Sensegrep FAQs

Question 1

How does Sensegrep use AI embeddings and AST parsing?

Accepted Answer

It uses Tree-sitter to parse code into an Abstract Syntax Tree (AST), extracting symbols and metadata. Code chunks are then embedded using local models or Gemini API, allowing for vector-based semantic search and precise structural filtering.

Question 2

What programming languages does Sensegrep currently support?

Accepted Answer

Sensegrep currently supports TypeScript/JavaScript (including TSX/JSX) and Python. Support for more languages like C#, Java, and HTML is actively in development.

Question 3

Can Sensegrep be integrated with AI agents and popular development environments?

Accepted Answer

Yes, Sensegrep offers deep integration with AI agents via its MCP server and dedicated plugins for Claude Code and Cursor. It also has a VS Code extension with semantic search, duplicate detection, and auto-indexing features.

Question 4

What are Sensegrep's core features for developers?

Accepted Answer

Key features include AI embedding-powered semantic search, AST-aware parsing, 30+ structural and symbol metadata filters, logical duplicate code detection, and tree-shaking output to collapse irrelevant code in results.

Question 5

What is Sensegrep and how does it differ from traditional code search?

Accepted Answer

Sensegrep performs semantic and structural code search, understanding code by its meaning using AI embeddings and Tree-sitter AST parsing. Unlike text-pattern tools (grep, ripgrep), it finds code based on concepts like 'authentication logic' even if the exact words aren't present.

Sensegrep

Sensegrep

Key Features

Use Cases

Key Features

Use Cases