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

Three-layer memory architecture for persistent OpenClaw agent context
A developer built a 3-layer memory system on top of OpenClaw's infrastructure to prevent agents from starting each session without context. The architecture includes L1 workspace files injected every turn, L2 semantic memory search, and L3 reference documents opened on demand.

Reddit Post: Developers Need Better AI Coding Practices, Not Just Better Tools
A Reddit post argues that developers' dissatisfaction with AI coding tools stems from poor prompting practices, specifically 'raw prompting' without context or structure. The author recommends using scaffolding like CLAUDE.md and structured workflows to get production-ready code from Claude.

Practical techniques to reduce state drift in multi-step AI agents
A developer shares concrete methods to fix state drift in multi-agent workflows, including snapshot-based reads, append-only writes, and separating state from context. These approaches made runs reproducible and debugging traceable.

Giving Claude M365 Access via Power Automate and a FastMCP Server
A developer built a lightweight MCP server that lets Claude interact with Microsoft 365 (inbox, calendar, OneDrive, Planner, Excel, Word) using Power Automate webhooks — no admin Graph permissions needed.