Smart Memory

Smart Memory v2 Skill

Smart Memory v2 is a persistent cognitive memory runtime, not a legacy vector-memory CLI. Core runtime: Node adapter: smart-memory/index.js Local API: server.py (FastAPI) Orchestrator: cognitive_memory_system.py

Core Capabilities

Structured long-term memory (episodic, semantic, belief, goal) Entity-aware retrieval and reranking Hot working memory Background cognition (reflection, consolidation, decay, conflict resolution) Strict token-bounded prompt composition Observability endpoints (/health, /memories, /memory/{id}, /insights/pending)

Native OpenClaw Integration (v2.5)

Use the native OpenClaw skill package: skills/smart-memory-v25/index.js Optional hook helper: skills/smart-memory-v25/openclaw-hooks.js Skill descriptor: skills/smart-memory-v25/SKILL.md Primary exports: createSmartMemorySkill(options) createOpenClawHooks({ skill, agentIdentity, summarizeWithLLM })

Tool Interface (for agent tool use)

memory_search Purpose: query long-term memory. Input: query (string, required) type (all|semantic|episodic|belief|goal, default all) limit (number, default 5) min_relevance (number, default 0.6) Behavior: checks /health first, then retrieves via /retrieve and returns formatted memory results. memory_commit Purpose: explicitly persist important facts/decisions/beliefs/goals. Input: content (string, required) type (semantic|episodic|belief|goal, required) importance (1-10, default 5) tags (string array, optional) Behavior: checks /health first auto-tags if missing (working_question, decision heuristics) commits are serialized (sequential) to protect local CPU embedding throughput if server is unreachable, payload is queued to .memory_retry_queue.json unreachable response is explicit: Memory commit failed - server unreachable. Queued for retry. memory_insights Purpose: surface pending background insights. Input: limit (number, default 10) Behavior: checks /health first, calls /insights/pending, returns formatted insight list.

Reliability Guarantees

Mandatory health gate before each tool call (GET /health). Retry queue flushes automatically on healthy tool calls and heartbeat. Heartbeat supports automatic retry recovery and background maintenance.

Session Arc Lifecycle Hooks

The v2.5 skill supports episodic session arc capture: checkpoint capture every 20 turns session-end capture during teardown/reset Flow: Extract recent conversation turns (up to 20). Run summarization with prompt: Summarize this session arc: What was the goal? What approaches were tried? What decisions were made? What remains open? Persist summary through internal memory_commit as: type: "episodic" tags: ["session_arc", "YYYY-MM-DD"]

Passive Context Injection

• Use inject_active_context (or createOpenClawHooks().beforeModelResponse) before response generation.
• This adds the standardized block:
• [ACTIVE CONTEXT]
• Status: {status}
• Active Projects: {active_projects}
• Working Questions: {working_questions}
• Top of Mind: {top_of_mind}
• Pending Insights:
• {insight_1}
• {insight_2}
• [/ACTIVE CONTEXT]
• Add this guidance line to your agent base prompt:
• If pending insights appear in your context that relate to the current conversation, surface them naturally to the user. Do not force it - but if there is a genuine connection, seamlessly bring it up.

Minimal OpenClaw Wiring Example

const { createSmartMemorySkill, createOpenClawHooks, } = require("./skills/smart-memory-v25"); const memory = createSmartMemorySkill({ baseUrl: "http://127.0.0.1:8000", summarizeSessionArc: async ({ prompt, conversationText }) => { return openclaw.llm.complete({ system: prompt, user: conversationText }); }, }); const hooks = createOpenClawHooks({ skill: memory.skill, agentIdentity: "OpenClaw Agent", summarizeWithLLM: async ({ prompt, conversationText }) => { return openclaw.llm.complete({ system: prompt, user: conversationText }); }, }); // Register memory.tools as callable tools: // - memory_search // - memory_commit // - memory_insights // and call hooks.beforeModelResponse / hooks.onTurn / hooks.onSessionEnd at lifecycle points.

Node Adapter Methods (Base Adapter)

start() / init() ingestMessage(interaction) retrieveContext({ user_message, conversation_history }) getPromptContext(promptComposerRequest) runBackground(scheduled) stop()

API Endpoints

GET /health POST /ingest POST /retrieve POST /compose POST /run_background GET /memories GET /memory/{memory_id} GET /insights/pending

Install (CPU-Only Required)

For Docker, WSL, and laptops without NVIDIA GPUs, use CPU-only PyTorch. # from repository root cd smart-memory # Create Python venv python3 -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate # Install CPU-only PyTorch FIRST pip install torch --index-url https://download.pytorch.org/whl/cpu # Then install remaining dependencies pip install -r requirements-cognitive.txt # Finally, install Node dependencies npm install

PyTorch Policy

Smart Memory v2 supports CPU-only PyTorch only. Do not install GPU/CUDA PyTorch builds for this project. Use the bundled installer flow (npm install -> postinstall.js) so CPU wheels are always used.

Deprecated

Legacy vector-memory CLI artifacts (smart_memory.js, vector_memory_local.js, focus_agent.js) are removed in v2.