[HN Gopher] Show HN: Pipelex - Declarative language for repeatab...
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Show HN: Pipelex - Declarative language for repeatable AI workflows
We're Robin, Louis, and Thomas. Pipelex is a DSL and a Python
runtime for repeatable AI workflows. Think Dockerfile/SQL for
multi-step LLM pipelines: you declare steps and interfaces; any
model/provider can fill them. Why this instead of yet another
workflow builder? - Declarative, not glue code: you state what to
do; the runtime figures out how. - Agent-first: each step carries
natural-language context (purpose, inputs/outputs with meaning) so
LLMs can follow, audit, and optimize. Our MCP server enables agents
to run pipelines but also to build new pipelines on demand. - Open
standard under MIT: language spec, runtime, API server, editor
extensions, MCP server, n8n node. - Composable: pipes can call
other pipes, created by you or shared in the community. Why a
domain-specific language? - We need context, meaning and nuances
preserved in a structured syntax that both humans and LLMs can
understand - We need determinism, control, and reproducibility that
pure prompts can't deliver - Bonus: editors, diffs, semantic
coloring, easy sharing, search & replace, version control,
linters... How we got there: Initially, we just wanted to solve
every use-case with LLMs but kept rebuilding the same agentic
patterns across different projects. So we challenged ourselves to
keep the code generic and separate from use-case specifics, which
meant modeling workflows from the relevant knowledge and know-how.
Unlike existing code/no-code frameworks for AI workflows, our
abstraction layer doesn't wrap APIs, it transcribes business logic
into a structured, unambiguous script executable by software and
AI. Hence the "declarative" aspect: the script says what should be
done, not how to do it. It's like a Dockerfile or SQL for AI
workflows. Additionally, we wanted the language to be LLM-
friendly. Classic programming languages hide logic and context in
variable names, functions, and comments: all invisible to the
interpreter. In Pipelex, these elements are explicitly stated in
natural language, giving AI full visibility: it's all logic and
context, with minimal syntax. Then, we didn't want to write
Pipelex scripts ourselves so we dogfooded: we built a Pipelex
workflow that writes Pipelex workflows. It's in the MCP and CLI:
"pipelex build pipe '...'" runs a multi-step, structured generation
flow that produces a validated workflow ready to execute with
"pipelex run". Then you can iterate on it yourself or with any
coding agent. What's included: Python library, FastAPI and Docker,
MCP server, n8n node, VS Code extension. What we'd like from you
1. Build a workflow: did the language work for you or against you?
2. Agent/MCP workflows and n8n node usability. 3. Suggest new kinds
of pipes and other AI models we could integrate 4. Looking for OSS
contributors to the core library but also to share pipes with the
community Known limitations - Connectors: Pipelex doesn't
integrate with "your apps", we focus on the cognitive steps, and
you can integrate through code/API or using MCP or n8n -
Visualization: we need to generate flow-charts - The pipe builder
is still buggy - Run it yourself: we don't yet provide a hosted
Pipelex API, it's in the works - Cost-tracking: we only track LLM
costs, not image generation or OCR costs yet - Caching and
reasoning options: not supported yet Links - GitHub:
https://github.com/Pipelex/pipelex - Cookbook:
https://github.com/Pipelex/pipelex-cookbook - Starter:
https://github.com/Pipelex/pipelex-starter - VS Code extension:
https://github.com/Pipelex/vscode-pipelex - Docs:
[https://docs.pipelex.com](https://docs.pipelex.com/) - Demo video
(2 min): https://youtu.be/dBigQa8M8pQ - Discord for support and
sharing: https://go.pipelex.com/discord Thanks for reading. If you
try Pipelex, tell us exactly where it hurts, that's the most
valuable feedback we can get.
Author : lchoquel
Score : 33 points
Date : 2025-10-28 16:19 UTC (1 days ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| RoyTyrell wrote:
| Sorry, I guess I'm not fully understanding what this is exactly.
| Would you describe this as a low-code/no-code agent generator? So
| if you can define requirements via a pipelex "config" file,
| Pipelex will generate a python-based agent?
| lchoquel wrote:
| Hi RoyTyrell, I guess you could call it low-code, a new kind of
| no-code where we have natural language in the mix. But no,
| Pipelex does not generate a python-based agent: the pipelex
| script is interpreted at runtime.
| ronaldgumo wrote:
| Very cool declarative + agent-first is the right direction. Love
| the "Dockerfile for AI reasoning" analogy. Excited to try
| composing Pipelex with Codiris workflows.
|
| Waiting for partnership to propose to our users
| lchoquel wrote:
| Thanks, Ronald! Yes, very interested in discussing
| integrations. Pipelex is super modular and open by design, so
| it should be a breeze.
| hartem_ wrote:
| Declarative DSL is a really interesting approach, especially
| since you're exposing it directly to the users. There are some
| applications where throwing the dice in production by having LLM
| as part of the runtime is not an option.
| lchoquel wrote:
| Yes! Clearly the introduction of LLMs into the mix raises the
| problem of throwing dice. The point of view we chose is: how to
| orchestrate the collaboration between AI, Software and people?
| With our aim to have repeatable workflows, this drove us away
| from building autonomous agents and towards a place where the
| software is in command of the orchestration. Then the Humans
| and AI can discuss "what you want to do" and have software run
| it and use AI where it's needed.
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