hmem — Humanlike Memory for AI Agents
AI 与智能体by bumblebiber
为 AI agents 提供持久化的 5 层分级记忆,基于 SQLite 存储,并支持 lazy-loaded 按需加载。
什么是 hmem — Humanlike Memory for AI Agents?
为 AI agents 提供持久化的 5 层分级记忆,基于 SQLite 存储,并支持 lazy-loaded 按需加载。
README
hmem — Humanlike Memory for AI Agents
Your AI forgets everything between sessions. hmem fixes that.
One read_memory() call. 5k tokens. Your agent knows every project, every past mistake, every decision you ever made together — across sessions, devices, and AI providers. No setup per conversation. No "let me re-read the codebase." It just remembers.
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 hmem"
Agent: [calls load_project("P0048") — 700 tokens]
Agent: "Got it. v5.0.0, TypeScript/SQLite/npm, 10 source files,
3 open tasks, 9 ideas. Last session you implemented
auto-checkpoints via Haiku. What's next?"
That's it. 700 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 700.
Nothing is summarized away. Level 1 is a summary, but Levels 2-5 hold the complete original text, word for word, accessible on demand.
What Makes v5 Different
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 PC? The next agent sees every exchange from every device — the conversation never dies.
Haiku Background Checkpoints
Every 20 exchanges, 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
- 10 messages on your laptop + 10 on your server = checkpoint fires on message 20
load_projectshows recent conversations with full context — across all devices
Key Features
| Feature | What it does |
|---|---|
| 5-level lazy loading | Tokens scale with need, not memory size |
| Smart bulk reads | Expands newest + most-accessed; compresses the rest to titles |
| Project gate | Activate a project — only relevant memories are expanded |
| Duplicate detection | Warns before creating entries that already exist |
| Encrypted sync | AES-256-GCM, zero-knowledge server, multi-server redundancy |
| Auto-logging | Every exchange recorded via Stop hook (O-prefix) |
| Auto-checkpoint | Haiku extracts L/D/E entries every N exchanges |
| Project handoff | Background agent maintains "current state" in Protocol section |
| User skill tracking | Agents track your expertise (1-10) and adapt communication |
| Hashtags | Cross-cutting tags for discovery across all categories |
| Obsolete chains | Mark entries wrong with correction reference — auto-follows |
| Cross-provider | Claude, Gemini, GPT, DeepSeek, local models — same memory |
| Cross-tool | Claude Code, Gemini CLI, Cursor, Windsurf, OpenCode, Cline |
| Import/Export | Share memories between agents or back up as Markdown |
Categories
| Prefix | Category | Example |
|---|---|---|
| P | Project | hmem-mcp | Active | TS/SQLite/npm | Persistent AI memory |
| L | Lesson | HMEM_AGENT_ID must be set in hooks — resolveHmemPath falls back to wrong DB |
| E | Error | 158 spurious O-entries created when Haiku MCP lacked HMEM_NO_SESSION guard |
| D | Decision | Project-based O-entries over session-based — sessions are meaningless |
| H | Human | User Skill: TypeScript 9, Architecture 9, React 3 |
| R | Rule | Max one npm publish per day — batch changes |
| O | Original | Auto-recorded conversation history (every exchange, every device) |
| I | Infra | Strato Server | Active | Linux | 87.106.22.11 |
Quick Start
1. Install
npm install -g hmem-mcp
2. Run the interactive installer
npx hmem init
This detects your AI tools, creates the memory directory, configures MCP, and installs all 4 hooks:
| Hook | When | What |
|---|---|---|
UserPromptSubmit | Every message | First message: load memory. Every Nth: checkpoint reminder |
Stop (sync) | Every response | Log exchange to active O-entry |
Stop (async) | Every response | Haiku auto-titles untitled sessions |
SessionStart[clear] | After /clear | Re-inject project context |
3. Verify
Restart your AI tool, then:
read_memory()
Empty response = working (first run). Error = check the troubleshooting section.
Manual setup
If you prefer manual configuration over hmem init:
{
"mcpServers": {
"hmem": {
"command": "/absolute/path/to/node",
"args": ["/absolute/path/to/hmem-mcp/dist/mcp-server.js"],
"env": {
"HMEM_PROJECT_DIR": "/home/yourname/.hmem",
"HMEM_AGENT_ID": "DEVELOPER"
}
}
}
}
Find the paths:
echo "Node: $(which node)"
echo "Server: $(npm root -g)/hmem-mcp/dist/mcp-server.js"
{
"mcp": {
"hmem": {
"type": "local",
"command": ["/absolute/path/to/node", "/absolute/path/to/hmem-mcp/dist/mcp-server.js"],
"environment": { "HMEM_PROJECT_DIR": "/home/yourname/.hmem" },
"enabled": true
}
}
}
Edit ~/.cursor/mcp.json, ~/.codeium/windsurf/mcp_config.json, or .vscode/mcp.json:
{
"mcpServers": {
"hmem": {
"command": "/absolute/path/to/node",
"args": ["/absolute/path/to/hmem-mcp/dist/mcp-server.js"],
"env": { "HMEM_PROJECT_DIR": "/home/yourname/.hmem" }
}
}
}
Configuration
hmem.config.json in your HMEM_PROJECT_DIR (or Agents/NAME/):
{
"memory": {
"maxCharsPerLevel": [200, 2500, 10000, 25000, 50000],
"maxDepth": 5,
"checkpointMode": "auto",
"checkpointInterval": 20,
"recentOEntries": 10,
"maxTitleChars": 50,
"prefixes": { "X": "Custom" }
},
"sync": {
"serverUrl": "https://your-server/hmem-sync",
"userId": "yourname",
"salt": "...",
"token": "..."
}
}
| Key | Default | What it does |
|---|---|---|
checkpointMode | "remind" | "auto" = Haiku writes L/D/E in background. "remind" = asks the main agent |
checkpointInterval | 20 | Exchanges between checkpoints. Set 0 to disable |
recentOEntries | 10 | How many recent sessions to show in load_project |
All keys are optional. Missing keys use defaults.
Cross-Device Sync
Sync memories across all devices with zero-knowledge encryption.
npm install -g hmem-sync
npx hmem-sync connect # Interactive wizard — first device creates, others join
Add HMEM_SYNC_PASSPHRASE to your MCP config for automatic sync on every read/write.
Multi-server redundancy
{
"sync": [
{ "name": "primary", "serverUrl": "https://server1/hmem-sync", "userId": "me", "salt": "...", "token": "..." },
{ "name": "backup", "serverUrl": "https://server2/hmem-sync", "userId": "me", "salt": "...", "token": "..." }
]
}
Announcements
Broadcast to all synced agents across all devices:
npx hmem-sync announce --message "Server URL changing — update your config!"
Troubleshooting
| Problem | Fix |
|---|---|
read_memory() fails | Check HMEM_PROJECT_DIR is absolute path and directory exists |
nvm: node not found | Use absolute path: which node → use as "command" |
| Hooks not firing | Restart Claude Code. Check ~/.claude/settings.json has all 4 hooks |
| Exchanges not logged | Check HMEM_AGENT_ID matches your Agents/ directory name |
| Sync fails | Run npx hmem-sync connect to re-authenticate |
Updating
npm update -g hmem-mcp # MCP server
npm update -g hmem-sync # Sync (if installed)
npx hmem update-skills # Refresh skill files
License
MIT
常见问题
hmem — Humanlike Memory for AI Agents 是什么?
为 AI agents 提供持久化的 5 层分级记忆,基于 SQLite 存储,并支持 lazy-loaded 按需加载。
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