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Research map / Multi-agent systems and games / LLM agents

BootstrapAgent: Turning Repository Setup into Reusable Agent Knowledge

NeurIPS 2026 · Multi-agent systems and games › LLM agents · approach: Robust and efficient agent execution

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Proposes BootstrapAgent, a multi-agent pipeline that distills repository setup exploration into a verified, reusable .bootstrap contract, using Docker-based clean replay and trace-driven delta repair.

Problem

Repository bootstrapping (getting an unfamiliar repo into a usable dev state) is costly for code agents, and the resulting setup knowledge is lost after each conversation.

Approach

Persist bootstrap exploration as a repository-local, verifiable .bootstrap contract (setup, doctor, verify, repair knowledge) produced by discovery, planning, generation and trace-driven delta repair, with warm repair for speed and clean-replay cold verification plus a sanity check against verification weakening.

Key results

Surprising findings

Limitations

Experimental setup

Datasets: Repo2Run-Bench ExecutionAgent-Bench Installamatic-Bench

Benchmarks: Repo2Run-Bench ExecutionAgent-Bench Installamatic-Bench

Metrics: bootstrap success rate token usage wall-clock build time

Baselines: HerAgent ExecutionAgent Repo2Run Installamatic

Builds on: HerAgent Claude Code

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