"Headroom open-notebook resources" matching MCP servers:

Open-source MCP server for mem0 that enables memory storage and retrieval using local LLMs, self-hosted, and Docker-free.

将 pgvector 知识库检索能力封装为标准 MCP 服务器，支持 Tools、Resources 和 Prompts，使 Claude Desktop 等客户端可通过自然语言查询知识库。

Enables an LLM to maintain and query durable layered context across sessions, retrieving standing rules, current state, open loops, compiled pages, historical facts, and hard records with explicit conflict handling and abstention when nothing matches.

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

Mem0-compatible persistent memory for AI agents - write facts once, recall them semantically in any session. Self-hostable open-source server, or managed cloud with a remote MCP endpoint at https://deepmem.dev/mcp.

A self-hosted second brain MCP server that enables capturing thoughts with deduplication and semantic search using local embeddings and PostgreSQL with pgvector, all running on your own hardware.

A Model Context Protocol server integration that creates a persistent, searchable working memory for AI-assisted development by enabling automated context recall and knowledge persistence in Chroma, the open-source embedding database.

Stores what AI agents learn — thoughts, decisions, session events and repo facts — in PostgreSQL with pgvector and serves it back through versioned MCP tools with hybrid vector-plus-full-text retrieval and budgeted context packs. Enables multiple agents to share one scoped, role-isolated long-term memory, with deliberate promotion of distilled candidates into durable memories.

Enables semantic search and interaction with Fuel Network documentation and Sway Language resources within AI-powered development environments. It provides high-quality technical context using a local vector database to assist with smart contract development and FuelVM queries.

Enables coding agents to persistently store memories, artifacts, and context discarded by compaction into a searchable store, then automatically recall and inject the items relevant to the current task. It fuses BM25 keyword search with embedding-vector retrieval so differently-phrased memories still match, running as a fail-open sidecar that never blocks the agent session.

A remote MCP server enabling semantic memory across multiple AI platforms like Perplexity Comet and Claude Desktop, with vector embeddings for intelligent retrieval.

Persistent, searchable archive and memory for Claude and ChatGPT. Native macOS MCP server, install from the App Store, no setup. Private, local and open source.

Enables chat.z.ai agents to keep persistent vector memory, auto-enhance context, compress long content to save tokens, and orchestrate smart skills, with every session mirrored to the user's own private GitHub repository as a human-readable cloud database. Exposes these capabilities through a streamable HTTP MCP endpoint with 12 tools, resources, and prompt templates.

Open-source MCP server that gives any LLM long-term memory using a knowledge graph and vector search hybrid. It stores entities, observations, and relationships, enabling semantic recall across sessions with automatic clustering and fail-loud infrastructure.

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.

Provides token-efficient semantic search and document retrieval by indexing PDFs, text, and markdown files into local notebooks using ChromaDB. It enables AI agents to query relevant passages from large documents through local embedding models like Hugging Face or Ollama.

Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.

Enables LLM-based agents and MCP-compatible clients to perform full CRUD operations on FHIR resources, plus AI-powered document ingestion, semantic search, and LOINC terminology lookup. Users can query and manipulate clinical data using natural-language prompts.

Enables LLM-based agents to perform full CRUD operations on FHIR resources, process clinical documents, and use semantic search through MCP-compatible clients using natural-language prompts.

Demonstrates an MCP server with tools, resources, and prompts for a knowledgebase chatbot that queries a ChromaDB vector database for RAG responses.

Enables LLM-based agents and MCP-compatible clients such as Claude Desktop to perform full CRUD operations on FHIR resources, manage documents with semantic vector search and a RAG-ready pipeline, and look up standardized LOINC terminology, all through natural-language prompts. It connects to FHIR-compliant backends like Medplum with OAuth2 authentication and supports Docker or uv deployment.

Enables LLM-based agents and MCP-compatible clients to query and manipulate clinical data through natural-language prompts by providing full CRUD operations on FHIR resources, document ingestion, semantic search, and LOINC terminology lookup.

Enables AI agents and MCP hosts to query locally indexed notes, docs, chat logs, and other files via hybrid vector/BM25 retrieval with section-level citations. Exposes tools, resources, and prompts for searching and asking over the indexed content.

Enables LLM-based agents and MCP-compatible clients to interact with FHIR-compliant backends, performing full CRUD operations on clinical resources through natural-language prompts, with document ingestion, semantic search, and LOINC terminology lookup.

Enables storing and searching personal notes, documents, and snippets using semantic search and RAG capabilities across Claude Desktop, VS Code, and Open WebUI.

Enables LLM-based agents and MCP-compatible clients such as Claude Desktop to query and manipulate clinical data through natural-language prompts, supporting full CRUD operations on FHIR resources, OAuth2-secured FHIR API access, AI-powered document ingestion, and semantic search over embedded documents via Pinecone. It also provides LOINC terminology lookup and validation, plus RAG-ready context retrieval for clinical data workflows.

An open protocol server that implements Anthropic's Model Context Protocol to enable seamless integration between LLM applications and RAG data sources using Sionic AI's Storm Platform.

Enables storing and retrieving semantic memories using LanceDB vector database, with tools to add memories and search by similarity.