How Cursor Indexes Code: Merkle Trees, Vector Embeddings, and Secure Search

Core Technologies and Index Architecture

Cursor builds a Merkle tree of the entire codebase to detect changes, uses Tree‑sitter to split files into syntax‑aware code blocks, and computes vector embeddings for each block with either OpenAI’s API or a self‑hosted model. The embeddings, together with obfuscated file‑path fragments and line‑range metadata, are stored in the Turbopuffer vector database.

Index Triggering and Updating

Initial full‑index is performed when a project is opened; subsequent updates are incremental, based on Merkle‑tree comparisons and file‑system monitoring (default every 10 minutes).

Ignore rules follow .gitignore, .cursorignore, etc.; users can add large binaries or private files to .cursorignore to improve efficiency and security.

Strategies to Improve Index Accuracy

Semantic chunking : code is split at function, class, or module boundaries using AST analysis, preserving context for embeddings.

Project structure awareness : enabling “Include Project Structure” lets Cursor index hierarchical information; custom rules in .cursor/rules can inject domain‑specific knowledge.

Cross‑file association : vector search spans the whole repository, allowing related snippets across multiple files to be retrieved for queries or completions.

Type and static information : while the index focuses on semantic embeddings, Cursor still leverages the VS Code language server for type hints during code‑completion scenarios.

Multi‑Language Support and Synchronization

Through Tree‑sitter, Cursor supports many languages (Python, Java, Swift, TypeScript, etc.) and can be extended with custom grammars. In multi‑root workspace mode, multiple projects are indexed concurrently, each kept in a separate namespace within Turbopuffer.

Code Security and Permission Policies

Privacy mode : source code is never stored in plaintext on the server; only encrypted chunks, vector embeddings, and obfuscated metadata persist.

Transport and storage encryption : all communication uses TLS 1.2, and stored data is encrypted with AES‑256. Cursor holds SOC 2 Type II compliance and supports SAML/SSO.

Team and data management : administrators can enforce privacy mode for all members, and users may delete indexed projects at any time to remove residual data.