Prometheus: Towards Long-Horizon Codebase Navigation for Repository-Level Problem Solving

TL;DR

Prometheus is a memory-centric coding agent framework that improves long-horizon codebase navigation and repository-level problem solving by maintaining contextual continuity and leveraging a knowledge graph, leading to state-of-the-art issue resolution performance.

Contribution

It introduces a memory-enhanced, knowledge graph-based approach for long-horizon code navigation, addressing the limitations of stateless agents and improving scalability in repository-level tasks.

Findings

Achieves 74.4% and 33.8% resolution rates on SWE-bench benchmarks.

Ranks Top-6 and Top-1 among open-source systems.

Demonstrates effective long-horizon reasoning with memory and knowledge graph integration.

Abstract

Large Language Models (LLMs) have shown remarkable capabilities in automating software engineering tasks, spurring the emergence of coding agents that scaffold LLMs with external tools to resolve repository-level problems. However, existing agents still struggle to navigate large-scale codebases, as the Needle-in-a-Haystack problem persists even with million-token context windows, where relevant evidence is often overwhelmed by large volumes of irrelevant code and documentation. Prior codebase navigation approaches, including embedding-based retrieval, file-system exploration, and graph-based retrieval, address parts of this challenge but fail to capture the temporal continuity of agent reasoning, rendering agents stateless and causing repeated repository traversals that hinder scalable planning and reasoning. To address these limitations, we present Prometheus, a memory-centric coding agent framework for long-horizon codebase navigation. Prometheus represents the repository as a unified knowledge graph to encode semantic dependencies and employs a context engine augmented with working memory that retains and reuses previously explored contexts to ensure continuity across reasoning steps. Built upon this engine, Prometheus integrates memory-enhanced navigation into a multi-agent system for automated issue resolution, encompassing issue classification, bug reproduction, patch generation, and verification. Comprehensive experiments are conducted on two widely used issue resolution benchmarks, i.e., SWE-bench Verified and SWE-PolyBench Verified. Powered by GPT-5, Prometheus achieves state-of-the-art performance with 74.4% and 33.8% resolution rates on the two benchmarks, ranking Top-6 and Top-1 among open-source agent systems, respectively. Our data and code are available at https://github.com/EuniAI/Prometheus.