Skill: bluepointdigital/smart-memory (version 3.1.0)

--- name: smart-memory description: Persistent local transcript-first memory for OpenClaw via a Node adapter and FastAPI engine. --- # Smart Memory v3.1 Skill Smart Memory v3.1 is a local transcript-first cognitive memory runtime with revision-aware derivation, pinned context lanes, entity-aware retrieval, and bounded prompt composition. Core runtime: - Node adapter: `smart-memory/index.js` - Local API: `server.py` - System facade: `cognitive_memory_system.py` - Canonical store: `storage/sqlite_memory_store.py` plus `transcripts/` ## Core Capabilities - transcript-first ingest and per-message transcript logging - typed long-term memory including `preference`, `identity`, and `task_state` - evidence-backed revision lifecycle decisions and supersession chains - explicit core and working memory lanes - entity-aware retrieval with lightweight relationship hints - deterministic rebuild from transcript history - hot-memory compatibility projection for working context - strict token-bounded prompt composition with trace metadata - inspection endpoints for transcripts, evidence, history, lanes, and eval runs ## OpenClaw Integration Use the native wrapper package in `skills/smart-memory-openclaw/`. Primary exports: - `createSmartMemorySkill(options)` - `createOpenClawHooks({ skill, agentIdentity, summarizeWithLLM })` The wrapper remains stable while the backend is now transcript-first under the hood. ### Tool Interface 1. `memory_search` - purpose: query relevant memory through `/retrieve` - supports `query`, `type`, `limit`, `min_relevance`, and optional `conversation_history` - health-checks the backend before execution 2. `memory_commit` - purpose: persist important facts, decisions, beliefs, goals, or session summaries - health-checks the backend before execution - serializes commits to protect local embedding throughput - queues failed commits in `.memory_retry_queue.json` 3. `memory_insights` - purpose: surface pending background insights - health-checks the backend before execution - calls `/insights/pending` ## API Endpoints Core endpoints: - `GET /health` - `POST /ingest` - `POST /retrieve` - `POST /compose` - `POST /run_background` - `GET /memories` - `GET /memory/{memory_id}` - `GET /insights/pending` Transcript and inspection endpoints: - `POST /transcripts/message` - `GET /transcripts/{session_id}` - `GET /transcript/message/{message_id}` - `GET /memory/{memory_id}/evidence` - `POST /revise` - `GET /memory/{memory_id}/history` - `GET /memory/{memory_id}/active` - `GET /memory/{memory_id}/chain` - `GET /lanes/{lane_name}` - `POST /lanes/{lane_name}/{memory_id}` - `DELETE /lanes/{lane_name}/{memory_id}` - `POST /rebuild` - `POST /rebuild/{session_id}` - `GET /eval/suite/{suite_name}` - `GET /eval/case/{case_id}` ## Operating guidance - query memory before speaking when continuity matters - do not claim prior context unless retrieval actually supports it - transcripts are canonical, memories are derived - treat SQLite as canonical runtime storage - treat JSON as offline export or backup only - keep CPU-only PyTorch policy intact ## Deprecated Legacy vector-memory CLI artifacts remain deprecated and should not be revived.

path: .gitignore
content: # Runtime memory and cognitive state (never commit personal memories)
data/

# Python environments
.venv/
venv/
env/

# Python caches
__pycache__/
*.pyc
*.pyo
*.pyd
.pytest_cache/

# Node artifacts
node_modules/
smart-memory/node_modules/

# Local DB and vector artifacts
*.db
*.db-shm
*.db-wal
*.sqlite

# OS/editor artifacts
.DS_Store
Thumbs.db


path: requirements-cognitive.txt
content: pydantic>=2,<3
numpy>=1.26
pytest>=8,<10
sentence-transformers>=3,<4
einops>=0.8.0
qdrant-client>=1.9
fastapi>=0.115,<1
uvicorn>=0.30,<1

path: requirements-cpu.txt
content: # CPU-only PyTorch requirements
# Usage: pip install -r requirements-cpu.txt
#
# This installs CPU-only PyTorch (~200MB) instead of the full GPU version (~2GB)
# Ideal for: Docker containers, WSL, laptops without NVIDIA GPUs, edge deployment

--index-url https://download.pytorch.org/whl/cpu
torch==2.5.1+cpu
torchvision==0.20.1+cpu
torchaudio==2.5.1+cpu

# Core dependencies (same as main requirements)
pydantic>=2,<3
numpy>=1.26
einops>=0.8.0
pytest>=8,<10

# Embeddings and vector store
sentence-transformers>=3,<4
qdrant-client>=1.9

# API server
fastapi>=0.115,<1
uvicorn>=0.30,<1


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)