"Remember Codebase feature in Cursor text editor" matching MCP servers:

Multi-tenant memory system MCP server with vector search, relationships, and trust-based access control for AI assistants.

A semantic codebase indexer MCP server that chunks source code, generates embeddings via Ollama, and stores them in Qdrant for natural-language code search.

An MCP server that transforms a local codebase into a queryable, citation-grounded knowledge base using hybrid FAISS and BM25 retrieval, with optional reranking and swappable LLM providers.

Enables semantic code search across multiple repositories using AST-aware chunking and relationship tracking. Supports local LLM embeddings, real-time indexing, and cross-codebase dependency analysis through vector and graph databases.

Enables semantic search across your codebase using Google's Gemini embeddings and Qdrant Cloud vector storage. Supports 15+ programming languages with smart code chunking and real-time file change monitoring.

A minimalist indexing tool that provides AI agents with semantic search and structural AST parsing for deep codebase understanding. It enables autonomous agents to navigate large codebases predictably using vector embeddings and native language server capabilities like definition and reference tracking.

A server component of the Model Context Protocol that provides intelligent analysis of codebases using vector search and machine learning to understand code patterns, architectural decisions, and documentation.

Enables AI agents to chat with codebases by indexing local directories or GitHub repositories into a vector database for semantic search and code retrieval.

Indexes codebases into Qdrant for semantic search, enabling AI assistants to find relevant code by meaning without re-exploring the repo.

Enables searching through vectorized Cursor IDE chat history via a FastAPI service powered by LanceDB and Ollama.

Enables semantic search over your Cursor IDE chat history by vectorizing prompts and storing them in LanceDB. Provides a Dockerized API to perform vector similarity searches against your chat history.

Dual-mode tool providing long-term memory management for Claude Code and MCP clients, with semantic search and user/session isolation via Qdrant.

A Python-based local indexing server that creates semantic search capabilities for codebases using ChromaDB, allowing Cursor IDE to perform vector searches on your code without sending data to external services.

An MCP server that analyzes Python codebases using AST, stores code elements in a vector database, and enables natural language queries about code structure and functionality using RAG with Google's Gemini models.

An MCP server that enables clients like Cursor, Claude, and Windsurf to remember user information and preferences across conversations using vector search technology.

MCP server that gives MCP clients (like Cursor, Claude, Windsurf) the ability to remember user information across conversations using vector search technology.

A server that gives MCP clients (Cursor, Claude, Windsurf, etc.) the ability to remember user information across conversations using vector search technology.

A cognitive memory system for MCP that provides hybrid retrieval, memory layers, entity graph, and 83 tools for recall, remember, codebase scanning, and more.

Enables AI agents to interact with an embedded graph database (GrafeoDB) via the Model Context Protocol, providing tools for graph CRUD, GQL queries, full-text and vector search, and graph algorithms.

Single-binary local RAG MCP server in Rust — MiniLM compiled in, hybrid BM25+dense, zero runtime downloads