The Language for Knowledge Pipelines
Harnessing LLMs for critical knowledge work often means wrestling with complex custom code. This makes building reliable, scalable, and replicable AI applications slow, expensive, and prone to inconsistent results.
That’s precisely why we built Pipelex
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
The Pipelex language uses pipelines, or "pipes", each capable of integrating different language models (LLMs) or software to process knowledge. Pipes consistently deliver structured, predictable outputs at each stage.
Pipelex employs user-friendly TOML syntax, enabling developers to intuitively define workflows in a narrative-like manner. This approach facilitates collaboration between business professionals, developers, and language models (LLMs), ensuring clarity and ease of communication.
Pipes work like modular building blocks, assembled by connecting other pipes sequentially, in parallel, or by calling sub-pipes. This assembly resembles function calls in traditional programming but emphasizes a more intuitive, plug-and-play structure, focused explicitly on clear knowledge input and output.
Pipelex is distributed as an open-source Python library, with a hosted API launching soon, enabling effortless integration into existing software systems and automation frameworks. Additionally, Pipelex will provide an MCP server that will enable AI Agents to run pipelines like any other tool.
