OpenClaw Cost Optimization: Five Settings for Continuous Agent Usage
OpenClaw Configuration for Cost Reduction
A developer running OpenClaw as a personal agent layer on a Raspberry Pi 24/7 found that the agent was functioning correctly but using the most expensive operational path. After reviewing billing, they identified specific configuration adjustments that made a significant difference in cost.
Key Configuration Settings
The source lists five specific settings to adjust:
contextTokens: 80000– Caps the history sent per request instead of transmitting the full context window every time.compaction.mode: "safeguard"– Enables proactive, chunked summarization rather than reactive, one-shot context compaction.heartbeat.model: "<cheapest-model>"– Directs the 48 daily agent heartbeats to use the most economical model instead of the primary one.fallbacks– Recommends auditing provider logs to verify which model is actually handling requests, not relying on assumptions.reserveTokensFloor: 24000– Prevents context-limit errors that can trigger cascading retries and fallback mechanisms.
The underlying principle noted is that OpenClaw's default settings are optimized for capability. When running an agent continuously, you must explicitly configure for cost optimization.
The original setup involved using OpenClaw as a continuous personal agent on a Raspberry Pi. The full explanation and context for these settings are available in the linked post.
📖 Read the full source: r/openclaw
👀 See Also
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