How to Manage OpenClaw Sessions and Context Pruning
Long conversations fill the context window, causing errors, higher costs, and slower responses. Effective session management with intelligent context pruning keeps OpenClaw responsive while preserving important conversation history. This guide covers session lifecycle, pruning strategies, archiving, and monitoring.
Why This Is Hard to Do Yourself
These are the common pitfalls that trip people up.
Context window limits
Claude models have finite context windows (200k tokens). Long sessions hit the limit and fail.
What to prune
Pruning too aggressively loses important context. Pruning too little fills the window. Finding the balance is hard.
Cost vs. quality tradeoff
Larger context windows cost more. Aggressive pruning saves money but degrades conversation quality.
Session storage
Thousands of sessions create storage overhead. Old sessions need cleanup without losing valuable data.
Step-by-Step Guide
Understand OpenClaw session lifecycle
Sessions have distinct phases that affect management strategy.
Configure session TTL and cleanup
Set time-to-live for different session states.
Warning: Setting TTL too short will delete active sessions. Start with conservative values (24h for idle, 90d for archives) and adjust based on usage patterns.
Set up context window limits
Configure maximum context size per model.
Configure pruning strategies (FIFO, relevance-based)
Choose how to prune old messages.
Warning: Relevance-based pruning requires computing embeddings, which adds latency and cost. Use FIFO for most use cases and relevance only when conversation quality is critical.
Implement session archiving
Archive old sessions to cold storage.
Monitor context usage
Track token usage and pruning effectiveness.
Session Management Is Easy to Get Wrong
TTL configuration, pruning strategies, archiving, monitoring โ balancing context quality with performance and cost requires careful tuning. Our experts configure production-grade session management for OpenClaw deployments.
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