Best AI Agent Frameworks

Control vs. speed of setup

LangGraph gives you the most control over agent state and execution flow, but requires the most configuration. CrewAI and Smolagents are faster to get working but give you less control over how agents step through tasks. If you are still learning, start with CrewAI and move to LangGraph when you need finer control.

Orchestration vs. retrieval-first

LlamaIndex is the strongest choice when your primary problem is connecting an LLM to documents, databases, or external knowledge — it is a data framework first. LangGraph, CrewAI, and AutoGen are orchestration frameworks — they coordinate agent behaviour and task state but do not specialise in retrieval. Many production systems combine both: LlamaIndex for the RAG layer, LangGraph for the orchestration layer.

Code-first vs. no-code

Dify and Flowise let you build agent workflows visually without writing orchestration code — faster to prototype, easier to share with non-developers, but harder to customise for complex logic. All other frameworks require Python or TypeScript. If prototyping speed matters more than customisation, start with Dify or Flowise.

Self-hosted only vs. hosted option

All 11 frameworks are open source and self-hostable. CrewAI, Dify, Flowise, and LlamaIndex also offer managed cloud hosting — which removes infrastructure overhead but introduces a cost and vendor dependency. The rest are self-hosted only.

Experimentation vs. production

Smolagents and Pydantic AI are optimised for simplicity and fast iteration — small codebases, easy to read, good for prototypes. LangGraph and Semantic Kernel are built with production deployments in mind — better observability hooks, finer control over failure handling, and more mature documentation. AutoGen and CrewAI sit in between.

What is an AI agent framework?

An AI agent framework is a library or platform that provides the building blocks for creating AI agents — programs that can reason, use tools, and take actions autonomously. Frameworks handle orchestration, state management, and LLM integration so you can focus on defining agent behaviour.

Which framework should I use if I only know Python?

Most frameworks in this list are Python-first: LangGraph, CrewAI, Pydantic AI, Smolagents, and AutoGen all support Python. LangGraph and CrewAI are the most full-featured; Pydantic AI is strongest for type safety; Smolagents is the most minimal.

Which frameworks have a visual builder?

Dify and Flowise both offer drag-and-drop visual interfaces for building agent workflows without writing orchestration code. The other frameworks are code-only.

Which framework supports TypeScript?

Mastra and VoltAgent are the TypeScript-native frameworks in this list. Mastra integrates with Next.js, Express, and Hono. VoltAgent is designed for building multi-agent systems in TypeScript with a focus on real-time streaming. Semantic Kernel also has some TypeScript/JavaScript support alongside .NET and Python.

Are these frameworks free to use?

All 11 are open source. LangGraph, AutoGen, Semantic Kernel, Pydantic AI, Smolagents, Mastra, and VoltAgent are fully free. CrewAI, Dify, Flowise, and LlamaIndex offer free open-source cores with optional paid cloud hosting.

LangGraph vs CrewAI: which should I use?

LangGraph gives you fine-grained control over agent state and execution flow using a graph-based model — good for complex, custom workflows where you need predictability. CrewAI abstracts that away with role-based agents and a simpler API — good for multi-agent pipelines where each agent has a clear job. LangGraph has a steeper learning curve; CrewAI is faster to prototype with.

LlamaIndex vs LangGraph: what's the difference?

LlamaIndex is primarily a data framework — it excels at connecting LLMs to external data sources, building RAG pipelines, and indexing documents. LangGraph is an orchestration framework — it excels at controlling agent behaviour, state, and multi-step reasoning. They solve different problems; many teams use both together.

Which framework is easiest to get started with?

Smolagents (by Hugging Face) is the most minimal — a small codebase with a straightforward API for single-agent tasks. CrewAI is the easiest multi-agent framework. Dify and Flowise require no code at all if you prefer a visual builder. LangGraph has the highest learning curve of the code-first options.

AutoGen vs CrewAI: what's the difference?

AutoGen models agents as conversational participants that exchange messages to complete tasks — well-suited for research workflows, iterative problem solving, and human-in-the-loop scenarios. CrewAI models agents as role-based crew members with explicit tasks and goals — better suited for structured multi-step pipelines where each agent has a clearly defined job. AutoGen is more flexible for open-ended conversational workflows; CrewAI is faster to configure for defined pipelines.

Do these frameworks work with any LLM provider?

Most do. LangGraph, CrewAI, AutoGen, Pydantic AI, Smolagents, Mastra, and VoltAgent are provider-agnostic and support OpenAI, Anthropic, Google, and local models via APIs. LlamaIndex supports most major providers through its integration layer. Semantic Kernel is officially backed by Microsoft and integrates tightly with Azure OpenAI but also supports other providers. Dify and Flowise let you configure any compatible LLM through their visual interface.