PicoFlow – a tiny DSL-style Python library for LLM agent workflows
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…
In plain words
PicoFlow is a lightweight Python library for building LLM agent workflows using a domain-specific language approach. It enables developers to compose async functions with operators for sequential, looping, and parallel execution patterns while maintaining explicit data flow through shared context. Designed for those who find larger frameworks like LangChain or CrewAI overly complex, PicoFlow prioritizes simplicity, minimal concepts, and straightforward embedding into existing Python services, with workflows expressed using intuitive operator syntax like `plan >> retrieve >> answer`.
written from the facts on this page · September 2026
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In the maker’s words, at launch
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
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