Agno

Python framework for multi-agent systems with shared state, session storage and teams: build agent products, not prototypes.

Agno is a Python framework for building multi-agent systems: agents with tools, memory, teams that delegate to each other, and workflows that run with structured state. It focuses on the plumbing that makes agents production-shaped, session storage, knowledge bases, human-in-the-loop steps, and it stays close to plain Python rather than hiding everything behind abstractions.

Why it matters to an indie builder: multi-agent patterns are how complex AI features get organized. A research feature becomes a planner agent, a retrieval agent and a writer agent with a shared state object; a support bot becomes a router with specialized handlers. Agno gives that structure with minimal ceremony, and its storage layer means conversations survive restarts instead of living in a process's memory.

A realistic use: an internal tool that answers questions over your own documents and takes actions. One agent retrieves from a vector store, another drafts, a third checks the draft against policy before anything is sent, and the whole run is logged for review. The framework's session and memory primitives save the state-management code that otherwise gets hand-rolled badly in every project.

Trade-offs: the multi-agent space moves fast and abstractions leak when your use case is unusual; be ready to drop to raw API calls. Compare with the other agent frameworks here on three axes: how state is persisted, how tools are registered, and how easy it is to test a single agent in isolation. Agno's Python-first approach makes that last one genuinely pleasant.