agents has become a critical skill. AI Agents are no longer just for large tech companies, but also for small businesses and individuals looking to stay competitive.

This growing interest is no surprise, considering that the global AI agents market is projected to grow from USD 5.29 billion in 2024 to USD 216.8 billion by 2035, reflecting a CAGR of 40.15%.[Autogen][autogen]

In this fast-growing landscape, choosing the right frameworks is key. With so many options available, this article will help AI enthusiasts and developers identify the best frameworks to fit their needs.

What Are AI Agent Frameworks?

AI agent frameworks are development toolkits that simplify the creation of AI agents. These frameworks provide essential infrastructure for:

• Streamlining agent development by handling complex backend operations like state management and communication protocols
• Enabling efficient collaboration between multiple intelligent agents
• Abstracting technical complexities, allowing developers to concentrate on designing application logic

State of AI Agent Frameworks in 2025

In 2024, many new frameworks emerged for building AI agents. Since the technology is still young, most of these focus on fixing the shortcomings of others and are not yet fully ready for production.

By 2025, choosing the right AI Agent framework will be crucial. The right choice can simplify complex work, improve efficiency, and save time, while a poor choice may lead to unnecessary problems.

Because there are so many options, we have selected 4 of the most widely used frameworks for building AI agents. If you plan to create an AI agent in 2025 and have some technical background, start by exploring these frameworks to stay updated with the latest advancements.

Autogen

Autogen is an open-source AI agent development framework created by Microsoft in collaboration with Penn State and the University of Washington. It focuses on simplifying the creation of conversational agents, providing abstractions that reduce complexity and enable seamless agent-to-agent interactions.

Pros:

• Strong Community: Its open-source model fosters a vibrant, active community that contributes features, shares best practices, and supports ongoing innovation. This engagement is so strong that a community fork, AG2, has emerged to maintain stability against upcoming breaking changes in version 0.4.
• Conversational Focus: Autogen excels at building agents designed for interactive dialogue, handling the underlying complexities so developers can focus on conversation logic rather than integration details.
• Flexible and Customizable: The upcoming Autogen 0.4 release introduces an event-driven architecture, enabling greater flexibility, customizable workflows, and easier integration with diverse systems.

Cons:

• Not Production-Ready: Autogen remains a research-driven project with evolving features and occasional instability. Ongoing changes and lack of backward compatibility may pose challenges for immediate production use.
• Future Migration Effort: Autogen’s long-term plan is to align with the Semantic Kernel’s multi-agent runtime. As this alignment takes shape, teams currently using Autogen for early testing may have extra work in the future to move their projects into a stable, enterprise-ready environment.

Conclusion

Autogen offers a dynamic platform for experimenting with advanced agent capabilities and conversational flows. However, its evolving nature and planned roadmap shifts demand careful consideration, making it best suited for exploration and prototyping rather than production deployment.

Semantic Kernel

Semantic Kernel is an open-source, production-ready SDK maintained by Microsoft. It is designed to integrate Large Language Models (LLMs) and data stores into enterprise applications. By supporting programming languages C#, Python, and Java, it enables the development of Generative AI solutions.

Pros

• Agent and Process Frameworks (experimental): Semantic Kernel provides early-stage frameworks for both single-agent and multi-agent solutions, offering developers a head start in implementing agent-based architectures.
• Multi-Language Support: Although it has a strong foundation in C# and .NET, Semantic Kernel also supports Python and Java, providing flexibility for diverse technology stacks.

Cons

• C# and .NET Bias: Despite supporting multiple languages, much of the community and existing resources are oriented toward C# and .NET, potentially limiting engagement and knowledge-sharing for Python or Java developers.
• Experimental Agent Framework: The agent-related features remain experimental, which means they may change significantly and require future rework as the technology matures.

Conclusion

Semantic Kernel stands as a robust, production-ready SDK suited for integrating LLMs into enterprise applications. While its emerging agent frameworks and language diversity highlight its potential, the current ecosystem leans heavily toward C# and involves experimental features. As the project evolves and the agent framework becomes stable, its community and feature set are likely to broaden, making it a compelling option in the future.

CrewAI

CrewAI is an open-source AI agent framework introduced in 2023 that focuses on assembling a team of specialized agents to achieve a common goal. Inspired by real-world organizational structures, CrewAI assigns each agent a distinct role and integrated tools, enabling them to collaborate on tasks through defined processes and flows. With $18 million in funding, CrewAI emphasizes simplicity and ease-of-use, making it accessible for rapid prototyping and straightforward projects.

Pros

• Extensive Built-In Tool Integrations: CrewAI provides immediate access to tools like Selenium Scraper and Firecrawl, reducing setup time and accelerating development.
• Easy Onboarding and Modular Design: Its modular approach allows developers to quickly create agent systems by defining agents, tasks, flows, and tools, abstracting away orchestration and state management.
• Automatic Memory Management: CrewAI retains context and recalls previous steps without additional coding, streamlining the development process.
• Straightforward Structure: Its simplicity makes CrewAI ideal for quick builds and less complex projects, where extensive customization isn’t required.

Cons

• Limited Customization: The framework’s rigid structure offers little flexibility in designing custom workflows, making it challenging to accommodate special or unique requirements.
• Not Suited for Advanced Use Cases: Complex scenarios may be difficult to implement due to CrewAI’s strict architecture and limited adaptability.
• Restricted Human Input: Integrating human oversight or intervention during flows is less straightforward compared to other frameworks, limiting opportunities for guided decision-making.

Conclusion

CrewAI is a convenient, user-friendly solution for rapidly creating agent-based systems, offering built-in tool integrations, automatic memory, and a simplified design. While it excels at enabling fast prototyping and straightforward implementations, its rigidity and limited customization options may not meet the needs of developers seeking advanced, highly specialized workflows. For simpler projects and quick experimentation, CrewAI stands as a practical choice.

LangGraph

LangGraph is an open-source framework developed by Langchain to create sophisticated multi-agent workflows using directed acyclic graphs (DAGs). By representing processes as interconnected nodes, LangGraph offers granular control over the application state, making it well-suited for complex, production-ready scenarios that optionally require human oversight.

Pros

• Highly Customizable Workflows: Its graph-based architecture enables fine-grained orchestration of agents, allowing you to create detailed processes tailored to specific needs.
• Robust State Management: LangGraph’s explicit state definitions simplify debugging and scaling by making it easier to track and adjust agent states throughout the workflow.
• Human-in-the-Loop Integration: With built-in support for execution breakpoints, developers can insert human input at critical points, enhancing reliability and control.

Cons

• Steep Learning Curve: The complexity of DAG-based workflows may be intimidating for beginners or those working on simpler use cases, requiring more time to fully understand and utilize the framework’s capabilities.
• Higher Development Overhead: Designing workflows with explicit state management can slow initial development, making quick prototypes more challenging compared to other frameworks.
• Limited Accessibility for Newcomers: Advanced customization features may overwhelm developers new to agent frameworks, hindering rapid adoption.

Conclusion

LangGraph’s powerful, graph-driven approach excels in complex, high-stakes environments where precision, flexibility, and human oversight are important. While its sophistication enables rich functionality and detailed control, this comes at the cost of a steeper learning curve and increased development effort. For advanced users and complicated workflows, LangGraph stands as a compelling choice, though simpler projects may benefit from more accessible frameworks.

Key Factors to Consider When Choosing AI Agent Frameworks in 2025

When choosing an AI agent framework, there isn’t a one-size-fits-all solution. Your decision should rely on three key factors:

• Technical Background: What’s your overall technical expertise? Are you more comfortable with C# or Python?
• Customization Needs: Are you building a simple chatbot or a sophisticated, multi-agent system that requires extensive customization and human oversight?
• Project Objective: Are you exploring a quick prototype or aiming for a production-ready solution that can scale?

Figure 1: A step-by-step decision tree for selecting the right AI agent framework in 2025.

If you’re building a customer support chatbot integrated with enterprise systems and prefer .NET, Semantic Kernel might be ideal. It aligns with Microsoft’s ecosystem and offers robust features for production-grade applications.

If you’re working in Python, you should determine whether you’re better suited for a production environment or just prototyping.

For production scenarios requiring a high level of customization and control, frameworks like LangGraph shine. On the other hand, if you prioritize ease of use and faster iteration, CrewAI might be a better fit.

For those looking to stay on the cutting edge, especially when still in the prototyping phase, Autogen can be a compelling choice due to its rapid adoption of new agentic capabilities.

Agentic Framework Popularity

The following image, a GitHub star history chart, shows how these frameworks have grown in popularity over time: Figure 2: GitHub star trends highlighting the growing interest and adoption rates of AI agent frameworks Autogen, CrewAI, Semantic Kernel and Langraph.

While popularity can suggest a thriving community and frequent updates, it shouldn’t be your sole criterion. An over-reliance on a popular but overly complex framework might lead to unnecessary complexity, slowing down development and maintenance.

In short, start by asking if your goal is a quick prototype or a production-ready system. Then, factor in your programming language preference, the complexity of your intended solution, and how much customization you’ll need.

By following the logic of the decision tree and considering the frameworks’ evolving popularity, you’ll be better equipped to select the right AI agent framework for your project’s unique requirements.

Sources:

AI Agents growth | Autogen | Semantic kernel | Autogen merge with Semantic Kernel | CrewAI | Langraph