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

Maximizing AI Agent Capabilities in OpenClaw
OpenClaw's AI can be optimized by selecting the right model and providing specific system context. The Qwen models excel in tool use, critical for autonomous workflows.

Components of a Coding Agent: How Tools, Memory, and Context Extend LLMs
Sebastian Raschka breaks down the six building blocks of coding agents like Claude Code and Codex CLI, explaining how agent harnesses combine models with tools, memory, and repository context to make LLMs more effective for software work.

Claude Code O365 MCP Conditional Access Setup Issues and Solutions
A developer shares specific solutions for two problems encountered when setting up Claude Code's O365 MCP connector under conditional access policies: finding the correct application IDs for policy rules and resolving authentication errors related to server locations.

Practical fixes for OpenClaw reliability issues
A developer shares eight specific techniques that improved their OpenClaw setup, including a 3-tier memory system with daily logs and a knowledge graph, activation score management, and file-based rule enforcement.