DSPy vs Flue: Which Agent Framework to Use?

DSPy 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.

DSPy replaces hand-written prompts with compiled modules.

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 DSPy if dSPy's real innovation is automated prompt optimization — replacing manual prompt engineering with algorithmic tuning. This is genuinely novel and valuable for production systems where prompt quality matters at scale. For simple agents or learning, hand-written prompts are easier to understand and modify. 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; DSPy 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. DSPy 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.