dupehound is a duplicate-code detector built for codebases where agents write most of the code. It finds functions that exist more than once, even after every identifier and literal has been renamed, because it fingerprints the structure of the code instead of its text.
dupehound runs three commands: scan
reports every duplicate cluster and a repo-level slop score, history
charts duplication across the git log and pinpoints when it took off, and check
fails CI when a change duplicates code that already exists, naming the original to reuse. Everything runs locally and deterministically: no network, no API keys, no machine learning.
Prebuilt binaries for macOS, Linux and Windows are on the releases page, or:
cargo install dupehound
history
and check
require git
on PATH. scan
works on any directory.
dupehound scan [path]
ranks duplicate clusters by deletable lines:
$ dupehound scan .
dupehound v0.1.0 — scanned 19 files · 370 lines · 27 functions in 21ms
╭─────────────────────────────────────────────────────────╮
│ SLOP SCORE 36.1% grade F │
│ 127 of 352 significant lines are deletable duplicates │
╰─────────────────────────────────────────────────────────╯
● Cluster 1 ─ 4 copies · 100% similar · 42 deletable lines ─────────────
★ src/utils/date.ts:1 formatDate 14 lines
src/api/timestamps.ts:1 renderTimestamp 14 lines 100% █████████
src/jobs/report_dates.ts:1 stringifyDate 14 lines 100% █████████
src/billing/dates.ts:1 humanizeDate 14 lines 100% █████████
★ = representative (kept) · dupehound scan --explain 1 shows the code
The slop score is the percentage of code you could delete if every cluster kept only one copy; the largest copy is exempt and test files are excluded by default, since table-driven tests are repetitive by design. --explain N
prints a cluster's code as proof, --json
emits a versioned schema, --card
writes a score card as SVG and PNG. Languages: TypeScript, TSX, JavaScript, Python, Rust, Go, Java.
dupehound history
measures the slop score at monthly snapshots, reading blobs straight from the object database (no checkouts), and reports when duplication took off:
36.1% ┤ ██
┤ ▂▂▆▆██
┤ ▂▂████████
┤ ▁▁████████████
0.0% ┤ ██████████████
└────────────────────────
2025-01 2025-12
current slop score: 36.1% (grade F)
duplication went from ~0 to 36.1% since 2025-05
dupehound check
gates CI and pre-commit. It indexes the codebase at the base revision and probes only the functions a change adds or touches. Moved functions and in-place edits don't fire. Exit codes: 0 clean, 1 findings, 2 error.
$ dupehound check --diff main .
src/api/orders.ts:1 calculateOrderAmount() is a 100% duplicate of src/billing/invoice.ts:1 computeInvoiceTotal() — reuse it
A GitHub Actions recipe and a pre-commit setup are in docs/ci.md. To make a coding agent reuse code instead of rewriting it, feed check
back to it from CLAUDE.md
or AGENTS.md
; the snippet is there too.
Function bodies are parsed with tree-sitter and normalized: identifiers, strings and numbers become sentinels, comments are dropped, structure stays. k-grams of 10 tokens are rolling-hashed and selected by robust winnowing (Schleimer, Wilkerson & Aiken, SIGMOD 2003), which guarantees any shared run of 17 normalized tokens is caught. An inverted fingerprint index generates candidate pairs, boilerplate fingerprints are culled, similarity is exact Jaccard, union-find builds the clusters.
The defaults are conservative about false positives: generated, minified and vendored files are skipped, functions under 40 normalized tokens are ignored, and every match is verifiable with --explain
. Grade buckets were calibrated against express (0.0%), gin (0.2%), tokio (1.1%), fastapi (1.7%) and vscode (2.8%), all grade A. vscode, at 2.97M lines and 53k functions, scans in 3.6s on a laptop. Full design notes in docs/design.md.
Coding agents don't know what a codebase already contains, so they re-implement it. formatDate
becomes renderTimestamp
, then stringifyDate
: the same logic under several names, each copy aging independently. Analyses of millions of commits report duplication roughly doubled since AI assistants went mainstream.
An LLM can't do this job. Duplicate detection compares every function against every other; a model samples what fits in context, an index checks everything. A merge gate must be reproducible: same input, same verdict, an algorithm you can read. dupehound is the deterministic side of the loop: the agent writes, the index remembers.
Please file issues on the issue tracker. The most useful false-positive report is a small code pair that matches but shouldn't, plus the --explain
output; these become regression fixtures directly.
PRs welcome. Adding a language is the most wanted contribution and is roughly one tree-sitter query file; see CONTRIBUTING.md.
MIT. Bundled JetBrains Mono subsets are under the SIL OFL 1.1. The diagram uses Excalidraw's Virgil font (OFL).