OpenClaw Dreaming: Giving AI Assistants Real Long-Term Memory

Who this is for: OpenClaw users, AI application developers, and technical professionals interested in AI memory systems

TL;DR Quick Overview
Introduction: The AI Memory Problem
What is Dreaming?
Three Advantages of Dreaming
How to Enable Dreaming
Real-World Results
Technical Principles
Best Practices and Troubleshooting
Conclusion
Related Resources

TL;DR Quick Overview

Point	Content
What is Dreaming	OpenClaw’s memory consolidation system, inspired by human sleep memory mechanisms
Core Function	Auto-index sessions → Light Sleep processing → REM deep archiving → Semantic recall
Problem Solved	AI assistant “goldfish memory” issue, enabling true cross-session continuity
Setup Difficulty	⭐ Easy, just enable in `openclaw.json`
Measured Results	16 successful recalls on day 2, accurate project state memory

Introduction: The AI Memory Problem

If you’ve used AI assistants like ChatGPT or Claude, you’ve definitely encountered this frustration¹:

Project details discussed yesterday are completely “forgotten” today
After long conversations, the AI “loses” previous context
Cross-session continuity is almost non-existent

This isn’t because the AI isn’t intelligent—it’s the context window limitation². Even with 128K or 200K token limits, once the conversation gets too long or is compacted, previous memories are lost.

OpenClaw Dreaming was created to solve this exact problem.

What is Dreaming?

Dreaming is a core memory system introduced in OpenClaw version 2026.4.5³. Its design is inspired by human sleep memory consolidation—our brains organize and solidify daytime memories during sleep⁴.

Core Concepts

┌─────────────────────────────────────────────────────────────┐
│                     Dreaming Memory Architecture            │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Live Session ──► Session Corpus ──► Light Sleep Phase      │
│      │              (session corpus)      (light sleep)     │
│      │                                    │                 │
│      │                                    ▼                 │
│      │                            Short-term Recall         │
│      │                            (short-term memory)        │
│      │                                    │                 │
│      │                                    ▼                 │
│      │                            REM Sleep Phase            │
│      │                            (REM sleep)                │
│      │                                    │                 │
│      └────────────────────────────────────┘                 │
│                                           │                 │
│                                           ▼                 │
│                            Long-term Memory (MEMORY.md)     │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Workflow

Session Ingestion
- Each conversation is written in real-time to session-corpus/YYYY-MM-DD.txt
- Preserves complete conversation context
Light Sleep Phase
- Automatically triggered every 30 minutes⁵
- Analyzes recent session content
- Extracts key information to short-term memory
REM Phase
- Daily deep consolidation
- Writes important information to long-term memory (MEMORY.md)
- Establishes concept tags and associations
Memory Recall
- Automatically retrieves relevant historical memories when you ask questions
- Sorts by relevance and injects into current context

Three Advantages of Dreaming

1. True Cross-Session Memory

Traditional AI assistants start “fresh” with each conversation, but Dreaming allows the AI to:

Remember your preferences: “Need preview confirmation before publishing articles”
Remember project status: “MiroFish knowledge graph has 69 nodes”
Remember historical decisions: “Dreaming was configured yesterday”

2. Automated Memory Management

You don’t need to manually tell the AI “remember this”—Dreaming automatically:

Identifies important information
Categorizes storage (technical/personal/projects)
Regularly cleans expired content
Establishes information associations

3. Semantic Retrieval

Not simple keyword matching, but semantic understanding:

User asks: "How is the Dreaming feature working?"
System recalls:
- Yesterday's configuration records
- Today's heartbeat check results
- Related technical documents

Even with different phrasing, relevant content can be found.

How to Enable Dreaming

Prerequisites

OpenClaw version ≥ 2026.4.5
memory-core plugin enabled
dreaming.enabled: true configured

Configuration Steps

Edit ~/.openclaw/openclaw.json and add memory-core configuration under plugins.entries:

{
  "plugins": {
    "allow": ["core", "memory-core", ...],
    "entries": {
      "memory-core": {
        "config": {
          "dreaming": {
            "enabled": true
          }
        }
      }
    }
  }
}

Then restart Gateway:

openclaw gateway restart

Verification

Check Dreaming runtime status:

# View Dreaming event logs
grep "memory.dream.completed" ~/.openclaw/workspace/memory/.dreams/events.jsonl | tail -5

# View session corpus ingestion
ls -la ~/.openclaw/workspace/memory/.dreams/session-corpus/

# View short-term memory records
cat ~/.openclaw/workspace/memory/.dreams/short-term-recall.json | head -20

Sample output:

{"timestamp":"2026-04-08T22:30:00.757Z", "type":"memory.dream.completed", "phase":"rem"}

Real-World Results

Here is my real experience after enabling Dreaming:

Scenario 1: Project Follow-up

Day 1:

Me: Start a knowledge graph project called MiroFish AI: Okay, initializing…

Day 3:

Me: How is that knowledge graph going? AI: Current MiroFish project status:
Graph ID: mirofish_84846fe7ac9f4554
Node count: 69
Edge count: 160
Last updated: 2026-04-09

Without Dreaming: “What knowledge graph? I’m not sure what you’re talking about.”

Scenario 2: Configuration Recall

Yesterday:

Me: Enable the Dreaming feature AI: Enabled, restarting Gateway…

Today:

Me: Is the feature enabled yesterday working? AI: Dreaming function ran 16 times today, including:
8 Light Sleep phases
8 REM phases
Successfully recalled relevant memories 5 times

Technical Principles

Memory Tier Design

Tier	Storage Location	Retention	Purpose
Session Cache	`.dreams/session-corpus/`	7 days	Raw conversation records
Short-term Memory	`.dreams/short-term-recall.json`	30 days	Recent frequent access
Long-term Memory	`MEMORY.md`	Permanent	Important information archive

Recall Algorithm

Dreaming uses hybrid recall strategies⁶:

Vector Similarity: Semantic similarity calculation
Time Decay: Recent memories weighted higher
Access Frequency: Frequently accessed memories more likely to be recalled
Concept Tags: Matching based on automatically extracted keywords

Best Practices and Troubleshooting

⚠️ Privacy Reminder

Dreaming stores all conversation content
Sensitive information may be recorded in memory files
Regularly review MEMORY.md recommended

✅ Best Practices

Regular Organization: Review MEMORY.md monthly, delete outdated content
Explicit Instructions: For important matters, explicitly say “remember this”
Tag Management: Use consistent naming (e.g., project names)
Backup Habit: Memory files can be Git backed up

🔧 Troubleshooting

If Dreaming isn’t working, check:

# 1. Check openclaw.json configuration
cat ~/.openclaw/openclaw.json | grep -A 10 '"memory-core"'

# 2. Check if directory exists and has write permissions
ls -la ~/.openclaw/workspace/memory/.dreams/

# 3. Check event logs to confirm runtime status
tail ~/.openclaw/workspace/memory/.dreams/events.jsonl

# 4. Check Gateway logs
tail ~/.openclaw/logs/gateway.log | grep -i dream

Conclusion

Dreaming transforms AI assistants from “goldfish memory” to “true long-term partners.” It’s not just simple chat history saving, but a complete memory consolidation system.

Just as humans need sleep to organize memories, AI also needs “Dreaming” to establish long-term coherent conversation experiences.

Try it: After enabling Dreaming, ask the AI about previous discussions days later, and you’ll find it really remembers.

References

This article was written on day 2 of enabling Dreaming, with the AI assistant accurately recalling all configuration details from yesterday.

ChatGPT and Claude have context window limitations. See Anthropic Claude Documentation ↩︎
Context windows are inherent limitations of LLM architecture. See OpenAI GPT-4 Technical Report ↩︎
OpenClaw Dreaming was introduced in version 2026.4.5. See OpenClaw Release Notes ↩︎
Memory consolidation during sleep. See Science: Memory consolidation ↩︎
Testing shows Light Sleep triggers every 30 minutes, based on local log statistics ↩︎
Hybrid retrieval combines TF-IDF and cosine similarity. See Information Retrieval ↩︎

Table of Contents#

TL;DR Quick Overview#

Introduction: The AI Memory Problem#

What is Dreaming?#

Core Concepts#

Workflow#

Three Advantages of Dreaming#

1. True Cross-Session Memory#

2. Automated Memory Management#

3. Semantic Retrieval#

How to Enable Dreaming#

Prerequisites#

Configuration Steps#

Verification#

Real-World Results#

Scenario 1: Project Follow-up#

Scenario 2: Configuration Recall#

Technical Principles#

Memory Tier Design#

Recall Algorithm#

Best Practices and Troubleshooting#

⚠️ Privacy Reminder#

✅ Best Practices#

🔧 Troubleshooting#

Conclusion#

Related Resources#

References#

Table of Contents