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Tech riderRev. 20 Sept 2026

semdup

Ch 195.5of 23 Code Clone Detection Tools
  1. 1Runs onLinux, Mac, self-hosted, Windows
  2. 2CostsNot stated by the maker
  3. 3Clone detection methodsemantic
  4. 4Scan scoperepository
  5. 5CI integrationYes
  6. 6Self-hosted optionYes
5 lines stated Written from the maker's own pages: github.com
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The semdup homepage

Overview

semdup is ranked #19 of 23 in code clone detection tools on Specifiction. It runs on Linux, macOS, Self-hosted, Windows.

Compared on code clone detection tools

Clone detection method
semanticgithub.com
Scan scope
repositorygithub.com
CI integration
Yesgithub.com
Self-hosted option
Yesgithub.com

Facts

Purpose
semdup is a fuzzy-search tool that detects near-duplicate code by comparing semantic meaning rather than token sequences.github.com · 4 Oct 2026
Use case
The maker describes it as useful for catching AI-generated code duplication and preventing agents from reimplementing existing code.github.com · 4 Oct 2026
Local inference
The default model runs locally on CPU or CUDA, and the maker says semdup keeps everything local unless an external backend is chosen.github.com · 4 Oct 2026
Languages
The project lists Rust, TypeScript, Python, Go, Java, C#, PHP, Ruby, C, and C++ as supported languages, with a caveat that heavily macro-based C/C++ may perform less well.github.com · 4 Oct 2026
Matching controls
Users can tune similarity thresholds, configure separate warning and error thresholds, and require matches to appear a chosen number of times.github.com · 4 Oct 2026
Workflow integration
The project provides a GitHub Actions workflow and action that can run semdup checks on pull requests and pushes.github.com · 4 Oct 2026
Bedrock integration
An optional Amazon Bedrock backend uses Titan Text Embeddings V2 through the AWS SDK credential chain and requires bedrock:InvokeModel permission.github.com · 4 Oct 2026
Model choice
The default embedding model is nomic-ai/CodeRankEmbed, and users can configure a compatible model or use a Python sidecar for other backends.github.com · 4 Oct 2026
Download integrity
The first initialization, refresh, or scan downloads the model into a user cache and verifies files against compiled BLAKE3 hashes before loading them.github.com · 4 Oct 2026
Index limitation
Sparse search is approximate and can miss pairs found by exact search, while exact search compares every pair and avoids dropping matches.github.com · 4 Oct 2026
Known limitation
The maker says the tool is still young, may contain bugs, and can produce false positives because it uses fuzzy search.github.com · 4 Oct 2026
License
The project is available under either the MIT or Apache-2.0 license, at the user's option.github.com · 4 Oct 2026
Maintainer support
The maker welcomes contributions to the project.github.com · 4 Oct 2026

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Sources