"A server for handling vector memory operations" matching MCP servers:

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.

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 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.

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.

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

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

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.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables read-only access to operations and telemetry data across 12 domains through 45 agent tools, including asset inventory, metrics, outages, incidents, and platform settings, with support for AWS SigV4-signed live access and a fixture-based mock mode that runs without a backend.

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

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

Enables comprehensive Windows system management through Claude Desktop, including PowerShell/CMD execution, file operations, archive handling, Git tools, network testing, and system monitoring. Provides a complete toolkit for Windows automation and administration tasks.

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

A Model Context Protocol server that enables enhanced file system operations including reading, writing, copying, moving files with streaming capabilities, directory management, file watching, and change tracking.

Enables AI assistants to run local-first operations checks, review evidence, and manage durable tickets in a SQLite-backed ledger through self-describing MCP tools.

A deterministic SVG perception tool for LLM agents that renders SVGs in a real browser to measure geometry, colors, and spatial constraints, reporting results in grid grammar and W3C color names without guessing.

A secure, vector-based task management server for Claude Desktop using sqlite-vec and sentence-transformers. It provides intelligent task tracking with semantic search capabilities to organize and retrieve development tasks efficiently.

In-memory vector store with TF-IDF vectorization and cosine similarity search, paid per call via x402 micropayments.

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.