"Memory and Database Solutions for AI Systems" matching MCP servers:

Provides comprehensive GDPR compliance assessment tools for AI/ML systems, including lawful basis determination, DPIA generation, and data subject rights handling. It also crosswalks GDPR requirements to EU AI Act obligations with AI-specific considerations throughout.

Gives agents a stable interface for durable memory, combining exact, lexical, semantic, and graph search results.

Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.

Provides AI coding agents with a local, Markdown-first shared memory, enabling them to record decisions, failures, progress, and handoffs and retrieve them across sessions via MCP tools.

MCP server that bridges Ableton Live with AI models, enabling real-time project inspection and control such as track overview, device parameters, and audio analysis.

Provides a shared, persistent memory layer for AI assistants, letting them read and write to a local Obsidian vault through MCP with validation, secret rejection, and deduplication.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Local MCP server for Survey Solutions Headquarters workflows, enabling connection profile management, read operations, export handling, and guarded fieldwork/admin actions through natural language.

A persistent, project-scoped memory layer for AI agents, supporting hybrid retrieval (vector, keyword, and tag matching) and sharing across different MCP clients like Claude Code, Qoder, or Cursor.

Enables SQL database interaction with true server-side result paging, runtime-managed connection profiles, and SSH-bridged access for PostgreSQL.

MCP server for querying and managing multiple databases (SQLite, PostgreSQL, MySQL) with read-only mode and schema inspection.

A database MCP server providing 33 tools across PostgreSQL, MySQL, and SQLite, with connection groups, rollback, dump/restore, and schema auto-discovery via natural language.

Provides a local-first secure memory store with encrypted payloads, entity extraction, and vector persistence, exposing read, write, and delete tools with granular capability controls.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.

MCP server providing persistent memory for AI applications, enabling Claude to store and recall memories with hybrid search and knowledge graph.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

A different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_rep

A zero-dependency, file-based persistent memory system for AI agents with tiered memory, Ebbinghaus decay, and keyword retrieval.