OpenClaw Memory Management: Complete Guide

OpenClaw Memory Management: Complete Guide
Memory management is one of the most common pain points for OpenClaw newcomers. This guide compiles best practices from dozens of Reddit discussions.
The Problem
"It forgets what you are talking about mid-sentence"
Unlike ChatGPT which warns about losing context, OpenClaw automatically compacts and forgets. This is a feature, not a bug—but requires proper setup.
Basic Memory Setup
Key prompt:
Enable memory flush before compaction
Set compaction.memoryFlush.enabled to true
Set memorySearch.experimental.sessionMemory to true
Memory Rules
Before training — /compact
- Run
/compactBEFORE any new task - This clears context for fresh information
- Run
After training — commit
- Ask: "Commit this to memory"
- Then: "Repeat back what you committed"
- Verify everything is correct
Before new task — recall
- Ask: "Check memory for related tasks"
- Agent loads relevant context
File Structure
.openclaw/
├── memory/
│ ├── YYYY-MM-DD.md # Daily logs
│ └── ...
├── MEMORY.md # Long-term memory
├── HEARTBEAT.md # Periodic tasks
└── TOOLS.md # Tool configuration
Supermemory.ai Integration
Why:
- Memory backup outside the agent
- Recovery after failures
- Structured storage
Setup:
- Connect API
- Configure automatic backup (every 6 hours)
- Use tags:
project-{name},decision,action-item
Common Mistakes
| Mistake | Solution |
|---|---|
| No /compact | Always before new task |
| Forgets after restart | Configure memory flush |
| Mixes old and new | Commit more often |
| Loses project context | Use Supermemory |
Memory management is not optional—it's essential.
👀 See Also

Fix Remote Browser Automation with OpenClaw Node Setup
Use a local OpenClaw node to avoid CDP/RDP headaches — run browser visible, keep your IP and cookies.

Splitting Agent Context into Three Layers to Solve the 700-Line Monolith Problem
A team building a 6-agent autonomous system solved context file bloat by separating agent context into three layers based on concern type and change frequency: CLAUDE.md for identity, BRIEFING.md for mission, and PLAYBOOK.md for operations. This approach prevents silent failures from argument limits and makes editing predictable.

How to run OpenClaw agents for free using cloud APIs or local models
A detailed guide explains how to run OpenClaw agents at zero cost using free cloud tiers from OpenRouter, Gemini, and Groq, or by running local models via Ollama with specific configuration tips to avoid common pitfalls.

How to Fix OpenClaw Response Times by Reducing Context Bloat
A developer resolved 10-minute response times in OpenClaw by reducing injected workspace files from 47,000 characters to 16,000 characters through file restructuring and configuration changes, including setting bootstrapMaxChars to 8000 and adding compaction safeguards.