"Hybrid Memory Models Combining Relational, Graph, and RAG Approaches" matching MCP servers:

An MCP server that wraps the @apitomy/data-models library for querying, validating, and editing OpenAPI and AsyncAPI documents.

Provides AI agents and teams with self-hosted adaptive memory, combining hybrid search and a self-improving neural memory graph to enable precise recall and serendipitous discovery across MCP-compatible clients.

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.

Enables AI agents to synthesize and update hierarchical episodic and semantic user profile memory graphs through MCP, using deterministic zero-dependency Python processing with JSON Schema validation.

Exposes a RAG document-search API as MCP tools (rag_health, rag_ingest, rag_query), enabling agents to index and search markdown documents with cited results through natural language.

Provides persistent memory for Claude with hierarchical categorization, cross-corpus recall, session journals, and customizable persona, enabling memory continuity across sessions.

AI memory server with 53 tools. Knowledge Graph, semantic search, session tracking, multi-agent support.

Enables local knowledge base management with retrieval-augmented generation (RAG), providing semantic search, document reading, listing, and Q&A via MCP tools and REST endpoints, all running locally without cloud dependencies.

A portable, user-owned memory for MCP-capable agents in VS Code, enabling cross-agent recall and memory creation.

Enables fusing and reranking results from multiple search or retrieval sources using Reciprocal Rank Fusion and hybrid rank fusion. It provides deterministic scoring, robust parsing, and structured JSON output for autonomous agent workflows.

Minimal local RAG stack with an MCP server that provides document search for any agent.

Provides persistent long-term memory for AI coding agents by storing entities, relations, and observations across different sessions. It enables users to manage and query structured knowledge like coding preferences, project patterns, and technical solutions via a graph-based storage system.

Exposes local memory vaults as 6 MCP tools over stdio, enabling clients like Windsurf, Zed, and Claude Desktop to read and write memory.

Dynamic semantic memory for AI assistants that persists across sessions and clients, enabling natural language recall and project-scoped isolation.

Enables async-first working practices by providing tools to draft decision docs, convert meetings to async artifacts, score status updates, and triage sync vs async tasks. It also offers reference tools for the Open and Async book's principles and coaching prompts.

Enhanced MCP knowledge graph memory server with cloud persistence and semantic search, acting as a drop-in replacement for the standard memory server.

A tiny local MCP server that learns a user's conversational style, catchphrases, dialect markers, emoji habits, tone preferences, and concrete collaboration preferences without storing private memories.

A Model Context Protocol server for indexing and searching local document collections with hybrid BM25+vector search, optimized for Korean and English, supporting DOCX, PDF, audio, and more.

Enables AI agents to persist, recall, expand, trace, and inspect durable memories with provenance through MCP tools.