"Using Cline as a Knowledge Graph for Coding" matching MCP servers:

Provides LLM clients with a persistent, scalable knowledge graph memory system that supports semantic retrieval, contextual recall, and temporal awareness.

Enables building and querying vector-based knowledge graphs with node and edge management and semantic search.

Exposes a Neo4j knowledge-graph index over personal markdown notes to LLM agents via MCP, enabling semantic search, tag navigation, and relationship discovery.

An MCP server that enables semantic search over a private knowledge base via pgvector, with GitHub OAuth authentication and a RAG pipeline.

A local-first Graph-RAG system combining ChromaDB with metadata-based graph relationships and Gemini 2.5 Flash for intelligent Q&A over Obsidian vaults, supporting MCP clients like Claude Desktop, Cursor, and Raycast.

An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.

Provides Claude Desktop with persistent, structured memory and semantic search via a local SQLite knowledge graph, plus a web UI for visualization and management.

Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.

Local-first memory for Claude Code and any MCP client: hybrid vector + keyword search and a bi-temporal knowledge graph in one SQLite file. Local embeddings, no API key, $0/token.

Enables MCP hosts to query a layered, read-only memory backend that fuses lexical, CJK, vector, and graph retrieval into explainable ranked results with relationships.

Enables private, self-hosted storage and semantic/full-text retrieval of durable facts extracted from conversations, with provenance, corrections, conflict records, and scoped bearer authentication.

This server enables LLM agents to store, search, and traverse persistent semantic knowledge-graph memories through MCP, with duplicate refusal, contradiction marking, TTL handling, versioning, and isolated per-owner graphs.

This server enables AI assistants (CLINE, Cursor, Windsurf, Claude Desktop) to share a common knowledge base through Retrieval Augmented Generation (RAG), providing consistent information access across multiple tools.

A read-only Model Context Protocol server that exposes a semantic knowledge base to AI agents via 27 tools. It enables querying of documents and data integrated from sources like Notion, SharePoint, HubSpot, and Slack.

Indexes YouTube channel transcripts and exposes semantic search and excerpt retrieval to MCP clients, returning cited fragments with timestamps and video links. Supports channel/date filtering and subtitle-first transcription with Whisper fallback.

A personal knowledge MCP server with vector database for the Opencode ecosystem. It enables semantic search, tag organization, and session memory logging, all running locally without external API keys.

Enables querying MaxKB knowledge bases through MCP, supporting vector, hybrid, and full-text retrieval of relevant segments, as well as listing knowledge bases, documents, and workspaces.

Enables Claude to recall and query past conversations through a local knowledge graph and vector index, with tools for searching memory, retrieving related entities, and adding new memories.

Provides a project memory bank and RAG context provider for enhanced code understanding and management through vector embeddings, integrated with RooCode and Cline.

A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.

Enables AI assistants to build and query temporally-aware knowledge graphs from conversations and data. Supports adding episodes, searching entities and facts, and maintaining persistent memory across interactions.

Enables semantic search and document management with support for text, PDF, and image uploads using your own Supabase database and OpenAI API keys. Supports multi-tenant deployment on Cloudflare Workers or local hosting.

Enables storage and semantic search of facts, and interactive 3D visualization of embeddings via MCP tools.

Enables indexing a Markdown knowledge base with local sentence embeddings and serving retrieval over MCP, allowing LLM clients to query the indexed knowledge.

Enables storing, querying, and managing decision traces with semantic search using Voyage AI embeddings and ChromaDB. Supports outcome tracking and category filtering for software development decisions.

A local knowledge base system based on ChromaDB that supports automatic chunking, vector storage, and efficient similarity retrieval of txt and pdf documents, with MCP protocol support allowing AI assistants to directly access knowledge management functions.

Exposes fully offline semantic search over a local ChromaDB knowledge base of chunked notes and source code, letting agents run a search tool that returns matching passages with sources and relevance scores, plus a tool that lists the available content collections. Embeddings are computed locally with fastembed, so no cloud API or network connection is required.

Enables AI-powered document analysis and querying for project documentation using vector embeddings stored in Redis. Supports document upload, context-aware Q\&A, automatic test case generation, and requirements traceability through OpenAI integration.

Enables semantic search across multiple knowledge datasets using FAISS vector embeddings, allowing natural language queries to find relevant documents with fast retrieval.

Enables storing and retrieving information using vector embeddings with semantic search capabilities. Integrates with the AI Embeddings API to automatically generate embeddings for content and perform similarity-based searches through natural language queries.

Enables managing knowledge bases in Open WebUI and retrieving relevant fragments from Qdrant-stored knowledge through an MCP client.

A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.

Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.

A server implementation that allows secure communication between MCP clients and privateGPT, enabling users to chat with privateGPT using knowledge bases and manage sources, groups, and users through a standardized Model Context Protocol.

An integration server implementing the Model Context Protocol that enables LLM applications to interact with Milvus vector database functionality, allowing vector search, collection management, and data operations through natural language.

An MCP server that integrates Crawl4AI with Supabase to enable AI agents to crawl websites, store content in a vector database, and perform RAG queries.

Enables semantic search across text documents using vector embeddings stored in PostgreSQL. Provides multiple search modalities including semantic similarity, question/answer, and style-based search through a retrieval-augmented generation system.

A retrieval-only MCP server that searches a vectorized Wangsu Terraform Provider documentation knowledge base and returns relevant snippets with rules to guide the caller's LLM in generating accurate, provider-specific answers. It offloads all inference to the calling client, as it does not invoke any LLM or require API keys on the server side.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.