"Using PostgreSQL with n8n Workflow Automation" matching MCP servers:

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

A Model Context Protocol server that allows AI assistants to save structured information to n8n via webhooks for storage, indexing, and retrieval.

Enables AI assistants to use Neo4j knowledge graphs and Qdrant vector databases for hybrid reasoning, combining structured facts with semantic search for advanced knowledge management, research analysis, and standardized coding workflows.

Eleven local stdio MCP servers exposing a reproducible EPA SWMM stormwater-modelling workflow: model building, simulation with run manifests and continuity checks, calibration, GIS/QGIS, design storms and climate scenarios, uncertainty analysis, plotting, and modelling memory.

Memory bank with Server as SSH support for central knowledge base

Serves as a universal interface between AI Agents and AnalyticDB PostgreSQL databases, enabling metadata retrieval and SQL execution, with additional capabilities for knowledge graph and LLM memory management.

Enables chat with multiple LLM providers (OpenAI and Anthropic) while maintaining persistent conversation memory. Provides extensible tool framework for various operations including echo functionality and conversation storage/retrieval.

MCP server for alive-analysis. Query, search, and retrieve structured analysis history written with the ALIVE loop (Ask→Look→Investigate→Voice→Evolve), plus dashboard JSON export.

Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.

Connects Bear Notes to AI assistants using semantic search and RAG (Retrieval-Augmented Generation), allowing AI systems to access and understand your personal knowledge base through meaningful search rather than just keyword matching.

Enhances user interaction through a persistent memory system that remembers information across chats and learns from past errors by utilizing a local knowledge graph and lesson management.

Enables AI tools to query context from a local JSON data source via stdio, demonstrating the Model Context Protocol.

A multi-agent AI tutor that delivers personalized lessons, resolves doubts with RAG, generates quizzes, and tracks progress, all accessible via MCP for Claude Desktop.

A Model Context Protocol server that enables AI agents to query a Graphiti knowledge graph and pgvector document store for evidence-backed responses via hybrid search and RAG.

Enables users to query weather, perform web research, and manage a graph database through natural language.

Enables ingesting documents or text to extract entities and relationships via LLM, storing them in Neo4j, and searching or retrieving episodes.

An MCP server that enforces a pedagogical workflow to teach topics step by step, using research, decomposition, and spaced retrieval practice.

An MCP server that provides AI agents with a persistent, PostgreSQL-backed virtual filesystem, supporting session-isolated file operations, cross-session shared stores, and glob/grep search.

Local-first shared memory and coordination layer for AI coding agents, with repository evidence, reservations, handoffs, code graph context, and dashboard review backed by PostgreSQL/pgvector.

Self-hosted MCP server for Obsidian with semantic + full-text search over PostgreSQL/pgvector, wikilink graph traversal, atomic note CRUD, OAuth 2.0, and a self-describing vault guide.