Smart Memory

Generated note: shared plugin assets for this package live at the plugin root. Common local references were rewritten when they appeared in backticks or markdown links.

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)

1. 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.

2. 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.

3. 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:

1. Extract recent conversation turns (up to 20).
2. Run summarization with prompt:
   • Summarize this session arc: What was the goal? What approaches were tried? What decisions were made? What remains open?
3. 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.