About¶
pyDEXPI is an open-source engineering toolkit for importing, analyzing, and visualizing DEXPI models of Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs). It supports engineering applications and automation through a Python library, CLI, and MCP server.
Relationship to DEXPI¶
DEXPI defines the standard for exchanging engineering information. pyDEXPI provides the implementation and tools for working with that information: import/export, queries and graph analysis, rendering, and validation.
The Pydantic model classes are generated from the official specification by pyDEXPI-builder. pyDEXPI maintains the runtime behavior and engineering tools around those classes, including object identity and references, serialization, and validation.
Choose the Python library for integrations, the CLI for conversions and exports, or the MCP server to expose model operations to an LLM client.
Process Intelligence Research¶
The Process Intelligence Research group at TU Delft develops novel methods for process systems engineering using artificial intelligence and machine learning. We focus on leveraging data-driven approaches and generative AI to advance the field of chemical and process engineering.
Our research aims to unlock innovation in the process industry by making process data machine-readable and accessible for emerging AI applications.
🌐 Website: pi-research.org
Team¶
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Dominik P. Goldstein |   |
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Lukas Schulze Balhorn | |
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Achmad Anggawirya Alimin | |
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Artur M. Schweidtmann | |
Contact¶
For collaborations, questions, or more information:
Prof. Artur M. Schweidtmann¶
Process Intelligence Research Group¶
Acknowledgments¶
We thank all contributors who have helped make pyDEXPI possible. Special thanks to the DEXPI organization for developing and maintaining the DEXPI standard.