What is OpenClaw context bloat?

OpenClaw retains all conversation history by default, causing context to grow from ~5K tokens in round 1 to ~150K tokens by round 10. This increases API costs, latency, and memory usage significantly.

How much does OpenClaw cost per day with default settings?

Users running OpenClaw out of the box routinely hit $50 to $100 per day in API costs due to context bloat.

How do I fix OpenClaw context bloat?

Use context pruning, keep MEMORY.md under 800 words, implement vector search for long-term memory, and use the openclaw-optimization-guide for production-ready patterns.

How much RAM does OpenClaw use?

The core process can climb to 28GB by day three even with a 32GB container limit. Minimum 4GB for personal dev, 8GB for teams, 16GB for production.

What is the openclaw-optimization-guide?

A 33-part guide on GitHub (312 stars) covering speed optimization, memory architecture, context management, model selection, and one-shot development patterns for production OpenClaw.

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clawsmith.com/signal/openclaw-context-bloat-production-pain

⚠ IssueWide OpenLive

OpenClaw Context Bloat: 5K to 150K Tokens in 10 Rounds — The #1 Production Cost and Latency Killer

OpenClaw retains all conversation history by default. First round ~5K tokens; tenth round 150K. Users routinely hit $50-$100/day in API costs. Memory climbs to 28GB by day three. Long-running sessions accumulate context state with no garbage collection. Optimization guides emerged: openclaw-optimization-guide (312 stars), openclaw-mem, Openclaw-Token-memory-optimizer.

Product Idea from this Signal

A background service that enforces per-session token budgets on OpenClaw agents, auto-prunes context when limits approach, and reports cost per task

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OpenClaw retains all conversation history by default, growing from 5K tokens in round 1 to 150K by round 10. Users routinely hit $50-100/day in API costs running stock configurations. Memory consumption climbs to 28GB by day three. Existing tools show token usage after the fact or add a kill-switch, but nothing actively manages the context window in real time by pruning low-value messages, enforcing per-session budgets, and attributing cost to individual tasks. This service sits between OpenClaw and the LLM provider, intercepts every request, enforces configurable token budgets, and auto-prunes context using a relevance scorer before the request hits the API.

BACKGROUND-SERVICECOST-OPTIMIZATIONOPEN-SOURCEDEVTOOL

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