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BytesAgain β€Ί Skills β€Ί Smart Memory
πŸ¦€ ClawHub

Smart Memory

by @bluepointdigital

Persistent local transcript-first memory for OpenClaw via a Node adapter and FastAPI engine.

Versionv3.1.0
Downloads7,523
Installs66
Stars⭐ 3
#writing#video#legal#communication
TERMINAL
clawhub install smart-memory

πŸ“– About This Skill

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.