"Building a solid memory base for AI development" matching MCP servers:

Enables natural-language Q&A over multi-source documents (PDF/Markdown/web) with a self-reflective retrieval-augmented agent that cites sources, retries on poor retrieval or grounding, and supports multi-turn memory.

Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.

A persistent, searchable memory layer for AI assistants, exposed as an MCP server. It supports CRUD, namespaces, hybrid ranked search, relationships, chat-context assembly, import/export, and optional LLM-powered auto-extraction.

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.

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

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Provides read-only hybrid RAG search and discovery over a local-first AI knowledge corpus, enabling semantic and keyword search, browse, digest, and status tools.

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 AI-assisted long-term memory storage and retrieval with exact-phrase and semantic hybrid search, enabling save, read, delete, and admin recovery via MCP tools.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

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.

Munin is a high-performance, pragmatic memory layer for AI agents (Cursor, Claude Code, OpenClaw, Gemini CLI,...). Unlike other solutions, Munin focuses on developer productivity with: * Multi-Project Support: Isolate memories into separate "brains" (Context Cores). * GraphRAG: Automatically builds a knowledge graph from your context. * Sub-200ms Search: Blazing fast Hybrid & Semantic

Provides semantic search and data retrieval capabilities over a knowledge base with multiple tools including keyword search, category filtering, and ID-based lookup with in-memory caching.

MCP server providing permanent, intelligent memory for AI coding assistants with retrieval, Hebbian learning, and governance, plus personal memory modules for accounts, habits, experiences, knowledge, and lifestyle. Enables AI assistants to recall project context across sessions and manage personal data securely.

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

MCP server providing durable Substrate organizational memory tools, including search, read, query, ingest, remember, and sync. It integrates with Codex to automatically capture completed turns and session boundaries for persistent memory.

Provides cited retrieval over local-first knowledge sources (TXT, CSV, Google Sheets, Zoho Mail, MBOX) via SQLite FTS5, with tools for searching and checking source status, returning evidence links instead of generated answers.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

Provides a production-ready Model Context Protocol server with dual STDIO and Streamable HTTP transports, enabling file operations, memory, database queries, RAG, web search, GitHub integration, background tasks, and prompt-based workflows.

A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents, turning raw information into refined, retrievable knowledge that compounds intelligence over time.