Hi HN, I’m experimenting with a small Python library called PicoFlow for building LLM agent workflows using a lightweight DSL.

I’ve been using tools like LangChain and CrewAI, and wanted to explore a simpler, more function-oriented way to compose agent logic, closer to normal Python control flow and async functions.

PicoFlow focuses on: - composing async functions with operators - minimal core and few concepts to learn - explicit data flow through a shared context - easy embedding into existing services

A typical flow looks like:

  flow = plan >> retrieve >> answer
  await flow(ctx)

Patterns like looping and fork/merge are also expressed as operators rather than separate graph or config layers.

This is still early and very much a learning project. I’d really appreciate any feedback on the DSL design, missing primitives, or whether this style feels useful for real agent workloads.

Repo: https://github.com/the-picoflow/picoflow