AI Agent Frameworks, Compared
Side-by-side comparison of 15 AI agent frameworks. Filter by use case (RAG, multi-agent, structured output) and language (Python, TypeScript, .NET). Updated weekly.
LangGraph
Graph-based control for stateful LLM agents with explicit transitions and human gates.
CrewAI
Role-based crew orchestration with sequential or hierarchical task delegation.
AutoGen
Conversational multi-agent framework from Microsoft Research with deep tool use.
Pydantic AI
Typed agents built on Pydantic validation. Production-first, structured output as first class.
OpenAI Agents SDK
OpenAI official SDK with handoffs, guardrails, and built-in tracing UI.
Mastra
TypeScript-native framework with workflows, agents, RAG, and built-in eval.
Vercel AI SDK
TypeScript SDK with streaming UI primitives and provider abstraction.
LangChain
The original LLM framework. Broad integration surface, mature ecosystem.
LlamaIndex
Data-first framework. Strongest for RAG and structured data agent.
Haystack
Pipeline-based framework from deepset. Production search and RAG focus.
Dify
Open-source LLM app platform with visual workflow builder. Self-hostable.
Flowise
Drag-and-drop LangChain workflow builder. Open source, self-host first.
n8n
Workflow automation with AI agent nodes. Strong integration library.
Semantic Kernel
Microsoft enterprise SDK. .NET / Python / Java with plugin orchestration.
smolagents
Hugging Face minimal agent framework. Code-as-action approach, small footprint.