liustack/modlens

The first vision plugin for DeepSeek Harness, and the vision bridge for every text-only coding agent. Paste an image, get structured JSON evidence (OCR,...

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Summary Information

Updated 27 minutes ago
Added to GitGenius on September 17th, 2026
Created on February 22nd, 2026
Open Issues & Pull Requests: 7 (+0)
GitHub issues: Enabled
Number of forks: 123
Total Stargazers: 4,004 (+0)
Total Subscribers: 4 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.3 hours
Mean response time: 14.7 hours
90th percentile: 36.1 hours
Tracked items: 97

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How this project is maintained

About 3% of issues opened in the past year have never received a reply. 95% of issues opened in the past year have since been closed. Three people close 95% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 5
New in 7 days: 6
Closed in 7 days: 3
Avg open age: 2 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 5
Closed in 7 days: 3
Comments in 7 days: 4
Events in 7 days: 9

Top labels

  • bug (19)
  • enhancement (14)
  • invalid (1)
  • question (1)

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Detailed Description

ModLens is a vision plugin for DeepSeek Harness that extends text-only coding models with image understanding capabilities.

The tool solves the problem that DeepSeek's flagship chat models and GLM are text-only and cannot process images. ModLens works by accepting images pasted directly into chat and returning structured JSON evidence including OCR results, layout analysis, and semantic information. This approach eliminates the need to save images to files and pass file paths manually.

Developers should choose ModLens if they work with DeepSeek Harness or other text-only language models and need vision capabilities without switching to multimodal models. The tool installs as a single command within the DeepSeek Harness ecosystem. For those preferring a graphical interface over command-line setup, the README mentions AIManager as a desktop wrapper that handles installation and dependency management automatically.

The maintainers respond to issues and pull requests within hours. Work in the issue tracker centers on bug reports, enhancement requests, and user questions.