# How Agno compares

> Honest comparisons with the other agent stacks. Every claim carries a source, and every page states plainly when the other tool is the better choice.

- Canonical: https://agno-com-nine.vercel.app/compare

## Agno vs LangGraph

Both frameworks produce durable, resumable agents; a feature checklist settles little. The difference is one of kind. LangGraph is a formalism: you express the system as a graph and compile it. Agno is infrastructure: plain Python above, with a runtime, approvals with real access control and a control plane beneath. For teams serving many users, that is the deciding line.

Full comparison: https://agno-com-nine.vercel.app/compare/agno-vs-langgraph.md

## Agno vs CrewAI

CrewAI is a quick way to prototype a crew of role-playing agents, and Flows add structure when needed. Agno is infrastructure for running agents in production. The divide appears after the demo, and it concerns isolation: CrewAI keeps memory in local files and separates users by convention. Agno separates them by construction, giving every request its own agent, backed by your database, with approvals and access control built in.

Full comparison: https://agno-com-nine.vercel.app/compare/agno-vs-crewai.md

## Agno vs OpenAI Agents SDK

The OpenAI Agents SDK is a small, well-designed library, and inside the OpenAI platform it is the natural choice. A library, though, hands you the loop and leaves the service around it to you. Agno covers the same primitives, remains indifferent to your choice of model and supplies the service: a runtime, crash recovery, approvals and tracing that never leaves your system.

Full comparison: https://agno-com-nine.vercel.app/compare/agno-vs-openai-agents-sdk.md

## Benchmarks

Agent instantiation time and memory for Agno, LangGraph and CrewAI, measured with a public harness: https://agno-com-nine.vercel.app/benchmarks.md