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

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 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 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.

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.

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

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.

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.

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 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.

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.

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.

An MCP server that provides semantic memory on top of Qdrant vector search, with tools to store and retrieve information using embeddings.

Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.