"A server for local file operations on Windows that integrates with Ollama" matching MCP servers:

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

A read-only MCP server that provides document awareness for agents by parsing local files into structured profiles, blocks, chunks, and search results, enabling agents to understand and cite document content without dealing with raw file formats.

Enables persistent, project-scoped memory for OpenAI Codex Desktop and CLI on Windows, capturing prompts and responses through managed hooks, with federated recall across projects and optional local graph extraction.

Local-only MCP server for saving and restoring conversation context across AI agent sessions using JSON files on disk.

Gives AI agents a persistent vector memory by embedding text with Ollama and storing it in Qdrant, so they can save, batch-save, semantically search with payload filters, and delete memories across collections.

Local-first temporal knowledge graph with six read-only MCP retrieval tools. Supports Ollama and embedded LadybugDB; additions use a separate CLI proposal queue with human approval and explicit application.

A local-first MCP memory server that gives AI coding agents persistent memory with hybrid semantic and keyword retrieval, working fully offline.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

An MCP server that allows Claude and other LLMs to manage persistent memories across conversations through text file storage, enabling commands to add, search, delete and list memory entries.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Local MCP server to access Obsidian vault via the obsidian-local-rest-api, with semantic search and project memory using Ollama embeddings.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

A fully offline local RAG server that utilizes ChromaDB and Ollama to index and query PDF, text, and Markdown documents. It allows users to manage local knowledge bases and perform semantic searches with AI-generated responses.

Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.

Enables AI coding assistants to recall past lessons, bugs, and conventions from local git history with sub-5ms latency and full privacy.

A local-first MCP server providing persistent, project-scoped and global memory for AI agents using ChromaDB and Ollama embeddings, fully offline.

Provides read-only access to your local Zotero library via the Zotero Local API, enabling search, retrieval, and bibliography generation without API keys.

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.

A local-first MCP server for PageIndex — the vectorless, reasoning-based RAG framework. It lets local AI agents index and query local PDF and Markdown documents through a self-hosted PageIndex installation, without requiring any PageIndex cloud API key.

An MCP server that provides AI tools with relational tech principles, patterns, and the Studio library to guide builders in creating community-centered tools using embedded design.