Smart Memory v2 Skill

---

name: smart-memory

description: Persistent local cognitive memory for OpenClaw via a Node adapter and FastAPI engine.

---

# Smart Memory v2 Skill

Smart Memory v2 is a persistent cognitive memory runtime, not a legacy vector-memory CLI.

Core runtime:

Core Capabilities

Native OpenClaw Integration (v2.5)

Use the native OpenClaw skill package:

Primary exports:

Tool Interface (for agent tool use)

1. `memory_search`

- `query` (string, required)

- `type` (`all|semantic|episodic|belief|goal`, default `all`)

- `limit` (number, default `5`)

- `min_relevance` (number, default `0.6`)

2. `memory_commit`

- `content` (string, required)

- `type` (`semantic|episodic|belief|goal`, required)

- `importance` (1-10, default `5`)

- `tags` (string array, optional)

- 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`

- `limit` (number, default `10`)

Reliability Guarantees

Session Arc Lifecycle Hooks

The v2.5 skill supports episodic session arc capture:

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)

API Endpoints

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

Deprecated

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