OpenClaw token usage investigation reveals configuration issues

Token usage investigation reveals configuration problems

A developer reported burning through their OpenAI Codex weekly subscription in about 1.5 days while using OpenClaw for daily AI news updates. They used Claude Code to perform a deep review of their setup and identified several configuration issues causing excessive token consumption.

Key findings from the investigation

The investigation revealed multiple configuration problems:

• Telegram requireMention setting: All group chats had requireMention: false, meaning every message triggered the agent. Setting this to true makes bots only fire on @mentions.
• Web fetch defaults: The readability setting was off by default, causing fetches to return raw CSS/JS content even on failed requests. 21 out of 21 web_fetch calls had failed but still dumped page shells into context.
• Model inheritance: Per-agent model overrides don't inherit from defaults. Changing agents.defaults.model.primary to gpt-5.4-mini didn't cascade to four agents with hardcoded overrides to gpt-5.4.
• Orphan session files: Found 41 .reset.*/.deleted.* transcript files (~56MB) that nothing references anymore.
• No guardrails on web research: A web-research agent (responsible for 78% of token spend) had no fetch budget, no stopping rules, and no "check local memory before searching" pattern.

Configuration fixes

The investigation recommended these specific configuration changes:

openclaw config set tools.web.fetch.readability true
openclaw config set tools.web.fetch.maxChars 12000
openclaw config set tools.web.fetch.timeoutSeconds 15
openclaw config set tools.web.fetch.cacheTtlMinutes 30

For session cleanup:

openclaw sessions cleanup --all-agents --enforce --fix-missing

Quick checklist

• Check requireMention on all group chats
• Enable tools.web.fetch.readability and set maxChars/timeoutSeconds
• Audit per-agent model overrides — defaults don't cascade
• Run openclaw sessions cleanup --all-agents --dry-run --fix-missing
• Add fetch budgets and stopping rules to research-heavy agents

The developer noted that Claude Code was particularly useful for forensic analysis: it grep'd through 2 days of gateway logs, counted 510 gpt-5.4 references vs 23 gpt-5.4-mini in active sessions, found 198 CSS variable references in failed fetch results, and identified 56% redundancy in startup files.