hmem — Humanlike Memory for AI Agents

AI agents forget everything when a session ends. hmem changes that.

hmem is actively used in production. APIs are stable since v2.0. Feedback and bug reports welcome.

hmem is a Model Context Protocol (MCP) server that gives AI agents persistent, humanlike memory — modeled after how human memory actually works.

Born as a side project of a multi-agent AI system, hmem solves a real problem: when you work across multiple machines or sessions, your AI instances start from zero every time. They duplicate work, contradict previous decisions, and lose hard-won context.

hmem fixes this.

The Problem

When working across multiple PCs with AI coding agents, every new session was a fresh start. Agents had no knowledge of previous decisions, duplicated work, produced inconsistencies, and wasted tokens catching up.

Existing RAG solutions are flat — every memory fragment has the same abstraction level. The agent either gets too much detail and wastes tokens, or too little and loses nuance.

The Solution: 5-Level Humanlike Memory

hmem stores and retrieves memory in five nested levels of detail — mirroring how human memory works.

Level 1  ──  Coarse summary         (always loaded on spawn)
  Level 2  ──  More detail
    Level 3  ──  Deep context
      Level 4  ──  Fine-grained specifics
        Level 5  ──  Full verbatim detail

A freshly spawned agent receives only Level 1 — the broadest strokes. When it needs more detail on a specific topic, it makes a tool call to retrieve Level 2 for that entry. And so on, down to full detail.

Result: Agents load exactly as much context as they need — no more, no less.

How It Works

Saving Memory

After completing a task, an agent calls write_memory with tab-indented content. The indentation depth maps to memory levels — multiple entries at the same depth become siblings.

write_memory(prefix="L", content="Always restart MCP server after recompiling TypeScript
	Running process holds old dist — tool calls return stale results
	Fix: kill $(pgrep -f mcp-server)")

Loading Memory

On spawn, the agent receives all Level 1 summaries. Deeper levels are fetched on demand — by ID, one branch at a time.

read_memory()              # → all L1 summaries
read_memory(id="L0003")    # → L1 + direct L2 children for this entry
read_memory(id="L0003.2")  # → that L2 node + its L3 children

Each node gets a compound ID (L0003.2.1) so any branch is individually addressable.

Updating Memory

Entries can be updated without deleting and recreating them:

update_memory(id="L0003", content="Corrected L1 summary")   # update text
update_memory(id="L0003", favorite=true)                     # toggle flag only
update_memory(id="L0003.2", content="Fixed sub-node text")   # fix a sub-node
append_memory(id="L0003", content="New finding\n\tSub-detail")

update_memory replaces the text of a single node (children preserved). Content is optional — pass only flags to toggle them without repeating the text. append_memory adds new child nodes to an existing entry.

Obsolete Entries

When an entry is outdated, mark it as obsolete — never delete it:

update_memory(id="E0023", content="...", obsolete=true)

Obsolete entries are hidden from bulk reads and replaced by a summary line at the bottom:

--- 3 obsolete entries hidden (E0023, D0007, L0012) — use read_memory(id=X) to view ---

They remain fully searchable and accessible by ID. Past errors still teach future agents what not to do — knowledge is never destroyed, only archived.

Memory Curation

A dedicated curator agent runs periodically to maintain memory health. It detects duplicates, merges fragmented entries, marks stale pointers, and prunes low-value content — a form of the Ebbinghaus Forgetting Curve.

Key Features

Hierarchical retrieval — lazy loading of detail levels saves tokens
True tree structure — multiple siblings at the same depth (not just one chain)
Compact output — child IDs render as .7 instead of P0029.7; dates shown only when differing from parent
Persistent across sessions — agents remember previous work even after restart
Editable without deletion — update_memory and append_memory modify entries in place; content is optional when toggling flags
Markers — [♥] favorite, [P] pinned, [!] obsolete, [-] irrelevant, [*] active, [s] secret — on root entries and sub-nodes
Pinned entries — super-favorites that show all children titles in bulk reads (not just the latest); use for reference entries you need in full every session
Hashtags — cross-cutting tags (#hmem, #security) for filtering and discovering related entries across prefixes
Import/Export — export_memory as Markdown or .hmem SQLite clone (excluding secrets); import_memory with L1 deduplication, sub-node merge, and automatic ID remapping on conflict
Obsolete chain resolution — mark entries/sub-nodes obsolete with [✓ID] reference; read_memory auto-follows the chain to the current version
Access-count promotion — most-accessed entries get expanded automatically ([★]); most-referenced sub-nodes shown as "Hot Nodes"
Session cache — bulk reads suppress already-seen entries with Fibonacci decay; two modes: discover (newest-heavy) and essentials (importance-heavy)
Active-prefix filtering — mark entries as [*] active to focus bulk reads on what matters now; non-active entries still show as compact titles
Secret entries — [s] entries/nodes excluded from export_memory
Titles & compact views — auto-extracted titles; titles_only mode for table-of-contents view
Effective-date sorting — entries with recent appends surface to the top
Per-agent memory — each agent has its own .hmem file (SQLite)
Skill-file driven — agents are instructed via skill files, no hardcoded logic
MCP-native — works with Claude Code, Gemini CLI, OpenCode, and any MCP-compatible tool

Quick Start

Option A: Install from npm (Recommended)

npx hmem-mcp init

That's it. The interactive installer will:

Detect your installed AI coding tools (Claude Code, OpenCode, Cursor, Windsurf, Cline)
Ask whether to install system-wide (memories in ~/.hmem/) or project-local (memories in current directory)
Offer an example memory with 67 real entries from hmem development — or start fresh
Configure each tool's MCP settings automatically
Create the memory directory and hmem.config.json

After the installer finishes, restart your AI tool and call read_memory() to verify.

Example memory: The installer includes hmem_developer.hmem — a real .hmem database with 67 entries and 287 nodes from actual hmem development. It contains lessons learned, architecture decisions, error fixes, and project milestones — a great way to see how hmem works in practice before writing your own entries. You can explore it immediately with read_memory() after install, or browse it in the TUI viewer with python3 hmem-reader.py path/to/memory.hmem.

Don't forget the skill files! The MCP server provides the tools (read_memory, write_memory, etc.), but the slash commands (/hmem-save, /hmem-read) require skill files to be copied to your tool's skills directory. See the Skill Files section below — it's a one-time copy-paste.

Coming from the MCP Registry? Run npx hmem-mcp init first — it configures your tools and creates the memory directory. Then copy the skill files as described below.

Option B: Install from source

git clone https://github.com/Bumblebiber/hmem.git
cd hmem
npm install && npm run build
node dist/cli.js init

Option C: Manual Setup (no installer)

If you prefer to configure everything yourself:

1. Install

npm install -g hmem-mcp

Or from source: git clone https://github.com/Bumblebiber/hmem.git && cd hmem && npm install && npm run build

2. Register the MCP server

Claude Code — global registration:

claude mcp add hmem -s user -- npx hmem-mcp serve \
  --env HMEM_PROJECT_DIR="$HOME/.hmem"

OpenCode — add to ~/.config/opencode/opencode.json (or project-level opencode.json):

{
  "mcp": {
    "hmem": {
      "type": "local",
      "command": ["npx", "hmem", "serve"],
      "environment": {
        "HMEM_PROJECT_DIR": "~/.hmem"
      },
      "enabled": true
    }
  }
}

Cursor / Windsurf / Cline — add to ~/.cursor/mcp.json (or equivalent):

{
  "mcpServers": {
    "hmem": {
      "command": "npx",
      "args": ["hmem", "serve"],
      "env": {
        "HMEM_PROJECT_DIR": "~/.hmem"
      }
    }
  }
}

Windows note: Use forward slashes or double backslashes in JSON paths.

3. Verify the connection

Fully restart your AI tool, then call read_memory(). You should see a memory listing (empty on first run is fine).

In Claude Code, run /mcp to check the server status.

Skill Files

Skill files teach your AI tool how to use hmem correctly. Copy them to your tool's global skills directory, then restart your AI tool.

After copying skills, fully restart your terminal and AI tool — skills are loaded at startup and won't appear in a running session.

Available skills

Slash command	What it does
`/hmem-read`	Load your memory at session start — call at the beginning of every session
`/hmem-write`	Protocol for writing memories correctly (prefixes, hierarchy, anti-patterns)
`/hmem-save`	Save session learnings to memory, then commit + push
`/hmem-config`	View and adjust memory settings (`hmem.config.json`) interactively
`/hmem-curate`	Audit and clean up memory entries (curator role required)

Copy skills to your tool

Find the skills directory in the installed package:

HMEM_DIR="$(npm root -g)/hmem-mcp"

If you cloned from source, the skills are in the skills/ directory.

Claude Code:

for skill in hmem-read hmem-write hmem-save hmem-config hmem-curate; do
  mkdir -p ~/.claude/skills/$skill
  cp "$HMEM_DIR/skills/$skill/SKILL.md" ~/.claude/skills/$skill/SKILL.md
done

Gemini CLI:

for skill in hmem-read hmem-write hmem-save hmem-config hmem-curate; do
  mkdir -p ~/.gemini/skills/$skill
  cp "$HMEM_DIR/skills/$skill/SKILL.md" ~/.gemini/skills/$skill/SKILL.md
done

OpenCode:

for skill in hmem-read hmem-write hmem-save hmem-config hmem-curate; do
  mkdir -p ~/.config/opencode/skills/$skill
  cp "$HMEM_DIR/skills/$skill/SKILL.md" ~/.config/opencode/skills/$skill/SKILL.md
done

MCP Tools

Memory Tools

Tool	Description
`read_memory`	Read memories — L1 summaries, drill by ID, filter by prefix, search by time
`write_memory`	Save new memory entries with tab-indented hierarchy
`update_memory`	Update text and/or flags of an entry or sub-node (content optional)
`append_memory`	Append new child nodes to an existing entry or sub-node
`export_memory`	Export non-secret entries as Markdown text or `.hmem` SQLite file
`import_memory`	Import entries from a `.hmem` file with deduplication and ID remapping
`reset_memory_cache`	Clear session cache so all entries are treated as unseen
`search_memory`	Full-text search across all agent `.hmem` databases

Curator Tools (role: ceo)

Tool	Description
`get_audit_queue`	List agents whose memory has changed since last audit
`read_agent_memory`	Read any agent's full memory (for curation)
`fix_agent_memory`	Correct a specific entry or sub-node in any agent's memory
`append_agent_memory`	Add content to an existing entry in any agent's memory (for merging duplicates)
`delete_agent_memory`	Delete a memory entry (prefer `fix_agent_memory(obsolete=true)` — deletion is permanent)
`mark_audited`	Mark an agent as audited

Memory Directory

hmem stores all memory files (.hmem SQLite databases) and its configuration (hmem.config.json) in a single directory. The location depends on how you install:

Install mode	Memory directory	Example
System-wide	`~/.hmem/`	`/home/alice/.hmem/` or `C:\Users\Alice\.hmem\`
Project-local	Project root (cwd)	`/home/alice/my-project/`

The hmem init installer asks which mode you prefer and creates the directory automatically.

Directory structure

~/.hmem/                     # System-wide memory directory
  memory.hmem                # Default agent memory (when no HMEM_AGENT_ID is set)
  SIGURD.hmem                # Named agent memory (HMEM_AGENT_ID=SIGURD)
  hmem.config.json           # Configuration file
  audit_state.json           # Curator state (optional)

The MCP configuration files are written to each tool's own config directory — not into ~/.hmem/:

Tool	Global MCP config path
Claude Code	`~/.claude/.mcp.json`
OpenCode	`~/.config/opencode/opencode.json`
Cursor	`~/.cursor/mcp.json`
Windsurf	`~/.codeium/windsurf/mcp_config.json`
Cline / Roo Code	`.vscode/mcp.json` (project-only)

Environment Variables

Variable	Description	Default
`HMEM_PROJECT_DIR`	Root directory where `.hmem` files are stored	(required)
`HMEM_AGENT_ID`	Agent identifier — used as filename and directory name	`""` → `memory.hmem`
`HMEM_AGENT_ROLE`	Permission level: `worker` · `al` · `pl` · `ceo`	`worker`

Configuration (hmem.config.json)

Place an optional hmem.config.json in your HMEM_PROJECT_DIR to tune behavior. All keys are optional — missing keys fall back to defaults.

{
  "maxL1Chars": 120,
  "maxLnChars": 50000,
  "maxDepth": 5,
  "maxTitleChars": 50,
  "accessCountTopN": 5,
  "bulkReadV2": {
    "topNewestCount": 5,
    "topAccessCount": 3,
    "topObsoleteCount": 3
  },
  "prefixes": {
    "R": "Research"
  }
}

Memory prefixes

The default prefixes cover most use cases:

Prefix	Category	When to use
`P`	Project	Project experiences, summaries
`L`	Lesson	Lessons learned, best practices
`E`	Error	Bugs, errors + their fix
`D`	Decision	Architecture decisions with reasoning
`T`	Task	Task notes, work progress
`M`	Milestone	Key milestones, releases
`S`	Skill	Skills, processes, how-to guides
`N`	Navigator	Code pointers — where something lives in the codebase

To add your own, add entries to the "prefixes" key in hmem.config.json. Custom prefixes are merged with the defaults — you don't need to repeat the built-in ones.

Favorites

Any entry can be marked as a favorite — regardless of its prefix category. Favorites always appear with their L2 detail in bulk reads, marked with [♥].

write_memory(prefix="D", content="...", favorite=true)   # set at creation
update_memory(id="D0010", favorite=true)                 # set on existing entry
update_memory(id="D0010", favorite=false)                # clear the flag

Use favorites for reference info you need to see every session — key decisions, API endpoints, frequently consulted patterns. Use sparingly: if everything is a favorite, nothing is.

Pinned entries

Pinned entries are "super-favorites" — they show all children titles in bulk reads, not just the latest one. While favorites show the newest child + [+N more →], pinned entries give you the full table of contents at a glance.

write_memory(prefix="S", content="...", pinned=true)   # set at creation
update_memory(id="S0005", pinned=true)                 # set on existing entry

Display	Normal	Favorite `[♥]`	Pinned `[P]`	`expand=true`
Children shown	Latest only	All titles	All titles	All with full content
`[+N more →]` hint	Yes	No	No	No

Use pinned for entries with many structured sub-entries (handbooks, reference lists, project summaries) where you always want to see the full outline.

Hashtags

Tag entries for cross-cutting search across prefix categories:

write_memory(prefix="L", content="...", tags=["#security", "#hmem"])
read_memory(tag="#security")      # filter bulk reads by tag
read_memory(id="L0042")          # shows related entries (2+ shared tags)

Tags are lowercase, must start with #, max 10 per entry. They work on root entries and sub-nodes.

Access-count auto-promotion (`accessCountTopN`)

The top-N most-accessed entries are automatically promoted to L2 depth in bulk reads, marked with [★]. This creates "organic favorites" — entries that proved important in practice rise to the surface automatically.

{ "accessCountTopN": 5 }

Set to 0 to disable. Default: 5.

Time-weighted scoring: Raw access counts would systematically favor older entries (more time to accumulate accesses). hmem uses a logarithmic age decay instead:

score = access_count / log2(age_in_days + 2)

This means a 1-day-old entry with 5 accesses (score 3.16) outranks a 1-year-old entry with 6 accesses (score 0.70) — but a genuinely important old entry with 50 accesses (score 5.87) still stays at the top. The decay is gentle enough that consistently useful entries are never buried.

Mechanism	When useful
favorite flag	Entries you know are important from day 1 — even with zero access history
accessCountTopN	Entries that proved important over time — emerges from actual usage

Bulk reads (V2 algorithm)

read_memory() groups entries by prefix category. Each category expands a limited number of entries (newest + most-accessed + favorites) with their L2 children; the rest show only the L1 title. Two modes:

discover (default on first read): newest-heavy — good for getting an overview after session start.
essentials (auto-selected after context compression): importance-heavy — more favorites + most-accessed, fewer newest.

A session cache tracks which entries were already shown. Subsequent bulk reads suppress seen entries with Fibonacci decay [5,3,2,1,0], keeping output fresh without repetition. Use reset_memory_cache to clear the cache.

To see all children of an entry, use read_memory(id="P0005"). For a deep dive with full content, use read_memory(id="P0005", expand=true). For a compact table of contents, use read_memory(titles_only=true).

Effective-date sorting

Entries are sorted by effective_date — the most recent timestamp across the entry and all its nodes. This means a project entry (P0005) that was first written months ago but had a new session note appended today will appear near the top of the listing, alongside truly recent entries.

Character limits

Two ways to set per-level character limits:

Option A — linear interpolation (recommended): set only the endpoints; all levels in between are computed automatically.

{ "maxL1Chars": 120, "maxLnChars": 50000 }

With 5 depth levels this yields: [120, 12780, 25440, 38120, 50000]

Option B — explicit per-level array: set each level individually. If fewer entries than maxDepth, the last value is repeated.

{ "maxCharsPerLevel": [120, 2500, 10000, 25000, 50000] }

Bulk read tuning (`bulkReadV2`)

Controls how many entries are expanded in each category during a bulk read:

"bulkReadV2": {
  "topNewestCount": 5,     // expand the 5 newest entries per prefix
  "topAccessCount": 3,     // expand the 3 most-accessed per prefix
  "topObsoleteCount": 3    // show up to 3 obsolete entries (by access count)
}

Favorites are always expanded regardless of these limits. Entries expanded by one slot (e.g. newest) don't count against another (e.g. access).

TUI Viewer (hmem-reader.py)

A terminal-based interactive viewer for browsing .hmem memory files. Built with Textual.

pip install textual     # one-time dependency
python3 hmem-reader.py         # agent selection screen (scans Agents/ directory)
python3 hmem-reader.py THOR    # open a specific agent's memory
python3 hmem-reader.py ~/path/to/file.hmem  # open any .hmem file directly

Keys:

Key	Action
`r`	Toggle V2 bulk-read view (what agents see on `read_memory()`)
`e` / `c`	Expand / collapse all nodes
`q`	Quit
`Escape`	Back to agent list

The V2 view mirrors the MCP server's bulk-read algorithm — including time-weighted access scoring, per-prefix selection, active-prefix filtering, and all markers ([♥], [!], [*], [s], [-]) — so you can see exactly what an agent sees at session start.

Origin

hmem was developed out of necessity: working on a large AI project across multiple machines meant every new Claude Code session started blind. Agents redid work, lost decisions, and contradicted each other.

The solution was a memory protocol that works the way humans remember — broad strokes first, details on demand.

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 91 Commits
.github/workflows		.github/workflows
dist		dist
skills		skills
src		src
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
generate_sample_hmem.py		generate_sample_hmem.py
hmem-reader.py		hmem-reader.py
hmem.config.example.json		hmem.config.example.json
hmem_developer.hmem		hmem_developer.hmem
package-lock.json		package-lock.json
package.json		package.json
server.json		server.json
tsconfig.json		tsconfig.json

License

Bumblebiber/hmem

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hmem — Humanlike Memory for AI Agents

The Problem

The Solution: 5-Level Humanlike Memory

How It Works

Saving Memory

Loading Memory

Updating Memory

Obsolete Entries

Memory Curation

Key Features

Quick Start

Option A: Install from npm (Recommended)

Option B: Install from source

Option C: Manual Setup (no installer)

1. Install

2. Register the MCP server

3. Verify the connection

Skill Files

Available skills

Copy skills to your tool

MCP Tools

Memory Tools

Curator Tools (role: ceo)

Memory Directory

Directory structure

Environment Variables

Configuration (hmem.config.json)

Memory prefixes

Favorites

Pinned entries

Hashtags

Access-count auto-promotion (accessCountTopN)

Bulk reads (V2 algorithm)

Effective-date sorting

Character limits

Bulk read tuning (bulkReadV2)

TUI Viewer (hmem-reader.py)

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Access-count auto-promotion (`accessCountTopN`)

Bulk read tuning (`bulkReadV2`)

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