Top 5 AI Agent Frameworks for 2025

Developing a game or app is no easy task. But in the age of agentic AI, achieving this is much easier to accomplish in a fraction of the time. 

And with 85% of organizations using some form of AI, you’re risking falling way behind if you don’t start now.

Autonomous agents are capable of understanding and collaborating to smash all kinds of goals. If you want to build your own agents, then choosing the right AI agent framework is essential.

This especially applies if you’re handling automation, LLMs, and workflows. These frameworks often provide various tools and capabilities to ensure quicker development.

What Are AI Agent Frameworks?

AI agent frameworks are tools (often platforms or libraries) for developers to create autonomous agents to execute various workflows. 

These agents receive some kind of input, use LLMs or algorithms to process them, and then supply an output (often data-oriented).

These frameworks usually include pre-built modules for typical functions. This helps to streamline agentic workflows, save developers time, and improve scalability.

But which framework is best for your specific needs? We’ve done the heavy lifting for you.

What to Look for in AI Agent Frameworks

You need to ask yourself many questions when selecting the ideal AI agent framework for your specific needs. These include:

• Ease of Use: Is it beginner-friendly, intuitive, and easy to set up?
• Scalability: Can it handle large workloads, high traffic, and large-scale applications?
• Customizability: How easy is it to tailor workflows, pipelines, and modular components?
• Memory Handling: How well does it retain memory and handle long conversations?
• Compatibility/Integrations: Which databases, APIs, cloud services, LLMs, and other tools does it integrate with?
• License: Is it open-source, commercial, or a combination of the two?

With all these factors in mind, let’s explore how the best AI agent frameworks stack up against each other.

Best AI Agent Frameworks: Comparison Table

In the following table, we have broken down how the five best AI agent frameworks compare to each other based on the aforementioned factors.

AI Agent Framework	Ease of Use	Scalability	Customizability	Memory Handling	Compatibility/Integrations	License
LangGraph	3/5	5/5	5/5	5/5	LLMs, API tools, vector DBs	Open-source
CrewAI	5/5	3/5	5/5	4/5	LLMs, plugins, external tools	Open-source
Microsoft Autogen	3/5	5/5	3/5	5/5	Microsoft Azure, enterprise APIs	Proprietary
LangChain	5/5	4/5	5/5	3/5	LLMs, API connectors, databases	Open-source
LlamaIndex	4/5	5/5	5/5	5/5	LLMs, vector DBs, storage backends	Open-source

The Best AI Agent Frameworks Right Now

1. LangGraph

Best for: Complex LLM pipelines, detailed systems with human intervention

LangGraph is an open-source multi-agent systems framework that needs stateful, complex interactions. It leverages graph-based architectures. And it’s ideal for dynamic workflows, with user-friendly wrappers that streamline development. Very suitable for multi-step decision-making, planning, and problem-solving.

Key Features

• Stateful interactions
• Multi-agent coordination
• Error handling
• Graph-based execution flows
• Dynamic workflow support
• Flexible API integration
• LangChain integration
• Automatic workflow save

Pros

• Highly scalable
• Highly accessible SDK
• Graph-based control
• Good at offering detailed systems

Cons

• Steep learning curve
• Requires deep understanding of graph-based workflows
• Still establishing its ecosystem, which means fewer tutorials and support resources.

2. CrewAI

Best for: Automation, multi-agent teamwork

CrewAI focuses on collaboration, assigning multiple AI agents with specific roles and delegating tasks accordingly. This open-source framework enables the building of AI systems that replicate human team dynamics.

Key Features

• Task/workflow management
• Collaboration-based design for agent teamwork
• Customizable modules for various industries
• Conflict resolution
• Role-based architecture
• Hierarchical team structures
• API and tool integration
• Intelligent collaboration

Pros

• Easy setup
• Effective task delegation
• Offers high detail of orchestration

Cons

• Fewer integrations than other frameworks
• May provide inconsistent results
• Requires higher performance tuning to execute complex tasks.

3. Microsoft Autogen

Best for: Task automation, AI research, scalable systems

Open-source framework AutoGen is great for developing multi-agent systems and advanced AI agents. Developers can use this framework to build agents with interaction and high-level decision-making capabilities. Ideal for building large-scale applications with real-time data processing.

Key Features

• API and tool integration
• Event-driven architecture
• Task/workflow management
• Advanced reasoning

Pros

• Great for cloud automation
• Automated processes
• Self-improving

Cons

• Difficult to set up
• Complex framework with a steep learning curve
• Can result in high computational resource usage.

4. LangChain

Best for: AI workflows, RAG, Agents

For integrating LLMs with APIs and external data sources, there aren’t many better options than LangChain. This framework helps developers build various systems, from research assistants to conversational agents. LangChain simplifies context handling, memory management, and prompt engineering.

Key Features

• Task/workflow management
• Integrated memory management
• Prompt engineering
• Web/API interaction tools
• Semantic search
• Integrations with multiple APIs and LLMs

Pros

• Highly flexible and customizable
• Large ecosystem
• Strong community/support

Cons

• Not always the best framework for multi-agent collaboration
• More advanced workflows can become complex and hard to debug
• Extra abstraction layers can lead to performance issues.

5. LlamaIndex

Best for: Chatbot memory, Document search

LlamaIndex was built to simplify the retrieval and integration of complex data. This framework helps developers connect LLMs seamlessly with their data, allowing agents to act contextually and intelligently.

Key Features

• Modular architecture
• Indexing flexibility
• LLM data connectors
• Query engine

Pros

• Highly customizable indexing and querying
• Easy database integration
• Optimized search

Cons

• Fewer tools than other frameworks
• Not ideal for multi-agent systems
• Requires external storage.

Other AI Agent Frameworks Worth Considering

Here are some other frameworks that didn’t make the top 5, but we feel deserved a mention:

• PydanticAI: An ideal framework for providing consistent LLM outputs and structured AI data validation.
• Swarm: Decentralized AI agent framework for structured data and schema validation
• Microsoft Semantic Kernel: Integrates AI into existing applications.

So, Which AI Agent Framework Works For You?

Choosing your ideal AI agent framework ultimately depends on your needs. 

Do you need multi-agent workflows that are highly scalable? Then LangGraph might be best for you. Do you need enterprise-level AI automation? Then, you might want to consider going with AutoGen. Then, there’s CrewAI, which is ideal for collaborative agent teamwork. 

Others, like LlamaIndex and LangChain, are ideal for retrieval-augmented general (RAG) and strong integrations, respectively.

Ultimately, your framework of choice should be highly customizable, scalable, compatible, and easy to use. All five mentioned above tick these boxes to varying degrees.