LLM Memory Notes – semantic memory layer for AI agents (MCP)
LLM Memory Notes – semantic memory layer for AI agents (MCP)
Hi HN, I’ve been building LLM Memory Notes (LLMMN), a hosted semantic memory layer that gives AI agents persistent, searchable memory via the Model Context Protocol (MCP). You can create “Memories” (containers) and save “Notes” (entries) that are indexed using AI embeddings; agents can then search by meaning (e.g., searching “database error” will find relevant notes about “PostgreSQL timeout” or “MySQL connection failed”) . To get started, sign up on llm‑memory.com, create a Memory in the web UI, and obtain your API token . Add the server to your .mcp.json and use your MCP client’s ReadMcpResourceTool and AddNoteTool to query or add notes ; the docs have a full Quick Start guide . LLMMN is a hosted service (not open source), currently in a public beta with free early access; final pricing is still being determined . We’d love feedback from anyone building agent tooling or experimenting with persistent context layers!
Share cardActual performance
Launch Intel predictions
Analyze your own launch →Correct prediction on native model
Similar products
Governed Agent-agnostic Memory Layer.
Built a memory layer that stops AI agents from forgetting everything
I made an open-source memory layer for agents
I made an Neuroscience-Inspired Memory Layer for LLM Applications
An on-chain memory layer for X
AI memory layer for smarter agents
Continuum – an open, local-first memory layer for your Mac
Memory Layer for AI Agents, Persistent Memory
A local AI memory layer for your Mac
Unlost – Semantic search your memory