mlsanityData to 7 Oct 2026

Graph-Guided Repository Environment Construction

arXiv 2609.33429 ↗First posted 27 Sep 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Coding agents now increasingly rely on execution to validate their solutions, making the construction of reliable execution environments a critical enabling capability. However, repository environment construction is challenging because execution requirements are fragmented across repository artifacts and may only become apparent during execution. Existing agent-based approaches address this problem through iterative interaction, but information about the current construction state, including discovered requirements, satisfied and unresolved prerequisites, and their dependencies, can remain distributed across the interaction history. We present Graph2Env, an agent-based approach centered on DepGraph, a typed dependency graph that explicitly represents the environment requirements needed for repository execution, their dependency relations, and their states. Graph2Env uses DepGraph to guide environment construction and continuously refines it with execution feedback, while persisting successful repairs into a replayable construction procedure. The resulting artifacts are then applied in a fresh environment to verify that the constructed environment can be reproduced. We evaluate Graph2Env on a benchmark of 200 Python repositories drawn from RATBench and EnvBench, against a static dependency-inference baseline (pipreqs), three specialized environment-construction systems (Repo2Run, RAT, and SetupX), and two general-purpose coding agents (SWE-agent and Claude Code). Graph2Env achieves an 81.0% Environment Build Success Rate (EBSR) and a 59.3% Environment Setup Success Rate (ESSR), outperforming the strongest baseline by 9.5 and 9.0 percentage points, respectively.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 52 references that are in the map. First posted on arXiv 27 Sep 2026.

Builds on

14 of 52 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026SetupX: Can LLM Agents Learn from Past Failures in Functionality-Correct Code Repository Setup?25 May
  2. 2026EvoConfig: Self-Evolving Multi-Agent Systems for Efficient Autonomous Environment Configuration23 Jan
  3. 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projects13 Dec
  4. 2024Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projects9 Dec
  5. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May

Other references · 9

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  4. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  5. 2025Repo2Run: Automated Building Executable Environment for Code Repository at Scalecited by 39
  6. 2025EnvBench: A Benchmark for Automated Environment Setupcited by 36
  7. 2025From Experience to Strategy: Empowering LLM Agents with Trainable Graph Memorycited by 16
  8. 2019DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippetscited by 13
  9. 2026MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoningcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2025NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
  2. 2026Zero2Repo: Can Coding Agents Build Repositories from Scratch?
  3. 2026Needle in the Repo: A Benchmark for Maintainability in AI-Generated Repository Edits
  4. 2026Large-scale Repository Engineering via Agent-Native Reusable Code Primitives
  5. 2026Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations
  6. 2026ARISE: A Repository-level Graph Representation and Toolset for Agentic Program Repair and Fault Localization
  7. 2026Code That Works, Environments That Don't: Measuring Environment Reproducibility in AI-Generated Software
  8. 2026A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents