bepo 1.0.0

Detect duplicate PRs in GitHub repos

bepo

Detect duplicate pull requests in GitHub repos.

No ML, no embeddings, no API keys. Just static analysis of diffs.

A maintainer with 100 open PRs can run bepo check --repo foo/bar and in 5 minutes get a ranked list of "you should look at these pairs." That saves hours of manual review.

The Problem

Large repos waste engineering time on duplicate PRs. When multiple contributors fix the same bug independently, only one PR gets merged — the rest is wasted effort.

This actually happens. We analyzed 100 PRs from OpenClaw and found:

Cluster	PRs	What happened
Matrix startup bug	4 PRs	4 engineers independently fixed startupGraceMs = 0 → 5000
Media token regex	2 PRs	Identical fix submitted twice
Feishu bitable config	2 PRs	Same multi-account config fix

8 duplicate PRs across 3 bug fixes. That's real engineering time wasted.

Proof: OpenClaw Analysis

30-day window (recommended)

$ bepo check --repo openclaw/openclaw --since 30d

Found 52 potential duplicates:

#20472 <-> #20491
  Similarity: 100%
  Reason: Both fix #20468      ← Same Nextcloud Talk restart bug, two fixes

#19595 <-> #19624
  Similarity: 90%
  Reason: Both fix #19574      ← Identical PR titles, same elevatedDefault bug

#20419 <-> #20441
  Similarity: 83%
  Reason: Both fix #20410      ← Same WebChat markdown rendering fix

#19865 <-> #19945
  Similarity: 97%
  Reason: Same code: 100 lines overlap   ← Two embedding provider PRs duplicating core logic

#19770 <-> #20317
  Similarity: 87%
  Reason: Same code: 9 lines overlap     ← Same "hide tool calls" UI toggle, added twice

Precision: ~88% — verified by manual classification of all 52 pairs.

The remaining ~12% are concurrent PRs touching the same structural code — two provider integrations sharing schema boilerplate, two locale additions hitting the same type. The kind of overlap a reviewer would want to know about.

Full backlog (3,000 PRs)

$ bepo check --repo openclaw/openclaw --limit 3000

Analyzed 3000 PRs in 9.8s

Found 1022 potential duplicates:

#17518 <-> #17653
  Similarity: 100%
  Reason: Both fix #17499      ← Identical browser dialog fix submitted twice

#12936 <-> #19050
  Similarity: 80%
  Reason: Same code: 10 lines overlap   ← Same Telegram thread_id fix, one is literally "v2"

#15512 <-> #18994
  Similarity: 67%
  Reason: Same code: 10 lines overlap   ← Both normalize Brave search language codes

#14182 <-> #15051
  Similarity: 75%
  Reason: Same code: 2403 lines overlap ← Two Zulip implementations duplicating core logic

Precision by similarity band, verified by manual sampling:

Band	Pairs	Precision	Notes
100%	221	~75%	High code overlap but tiny sets — watch for short boilerplate
80–90%	318	~95%	Best signal — issue refs + code together
70–79%	283	~90%	Strong structural duplicates
65–69%	200	~70%	Noisier; raise --threshold 0.75 to cut this band
Overall	1022	~84%

For large backlogs, --threshold 0.75 drops to ~550 pairs at ~92% precision. --since 30d gives 52 actionable pairs at ~88% — the recommended default.

More Examples

VSCode — Found PRs touching same files for same feature:

#295823 <-> #295822
  Similarity: 77%
  Reason: Same files: chatModel.ts, chatForkActions.ts

  Both: "Use metadata flag for fork detection"

Next.js — Found related test updates:

#90121 <-> #90120
  Similarity: 86%
  Reason: Same files: test/

Install

pip install bepo

Requires GitHub CLI (gh) to be installed and authenticated.

Usage

# Check a repo for duplicate PRs
bepo check --repo owner/repo

# Check recent PRs (recommended — avoids stale noise)
bepo check --repo owner/repo --since 30d

# Adjust sensitivity (default: 0.65, higher = stricter)
bepo check --repo owner/repo --threshold 0.7

# JSON output for CI
bepo check --repo owner/repo --json

How It Works

bepo fingerprints each PR by extracting:

Signal	Weight	What it catches
Same issue ref (#123)	10.0	Definite duplicate
Same code changes (IDF-weighted)	8.0	Rare lines weighted more than common boilerplate
Same files touched	6.0	PRs modifying same code
Same feature domain	3.0	auth, messaging, database, etc.
Same imports	1.0	Similar dependencies

Then computes pairwise Jaccard similarity.

That's it. No embeddings, no LLM calls. Just:

• Parse +++ b/path from diffs
• Regex for #\d+ issue refs
• Compare actual code changes
• Set intersection for similarity

Cross-component filtering suppresses boilerplate FPs in integration/plugin monorepos (e.g. Home Assistant, VSCode extensions). Two unrelated integrations sharing scaffold code (config_flow.py, manifest.json) are filtered out when each PR is concentrated in a different component subtree. Pairs sharing a GitHub issue ref always bypass this filter.

~2,000 lines of Python.

As a Library

from bepo import fingerprint_pr, find_duplicates

# Fingerprint PRs
fp1 = fingerprint_pr("#123", diff1, title="Fix auth", body="Fixes #456")
fp2 = fingerprint_pr("#124", diff2, title="Auth fix", body="Fixes #456")

# Find duplicates
dups = find_duplicates([fp1, fp2], threshold=0.65)
for d in dups:
    print(f"{d.pr_a} ↔ {d.pr_b}: {d.similarity:.0%}")
    print(f"  Shared issues: {d.shared_issues}")
    print(f"  Shared files: {d.shared_files}")

GitHub Action

Add to your repo to automatically detect duplicate PRs and post a warning comment:

name: PR Duplicate Check
on: [pull_request]

jobs:
  check-duplicates:
    runs-on: ubuntu-latest
    steps:
      - uses: aardpark/bepo@v1
        with:
          github-token: ${{ secrets.GITHUB_TOKEN }}
          threshold: '0.65'  # optional, default 0.65

When a PR is opened that looks like a duplicate, bepo posts a comment:

⚠️ Potential Duplicate PRs Detected

This PR may be similar to existing open PRs:

PR	Similarity	Reason
#123	85%	Both fix #456
#124	71%	Same code: 10 lines overlap

---

Detected by bepo

Action Inputs

Input	Description	Default
github-token	GitHub token for API access	${{ github.token }}
threshold	Similarity threshold (0.0-1.0)	0.65
limit	Max PRs to compare against	50
comment	Post comment on PR	true

Action Outputs

Output	Description
has_duplicates	true if duplicates found
match_count	Number of matches
matches	JSON array of matches

Why This Works

Duplicates share obvious signals:

• Same code = Identical changes (639 shared lines caught SoundChain duplicates)
• Same issue ref = Same bug report (#19843 appeared in 4 Matrix PRs)
• Same files = Same bug location (100% overlap for Feishu cluster)

IDF weighting makes rare lines matter more than common boilerplate. A shared startupGraceMs = 5000 is a stronger signal than a shared return null.

Code overlap and issue refs catch most duplicates. Simple works.

Origin Story

This tool was vibe-coded in a single session with Claude.

We tried a few approaches and kept finding that simpler signals outperformed fancier ones. File overlap and issue refs catch most duplicates. Sometimes the obvious solution is the right one.

License

MIT