OpenClaw instances can consume excessive memory due to large conversation contexts, unbounded caches, memory leaks in skills, or simply having too many features enabled simultaneously. High memory usage leads to slow response times, system swap thrashing, and eventual OOM (out of memory) kills. This guide helps you identify memory hogs and optimize resource usage.
Why This Is Hard to Do Yourself
These are the common pitfalls that trip people up.
Memory usage creeping up over time
Slow leaks in skill code or unbounded conversation history buffers
Massive context windows
Keeping full conversation history in memory for long sessions
Unbounded caches and buffers
Embedding caches, skill result caches growing without eviction policies
Too many loaded skills
Each skill and its dependencies consuming heap space even when idle
Step-by-Step Guide
Measure current memory usage
Establish a baseline to understand how much memory OpenClaw is actually using.
Identify specific memory hogs
Use profiling tools to see which parts of OpenClaw consume the most memory.
Configure strict memory limits
Set hard limits to prevent runaway memory consumption and force garbage collection.
Reduce conversation context window
Limit how much conversation history OpenClaw keeps in memory.
Prune loaded skills and caches
Disable unused skills and configure cache eviction policies.
Set up memory monitoring and alerts
Track memory trends over time and get notified before OOM kills.
Memory Issues Slowing You Down?
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