The Language
for Knowledge Pipelines
Harnessing LLMs for critical knowledge work often means wrestling with complex glue code and mega-prompts. This makes building AI applications slow, expensive, and unreliable.​​
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That’s precisely why we built Pipelex
Pipelex is a declarative language to define and execute LLM-driven knowledge pipelines, delivering reliable, structured results.
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The core framework is an open-source Python library, available on GitHub.
Simply build reliable AI applications
Pipe-based structure
The Pipelex language uses pipelines, or "pipes", each capable of integrating different LLMs to process knowledge.
Pipes consistently deliver structured, predictable outputs at each stage.
User-friendly syntax
Pipelex employs a clear easy to read syntax, enabling developers to intuitively define workflows in a narrative-like manner.
It facilitates collaboration between business professionals, developers, and AI Agents.
Modular Building Blocks
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Our pipelines work like modular building blocks, assembling pipes sequentially, in parallel, and by calling sub-pipes.
It's intuitive and powerful plug-and-play for knowledge input / knowledge output.
Open-Source • API • MCP
Pipelex is meant to integrate into any software and automation framework.
It's an open-source Python library, with a hosted API launching soon, along with an MCP server enabling AI Agents to run pipelines like any other tool.​
Pipelex combines the reliability and replicability of software with the understanding and creativity of AI
Without Pipelex​​​
Custom glue code & mega-prompts wasting hours
Prompts, business rules, and SDK calls get hopelessly tangled
Endless prompt-tweaking is a time sink and barely gets you to 80% reliability
One model change? Outputs drift and brittle tests collapse
Each new AI feature feels like rebuilding from scratch
With ​​​
Declarative pipelines for knowledge workflows
Business logic lives in pipes / Python stays lean and testable
Every step emits typed, version-controlled outputs you can trust
Swap in cheaper or faster LLMs per pipe while keeping guard-railed quality
Plug-in community pipes speed new features: no reinvention needed
