"Information on GraphRAG, AutoGen, AG2, Daydream, and HybridRAG" matching MCP servers:

A native macOS app with a read-only stdio MCP server for searching a local work timeline by app, document, optional Chrome page context and time. Requires Apple silicon and macOS 15+; it runs on the user’s Mac, rather than in Docker or as a hosted endpoint.

Enables persona-grounded retrieval-augmented generation over your own documents by building a Neo4j knowledge graph and exposing tools for search, context, persona briefs, and graph queries via MCP.

Connects Claude Code to a local GraphRAG knowledge base (LightRAG + Ollama) for private, offline document retrieval. Provides tools to search, add documents, and check the RAG status, enabling Claude to reason over user's own notes, code, and papers without cloud dependencies.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

An MCP server that builds a knowledge graph from code and text documents, enabling Q\&A and implementation planning via tools like graph_create, graph_plan, and graph_query.

Enables building and querying a Markdown-based knowledge graph with hybrid vector, keyword, and graph retrieval, community detection, and evidence-grounded answers through isolated workspaces.

A Model Context Protocol server implementing Graph RAG with local, embedded Knowledge Graph using FAISS for vector search and NetworkX for graph traversal, enabling hybrid retrieval through anchor discovery and relationship expansion.

Enables building and querying knowledge graphs by ingesting documents into Neo4j using Gemini for entity extraction, and exposes MCP tools for graph health, document ingestion, and knowledge base querying.

Enables agents to run hybrid retrieval, build token-budgeted attributed context packs, answer questions with citation and groundedness-gated release, explore temporal graph entities, evaluate claim support, and ingest screened, embedded documents through typed MCP tools.

Exposes six Neo4j graph tools for tracing data lineage, checking compliance boundaries, and managing audit findings in a simulated infrastructure sandbox.

Enables enterprise document retrieval using graph-based reasoning and knowledge graphs. Allows agents to search and extract information from scattered documents through structured entity and relationship extraction.

A local-first MCP server that transforms crypto whitepapers into a knowledge graph and vector corpus, enabling entity-filtered RAG question answering with optional knowledge graph enrichment.

On Board is a local MCP server that gives multiple AI agents and IDEs a shared project memory, ticket queue, and handoff history, so agents can seamlessly continue each other's work. It supports agent-to-agent wake events, enabling autonomous workflows like reject-fix-resubmit cycles without human relay.

Enables AI agents to access detailed research findings, implementation patterns, and best practices for building graph-enhanced retrieval-augmented generation systems.

Enables graph-powered semantic search over the Grand Débat National dataset, providing fast, transparent answers with provenance tracing back to citizen contributions.

Enables agents to search a document collection by query, fetch a complete document by its ID, and generate bounded summaries of supplied text, with Pydantic-validated tool inputs.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Enables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.

Enables Claude and ChatGPT to share a single study-progress database for interview prep, coursework, and spaced repetition, with per-user data isolation and server-side SM-2 scheduling for due reviews and study plans.

MCP server for Ultrathink: save and search your knowledge base from Claude Desktop, Claude Code, and any MCP client.

Enables researchers to turn property-history questions into reviewable evidence dossiers by gathering sources, tracing people and places, linking conclusions to supporting or opposing passages, and exporting portable backups.

TheWeave is a markdown-native memory architecture for Claude and any MCP-aware agent. Your agent's memory lives as plain .md files you own: a 5-verb MCP core over the vault, query-driven PageRank retrieval, bi-temporal facts (valid_from / valid_until), and a persona-as-vault model. No database and no embeddings server. The files are the memory, inspectable in your editor and versionable in git.

Enables local MCP clients to list, read, write, and search notes in an Obsidian vault via an HTTP server running inside Obsidian.

AutoMem MCP provides persistent memory for AI assistants, enabling them to recall information across conversations and platforms with graph-vector retrieval.

MCP server for DocsMint, an AI-native knowledge workspace. It gives AI agents scoped access to search, read, create, update and organize documents using hybrid retrieval and GraphRAG.

The infrastructure for AI teams: a self-hosted server that gives a fleet of agents shared semantic memory, tasks, direct messages, and session handoff. Any agent that speaks HTTP participates: Claude Code, AutoGen, raw API scripts, anything.

Zero-trust, air-gapped Enterprise GraphRAG MCP server. Build knowledge graphs from local documents and run multi-hop, citation-grounded queries entirely offline with Ollama.

An MCP server for managing agent memory using provenance tracking, decay-weighted retrieval, and feedback loops to optimize information recall. It allows agents to store insights in a local SQLite database and rank them based on confidence, age, and usefulness.

Provides persistent, searchable memory for AI agents, enabling them to retain, recall, and reflect on information across conversations.

Enables AI assistants to maintain persistent project context across sessions by storing and retrieving structured information in markdown files organized in a memory bank directory.