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agent-tool-benchmark: hands-on benchmarks of AI agent tooling. Same machine, same tasks, full scripts.

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Every benchmark here is a real run on real tools — same machine, same task suite, same hour. Each one ships with its full methodology, rerunnable task scripts, fixtures, and honest limitations, so you can reproduce the numbers instead of trusting a vendor chart.

Benchmarks

Category Comparison Date Verdict
browser ego lite vs browser-use CLI 2026-07-28 ego lite wins for local everyday agent tasks (cross-origin iframe piercing, parallel Spaces, zero tab interference); browser-use CLI remains the only option for servers, CI, and cross-platform
editing udiff vs SEARCH/REPLACE vs str_replace vs apply_patch vs Cline vs whole-file 2026-08-19 git apply / GNU patch remain the only appliers with zero silent corruptions across 13 scenarios; Cline's fallback ladder and whole-file rewrite tie for most corruptions (4); three appliers silently append a trailing newline; OpenAI apply_patch safely rejects overlapping hunks but corrupts tab-indented files
documents pymupdf4llm vs markitdown vs docling vs pdftotext 2026-08-19 pymupdf4llm wins outright: 9/10 headings, correct two-column order, byte-exact CJK, ~1 s/doc at ~120 MB; docling matches structure but needs ~10 s/doc + 6 GB and silently maps CJK ideographs to lookalike radicals; markitdown/pdftotext emit plain text (0 headings)
retrieval ripgrep vs ctags vs tree-sitter vs jedi (find-all-references) 2026-08-19 jedi: perfect precision, follows re-exports and separates same-named methods, but blind to getattr dispatch; ripgrep: highest recall (0.95) including string-based dispatch, half its hits are noise; ctags indexes definitions, not references (0.00 recall); an aliased constructor call defeated all four tools
retrieval zg (zvec-grep) hybrid vs BM25 vs ripgrep (NL intent → code) 2026-09-07 On 12 natural-language intent queries over requests v2.32.3, zg answered 11–12/12 vs ripgrep's 6/12 — the vocab-gap tier is binary (zg 3–4/4, rg 0/4); but zg costs ~360 tokens and ~1.6 s per query vs rg's ~46 tokens at 5 ms, and pure BM25 --fts matched hybrid, so the official tool-call-reduction claim rests on avoided agent round-trips, which this does not measure
search Exa vs Tavily vs Serper vs Brave vs Firecrawl vs Jina pending Harness + query suite committed; awaiting API keys for a captured run — no numbers published until then

Principles

  • Same machine, same tasks. Every tool in a comparison runs the identical task suite on the identical hardware, within the same session.
  • Reproducible. All task scripts and fixtures are committed. bash tasks/<tool>/<task>.sh reruns any measurement.
  • Honest about limits. Single runs, network variance, and untested modes (such as autonomous LLM planning) are stated, not hidden.
  • No invented numbers. Every figure in every chart comes from a captured run. Vendor claims are labeled as claims.

Planned

  • Agent terminal and sandbox execution environments
  • Spreadsheet agent tools
  • Search-API captured run (harness ready, awaiting keys)

License

MIT

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Hands-on, reproducible benchmarks of AI agent tooling — same machine, same tasks, full scripts

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