Oak:智能体 Git
Oak: Git for Agents

原始链接: https://oak.space/

将 Oak 与 Git 的基准测试对比显示,Oak 在快照、状态检查及大文件操作的 p50 延迟方面明显优于 Git。在处理大量小文件、大型二进制文件以及庞大的脏工作树等多种场景下,Oak 均实现了 90% 到 95% 的性能提升。它在开发者“内循环”任务(如重构、代理设置和并行工作流操作)中表现同样出色,而这些任务的运行速度对日常工作流程至关重要。 尽管 Oak 在长时间运行的代理会话中表现出巨大优势,但在初始冷启动和进程生成阶段,其性能会有所下降,比 Git 慢 43% 到 188%。 归根结底,Oak 旨在优化开发周期中重复且高频的操作。尽管初始设置存在微小的固定成本,但对于在大型或复杂代码库中频繁进行快照和状态检查的开发者而言,它带来了显著的效率提升。用户可在 oak.space/oak/benchmarks 验证这些结果或提供反馈。

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原文

Median p50 latency from the same benchmark harness, comparing git and Oak on identical repositories. The delta column shows how much Oak changes the wait for each operation. Run the harness yourself if you're curious oak.space/oak/benchmarks. If you find anything wrong, please let us know.

Where Oak shines most p50, standard & large profiles

scenario / operation delta p50 time

many small files · 50kinitial snapshot−95%git29,723 msoak1,412 ms

many small filesinitial snapshot−95%git2,073 msoak99.6 ms

many large binariestask snapshot−95%git443 msoak23.2 ms

wide dirty treeinitial snapshot−94%git849 msoak50.9 ms

multi-GB binariestask snapshot−94%git5,218 msoak321 ms

multi-GB binariesfull diff (info recall)−93%git3,945 msoak271 ms

many large binariesdirty snapshot−93%git362 msoak25.4 ms

multi-GB binariesdirty status−90%git1,343 msoak128 ms

Inner-loop wins the devloop you live in

scenario / operation delta p50 time

wide config refactoragent setup (clone + checkout)−89%git540 msoak58.6 ms

large asset manifestagent setup−88%git319 msoak37.7 ms

few large binariestask snapshot−82%git77.7 msoak13.9 ms

shared checkout · 8 workersparallel worker−72%git419 msoak116 ms

Where Oak has the most room to improve git-faster work

scenario / operation delta p50 time

repo init · 50k filesone-time cost+188%git14.8 msoak42.6 ms

process spawncold start+143%git22.3 msoak54.2 ms

cold dirty status · 50k filesuncached+82%git89.4 msoak163 ms

dirty status · many small filesuncached+43%git16.3 msoak23.3 ms

Oak still pays small fixed costs for cold init and process startup. The reason to use it is the loop that follows: snapshots, status checks, and large-file work can be dramatically faster across a long-running agent session.

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