Agno vs Flue: Which Agent Framework to Use?

Agno and Flue both let you build an agent, but they sit in different parts of the stack and they assume different things about who's writing the code.

Agno (formerly Phidata) is a lightweight Python framework for building agents.

Flue is a declarative TypeScript agent framework from Fred K.

Underneath, both wrap the same thing: a model call, a tool dispatch, a loop. The decision is about which abstraction your team wants to think in day to day, and which ecosystem you're willing to inherit along with it. There's an honest, framework-free version of the same pattern in about 60 lines of Python in the lesson at the bottom of this page — useful as a baseline regardless of which framework wins.

Pick Agno if agno adds value when you want a batteries-included agent with minimal boilerplate — especially for multi-modal agents or team orchestration. But each of its abstractions maps to a small piece of plain Python. If your agent is straightforward, writing it directly gives you full control with zero framework overhead. The tradeoffs in its intro should match how your team already thinks about agents; Flue will feel like translation if they don't.

Pick Flue if flue is the natural choice when the deploy target is Cloudflare and you want a TypeScript-first, declarative agent framework tuned for Durable Objects. Its cross-runtime story (Cloudflare + Node + CI) is genuinely useful if agents run in more than one place. For a single-agent loop that doesn't need persistence, plain TypeScript is simpler. The tradeoffs in its intro should match how your team already thinks about agents; Agno will feel like translation if they don't.

Whichever you pick, you're inheriting a dependency tree and a vocabulary your team has to learn before they ship anything. Agno has its own class hierarchy and tool registration conventions; Flue has its. Either way, when something misbehaves you'll be reading framework source before you reach the actual HTTP call.

If the real workload is one model and a handful of tools, both can feel like a workbench for driving a nail. The lesson below builds the same pattern in plain Python — useful as a comparison point even if you ultimately keep the framework.