smart-memory

name	smart-memory
description	持久化认知记忆系统。基于本地向量存储的长期记忆管理。当用户说"保存记忆"、"回忆"、"长期记忆"时使用。
version	1.0.0
triggers	["智能记忆","smart memory"]

Smart Memory v2 Skill

Skill Graph： 领域 → [[_index-intelligence|情报与知识领域]] | 上游 ← [[_index-intelligence|情报领域]] | 下游 → 向量检索 → [[company-research|背调]] + [[knowledge-base|知识库]]

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.