"Search for the term pHqghUme" matching MCP servers:

Engram is a local-first memory server that lets AI agents remember things across sessions with zero cloud dependencies.

A local MCP server that gives AI assistants persistent long-term memory using a biological neural architecture with cortex, synapses, and hippocampus.

Provides AI assistants with long-term semantic memory capabilities through local vector-based storage. Enables storing, recalling, and managing information across sessions with complete privacy using ChromaDB, with no data ever leaving your machine.

Long-term memory for Claude Desktop and Claude Code over plain markdown files on your machine. It returns nothing when the corpus has no answer, naming the terms that appear nowhere — no least-bad match dressed as an answer. And it verifies cited commit SHAs against git, so an agent can tell what actually shipped from what was only discussed and abandoned. Local; nothing is transmitted.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Model-agnostic, agent-ready Obsidian MCP server with RBAC, SLSA provenance, and native search. 163 tools across 31 domains, multi-vault, pluggable embeddings.

Local MCP server that manages a job search using Markdown files, enabling skills tracking, gap analysis, and offer pipeline management via Claude.

Enables coding agents to use an Obsidian vault as long-term memory, with search, reading, and writing of notes, plus automatic capture of learnings that are propagated to MOCs, daily notes, and the knowledge index with git commits.

Enables AI coding agents to persist structured long-term memory (gotchas, architecture, API notes) in a .context folder and sync across devices and agents via Git.

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.

Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.

An intelligent memory MCP server that provides AI applications with semantic search, entity extraction, and knowledge graph capabilities using local Redis caching and optional cloud sync. It enables LLMs to store and retrieve long-term context across sessions with high-performance multi-tier storage.

A shared long-term memory server for MCP clients, enabling you to persist and retrieve decisions, gotchas, and context across sessions with a web dashboard.

Read, search, and write Obsidian vault notes via Team Relay collaborative server. Supports shared folders, real-time sync, and AI agent access.

A FastMCP server that exposes mem0 long-term memory to MCP clients over SSE, enabling AI agents to store and recall facts persistently.

Provides persistent long-term memory for AI agents through semantic search and automated knowledge graph extraction. It enables agents to store, recall, and reason over facts, preferences, and relationships across multiple conversations and sessions.

Provides AI agents with persistent long-term memory over MCP, extracting durable facts from conversations, storing them as plain files, and injecting ranked memories into later sessions via recall and search tools.

MemOS (Memory Operating System) is a memory management operating system designed for AI applications. Its goal is: to enable your AI system to have long-term memory like a human, not only remembering what users have said but also actively invoking, updating, and scheduling these memories.

Enables AI coding assistants to store and retrieve persistent long-term memory across sessions, remembering project preferences, build steps, and architecture decisions.

Enables agents to store and recall long-term memories by semantic relevance rather than exact keywords, with writes gated by verbatim evidence and separate memory stores per agent mode.