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.
Pipelex is a declarative language to define and execute LLM-driven knowledge pipelines, delivering reliable, structured results.
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
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 in, knowledge out.
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
