Papers › ProMoAI: Process Modeling with Generative AI

ProMoAI: Process Modeling with Generative AI

7 Mar 2024arXiv:2403.04327archive 2025-07-28

Humam Kourani, Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst

ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically generate process models from textual descriptions, incorporating advanced prompt engineering, error handling, and code generation techniques. Beyond automating the generation of complex process models, ProMoAI also supports process model optimization. Users can interact with the tool by providing feedback on the generated model, which is then used for refining the process model. ProMoAI utilizes the capabilities LLMs to offer a novel, AI-driven approach to process modeling, significantly reducing the barrier to entry for users without deep technical knowledge in process modeling.

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execute_code_and_get_variable humam-kourani/ProMoAI/promoai/model_generation/code_extraction.py found in paper text by Syntology ran AGPL-3.0 (copyleft) · pointer only · 129aa56e7cb327ab · report
extract_resources_from_code humam-kourani/ProMoAI/promoai/model_generation/code_extraction.py found in paper text by Syntology ran fingerprinted AGPL-3.0 (copyleft) · pointer only · dea9880e551dbe97 · report
get_simplified_xml_abstraction humam-kourani/ProMoAI/promoai/aipa/abstraction.py found in paper text by Syntology ran AGPL-3.0 (copyleft) · pointer only · 0e33ac6f4e2e6c9b · report
image_to_base64 humam-kourani/ProMoAI/promoai/agents/utils.py found in paper text by Syntology ran AGPL-3.0 (copyleft) · pointer only · 82f44d52c25b7887 · report
parse_dataframe_for_llms humam-kourani/ProMoAI/promoai/agents/utils.py found in paper text by Syntology ran AGPL-3.0 (copyleft) · pointer only · fd414e2041fdf0ca · report
transform_dataframe_for_llms humam-kourani/ProMoAI/promoai/agents/utils.py found in paper text by Syntology ran AGPL-3.0 (copyleft) · pointer only · 7ac3dc54028db6e8 · report
extract_final_python_code humam-kourani/ProMoAI/promoai/model_generation/code_extraction.py found in paper text by Syntology unverified AGPL-3.0 (copyleft) · pointer only · a421f782ecb8bab2 · report

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Code GenerationModel OptimizationPrompt Engineering

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