# hmem — Humanlike Memory for AI Agents MCP server

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.

## Install

### How to install hmem — Humanlike Memory for AI Agents in Claude Code

1. Add the server

```bash
claude mcp add hmem-mcp -- npx -y hmem-mcp
```

2. Verify it's connected

```bash
claude mcp list
```

3. 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).

4. 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 Desktop

1. Open your MCP config

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

2. Add this server

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

3. Restart Claude Desktop

   The tools appear under the 🔌 menu once the app reloads.

### How to install hmem — Humanlike Memory for AI Agents in Cursor

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

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

2. 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 CLI

1. Add to ~/.codex/config.toml

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

2. 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 Windsurf

1. Edit ~/.codeium/windsurf/mcp_config.json

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

2. Reload

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

### How to install hmem — Humanlike Memory for AI Agents in Cline

1. Open Cline → MCP Servers → Configure

   Adds to cline_mcp_settings.json.

2. Add the server

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

### How to install hmem — Humanlike Memory for AI Agents in VS Code

1. Create .vscode/mcp.json

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

2. Start it

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

## Tools

- `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_memory` — 5-level lazy read — by ID, prefix, search, time, or tag
- `write_memory` — Create new entries with title, body, tags, links
- `append_memory` — Add child nodes to existing entries
- `update_memory` — Patch fields: title, body, tags, irrelevant, links
- `search_memory` — FTS5 full-text search with sub-node attribution
- `find_related` — Find contextually related entries by tag overlap
- `load_project` — Activate a project + get full briefing + recent sessions
- `read_project` — Read project without activating (comparison/reference)
- `create_project` — Scaffold a new project entry with standard schema
- `list_projects` — List all projects with status summary
- `flush_context` — Persist current session context to long-term memory
- `move_nodes` — Move a subtree under a different parent (also in curate server)
- `set_active_device` — Register and switch between devices
- `memory_stats` — Per-prefix counts, total tokens, favorites, hashtags, stale-list
- `memory_health` — Find broken links, orphan tags, empty entries, dangling chains
- `export_memory` — Export the full .hmem to a portable JSON snapshot
- `import_memory` — Import a JSON snapshot back into a .hmem (destructive)
- `update_many` — Bulk-patch a set of entries (irrelevant, tags, body, etc.)
- `tag_bulk` — Add/remove a tag across many entries
- `tag_rename` — Rename a tag globally across all entries
- `move_memory` — Move an entire entry to a new ID slot
- `rename_id` — Rename an entry's ID; rewrites all inbound links
- `reset_memory_cache` — Invalidate the in-memory L1 cache (after raw SQL writes)

Source repository: https://github.com/Bumblebiber/hmem

---

Source: https://mcp-skills-hub.vercel.app/mcps/io-github-bumblebiber-hmem-mcp · MCP Skills Hub · data as of 2026-10-28T12:31:05Z