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LemonCrow vs fff (fff-mcp)
What fff (fff-mcp) says, vs. what it scored.
In-memory, frecency-ranked file and content search with a background watcher -- fast file discovery, not symbol-level code search.
What fff (fff-mcp) says about itself
“A file search toolkit for humans and AI agents. Really fast... Way faster than CLIs like ripgrep and fzf in any long-running process that searches more than once.”
“On a 500k-file Chromium checkout, FFF achieves sub-10ms per query compared to 3-9 seconds per ripgrep spawn.”
What it actually scored — same 14 repos, same 7,213 queries as every other tool
| Tool | MRR | p95 | p100 |
|---|---|---|---|
| ★ LemonCrow +semantic (BGE) | 0.727 | 390ms | 1057ms |
| ★ LemonCrow lexical (default) | 0.676 | 134ms | 319ms |
| fff (fff-mcp) | 0.430 | 46ms | 207ms |
Speed claim real -- 46ms p95, among the fastest measured. File finder, not symbol search: 0.430 MRR vs. LemonCrow's 0.676-0.727.
By query kind -- same benchmark, broken out (no reps in this eval: one deterministic pass per query)
| Kind | LemonCrow +semantic | LemonCrow lexical | fff (fff-mcp) |
|---|---|---|---|
| definition | 0.873 (n=1570) | 0.871 (n=1570) | 0.684 (n=1570) |
| content | 0.873 (n=1444) | 0.864 (n=1444) | 0.834 (n=1444) |
| semantic | 0.759 (n=1800) | 0.576 (n=1800) | 0.021 (n=1800) |
| swebench | 0.500 (n=1908) | 0.493 (n=1908) | 0.341 (n=1908) |
| sessions | 0.587 (n=491) | 0.571 (n=491) | 0.281 (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 | fff (fff-mcp) |
|---|---|---|---|
| astropy/astropy | 0.772 | 0.715 | 0.519 |
| django/django | 0.689 | 0.652 | 0.444 |
| lemoncrow-lab/lemoncrow-dev | 0.467 | 0.477 | 0.269 |
| lemoncrow/lemoncrow | 0.594 | 0.557 | 0.284 |
| matplotlib/matplotlib | 0.801 | 0.747 | 0.495 |
| mwaskom/seaborn | 0.814 | 0.768 | 0.481 |
| pallets/flask | 0.735 | 0.671 | 0.360 |
| psf/requests | 0.840 | 0.803 | 0.511 |
| pydata/xarray | 0.815 | 0.764 | 0.505 |
| pylint-dev/pylint | 0.856 | 0.784 | 0.574 |
| pytest-dev/pytest | 0.826 | 0.739 | 0.523 |
| scikit-learn/scikit-learn | 0.740 | 0.669 | 0.370 |
| sphinx-doc/sphinx | 0.637 | 0.580 | 0.354 |
| sympy/sympy | 0.694 | 0.637 | 0.397 |
| torvalds/linux | 0.726 | 0.668 | 0.464 |
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, fff (fff-mcp) included. Full methodology, every raw number, and the other9 tools →