BootstrapAgent: Distilling Repository Setup into Reusable Agent Knowledge
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Abstract (arXiv)
Code agents increasingly help developers work with unfamiliar repositories, but every such task depends on a costly prerequisite: bootstrapping the repository into a usable development state. This process requires substantial trial-and-error exploration, yet the resulting knowledge--resolved dependencies, repair strategies--stays trapped in a single conversation, unavailable to future agents. We therefore formulate repository bootstrapping as a reusable startup knowledge problem and introduce BootstrapAgent, a multi-agent framework that distills the heuristics discovered during bootstrap exploration into a persistent, verifiable, agent-consumable .bootstrap contract. Through evidence extraction, structured planning, deterministic Docker-based verification, and trace-driven repair, BootstrapAgent generates a contract covering environment setup, diagnostic checks, minimal verification, and accumulated repair knowledge. We further propose warm repair with clean replay to accelerate iterative debugging without sacrificing cold-start reproducibility, and a delta repair with sanity check to prevent reward hacking. Experiments on three benchmarks show that BootstrapAgent achieves a 92.9% success rate, outperforming the baseline by over 10% while reducing downstream agent token usage by 25.9% and build time by 22.3%. Our code is available at https://github.com/Vossera/BootstrapAgent.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 15 May 2026.
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32 of 51 references.
References · 32
- 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
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- 2025R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agentscited by 140
- 2022MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generationcited by 132
- 2024SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agentscited by 129
- 2022Repository-Level Prompt Generation for Large Language Models of Codecited by 106
- 2021Neural Program Repair with Execution-based Backpropagationcited by 50
- 2024You Name It, I Run It: An LLM Agent to Execute Tests of Arbitrary Projectscited by 45
- 2025Repo2Run: Automated Building Executable Environment for Code Repository at Scalecited by 39
- 2025EnvBench: A Benchmark for Automated Environment Setupcited by 36
20 more
- 2025SWE-Perf: Can Language Models Optimize Code Performance on Real-World Repositories?cited by 33
- 2025Understanding Code Agent Behaviour: An Empirical Study of Success and Failure Trajectoriescited by 30
- 2025SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositoriescited by 27
- 2024On the Impacts of Contexts on Repository-Level Code Generationcited by 21
- 2024Beyond pip install: Evaluating LLM Agents for the Automated Installation of Python Projectscited by 21
- 2025Agent READMEs: An Empirical Study of Context Files for Agentic Codingcited by 21
- 2025SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environmentscited by 18
- 2025CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science Research Repositoriescited by 15
- 2025Context Engineering for AI Agents in Open-Source Softwarecited by 14
- 2019DockerizeMe: Automatic Inference of Environment Dependencies for Python Code Snippetscited by 13
- 2025SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Developmentcited by 13
- 2025RepoST: Scalable Repository-Level Coding Environment Construction with Sandbox Testingcited by 12
- 2025CXXCrafter: An LLM-Based Agent for Automated C/C++ Open Source Software Buildingcited by 10
- 2025Can Agents Fix Agent Issues?cited by 8
- 2025DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scalecited by 7
- 2025Understanding Prompt Management in GitHub Repositories: A Call for Best Practicescited by 5
- 2023Automatically Generating Dockerfiles via Deep Learning: Challenges and Promisescited by 3
- 2025Treefix: Enabling Execution with a Tree of Prefixescited by 2
- 2026HerAgent: Rethinking the Automated Environment Deployment via Hierarchical Test Pyramidcited by 2
- 2024Local Software Buildability across Java Versions (Registered Report)cited by 1
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- 2026RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue
- 2026SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?
- 2026Large-scale Repository Engineering via Agent-Native Reusable Code Primitives
- 2026Correct Code, Broken Contributions? SWE-CC: Benchmarking Repository Policy Compliance for Coding Agents
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