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

OpenClaw 2026.3.7 breaks Kimi tool calls, downgrade to 2026.3.2 fixes regression
OpenClaw version 2026.3.7 has a regression where the Kimi API provider outputs raw <function_calls> XML instead of executing tools. The solution is to downgrade to version 2026.3.2 and restore a compatible config file.

Master OpenClaw on Your Android Smartphone: A Comprehensive Tutorial
Curious about harnessing the potential of OpenClaw on your Android smartphone? This tutorial provides step-by-step guidance on getting started, covering essential tips and tricks from the vibrant OpenClaw community.

Understanding AI Agent Architecture: Deterministic vs Probabilistic Layers
A Reddit user shares a mental model for AI agent systems that separates deterministic layers (scripts, commands, APIs) from probabilistic layers (LLM reasoning and decisions). The key insight: push as much work as possible to the deterministic side.

Practical Prompt Structure for Claude AI Execution Agents
A developer shares prompt engineering techniques that reduced hallucinations in Claude AI agents performing API calls, data extraction, and multi-step workflows. Key strategies include writing prompts as contracts, dedicating 40% of tokens to error handling, and separating 'wait' from 'stop' conditions.