Flue vs LlamaIndex: Which Agent Framework to Use?

Flue 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.

Flue is a declarative TypeScript agent framework from Fred K.

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 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; 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; Flue 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. Flue 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.