Summary

OpenClaw Auto-Dream addresses the critical issue of knowledge loss and context amnesia in AI agents. It provides a cognitive memory consolidation system for OpenClaw agents, enabling them to "sleep" and "dream," thereby transforming them into more intelligent, context-aware partners. This solution is specifically designed for users of the MyClaw.ai ecosystem who require persistent, evolving memory capabilities for their AI assistants.

How It Works

Auto-Dream operates via a periodic "dream cycle" that automatically scans, consolidates, and evaluates an agent's accumulated logs. The process involves three phases: Collect (extracting insights from recent logs and flagging important markers), Consolidate (routing insights to one of five distinct memory layers—Working, Episodic, Long-term, Procedural, Index—and linking related entries), and Evaluate (scoring importance using a formula incorporating recency and references, applying forgetting curves for archival, calculating a multi-metric health score, and generating actionable insights). This multi-layered, evaluated approach mimics cognitive memory processes for enhanced AI recall and learning.

Quick Start & Requirements

Installation is streamlined via ClawHub (clawhub install openclaw-auto-dream) or directly through a MyClaw agent command ("Install Auto-Dream"). Manual installation involves cloning the repository. Setup is initiated via an agent command ("Set up Auto-Dream"), which configures cron jobs and notification preferences. The system has no external dependencies beyond the OpenClaw runtime and operates entirely on files, requiring no API keys or databases. Official documentation and demos are accessible via MyClaw.ai.

Highlighted Details

• Five Cognitive Memory Layers: Differentiates storage and function for Working, Episodic, Long-term, Procedural, and Index memories.
• Sophisticated Importance Scoring & Forgetting: Employs a weighted formula (base × recency × references) and graceful archival for unreferenced memories, preventing data loss.
• Knowledge Graph & Reachability: Dynamically links memory entries via semantic relations, measuring overall coherence and connectivity.
• Holistic Memory Health Score: A composite metric (Freshness, Coverage, Coherence, Efficiency, Reachability) provides a quantitative assessment of memory quality.
• Interactive Dashboard: A zero-dependency HTML interface visualizes memory health, distribution, and knowledge graph connectivity.
• Cross-Instance Migration: Enables seamless export/import of memory bundles between different agent instances.

Maintenance & Community

The project is part of the MyClaw.ai ecosystem, indicating centralized development and support. Community engagement is facilitated via Reddit (r/myclaw) and X/Twitter ([@MyClaw_Official](https://x.com/MyClaw_Official)). Active development is evident through frequent release notes detailing new features and optimizations.

Licensing & Compatibility

Distributed under the permissive MIT license, allowing for broad adoption, commercial use, and integration into closed-source projects without significant restrictions. It is specifically designed for compatibility within the OpenClaw/MyClaw agent framework.

Limitations & Caveats

Auto-Dream is tightly coupled to the OpenClaw/MyClaw ecosystem and is not designed for standalone use. While actively developed, the cognitive memory architecture is complex and relatively new, with recent releases focusing on core functionality and stability. Performance may vary depending on the agent's data volume and the underlying hardware, as the dream cycle involves significant file I/O and processing.