Happy slice of mcp pizzaMCP.pizza

hmem

MCP.Pizza Chef: Bumblebiber

Never lose your AI’s memory between sessions or devices. hmem stores all important project details, past conversations, decisions, and open tasks so your AI agent can pick up exactly where it left off. It works with popular AI tools like Cursor, VS Code, Cline, and Windsurf, and requires only a simple configuration step — no API keys or complex setup. Your AI remembers everything efficiently, saving time and tokens every session.

Other

Use This MCP server To

Keep my AI agent remembering all past project decisions Continue AI work seamlessly across multiple devices Automatically save and recall every conversation session Brief my AI on project status without reloading context Have AI recall past bugs, fixes, and open tasks instantly Switch AI providers without losing memory Let AI summarize recent work and next steps automatically

README

hmem — Humanlike Memory for AI Agents

mcpindex

Your AI forgets everything between sessions. hmem fixes that.

One load_project() call. ~3000 tokens. Your agent knows everything important about a project — every past mistake, every decision, every open task — across sessions, devices, and AI providers. No setup per conversation. No "let me re-read the codebase." It just remembers.

AI agent? Skip this file. Read AGENT_SETUP.md — written for you, not for humans.

Naming note (v8.0): This package was briefly published as its-over-9k (1.x). It is now hmem again — pure memory framework, nothing else. The its-over-9k name lives on as a separate project: the o9k token-efficiency meta-framework, which combines skills/plugins like this one. Related: TIM — hmem's next-generation successor (hypergraph memory, CRDT sync).

What This Is

hmem is not a note-taking plugin. It's a memory framework for AI agents — a complete infrastructure layer for persistent, portable, token-efficient knowledge that survives session boundaries, device switches, and provider changes.

Four core guarantees:

Pillar What it means
Token efficiency 5-level lazy loading — you pay for what you read, never more
Portability Same memory across Claude, Gemini, GPT, local models, any MCP client
Efficient storage Hierarchical tree structure — context scales with depth, not flat append
No context waste Auto-session capture + project briefing = zero re-read overhead

The Problem

Every AI session starts from zero. Your agent asks the same questions, makes the same mistakes, contradicts last week's decisions, and wastes 50k tokens loading context it already processed yesterday.

You've tried workarounds — CLAUDE.md files, custom prompts, manually pasting context. They don't scale. You have 10 projects. You switch between 3 devices. You use different AI tools.

The Solution

You:    "Load project"
Agent:  [calls load_project("P0048") — 3000 tokens]
Agent:  "v1.2.9, TypeScript/SQLite/npm. 3 open bugs, 8 roadmap items.
         Last session: rebrand complete, rename_id bug fixed (89 changes).
         Next: O-Entry Auto-Purge. What's the focus today?"

That's it. 3000 tokens for a complete project briefing. The agent knows the stack, the architecture, the open bugs, the recent decisions, and exactly where you left off — even if "you" was a different AI on a different machine yesterday.

hmem FAQ

Can I use this to keep my AI agent remembering past project details?
Yes — hmem lets your AI remember all important project info across sessions and devices, so it never starts from zero again.
Can I use this to have my AI continue work seamlessly on different devices?
Yes — your AI’s memory travels with you, syncing across devices and AI tools like Claude, Gemini, and GPT.
Which apps support hmem?
It works with MCP clients like Cursor, VS Code, Cline, and Windsurf.
Do I need an API key or special account to use hmem?
No — hmem runs as a server you configure; no API keys are required.
How hard is it to set up hmem?
Setup is copy-paste-config — you add a small snippet to your AI client’s settings.
Will hmem slow down my AI sessions?
No — it loads memory efficiently, only fetching details when needed to save tokens.