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

Replacing OpenClaw's Default Memory with Redis and Qdrant for Production Multi-Agent Systems
A developer replaced OpenClaw's default SQLite memory with Redis for ephemeral state and Qdrant for persistent vector memory to solve scaling issues in multi-agent setups, implementing semantic search, cross-agent sharing, and concurrent writes.

OpenClaw Workspace Structure and Self-Improvement Approach from Longtime User
A longtime OpenClaw user shares their workspace structure with key markdown files like SOUL.md, AGENTS.md, and MEMORY.md, plus the critical lesson that allowing the agent to improve its own environment dramatically increases effectiveness.

Four aarch64-specific failure modes when running vLLM on Blackwell GB10 with CUDA 13.0
A developer encountered four specific failure modes when setting up vLLM v0.7.1 with DeepSeek-R1-32B on a Blackwell GB10 system running aarch64 architecture with CUDA 13.0, including ABI mismatches and missing dependencies.

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.