AI & MemoryMCP Registrynpm25 toolsA · 85

hmem — Humanlike Memory for AI Agents MCP server

Name: hmem — Humanlike Memory for AI Agents
Author: Bumblebiber

io.github.Bumblebiber · Updated 8mo ago

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

At a glance

hmem — Humanlike Memory for AI Agents is an MCP server in the AI & Memory category. It runs locally over stdio via `npx -y hmem-mcp`, and declares no required credentials. It exposes 25 tools, including hmem, hmem-curate and read_memory. It scores A (85/100) on the MCP Skills Hub health score as of Oct 28, 2026.

Source 24

One-click install

VS Code VS Code Insiders Cursor

Opens your editor and pre-fills the config. Your editor will ask before adding it.

How to install hmem — Humanlike Memory for AI Agents in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install hmem — Humanlike Memory for AI Agents in Claude CodeAnthropic's agentic CLI

Add the server

bash

claude mcp add hmem-mcp -- npx -y hmem-mcp

2
Verify it's connected
bash
```
claude mcp list
```
Sharing it with your team?
Add `--scope project` to write it to .mcp.json in the repository root, which you can commit (with the secrets left as environment variables).
Use it in a session
Ask Claude to use the tools this server exposes (e.g. hmem, hmem-curate, read_memory). It discovers them automatically.

How to install hmem — Humanlike Memory for AI Agents in Claude DesktopDesktop app (MCP config)

Open your MCP config
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

Add this server

json

{
  "mcpServers": {
    "hmem-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ]
    }
  }
}

Restart Claude Desktop
The tools appear under the 🔌 menu once the app reloads.

How to install hmem — Humanlike Memory for AI Agents in CursorAI-first code editor

Create / edit .cursor/mcp.json in your project

json

{
  "mcpServers": {
    "hmem-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ]
    }
  }
}

Enable it
Cursor Settings → MCP → toggle the server on. Tools become available to the Composer/Agent.

How to install hmem — Humanlike Memory for AI Agents in Codex CLIOpenAI coding agent

Add to ~/.codex/config.toml

toml

[mcp_servers.hmem-mcp]
command = "npx"
args = ["-y", "hmem-mcp"]

Run Codex
Codex loads MCP servers from config.toml on startup and exposes their tools to the agent.

How to install hmem — Humanlike Memory for AI Agents in WindsurfCodeium's agentic IDE

Edit ~/.codeium/windsurf/mcp_config.json

json

{
  "mcpServers": {
    "hmem-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ]
    }
  }
}

Reload
Windsurf → Cascade → refresh MCP servers. Cascade can now call the tools.

How to install hmem — Humanlike Memory for AI Agents in ClineVS Code autonomous agent

Open Cline → MCP Servers → Configure
Adds to cline_mcp_settings.json.

Add the server

json

{
  "mcpServers": {
    "hmem-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "hmem-mcp"
      ]
    }
  }
}

How to install hmem — Humanlike Memory for AI Agents in VS CodeCopilot / MCP support

Create .vscode/mcp.json

json

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

Start it
VS Code shows a ▶ Start action above the server entry. Agent mode (Copilot Chat) can then use the tools.

What does hmem — Humanlike Memory for AI Agents need from you?

Configuration only — Declares config values but no credentials.

HMEM_STORAGE_DIRoptionalEnvironment variable
Directory where .hmem SQLite databases are stored. Defaults to ~/.hmem/

Pass secrets through your shell environment rather than committing them into a config file.

What tools does hmem — Humanlike Memory for AI Agents expose? (25)

Taken from the project's README — the server itself was not queried, so check its docs for the exact schemas.

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.
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
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
rename_idRename an entry's ID; rewrites all inbound links
reset_memory_cacheInvalidate the in-memory L1 cache (after raw SQL writes)

What the code can do

Read from the published package source — never executed. Parsed 80 of 100 source files.

This server can run other programs on your machine, read and write files and make network requests.

How it was published

Runs postinstall on install — this executes when you install the package, before you deliberately run anything.
No provenance attestation — there is nothing linking this published artifact to a specific source commit.
1 npm maintainer — a single point of compromise · last published by bumblebiber.

Maintainer of this package and think something here is wrong? Tell us and we'll correct it.

Before you install

Signals gathered from the source repo and package registry — not a safety verdict.

What it can reach

Runs code on your machine — Executes as a local process with your user's permissions, including filesystem access.
Downloads at launch — The package is fetched from its registry each run unless you pin a version.

Last commit 3 months ago — Actively maintained.
24 GitHub stars — Community adoption signal.

Client compatibility

Claude Code: NativeClaude Desktop: NativeCursor: NativeCodex CLI: NativeWindsurf: NativeCline: NativeVS Code: Native

Health score

A · 85

Maintenance, adoption, transparency and packaging — not a safety audit. How this is calculated

Maintenance30%90%
Last commit 100 days ago.
Adoption20%42%
24 stars.
Transparency20%100%
4/4: ✓ public source repo, ✓ declared license, ✓ documentation, ✓ discoverable tool list.
Provenance15%100%
Package and registry entry point at the same repository.
Packaging15%100%
Publishes a pinned version (1.6.7).

Documentation

Full README

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:

| 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.

Excerpt — the full document is on GitHub.

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