"Creating a Postgres MCP server in Python" matching MCP servers:

A simple note-taking MCP server that allows adding notes and summarizing them via a prompt.

Persistent memory and pattern learning for AI coding assistants via MCP.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables natural language queries to a Neo4j knowledge graph built from PostgreSQL data, integrating with Claude Desktop and REST API.

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.

A sophisticated AI-powered server providing intelligent, context-aware conversational capabilities with role-based advisors, semantic memory, multi-LLM support, and web browsing.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

A Python-based server providing persistent memory management for AI models with SQLite and Markdown dual backend storage. It features full-text search, RAG-enhanced querying, and cross-project knowledge sharing for integration with Claude, Cursor, and Rovo Dev.

Enables personal knowledge management through Claude Desktop, allowing users to capture thoughts, connect ideas, and reflect on thinking changes via natural conversation.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

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

Project management and session memory for AI agents that provides 13 tools for tracking projects, tasks, and context during AI coding sessions.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

Provides knowledge graph functionality for managing entities, relations, and observations in memory with strict validation rules to maintain data consistency.

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

MCP server that exposes Open WebUI Knowledge Bases as tools and resources, enabling AI assistants to search and access knowledge bases.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Enables AI models to securely and structurally assimilate experiences, thoughts, and reasoning processes across different AI systems, fostering intellectual collaboration and evolutionary dialogue.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.