# GDS Agent for Graph Algorithmic Reasoning

## TL;DR

The paper introduces the GDS agent, a system that enhances large language models with graph algorithms and retrieval techniques, enabling effective reasoning over large-scale graph data and improving performance on graph-related tasks.

## Contribution

It presents the GDS agent, integrating graph algorithms as tools within LLMs, along with a model protocol server, to facilitate graph reasoning and new benchmark evaluations.

## Key findings

- GDS agent effectively solves diverse graph tasks.
- Introduces benchmarks for intermediate tool call evaluation.
- Provides case studies and discusses future challenges.

## 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 introduce new benchmarks that evaluate 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.