"Google Cloud Composer" matching MCP servers:

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.

Simple, fast, long-term memory for your AI chatbots, agents, and MCP tools. Give your LLM a persistent memory using a local SQLite file. No complex database servers, no heavy cloud setup, and zero C++ compilation errors. Works everywhere.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.

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

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Indexes local Markdown/text files into a SQLite database with vector embeddings and provides MCP tools for semantic search without cloud dependencies.

Enhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.

MCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.

Local offline semantic search over documents (txt, md, pdf, docx, pptx, csv). Indexes folders into a LanceDB vector database with multilingual embeddings and supports hybrid vector + keyword search via Reciprocal Rank Fusion. No API keys, no cloud, no Docker required.

Enables Claude Desktop and compatible agents to search, read, add, update, and delete a locally stored, user-curated memory using hybrid retrieval with reranking and Qdrant. All processing runs locally without cloud services or API keys.

Enables making REST API calls to Teradata cloud services including Elastic Compute, Vector Store, OMS, and QueryGrid. Supports custom authentication, Socks5 proxy, and multipart file uploads.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

Persistent memory with knowledge graph visualization, semantic/hybrid search, importance scoring, and cloud sync (S3/R2) for cross-session context management.

Self-hosted MCP-native agent memory server. Gives AI agents persistent, decay-weighted memory via 83 MCP tools — no cloud, full control. RocksDB+HNSW backend. Works with Claude Code, Cursor, and any MCP-compatible agent.

Enables AI agents to interact with Milvus vector databases and Zilliz Cloud through natural language, allowing users to create clusters, manage collections, insert vector data, and perform semantic searches directly from their AI assistants.

A server that provides access to Baidu Cloud Vector Database functionality through the Model Context Protocol, enabling LLM applications to perform vector searches and database operations via natural language.

An MCP server that provides persistent semantic memory for LLMs by building a concept graph with vector search. It enables storing, linking, and retrieving concepts across conversations using Turso vector search and 256-dimensional embeddings.

A service discovery and proxy for MCP servers that enables registration, discovery, and execution of tools on remote MCP servers. Uses vector-based similarity search through Alibaba Cloud services to intelligently route requests to appropriate MCP services.

Headless geometric memory engine for AI agents — no Vector DB, no cloud, no API key. Store and retrieve by meaning using native Vector Symbolic Architecture (NVSA) math over O_DIRECT NVMe mapping. Runs entirely on your machine via MCP.

Mem0-compatible persistent memory for AI agents - write facts once, recall them semantically in any session. Self-hostable open-source server, or managed cloud with a remote MCP endpoint at https://deepmem.dev/mcp.

Persistent memory MCP server for Claude Code — self-hosted, n8n + PostgreSQL + pgvector. Team memory for AI agents with multi-user roles, multi-project namespacing, and hybrid vector + keyword search. No cloud required.

Enables semantic and hybrid search over documents and codebases backed by a Qdrant vector database, with collection management, document CRUD, metadata filtering, and AST-aware code indexing using local or cloud embedding providers. Supports natural language queries, incremental re-indexing of changed files, and custom configurable prompts for guided workflows.

A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.

A cloud-based vector memory service that provides AI assistants with persistent storage, semantic search, and entity management via the Model Context Protocol. It features multi-tenant isolation and bidirectional synchronization with macOS and Google contacts and calendars.

An MCP server that gives Claude (and any MCP client) a shared team knowledge base with semantic search. Backed by ChromaDB and Google Gemini embeddings — works across languages (RU + EN).

Provides a local vector memory store for AI agents with semantic search, offline embeddings, and MCP integration, enabling tools like Claude and Cursor to store and retrieve information without cloud dependencies.

Enables Claude to search and retrieve documents from Azure AI Search indexes with intelligent summarization and analysis using LangGraph workflows and optional Google Gemini integration.

Provides persistent cross-session memory for Claude and other AI agents by storing and automatically retrieving decisions, patterns, and knowledge across conversations and workspaces. It supports self-hosted Redis/Valkey or managed cloud deployments, helping preserve context beyond session restarts and context limits.

Enables Claude and other AI agents to store and retrieve persistent cross-session memories, so decisions, patterns, and knowledge survive context limits, restarts, and compaction. Supports self-hosted Redis/Valkey or managed cloud deployment with workspace organization and semantic search.

Enables AI assistants to semantically search your entire local conversation history from Claude Desktop, ChatGPT and Claude Code, and to retrieve, browse, ingest and report on those conversations. All embeddings run locally, so no cloud, API keys, or data leave your machine.

Serves agents two MCP tools, rag_search and rag_audit, for querying a local, session-aware vector archive of Hermes conversation history and SKILL.md procedures. Enables answering questions like "what did we decide about X three weeks ago" from incremental, per-corpus indexed SQLite data with fully local Ollama embeddings and no cloud keys.

Enables seamless integration with Weaviate vector databases, providing tools for semantic, keyword, and hybrid search across local or cloud instances. It supports schema management, collection retrieval, and multi-tenancy configurations through the Model Context Protocol.

Enables chat.z.ai agents to keep persistent vector memory, auto-enhance context, compress long content to save tokens, and orchestrate smart skills, with every session mirrored to the user's own private GitHub repository as a human-readable cloud database. Exposes these capabilities through a streamable HTTP MCP endpoint with 12 tools, resources, and prompt templates.

An MCP server providing semantic memory storage and retrieval using vector embeddings powered by LanceDB and Google Gemini. It supports multi-tenant isolation and bucket-based organization for managing structured memories through natural language queries.

Provides AI coding agents with persistent, long-term memory through local semantic search and SQLite storage. It enables agents to save and retrieve architectural decisions or project context across different conversation sessions without requiring cloud services.

A native, local-first, multilingual memory server for Codex, Claude Code, and any MCP client, providing hybrid semantic and exact memory recall using Go, Qdrant, and bge-m3. It enables stable, concurrent memory storage and retrieval without Docker or cloud dependencies.

A general-purpose MCP memory server that gives AI clients long-term memory with semantic search, memory classification, priority management, and progressive injection, supporting local JSON or cloud PostgreSQL storage.

A persistent long-term memory system that enables AI clients to store and recall notes, code, and research via semantic search. It utilizes Google Gemini embeddings and Supabase pgvector to provide a secure, searchable 'Second Brain' for MCP-compatible applications.

An MCP server that integrates Turso databases with LLMs, supporting organization and database-level operations with two-level authentication.

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.