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openclaw-auto-dream

Consolidate daily logs into structured long-term memory with importance scoring.

548|26|Updated Mar 28, 2026
Search Tags:#knowledge graph#cognitive architecture#openclaw#memory consolidation#importance scoring#dream cycles#myclaw
One-click install
npx skills add https://github.com/LeoYeAI/openclaw-auto-dream --skill openclaw-auto-dream
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openclaw-auto-dream
Source: https://github.com/LeoYeAI/openclaw-auto-dream/tree/main
Command: npx skills add https://github.com/LeoYeAI/openclaw-auto-dream --skill openclaw-auto-dream

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

AI agents suffer from amnesia after each session ends, losing valuable context accumulated over weeks of interaction. Daily logs pile up unsorted and unscored, preventing the agent from learning from history or connecting disparate pieces of information. Auto-Dream fixes this by giving agents a cognitive memory system that periodically consolidates raw logs into structured long-term memory, complete with importance scoring, knowledge graphs, and health dashboards.

Core Features & Use Cases

  • Cognitive Dream Cycles: Automatically scans daily logs, extracts key insights, and consolidates them into five memory layers on a configurable cron schedule.
  • Intelligent Memory Management: Applies importance scoring with forgetting curves, semantic deduplication, and graceful archival of stale entries to keep memory fresh and relevant.
  • Knowledge Graph & Health Monitoring: Builds a linked knowledge graph with reachability metrics and provides an interactive HTML dashboard showing memory health scores, trends, and insights.
  • Cross-Instance Migration: Export and import memory bundles between MyClaw instances to clone or merge agent personas.

Quick Start

Tell your OpenClaw agent to install Auto-Dream and run a dream cycle to consolidate your daily logs into structured long-term memory.

Frequently Asked Questions about openclaw-auto-dream

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I consolidate AI agent daily logs into long-term memory?▼

Consolidate AI agent daily logs into long-term memory by running periodic dream cycles that extract insights and organize them into structured memory layers with importance scoring. This solves agent amnesia by preserving valuable context across sessions.

How does importance scoring work for AI agent memory management?▼

Importance scoring for AI agent memory management applies forgetting curves and semantic deduplication to rank retained information. It automatically archives stale entries to keep long-term memory fresh, relevant, and optimized for cognitive architecture.

Can I export and migrate memory bundles between MyClaw instances?▼

You can export and import memory bundles between MyClaw instances to clone or merge agent personas. This cross-instance migration transfers structured long-term memory, including knowledge graphs and cognitive memory layers, seamlessly.

Do I need cron scheduling to run AI agent dream cycles?▼

You need cron scheduling to automate AI agent dream cycles, which run in isolated session execution to process file-based memory layers. This periodic scheduling ensures raw logs are consolidated into structured memory without manual intervention.

What file formats are required for AI agent persistent memory layers?▼

AI agent persistent memory layers require file-based formats including MEMORY.md, index.json, and episode files. These structured files store consolidated knowledge graphs and cognitive memory outputs generated during automated dream cycles.

Why does my AI agent lose context after sessions end?▼

AI agents lose context after sessions end because daily logs pile up unsorted without a cognitive memory system to consolidate them. Auto-Dream fixes this amnesia by applying importance scoring and dream cycles to build persistent long-term memory.

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