概述

OpenClaw Memory Pro System

An AI memory assistant that turns fragmented notes and conversations into searchable long-term memory, auto-distills actionable skills via a closed-loop feedback pipeline, and proactively reminds you.

When to Use

Goal	Command
------	---------
Store a memory	`memory-cli remember "Learned X today" --tag thought -i 0.8`
Assembled recall (skills + KG + evidence)	`memory-cli recall "X"`
Deep multi-hop reasoning	`memory-cli deep-recall "complex question"`
Inspiration collision (7 strategies)	`memory-cli collide`
Daily briefing	`memory-cli briefing`
List skills with utility stats	`memory-cli skills`
KG contradiction detection	`memory-cli contradictions`
KG blind spot scan	`memory-cli blindspots`
Thought threads	`memory-cli threads`
Skill feedback	`memory-cli skill-feedback success`

When Not to Use

For ephemeral throwaway messages that don't need persistence.
For real-time streaming data (this is a batch/on-demand system).

Architecture

Fragments --> [Ingest + Tag] --> Unified Corpus (Memora vectors + MSA documents)
                                        |
                              +---------+-----------+
                              v         v           v
                          [KG Weave] [Distill]  [Collide]
                          structural compression  novelty
                           _gain      _value      (1-5)
                              |         |           |
                              +----+----+-----+-----+
                                   |    v     |
                              [Skill Proposer]     <-- triggered when 2-of-3 scores pass
                                   v
                            [Skill Registry]       <-- utility tracking + feedback loop
                           (draft -> active -> deprecated)
                                   |
                         +---------+-----------+
                         v         v           v
                    [Question-   [Scheduled  [Nebius
                     Driven       Push]       Fine-
                     Recall]                  Tuning]
                         |
             +-----------+-----------+
             v           v           v
         [Skills]   [KG Relations] [Evidence]   <-- three-layer assembled output
             |
             v
        Use -> Feedback -> utility update -> low-utility auto-rewrite

Subsystems

Layer	Module	Role
-------	--------	------
Corpus	Memora	Primary vector store (nomic-embed-text, JSONL). All content enters here.
	MSA	Document-level storage for long text (>=100 words) or high importance (>=0.85). LLM-powered multi-hop interleave.
Intelligence	Second Brain	KG weaving, distillation, collision (7 strategies with attention focus + recency weighting).
	Skill Proposer	Auto-generates draft skills when 2-of-3 scores meet thresholds.
Skill	Skill Registry	Versioned skills with utility tracking, feedback loop, executable action bindings (prompt_template / tool_call / webhook).
Training	Chronos	Replay buffer, personality profile generation, training data export.

Ingestion Routing

All content -> Memora (always)
Long text (>=100 words) OR high importance (>=0.85) -> also MSA
High importance (>=0.85) -> also Chronos
Always writes daily log file
Post-remember hooks: KG extraction, access tracking

Recall

Three-layer assembled response with token budget control (default 4000 tokens):

Skills (score 1.0) — active skills matched by vector similarity, with executable prompts
KG Relations (score 0.9) — knowledge graph nodes + logical edges
Evidence (score 0.0-1.0) — Memora snippets + MSA documents

Collision Engine

7 strategies with attention-aware anchor selection:

RAG-based: Semantic Bridge, Dormant Revival, Temporal Echo, Chronos Cross-Ref, Digest Bridge
KG-driven: Contradiction-Based, Blind Spot-Based

Before each round, extracts 3-5 focus keywords from recent memories. Anchor selection biased toward current focus topics with recency weighting.

Requirements

Python 3.9+
macOS (Apple Silicon) or Linux
LLM API key: OpenRouter (preferred) or xAI (fallback)

Setup

See setup.md for installation instructions.

Source

GitHub: FluffyAIcode/openclaw-memory-pro-system

版本历史

共 1 个版本

v0.0.7 当前

2026-05-07 13:11 安全安全

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