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.

Smart Memory