"A simple memory MCP server with read/write tools and vector database search" matching MCP servers:

Gives multiple AI coding agents a shared, persistent memory by indexing your code and documentation into a vector database with AST-aware chunking and hybrid semantic/keyword search. Agents can search across projects and store, update, or forget decisions, conventions and lessons learned, all self-hosted with local embeddings.

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.

Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.

Enables AI assistants to securely search and retrieve information from a user's encrypted Joplin notebook via vector memory and MCP.

Enables AI assistants to save and recall information from files or free-form notes using natural language, acting as a long-term memory system.

Integrates RAG into AI agents via MCP Server, supporting multiple vector database technologies for collection management and search operations.

A production-grade MCP server for integrating RAG into AI agents, supporting multiple vector databases with enterprise security and dynamic tool selection.

A shared, persistent MCP memory server for coding agents that enables storing and retrieving project decisions and context across different tools like Claude Code, Codex, and Cursor using semantic vector search.

Persistent memory system for AI agents that records episodic memories with care-weighting and emotional valence, and provides full-text search with temporal chaining and automatic consolidation.

Enables local semantic and full-text search over Markdown notes through MCP, returning relevant passages ranked by meaning and exact terms without requiring a GPU or hosted APIs.

Enables MCP clients to run offline-first web search, fetch and extract web content, build a local hybrid-retrieval knowledge base, and use persistent session memory without external API keys. It also supports intent-aware federated search, semantic reranking, entity graph exploration, and source-cited deep-search answers using local models.

Enables semantic and full-text search over markdown notes, with tools to create, edit, move, and delete notes, all through MCP. Runs entirely locally using Ollama for embeddings and SQLite for storage, working with Obsidian or standalone.

Enables local vector database operations with a Pinecone-compatible API, supporting exact and approximate search, metadata filtering, and semantic text queries over MCP.

Enables integrating RAG into AI agents via MCP server, supporting multiple vector database technologies.

Enables agents to turn documents into vectors, perform approximate nearest neighbor search with HNSW, and filter by metadata via MCP tools for RAG workflows.

An MCP server that extends AI agents' context window by providing tools to store, retrieve, and search memories, allowing agents to maintain history and context across long interactions.

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.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Provides Claude Co-work with MCP tools to search and record organizational memory, including search, read, query, ingest, remember, sync, and status operations.

Provides AI coding agents with persistent memory by recording sessions and normalizing them into a searchable knowledge graph, then delivering relevant context at the start of the next session.