Embedded NoSQL Database for TypeScript & AI Agents. Zero-native document database with ACID transactions & in-memory cache — verified on Node.js & Bun.

MCP server for Stoolap embedded SQL database

Extensible MCP server for semantic code search with plugin architecture supporting multiple embedding providers, vector databases, and data sources.

A fast embedded graph database for AI applications and graph analytics

MCP server for the Python Projects to provide context for large databases via semantic precise meaningful vector embeddings

.NET SQL Agent MCP server featuring raw SQL input, strict AST validation, and an embedded Admin UI. Eliminates LLM hallucinations and security risks across 6 major databases.

Persistent memory for AI agents using git refs. MCP server + Claude Code skill. No vector database, no embeddings, no daemon — branches are scopes, push/fetch is sync.

An intelligent code memory system that leverages vector embeddings, structured databases, and knowledge graphs to store, retrieve, and analyze code patterns with semantic search capabilities, quality metrics, and relationship modeling. Designed to enhance programming workflows through contextual recall of best practices, algorithms, and solutions.

Local-first persistent memory for AI coding agents. Indexes git history, shell commands with exit codes, project docs and (opt-in) assistant transcripts into an embedded SQLite database, then serves back a ranked, token-budgeted context slice over MCP or

A local MCP server that gives code agents (Claude Code, Antigravity, and any other MCP client) a shared, persistent graph memory, backed by Kuzu — an embedded database purpose-built for graph storage and graph search — plus a live web dashboard showing the memory graph and an activity log.

An LLM wiki your coding agent writes — agent memory you can open and read. The agent does the writing; easymem does the storage, full-text search and knowledge graph. No embedding model, no vector database.

Ontology-driven codebase indexer and query service. Parses C#, TypeScript/JS, Go, Python, and SQL into an embedded graph database. Exposes Model Context Protocol (MCP) tools for AI agents to perform deep static analysis, cross-project call-graph tracing, and data lineage mapping.

Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.

A short-term planning system for engineering teams — keep engineers focused, run daily sprints, and track unplanned work. Built on GitHub Projects v2 (no database of its own): one Go binary with an embedded React UI, REST API, MCP server for AI agents, and live board updates over WebSocket.

Local RAG server for Claude Code, Cursor and other MCP clients: hybrid BM25 + vector search with cross-encoder reranking on by default, 13 MCP tools, 33 file types including code, re-indexing on file changes. One Python process with embedded ChromaDB and FastEmbed ONNX: no Docker, database server or API key; offline after the first model download.

Embedded knowledge base with 114 MCP tools, hybrid BM25+vector search and RAG. One Go binary.

MCP server for Zvec vector database

Knowledge management server

MCP server for agent memory over HexxlaDB—ring retrieval, embeddings + lexical search, seams, facets, YAML persistence policy, localhost HTTP transport.

Local-first encrypted memory for AI agents

MCP server with FAISS local vector database for RAG

Mnemo core — persistent semantic memory engine (ONNX embeddings + HNSW + sql.js)

Local memory for AI agents, with evidence, corrections and history. One DuckDB file.

Semantic memory server for AI agent teams. MCP server, ChromaDB or Qdrant, local ONNX embeddings.

Free SQLite MCP server — hosted cloud SQLite for Claude, ChatGPT and Cursor, no file to manage

Nautobot Model Context Protocol (MCP) Server - Contains STDIO and HTTP Deployments with Embedding Search and RAG.

Codai Better Database - High-Performance Vector Memory System with HPKV-inspired architecture and MCP server

Persistent, local-first memory for AI agents. No cloud, no Docker, no external services – just CozoDB embedded in Node.js.

Universal Qdrant MCP server with automatic dimension detection and search integrity. Supports any OpenAI-compatible embedding API (OpenAI, Qwen, Gemini, Ollama).

Rust graph-vector database: OpenCypher (99.9% of evaluated TCK scenarios pass), vector search, graph algorithms, RESP + HTTP. LDBC SNB Interactive and BI complete; 1B edges on one machine.

High-performance, SIMD-accelerated vector search engine and MCP server for .NET 10. Optimized for LLM embeddings and semantic search.

UI App for ChromaDB to build local RAG pipelines. Built for macOS, native Swift. Gives you full control over embedding for RAG.

MCP server for persistent memory using Qdrant vector store

RAG agent that ingests GitHub's official documentation, embeds it with local Ollama LLMs, and serves semantic search via a PostgreSQL pgvector database.

A Model Context Protocol (MCP) server that provides RAG capabilities to Cursor using Qdrant.

Xavier — Rust vector memory & knowledge graph for AI agents (HTTP/CLI/MCP). Powers SWAL communal context

MCP persistent memory service — let AI assistants remember across sessions. Powered by Qdrant vector search + official embeddings (via OpenAI-compatible relay or Gemini Vertex AI) with Ollama fallback and hybrid retrieval (vector + BM25).

A local-first vector database with an extra dimension: intent. The same corpus and query return different results depending on what the asker is doing; built for agent memory

MCP server that turns your Reddit content into a searchable knowledge base.

Local-first persistent memory system for AI agents with hybrid search, graph reasoning, and MCP integration

API service to search vectorized Cursor IDE chat history using LanceDB and Ollama

Cognitive memory database for AI agents — consolidates duplicates, detects contradictions, and fades stale memories via temporal decay, written in Rust (Apache-2.0) as a library, MCP server, or HTTP cluster.

Mindreader gives AI agents a memory they must curate, not a history they can search.

Self-hosted code intelligence hub. Uses VoyageAI embeddings for semantic searching code. CLI + server modes, MCP for Claude Desktop, FTS/regex, git history search, and SCIP searches. Automatic inter repository domain mapping. Automatic wiki generation for repos containing .md files and images.

MCP server for LogosDB: local semantic memory, file indexing, and hybrid search for Claude Code, Cursor, and other MCP clients

A Model Context Protocol (MCP) server that provides agentic search capabilities with support for vector search using Qdrant, full-text search using TiDB, or both combined.

Agent memory with a recall button - Every memory knows its sources. Recall a bad source, and everything learned from it goes too

MCP server for Qdrant: manage Qdrant Cloud (clusters, backups, keys) and your data (collections, hybrid search, text memory) from Claude Code, Codex, Cursor or any MCP client.

Persistent memory graph for AI agents. Facts, decisions, entities, and relationships that survive across sessions, tools, and providers. MCP server — works with Claude, Cursor, ChatGPT, and any MCP client.

MCP server for local vector search over portable RVF files: Claude Code, Cursor and other MCP clients create, fill and query HNSW-indexed .rvf stores over stdio or SSE.

Local memory infrastructure for AI agents. Typed vaults (semantic, procedural and episodic) with document indexing, skill management, bi-temporal facts, and autonomous agent access via MCP, HTTP API, or CLI. Multi-vault architecture, zero cloud dependencies.

Read-only PostgreSQL MCP server with multi-database semantic routing.

Memory database for LLM agents — persistent semantic + keyword memory, project namespacing, served over HTTP REST and MCP.

MCP server for vector database operations - Pinecone, Weaviate, Qdrant, and ChromaDB

Local semantic search over your personal knowledge bases. Index Obsidian notes, SQLite databases, and plaintext files into ChromaDB, exposed to Claude via an MCP server.

A context database for LLMs: structured storage and hybrid retrieval (BM25 + vectors, RRF-fused, token-budgeted) over SQLite, exposed to Claude as an MCP server

CP server exposing Qdrant vector DB store and GPU-accelerated embedding pipeline as tools for Claude Code, Claude.ai, and OpenClaw.

Local-first agentic RAG with citations - hybrid search, reranking and multimodal retrieval over your own documents, no database server required - Minimal dependencies

A semantic search engine that transforms your documents into an intelligent, searchable knowledge base using vector embeddings and AI

Neo4j knowledge graph MCP server with temporal versioning, semantic search, and vector embeddings for Claude AI