mixedbread-ai/mgrep

A calm, CLI-native way to semantically grep everything, like code, images, pdfs and more.

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Summary Information

Updated 30 minutes ago
Added to GitGenius on September 15th, 2026
Created on November 6th, 2025
Open Issues & Pull Requests: 54 (+0)
GitHub issues: Enabled
Number of forks: 175
Total Stargazers: 4,402 (+0)
Total Subscribers: 15 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 13.1 hours
Mean response time: 2.2 days
90th percentile: 4.9 days
Tracked items: 39

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How this project is maintained

Roughly one issue in three opened in the past year never receives a reply. 100% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. 98% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 29% of issues opened in the past year have been closed. Three people close 67% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 44
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 234 days
Stale 30+ days: 44
Stale 90+ days: 43

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Most active issues this week

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Detailed Description

mgrep is a command-line tool that performs semantic search across multiple file types including code, images, PDFs, and text.

The tool addresses the limitation of traditional grep, which matches only exact strings, by enabling natural-language search that understands meaning rather than syntax. It uses embeddings to index file content and supports multilingual queries. The approach combines local file indexing with optional web search capabilities, allowing users to query both their own files and the internet from a single interface. Background indexing via the watch command automatically keeps an index synchronized with changes in git repositories while respecting gitignore rules.

Developers should choose this tool if they work with mixed content types and want to search by meaning rather than keywords. It suits projects where team members need to find relevant code or documentation without knowing exact names or syntax. The tool is designed for both human developers and coding agents, with particular emphasis on reducing token usage for agent-based workflows. It enforces default limits on file size and count to maintain performance, though these can be customized. Authentication flows through either browser-based device login or API key environment variables for headless environments.

The project maintains active development with regular updates to core functionality. The tool receives ongoing refinement to its indexing and search capabilities. Integration patterns with coding agents continue to be a focus area for development. The team actively manages performance considerations through configurable limits and background sync behavior.