# An Agentic Approach to Automatic Creation of P&ID Diagrams from Natural Language Descriptions

arXiv: https://arxiv.org/abs/2412.12898 · HTML: https://mlsanity.com/paper/2412.12898/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 4 of 4)

- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [An Automated Survey of Generative Artificial Intelligence: Large Language Models, Architectures, Protocols, and Applications](https://mlsanity.com/paper/2306.02781/) (2023)
- [LLM-based Control Code Generation using Image Recognition](https://mlsanity.com/paper/2311.10401/) (2023)
- [Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering](https://mlsanity.com/paper/2409.00082/) (2024)

## Built on by (most cited first, 12 of 12)

- [Multi-agent systems for chemical engineering: A review and perspective](https://mlsanity.com/paper/2508.07880/) (2025)
- [AutoChemSchematic AI: Agentic Physics-Aware Automation for Chemical Manufacturing Scale-Up](https://mlsanity.com/paper/2505.24584/) (2025)
- [Sketch2Simulation: Automating Flowsheet Generation via Multi Agent Large Language Models](https://mlsanity.com/paper/2603.24629/) (2026)
- [TechImage-Bench: Rubric-Based Evaluation for Technical Image Generation](https://mlsanity.com/paper/2512.12220/) (2025)
- [CeProAgents: A Hierarchical Agents System for Automated Chemical Process Development](https://mlsanity.com/paper/2603.01654/) (2026)
- [From Detection to Action: Using LLM Agents for Fault-Tolerant Control](https://mlsanity.com/paper/2606.28011/) (2026)
- [Agentic Multimodal AI for Hyperpersonalized B2B and B2C Advertising in Competitive Markets: An AI-Driven Competitive Advertising Framework](https://mlsanity.com/paper/2504.00338/) (2025)
- [GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs](https://mlsanity.com/paper/2603.22528/) (2026)
- [Exploring Agentic Workflows for Generating High Quality Math Visual Aids](https://mlsanity.com/paper/2607.09839/) (2026)
- [Automating Cause-Effect Specification with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2606.31614/) (2026)
- [LLMs in Process Diagram Engineering: From Optimal PFDs to Validated P&IDs](https://mlsanity.com/paper/2608.11220/) (2026)
- [Separating Engineering Reasoning from DEXPI Serialization in LLM-Based Greenfield Surface-Process Design: A Three-Case Study for Underground Gas Storage](https://mlsanity.com/paper/2609.12656/) (2026)

## Abstract (arXiv)

The Piping and Instrumentation Diagrams (P&IDs) are foundational to the design, construction, and operation of workflows in the engineering and process industries. However, their manual creation is often labor-intensive, error-prone, and lacks robust mechanisms for error detection and correction. While recent advancements in Generative AI, particularly Large Language Models (LLMs) and Vision-Language Models (VLMs), have demonstrated significant potential across various domains, their application in automating generation of engineering workflows remains underexplored. In this work, we introduce a novel copilot for automating the generation of P&IDs from natural language descriptions. Leveraging a multi-step agentic workflow, our copilot provides a structured and iterative approach to diagram creation directly from Natural Language prompts. We demonstrate the feasibility of the generation process by evaluating the soundness and completeness of the workflow, and show improved results compared to vanilla zero-shot and few-shot generation approaches.