ClawMemory Skill

---

name: clawmemory

description: Sovereign agent memory engine — self-hosted, privacy-first SQLite store with LLM-based fact extraction (GLM-4.7), hybrid BM25+vector search, contradiction resolution, and OpenClaw plugin for auto-capture/auto-recall. Use when storing structured facts from conversations, querying agent memory semantically, or wiring persistent memory into an OpenClaw agent.

version: 1.0.0

---

# ClawMemory Skill

Sovereign agent memory engine — self-hosted, privacy-first. All data stays local (SQLite) with optional Turso cloud sync.

**Repo:** https://github.com/clawinfra/clawmemory

**Server port:** `localhost:7437`

**Last verified:** 2026-03-28

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✅ VERIFIED WORKING PATTERNS (copy-paste ready)

Start the server

cd /tmp/clawmemory && ./clawmemory serve --config config.json
# OR with defaults (SQLite at ./clawmemory.db, port 7437, Ollama at localhost:11434)
./clawmemory serve

**Guard rules:**

Store a fact manually

curl -s -X POST http://localhost:7437/facts \
  -H "Content-Type: application/json" \
  -d '{"text": "User prefers Python over Go for scripting", "category": "preference", "importance": 0.8}'

Search memory

# Hybrid BM25 + vector (best quality)
curl -s "http://localhost:7437/search?q=python+preference&limit=5" | python3 -m json.tool

# BM25-only (fast, no Ollama needed)
curl -s "http://localhost:7437/search?q=python+preference&limit=5&mode=bm25" | python3 -m json.tool

Extract facts from a conversation turn (auto-capture)

curl -s -X POST http://localhost:7437/extract \
  -H "Content-Type: application/json" \
  -d '{
    "turns": [
      {"role": "user", "content": "I always deploy to Hetzner, never AWS."},
      {"role": "assistant", "content": "Got it, using Hetzner for deployments."}
    ]
  }' | python3 -m json.tool

Get user profile

curl -s http://localhost:7437/profile | python3 -m json.tool

Forget a fact

curl -s -X DELETE http://localhost:7437/facts/<fact-id>

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OpenClaw Plugin (TypeScript) — Auto-wire

The plugin at `plugin/` auto-injects memory pre-turn and auto-captures post-turn.

cd /tmp/clawmemory/plugin && npm install && npm run build
# Copy plugin/dist/ to OpenClaw plugins dir and enable in config

Plugin config in `openclaw.config.json`:

{
  "plugins": [
    {
      "id": "clawmemory",
      "path": "./plugins/clawmemory/dist/index.js",
      "config": {
        "serverUrl": "http://localhost:7437",
        "maxContextFacts": 10,
        "minImportance": 0.3
      }
    }
  ]
}

**What it does automatically:**

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Config Reference (config.json)

{
  "server": { "host": "localhost", "port": 7437 },
  "store": {
    "sqlitePath": "./clawmemory.db",
    "tursoUrl": "",
    "tursoToken": ""
  },
  "extractor": {
    "endpoint": "http://localhost:8080/v1",
    "model": "glm-4.7",
    "apiKey": "placeholder"
  },
  "embed": {
    "ollamaUrl": "http://localhost:11434",
    "model": "qwen2.5:7b"
  },
  "decay": {
    "halfLifeDays": 30,
    "minImportance": 0.1,
    "intervalMinutes": 60
  }
}

**Key tunables:**

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❌ KNOWN BROKEN / DO NOT USE

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Build from source

git clone https://github.com/clawinfra/clawmemory /tmp/clawmemory
cd /tmp/clawmemory
go build ./...  # produces ./clawmemory binary
go test ./... -timeout 120s  # all tests should pass

**CI status:** `main` branch — golangci-lint v2 requires action@v7 (fixed 2026-03-28, commit `877384b`)

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API Reference (quick)

| Method | Path | Body / Params | Description |

|--------|------|---------------|-------------|

| POST | `/facts` | `{text, category, importance}` | Store a fact directly |

| POST | `/extract` | `{turns: [{role,content}]}` | LLM-extract + store facts from conversation |

| GET | `/search` | `?q=<query>&limit=N&mode=bm25|hybrid` | Search memory |

| GET | `/profile` | — | Get synthesized user profile |

| DELETE | `/facts/:id` | — | Soft-delete a fact (sets importance=0) |

| POST | `/forget` | `{query}` | Find + soft-delete facts matching query |

Categories: `person`, `preference`, `fact`, `skill`, `relationship`, `event`, `goal`

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Integration with OpenClaw workspace

ClawMemory replaces manual `memory/YYYY-MM-DD.md` writes for structured facts. Daily notes remain the primary context store for narrative/decisions; ClawMemory handles queryable structured facts (preferences, skills, relationships).

**When to use ClawMemory vs daily notes:**