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hierarchical memory plugin for OpenClaw — core memory block + archival storage with semantic search","maintainers":[{"name":"icex-dev","email":"g@icex.dev"}],"readme":"# @icex-labs/openclaw-memory-engine\n\n> Your agent remembers everything. Automatically.\n\n[![npm](https://img.shields.io/npm/v/@icex-labs/openclaw-memory-engine)](https://www.npmjs.com/package/@icex-labs/openclaw-memory-engine)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)\n\nAn [OpenClaw](https://openclaw.ai) plugin that gives your agent persistent, structured memory. Every conversation is automatically captured — the agent doesn't need to do anything. No \"let me save that\" messages, no manual tool calls. Memory just happens.\n\n---\n\n## The Problem\n\nOpenClaw agents wake up fresh every session. They forget who you are, what you discussed, and what matters to you.\n\nPrevious solutions required the agent to manually call memory tools — but agents forget to do that too.\n\n## The Solution\n\n**Passive memory capture via hooks** + five-layer architecture inspired by [MemGPT/Letta](https://github.com/cpacker/MemGPT):\n\n1. **Auto-Capture Hooks** — every message in/out is automatically analyzed and stored. Zero agent effort.\n2. **Core Memory** (~500 tokens) — identity, relationship, preferences. Always loaded.\n3. **Archival Memory** (unlimited) — facts with importance scoring. Hybrid semantic search.\n4. **Knowledge Graph** — entity relations, auto-extracted. \"Who is my doctor?\" → graph traversal.\n5. **Episodic Memory** — conversation summaries. \"What did we discuss last time?\"\n6. **Reflective Memory** — behavioral pattern analysis. Topic trends, mood shifts.\n\nThe agent doesn't say \"I'll remember that.\" It just remembers.\n\n---\n\n## Install\n\n```bash\nopenclaw plugins install @icex-labs/openclaw-memory-engine\nbash ~/.openclaw/extensions/memory-engine/setup.sh\nopenclaw gateway restart\n```\n\n### What setup.sh does\n\n1. **Interactive core memory setup** — prompts for name, location, role, relationship\n2. **Legacy data migration** — detects existing MEMORY.md / daily logs, imports into archival with dedup\n3. **Data quality pass** — re-classifies entities, re-rates importance, extracts graph triples\n4. **Platform scheduler** — daily maintenance (macOS LaunchAgent / Linux systemd / Windows schtasks)\n5. **Config + agent instructions** — patches `openclaw.json` and `AGENTS.md`\n6. **Cron registration** — 4 automated jobs (reflection, consolidation, dedup, dashboard)\n7. **Embedding backfill** — on next restart, missing embeddings auto-computed in background\n\n`--non-interactive` flag available for scripted installs.\n\n---\n\n## How It Works\n\n### Passive Capture (Hooks)\n\n```\nUser sends message on Telegram/Discord/WhatsApp\n    │\n    ├─→ Hook: message:received\n    │     Content ≥ 20 chars? Not a greeting? Not already stored?\n    │     → Auto-store in archival (entity inferred, importance scored)\n    │     → Auto-extract knowledge graph triples\n    │     → Embedding computed in background\n    │\n    ├─→ Agent processes and replies\n    │\n    └─→ Hook: message:sent\n          Reply ≥ 50 chars? Not duplicate (60s window)?\n          → Auto-store agent reply\n\nNo tool calls needed. No \"I'll remember that.\" Just memory.\n```\n\n### Dedup Safety\n\n- **60-second content hash** — prevents duplicate captures from streaming/retry events\n- **Keyword overlap check** — if agent already stored the same fact via manual `archival_insert`, hook skips it\n- **Result:** exactly one copy of each fact, regardless of who stores it first\n\n### Multi-Agent Isolation\n\n```\nSession key \"agent:wife:telegram:...\" → workspace-wife/\nSession key \"agent:main:telegram:...\" → workspace/\n\nEach agent's hooks + tools operate on their own workspace.\nZero cross-contamination. Automatic.\n```\n\n---\n\n## Architecture\n\n```\n┌──────────────────────────────────────────────────────────────────┐\n│                       Agent Context Window                       │\n│                                                                   │\n│  ┌─ Passive: Auto-Capture Hooks ──────────────────────────────┐  │\n│  │  message:received → analyze → store fact + graph + embed    │  │\n│  │  message:sent     → analyze → store reply (deduped)         │  │\n│  │  No agent action needed. Runs on every conversation.        │  │\n│  └────────────────────────────────────────────────────────────┘  │\n│                                                                   │\n│  ┌─ Layer 1: Core Memory ─────────────────────────────────────┐  │\n│  │  core_memory_read() → core.json (~500 tokens)              │  │\n│  │  Identity, relationship, preferences, current_focus         │  │\n│  └────────────────────────────────────────────────────────────┘  │\n│                                                                   │\n│  ┌─ Layer 2: Archival Memory ─────────────────────────────────┐  │\n│  │  archival_search(query) → hybrid 5-signal ranking          │  │\n│  │  Unlimited JSONL. Auto-populated by hooks.                  │  │\n│  └────────────────────────────────────────────────────────────┘  │\n│                                                                   │\n│  ┌─ Layer 3: Knowledge Graph ─────────────────────────────────┐  │\n│  │  graph_query(entity) → traverse relations                   │  │\n│  │  Auto-extracted from every captured message.                │  │\n│  └────────────────────────────────────────────────────────────┘  │\n│                                                                   │\n│  ┌─ Layer 4: Episodic Memory ─────────────────────────────────┐  │\n│  │  episode_save / episode_recall                              │  │\n│  │  Conversation summaries with decisions, mood, topics.       │  │\n│  └────────────────────────────────────────────────────────────┘  │\n│                                                                   │\n│  ┌─ Layer 5: Reflective Memory ───────────────────────────────┐  │\n│  │  memory_reflect → pattern analysis report                   │  │\n│  │  Topic trends, time distribution, mood shifts.              │  │\n│  └────────────────────────────────────────────────────────────┘  │\n└──────────────────────────────────────────────────────────────────┘\n```\n\n---\n\n## Tools (20) + Hooks (2)\n\n### Hooks (Passive — No Agent Action)\n\n| Hook | Trigger | What it does |\n|------|---------|-------------|\n| `memory-engine-capture-received` | Every incoming message | Auto-stores facts, infers entity + importance, extracts graph triples |\n| `memory-engine-capture-sent` | Every agent reply | Auto-stores replies (deduped, 60s window) |\n\n### Core Memory (3 tools)\n\n| Tool | Description |\n|------|-------------|\n| `core_memory_read` | Load identity block. Call every session start. |\n| `core_memory_replace` | Update field by dot-path. Auto-parses JSON strings. 3KB limit. |\n| `core_memory_append` | Append to array field. |\n\n### Archival Memory (5 tools)\n\n| Tool | Description |\n|------|-------------|\n| `archival_insert` | Manually store a fact (only needed for non-conversation sources). |\n| `archival_search` | Hybrid 5-signal search: keyword + semantic + recency + access + importance. |\n| `archival_update` | Correct an existing record. |\n| `archival_delete` | Remove outdated record. |\n| `archival_stats` | Record count, entity/tag distribution, embedding coverage. |\n\n### Knowledge Graph (2 tools)\n\n| Tool | Description |\n|------|-------------|\n| `graph_query` | Traverse entity relations with depth control. |\n| `graph_add` | Manually add a relation triple. |\n\n### Episodic Memory (2 tools)\n\n| Tool | Description |\n|------|-------------|\n| `episode_save` | Save conversation summary with decisions, mood, topics. |\n| `episode_recall` | Search past conversations by topic or get recent N. |\n\n### Intelligence (4 tools)\n\n| Tool | Description |\n|------|-------------|\n| `memory_reflect` | Analyze behavioral patterns over configurable time window. |\n| `archival_deduplicate` | Find/remove near-duplicates via embedding cosine similarity. |\n| `memory_consolidate` | Extract structured facts from text blocks. |\n| `memory_quality` | Re-classify entities, re-rate importance, extract missing graph triples. |\n\n### Backup & Admin (4 tools)\n\n| Tool | Description |\n|------|-------------|\n| `memory_export` | Full snapshot → JSON file. |\n| `memory_import` | Restore with merge or replace mode. |\n| `memory_migrate` | JSONL → SQLite with FTS5. |\n| `memory_dashboard` | Generate browsable HTML dashboard. |\n\n---\n\n## Search Scoring\n\n`archival_search` combines five signals:\n\n| Signal | Weight | Description |\n|--------|--------|-------------|\n| Keyword | 2× per term | Term presence in content + entity + tags |\n| Semantic | 5× | Cosine similarity via OpenAI `text-embedding-3-small` (512d) |\n| Recency | 0–1 | Linear decay over 1 year |\n| Access | 0–0.5 | Boost for recently accessed records |\n| Importance | 0.5× | Forgetting curve: `importance × e^(-0.01 × days)` |\n\nFalls back to keyword-only without OpenAI key. Cost: ~$0.001/session.\n\n---\n\n## Classification (v5.0)\n\nEntity and importance classification is **embedding-based** — no hardcoded keywords, works with any language.\n\n```\nWith OPENAI_API_KEY (recommended):\n  15 entity anchors (health, finance, immigration, legal, vehicles, ...)\n  4 importance anchors (critical / high / medium / low)\n  Anchor embeddings computed once, cached to classifier-anchors.json\n  → Language-agnostic: Japanese, French, Korean, Chinese, English all work\n\nWithout OPENAI_API_KEY (fallback):\n  Format-based heuristics:\n    $amounts → finance (importance 7)\n    URLs/code → technology\n    Dates → importance 6\n    Short messages → low importance\n    Long messages → high importance\n  → Basic but functional, no API cost\n```\n\nReal results on 2,751 records:\n\n| Metric | Before (regex v4) | After (embedding v5) |\n|--------|-------------------|---------------------|\n| \"general\" entities | 45% | **29%** |\n| flat importance=5 | 71% | **39%** |\n| Languages supported | English + Chinese | **any** |\n| Hardcoded keywords | 100+ | **zero** |\n\n---\n\n## Self-Healing\n\n| Issue | Auto-fix |\n|-------|----------|\n| Missing embeddings | Batch backfill on restart (all workspaces) |\n| Agent forgets to save | Hooks capture everything passively |\n| Duplicate facts | 60s dedup + keyword overlap + weekly cron |\n| Flat importance scores | Embedding-based re-rating via `memory_quality` |\n| General entity labels | Embedding-based re-classification via `memory_quality` |\n| No API key | Format-based fallback classifier (basic but functional) |\n\n---\n\n## Configuration\n\n```json\n{\n  \"plugins\": {\n    \"allow\": [\"memory-engine\"],\n    \"entries\": {\n      \"memory-engine\": {\n        \"enabled\": true,\n        \"config\": {\n          \"coreSizeLimit\": 3072,\n          \"sharing\": false\n        }\n      }\n    }\n  }\n}\n```\n\n| Option | Default | Description |\n|--------|---------|-------------|\n| `workspace` | Auto-resolved per agent | Override workspace directory |\n| `coreSizeLimit` | `3072` (3KB) | Max core.json size |\n| `sharing` | `true` | Cross-agent memory sharing. Set `false` for multi-user privacy. |\n\nSemantic search requires `OPENAI_API_KEY` in environment (optional — graceful degradation).\n\n---\n\n## Automated Maintenance\n\n| Schedule | Job | Description |\n|----------|-----|-------------|\n| Every 6h | Consolidate | Extract missed facts from daily logs |\n| Daily 9am | Reflect | Analyze patterns, store observations |\n| Daily 9:30am | Dashboard | Refresh browsable HTML report |\n| Weekly Sunday | Dedup | Clean near-duplicate records |\n| Daily 3am | File cleanup | Merge old logs, archive old summaries |\n\nPer-agent crons auto-registered for agents with separate workspaces.\n\n---\n\n## Project Structure\n\n```\nmemory-engine/\n├── index.js                  # Plugin entry: 20 tools + 2 hooks (ToolFactory pattern)\n├── lib/\n│   ├── paths.js              # Constants, multi-workspace resolution\n│   ├── core.js               # Core memory CRUD + auto-parse\n│   ├── archival.js           # JSONL storage + in-memory cache\n│   ├── embedding.js          # OpenAI embedding API + cache + batch backfill\n│   ├── search.js             # Hybrid 5-signal search with forgetting curve\n│   ├── graph.js              # Knowledge graph: triples + traversal + auto-extract\n│   ├── episodes.js           # Episodic memory: save + recall\n│   ├── reflection.js         # Statistical pattern analysis\n│   ├── consolidate.js        # Text → facts extraction\n│   ├── dedup.js              # Embedding similarity dedup\n│   ├── backup.js             # Export / import\n│   ├── store-sqlite.js       # SQLite backend (FTS5)\n│   ├── dashboard.js          # HTML dashboard generator\n│   ├── classifier.js          # Embedding-based entity + importance classification\n│   ├── quality.js             # Data quality pass (uses classifier)\n│   └── auto-capture.js        # Passive hooks: message → archival (uses classifier)\n├── extras/\n│   ├── memory-maintenance.sh\n│   ├── migrate-legacy.mjs\n│   └── auto-consolidation-crons.json\n├── setup.sh\n├── openclaw.plugin.json\n└── package.json\n```\n\n## Platforms\n\n| Platform | Scheduler | Status |\n|----------|----------|--------|\n| macOS | LaunchAgent | Full support |\n| Linux | systemd timer | Full support |\n| Windows | schtasks | Guided setup |\n\n---\n\n## License\n\nMIT — Built for [OpenClaw](https://openclaw.ai). Inspired by [MemGPT/Letta](https://github.com/cpacker/MemGPT).\n","readmeFilename":"README.md"}