"Wear OS" matching MCP servers:

Memory vaults on your own machine, one per life domain, retrieved with a local BM25 index fused with vector search, where the human decides which vaults a model may answer from. Requires the Mnemosyne OS desktop app running locally; it also reports what other coding agents on the same machine are doing.

Persistent long-term memory for AI coding assistants. Local SQLite, no cloud - 16 MCP tools to store, search, relate, and consolidate typed memories with a confidence lifecycle, hybrid BM25 + semantic search, namespaces, a knowledge graph, and a built-in OS keychain credential vault.

An MCP server that provides AI agents with a persistent, PostgreSQL-backed virtual filesystem, supporting session-isolated file operations, cross-session shared stores, and glob/grep search.

Stops AI agents reopening rejected decisions and forgetting project state. Persistent operational state for agents: decisions, plans, blockers, priorities and patterns that survive across sessions, with enforcement. Not conversation logs.

Local-first memory engine for AI-agent teams: private/team/project ACL, associative recall, and federated sync across nodes. One SQLite file, no LLM required.

Enables code agents to persist and recall durable project knowledge — rules, decisions, procedures, learnings and secrets — across sessions at low context cost, with accent-insensitive PT-BR search, scoped per repository/workspace, and secrets that stay out of the model's context.

Zero context compaction for Claude Code & LLM agents. Persistent memory powered by OS primitives (demand paging, kswapd eviction, mlock pinning). Single SQLite file, MCP-native, multi-agent shared.

Japan Operations OS for AI agents — 14 knowledge domains covering regulations, protocols, calendar, travel, food culture, language, disaster safety, daily life, and persistent memory. 31 MCP tools via REST + Streamable HTTP.

An agent-native teaching OS that gives AI assistants a persistent classroom with courses, lessons, flashcards, and graded exercises via MCP tools.

MCP server for coding-os, a cognitive operating system that gives AI agents memory, structure, and discipline. It exposes tools for project management, task tracking, knowledge graphs, and cognition traces to Claude Code and Codex.

Enables AI agents to search, read, and post to a public technical-knowledge forum, preserving insights and questions across sessions.

Enables source-first orchestration and knowledge indexing with inspectable architectural capabilities through a unified MCP interface, including named-attribute retrieval and atomic knowledge ingestion.

Enables searching imported AI conversations locally across different providers from an MCP client, with read-only access to the archive.

Enables AI agents to find semantic connections and perform text searches in Obsidian vaults using the Smart Connections plugin data.

Graph-level operations for Karpathy LLM Wiki knowledge bases, enabling LLM agents via MCP and humans via CLI to perform structured graph surgery including node/edge CRUD, wikilink management, index rebuilding, and graph metrics.

An MCP server that exposes nine local-first contract-ops CLIs as tools for contract extraction, linting, comparison, conversion, template vaults, and signed-contract vaults, with signing operations strictly human-gated.

Enables AI assistants to read and write Bear notes using SQLite for queries and x-callback-url for safe writes.

An LLM-native decision tracking system that captures unexpected engineering outcomes as 'pressure events' to build a persistent learning foundation. It enables AI assistants to manage cases, log surprises, and promote recurring insights into global or project-specific knowledge bases.

OpenCrab provides an MCP interface for the MetaOntology OS, enabling structured knowledge management across graph, vector, and document databases. It allows agents to perform hybrid searches, manage complex entity relationships, and conduct impact analysis within a multi-space ontological framework.

Enables sophisticated reasoning workflows using graph-based representations for AI models.