LemonCrow vs ripgrep

What ripgrep says, vs. what it scored.

Line-oriented regex search CLI -- no AST or symbol awareness, the fastest possible answer to a text-match question.

What ripgrep says about itself

“It can replace many use cases served by other search tools because it contains most of their features and is generally faster.”
Source →

Publishes some numbers ...against other search tools ~50k stars ↗

What it actually scored — same 14 repos, same 7,213 queries as every other tool

Text-search speed claim real and reproducible (creator's own 25-scenario benchmark). No AST/symbol awareness: 0.376 MRR vs. LemonCrow's 0.727.

By query kind -- same benchmark, broken out (no reps in this eval: one deterministic pass per query)

Kind	LemonCrow +semantic	LemonCrow lexical	ripgrep
definition	0.873 (n=1570)	0.871 (n=1570)	0.576(n=1570)
content	0.873 (n=1444)	0.864 (n=1444)	0.837(n=1444)
semantic	0.759 (n=1800)	0.576 (n=1800)	0.000(n=1800)
swebench	0.500 (n=1908)	0.493 (n=1908)	0.250(n=1908)
sessions	0.587 (n=491)	0.571 (n=491)	0.245(n=491)

n = query/gold pairs of that kind, out of 7,213 total -- every provider scored on all 5 kinds.

By repo -- all 15 repos in the corpus, same query set

Repo	LemonCrow +semantic	LemonCrow lexical	ripgrep
astropy/astropy	0.772	0.715	0.459
atelier-ws/atelier-dev	0.467	0.477	0.188
atelier/atelier	0.594	0.557	0.260
django/django	0.689	0.652	0.345
matplotlib/matplotlib	0.801	0.747	0.442
mwaskom/seaborn	0.814	0.768	0.450
pallets/flask	0.735	0.671	0.324
psf/requests	0.840	0.803	0.463
pydata/xarray	0.815	0.764	0.466
pylint-dev/pylint	0.856	0.784	0.451
pytest-dev/pytest	0.826	0.739	0.451
scikit-learn/scikit-learn	0.740	0.669	0.338
sphinx-doc/sphinx	0.637	0.580	0.300
sympy/sympy	0.694	0.637	0.365
torvalds/linux	0.726	0.668	0.440

MRR per repo: n-weighted blend across all 5 query kinds, same 7,213-query run.

The true story

Same 14 repositories, same 7,213 query/gold pairs as every tool here, ripgrep included. Full methodology, every raw number, and the other 9 tools →