Agno vs LlamaIndex: Which Agent Framework to Use?

Agno and LlamaIndex 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.

LlamaIndex started as a RAG framework — connect your data, query it with an LLM.

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; LlamaIndex will feel like translation if they don't.

Pick LlamaIndex if llamaIndex adds genuine value when your agent needs to query structured or unstructured data as part of its reasoning — that's the index-as-tool pattern, and it's well-executed. But if you're building a general-purpose agent that doesn't need RAG, the agent framework is overhead. The plain Python version of the agent loop is the same 60 lines either way. 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; LlamaIndex 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.