"SQLite" matching MCP servers:

Provides persistent memory and knowledge graph capabilities for AI assistants using local SQLite storage. Enables creating, searching, and managing entities, relationships, and observations with vector search support across conversations.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

Gives AI agents persistent, local-first memory using SQLite and on-device embeddings, enabling semantic search and recall across sessions with no cloud calls.

A Byzantine fault-tolerant MCP Memory server that uses SQLite write-ahead logging and FAISS hybrid vector search for deterministic, secure memory operations.

An MCP server that provides AI assistants with access to Multi Theft Auto: San Andreas function documentation through vector similarity search and smart keyword expansion. It enables efficient information retrieval with features like deprecation warnings and SQLite caching for technical documentation.

An MCP server that retrieves resume/experience evidence relevant to a job description via vector RAG, and tracks fit-analysis results in a configurable tracking store (Notion or SQLite), with tools like match_job, push_to_tracker, and list_applications.

A local MCP memory server for Kiro CLI that provides persistent semantic memory across sessions using SQLite for storage, Ollama for local vector embeddings, and Obsidian as a human-readable sync target.

A long-term memory MCP server for AI agents that stores memories (facts, decisions, etc.) in a single SQLite database with hybrid search and full edit history, ensuring consistency across sessions.

Local-first memory for Claude Code and any MCP client: hybrid vector + keyword search and a bi-temporal knowledge graph in one SQLite file. Local embeddings, no API key, $0/token.

A local, fully-offline MCP memory server that enables persistent storage and retrieval of information using SQLite with both keyword and semantic vector search capabilities.

Semantic vector DB as an MCP server for Claude Code — SQLite + FTS5 + local ONNX embeddings. Gives Claude persistent, searchable project knowledge across conversations.

A lightweight, local-first MCP server that automatically watches folders, chunks and embeds files using Transformers.js, and exposes semantic search capabilities to VS Code and Cursor. Runs completely offline with SQLite vector storage, designed for resource-constrained environments.

MCP server providing context usage estimation, conversation compaction, and durable semantic memory via local embeddings and SQLite.

Provides graph-based semantic memory storage and retrieval using vector embeddings and SQLite, enabling relationship creation between memories and similarity-based search through natural language.

A focused local-paper RAG MCP server that imports PDFs, indexes them into SQLite and ChromaDB, and retrieves section-aware paper chunks for use with Claude Code, Codex, or any MCP client.

Enables AI assistants to store and retrieve memories with semantic search capabilities using vector embeddings. Provides persistent memory storage with SQLite backend for context retention across conversations.

A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.

A privacy-preserving local RAG system integrated with MCP, enabling natural language queries over ingested documents and a SQLite database through vector search and local database tools.

RAG document search MCP server that allows AI assistants to search a local document set and retrieve grounded passages via keyword (SQLite FTS5) or semantic (Chroma) backends.

A persistent, human-like memory system for AI companions in LM Studio, using SQLite and ChromaDB for hybrid storage, enabling seamless recall across sessions and models.