agent-dream

Consolidate and prune OpenClaw agent memories across sessions with a nightly dream cycle.

Updated Feb 15, 2026
One-click install
npx skills add https://github.com/aleph23/Natasha --skill agent-dream
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: agent-dream
Source: https://github.com/aleph23/Natasha/tree/main/skills/agent-dream
Command: npx skills add https://github.com/aleph23/Natasha --skill agent-dream

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Your OpenClaw agent often forgets context between sessions, leading to drift in memory, conflicting decisions, and misalignment with user goals. Agent Dream provides a nightly dream cycle to review, organize, prune, and reflect on memories so the agent wakes with a coherent sense of self and recent changes.

Core Features & Use Cases

  • 5-phase dream cycle (Orient, Gather, Consolidate, Prune, Reflect) to process memories, decisions, and lessons.
  • Safe 2-pass deletion with backup copies (MEMORY.md.pre-dream and topic backups) to preserve history.
  • Automatic dream reporting and growth notifications to keep humans informed and confident in the agent's memory health.
  • Zero-config setup and auto-detection of workspace, memory structure, and sessions for quick deployment across OpenClaw agents.
  • Works with any OpenClaw agent to maintain long-term memory and identity, and to surface learnings over time.

Quick Start

Run the setup script to auto-detect your workspace and enable the nightly dream cycle.

Frequently Asked Questions about agent-dream

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

FAQPage Schema
How do I preserve OpenClaw agent memory across multiple sessions?

To preserve OpenClaw agent memory across multiple sessions, use a nightly dream cycle to consolidate, organize, and prune memories. This process maintains agent identity and learning coherence over time.

What is the best way to prune stale agent memory without losing data?

The best way to prune stale agent memory safely is using a 2-pass deletion method with automatic backups. This creates pre-dream and topic backup copies before removing stale data, ensuring full auditability and recovery.

How does a dream cycle work for autonomous agent memory management?

A dream cycle works by processing agent memories through five phases: Orient, Gather, Consolidate, Prune, and Reflect. This workflow organizes recent changes, removes stale data, and enables self-reflection to maintain identity.

Can I automate memory consolidation for OpenClaw agents with zero configuration?

Yes, you can automate memory consolidation with zero configuration. The setup script auto-detects your workspace, memory structure, and sessions to quickly deploy the nightly memory management workflow across OpenClaw agents.

Does automated memory pruning work with any OpenClaw agent?

Yes, automated memory pruning works with any OpenClaw agent. It maintains long-term memory and identity by surfacing learnings and applying configurable scheduling to ensure safe, auditable memory management across sessions.