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hmem — Humanlike Memory for AI Agents

by Bumblebiberversion 1.6.7i★ –iRuns on your computer
Registry nameio.github.Bumblebiber/hmem-mcp

Install

Overview

Description from the maker (original English)

Persistent 5-level hierarchical memory for AI agents. SQLite-backed, lazy-loaded.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: HMEM_STORAGE_DIR

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "hmem": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ],
      "env": {
        "HMEM_STORAGE_DIR": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add hmem --env HMEM_STORAGE_DIR=your-value -- npx -y hmem-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "hmem": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ],
      "env": {
        "HMEM_STORAGE_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "hmem": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ],
      "env": {
        "HMEM_STORAGE_DIR": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • HMEM_STORAGE_DIR env
    Directory where .hmem SQLite databases are stored. Defaults to ~/.hmem/

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Bumblebiber, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7266 servers in this category
  • Published to the registry more recently than 3% of the other 7266 servers in this category
  • Basic checks equal to or better than 100% of the other 7266 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is hmem — Humanlike Memory for AI Agents safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.6.7) reached the registry 9 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

hmem — Humanlike Memory for AI Agents

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:

PillarWhat it means
Token efficiency5-level lazy loading — you pay for what you read, never more
PortabilitySame memory across Claude, Gemini, GPT, local models, any MCP client
Efficient storageHierarchical tree structure — context scales with depth, not flat append
No context wasteAuto-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.


How It Works

Level 1  ──  One-line summary          (always loaded — ~5k tokens for 300+ entries)

Level 2 ── Paragraph detail (loaded on demand) Level 3 ── Full context (loaded on demand) Level 4 ── Extended detail (loaded on demand) Level 5 ── Raw/verbatim data (loaded on demand)

At session start, the agent loads Level 1 summaries — one line per memory. When it needs detail, it drills down. Your 300-entry memory costs 5k tokens to overview. A single project costs ~3000 tokens.

Nothing is summarized away. Level 1 is a compressed view, but Levels 2–5 hold the complete original text, word for word, accessible on demand.


Framework Features

Automatic Session Memory

Every conversation is recorded automatically. No "save your work" prompts. No manual checkpoints.

You type  →  Agent responds  →  Stop hook fires  →  Exchange saved to O-entry

→ Linked to active project → Haiku auto-titles the session

Switch projects mid-session? The O-entry switches too. Start a new session on a different device? The next agent sees every exchange from every device — the conversation never dies.

Haiku Background Checkpoints

Every N exchanges (configurable, default 5), a Haiku subagent wakes up in the background. It reads the recent conversation, extracts lessons learned, errors encountered, and decisions made, then writes them to long-term memory — with full MCP tool access. Your main agent is never interrupted.

The checkpoint also writes a handoff note to the project: "Here's what was done, here's what's in progress, here's the next step." The next agent — on any device, any provider — picks up exactly where you left off.

Project-Based, Not Session-Based

Sessions are meaningless. Projects are everything.

  • O-entries are linked to the active project, not the session
  • Checkpoint counters count project exchanges, not session messages
  • load_project shows recent conversations with full context — across all devices
Skills System

hmem ships with a complete skills layer — structured behavior files that agents load on demand. Skills define *how* an agent should do something (debug, write memory, curate entries, handle a session start) — separate from memory, separate from prompts.

npx hmem update-skills    # Pull latest skills to your AI tool's skill directory

Skills are versioned and updated independently. Your agents get smarter without reinstalling. 21 skills ship by default:

SkillTriggers when…
hmem-session-startEvery session start — loads project + surfaces pending git work, open tasks, misrouted O-entries
hmem-using-hmemMeta-skill loaded at session start; defines mandatory memory habits
hmem-readReading from long-term memory (search, prefix filter, find_related, cross-project read)
hmem-writeWriting to hmem — picks prefix, tree location, tags, detects duplicates
hmem-searchUser references something without an ID ("the bug we had", "letzte Woche")
hmem-new-projectCreating a P-entry — handles schema, sections, O-entry linking
hmem-new-errorCreating an E-entry with the strict 5-level scaffold
hmem-activateSwitching active project mid-session, plus fixing misrouted exchanges
hmem-contextLoading specific context when load_project output isn't enough
hmem-recallDispatching a Haiku sub-agent to search hmem
hmem-dispatchDispatching an isolated sub-agent for any search/lookup/calculation
hmem-curateCleaning up an .hmem file (mark obsolete, fix titles, consolidate dupes)
hmem-migrate-oMigrating O-entries to the project-bound 5-level structure
hmem-consolidateMerging session checkpoint summaries into one final O-entry summary
hmem-wipePrep for /clear — save high-value knowledge, update Next Steps
hmem-configView/change memory settings, hooks, sync, checkpoints
hmem-setupFirst-time install of hmem for Claude Code / Gemini CLI / OpenCode
hmem-sync-setupSet up hmem-sync for cross-device sync
hmem-updateUpdate flow — runs npm update -g, syncs skills, applies migrations
hmem-releasePre-publish checklist for hmem itself
hmem-subagentTemplate for sub-agents dispatched by hmem-dispatch
Company Memory

Beyond personal memory, agents can maintain a shared company store — a separate company.hmem that multiple agents and team members can read from. Personal and company memory coexist; agents query both simultaneously.

import { openCompanyMemory } from 'hmem';

const store = openCompanyMemory('/path/to/project');

Embeddable SDK

hmem ships as a fully documented TypeScript SDK — import HmemStore directly into your own agents, tools, or automation pipelines:

import {

HmemStore, openCompanyMemory, resolveHmemPath, loadHmemConfig, saveHmemConfig, DEFAULT_CONFIG, DEFAULT_PREFIXES, formatPrefixList, searchMemory, } from 'hmem'; import type { AgentRole, MemoryEntry, MemoryNode, HmemConfig, SearchResult, SearchOptions, SearchScope, } from 'hmem';

const store = new HmemStore('/path/to/agent.hmem'); const results = searchMemory('/path/to/project', 'auth token bug', { maxResults: 5 });


MCP Tools

hmem ships two MCP servers:

  • `hmem` (daily-use, 13 tools) — read, write, search, project lifecycle.
  • `hmem-curate` (maintenance, 11 tools) — bulk edits, schema migrations, backup/restore. Activate only when curating.
hmem — daily-use server (13)
ToolWhat it does
read_memory5-level lazy read — by ID, prefix, search, time, or tag
write_memoryCreate new entries with title, body, tags, links
append_memoryAdd child nodes to existing entries
update_memoryPatch fields: title, body, tags, irrelevant, links
search_memoryFTS5 full-text search with sub-node attribution
find_relatedFind contextually related entries by tag overlap
load_projectActivate a project + get full briefing + recent sessions
read_projectRead project without activating (comparison/reference)
create_projectScaffold a new project entry with standard schema
list_projectsList all projects with status summary
flush_contextPersist current session context to long-term memory
move_nodesMove a subtree under a different parent (also in curate server)
set_active_deviceRegister and switch between devices
hmem-curate — maintenance server (11)
ToolWhat it does
memory_statsPer-prefix counts, total tokens, favorites, hashtags, stale-list
memory_healthFind broken links, orphan tags, empty entries, dangling chains
export_memoryExport the full .hmem to a portable JSON snapshot
import_memoryImport a JSON snapshot back into a .hmem (destructive)
update_manyBulk-patch a set of entries (irrelevant, tags, body, etc.)
tag_bulkAdd/remove a tag across many entries
tag_renameRename a tag globally across all entries
move_memoryMove an entire entry to a new ID slot
move_nodesMove a subtree under a different parent (also in main server)
rename_idRename an entry's ID; rewrites all inbound links
reset_memory_cacheInvalidate the in-memory L1 cache (after raw SQL writes)

The daily hmem server is registered automatically by hmem init. hmem-curate is opt-in because its tools are destructive — register it when you need to curate:

npx hmem mcp-add-curate          # adds the curate server to Claude Code (user scope)

Then toggle it via /mcp only during curation sessions. For other MCP clients, see Manual setup.


CLI Commands

After npm install -g hmem-mcp, the hmem binary is on PATH.

User-facing
CommandPurpose
hmem initInteractive installer for AI tools (Claude Code, OpenCode, Gemini CLI, Cursor, Windsurf, Cline). Flags: --global / --local / --tools / --dir / --no-example
hmem update-skillsCopy/sync bundled skill files to detected AI tools (called automatically on npm install)
hmem mcp-add-curateRegister the hmem-curate MCP server with Claude Code (opt-in; toggle via /mcp when curating)
hmem doctorDetect stale or deprecated hmem MCP entries in host configs
hmem statsMemory statistics + per-project token estimates + 🔴 4k threshold flagging
hmem setup-hookRe-add the SessionStart hook to Claude Code settings (if removed)
hmem versionShow version
Hook drivers (called by AI tools, not by hand)
CommandWired intoWhat it does
hmem hook-startupUserPromptSubmitFirst-message context injection (memory overview, project list, sync status). Periodic checkpoint reminders. Reads JSON from stdin

| hmem log-exchange | Stop (sync) | Append the latest e

Only the beginning is shown; read the rest in the repo

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