"Y Combinator" matching MCP servers:

Agent memory for facts that change. When a user corrects something, Recall replaces the old fact, tells the agent what it replaced, and keeps the old value in a history. Ask what was believed on any date, see every change to a fact, or diff two dates. Runs locally on polign_db, Postgres with pgvector, MongoDB, or Qdrant. Apache 2.0.

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.

A multimodal local memory MCP server that lets AI agents search across text, PDFs, images, audio, and video using Gemini Embedding 2 and a local ChromaDB.

Connects AI assistants to a persistent memory engine with Neo4j knowledge graph and ProMem extraction, enabling long-term context and associative memory across chats and workspaces.

Enables Claude to store and query personal finance transactions using semantic search. Transactions are persisted in ChromaDB and JSON, allowing natural language questions about spending trends and portfolio allocations.

Wraps n8n with an MCP server and vector storage to enable semantic search, management, and execution of automated workflows. It integrates with other tools to make workflows searchable and orchestratable within a larger automation ecosystem.

Enables AI models to store lived queries, source text, embeddings, fractal paths, capsules, and chained digests, then recall them through REST and MCP with a signed append-only continuity record.

Enables MCP clients to curate YouTube watch history and locally search selected transcripts using SQLite FTS5, ChromaDB embeddings, and reciprocal rank fusion, with tools to browse unindexed history and add videos.

Turns local Codex session transcripts into a searchable memory that MCP-compatible coding agents can query to retrieve relevant past sessions or exact commands and fixes.

A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.

MCP server for a self-hosted RAG system that enables AI tools to search and retrieve grounded answers from locally ingested documents via MCP tools, with local embeddings and no API key required.

Enables any MCP-compatible client to persist long-term memories for AI agents, with LLM-based fact extraction, semantic vector search, update/delete/list operations, and queue status checks across sessions.

A local Mem0 MCP server for macOS with a menu bar toggle, storing memories locally in Chroma and using any OpenAI-compatible LLM for fact extraction.

A pluggable and observable modular RAG framework that enables AI assistants to perform semantic search, document Q\&A, and knowledge base retrieval. It supports hybrid search, reranking, and multiple LLM backends through a standardized Model Context Protocol interface.

Enables AI agents to use a neuro-symbolic memory fabric with bi-temporal knowledge graph and holographic VSA, providing tools for adding, searching, temporal queries, auditing, and proving memories with cryptographic provenance and zero-LLM ingest.

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.

Enables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.

Enables AI assistants to crawl websites, extract and store web content with semantic search capabilities using vector embeddings, and retrieve information through natural language queries with tag-based filtering and intelligent content cleaning.

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.

test

Enables AI clients to search and analyze code/docs through a central Pinecone-backed vector store, inspect project inventories and git sync status, rank work by urgency, and perform deep architectural audits via MCP.

A persistent memory layer for Claude Code agents: five MCP tools (recall, remember, session, distill, status) over a markdown vault, SQLite FTS5 and an optional self-hosted Hindsight vector store, so an agent remembers facts, decisions and corrections across sessions.