Large Language Models (LLMs) reasoning processes are challenging to analyze due to their complexity and the lack of organized visualization tools. We present ReasonGraph, a web-based platform for visualizing and analyzing LLM reasoning processes. It supports both sequential and tree-based reasoning methods while integrating with major LLM providers and over fifty state-of-the-art models. ReasonGraph incorporates an intuitive UI with meta reasoning method selection, configurable visualization parameters, and a modular framework that facilitates efficient extension. Our evaluation shows high parsing reliability, efficient processing, and strong usability across various downstream applications. By providing a unified visualization framework, ReasonGraph reduces cognitive load in analyzing complex reasoning paths, improves error detection in logical processes, and enables more effective development of LLM-based applications. The platform is open-source, promoting accessibility and reproducibility in LLM reasoning analysis.
ReasonGraph: Visualisation of Reasoning Paths
ReasonGraph is an open-source web platform that visualizes and analyzes the reasoning processes of Large Language Models, improving usability and error detection in logical processes.
- Year
- 2025
- Venue
- arXiv 2025
- Authors
- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2503.03979ARXIV-DEFAULT
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