GDS Agent: A Graph Algorithmic Reasoning Agent

• URL: http://arxiv.org/abs/2508.20637v1
• Date: Thu, 28 Aug 2025 10:35:44 GMT
• Title: GDS Agent: A Graph Algorithmic Reasoning Agent
• Authors: Borun Shi, Ioannis Panagiotas,
• Abstract summary: We introduce the GDS (Graph Data Science) agent in this technical report.<n>The GDS agent introduces a comprehensive set of graph algorithms as tools, together with preprocessing (retrieval) and postprocessing of algorithm results.<n>Results indicate that GDS agent is able to solve a wide spectrum of graph tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown remarkable multimodal information processing and reasoning ability. When equipped with tools through function calling and enhanced with retrieval-augmented techniques, compound LLM-based systems can access closed data sources and answer questions about them. However, they still struggle to process and reason over large-scale graph-structure data. We introduce the GDS (Graph Data Science) agent in this technical report. The GDS agent introduces a comprehensive set of graph algorithms as tools, together with preprocessing (retrieval) and postprocessing of algorithm results, in a model context protocol (MCP) server. The server can be used with any modern LLM out-of-the-box. GDS agent allows users to ask any question that implicitly and intrinsically requires graph algorithmic reasoning about their data, and quickly obtain accurate and grounded answers. We also introduce a new benchmark that evaluates intermediate tool calls as well as final responses. The results indicate that GDS agent is able to solve a wide spectrum of graph tasks. We also provide detailed case studies for more open-ended tasks and study scenarios where the agent struggles. Finally, we discuss the remaining challenges and the future roadmap.

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