How an Idle Agent Burned 50M Tokens a Day – and How to Fix It

An OpenClaw user on Reddit reported their LLM API usage spiking from 11M to 51M tokens per day over six days, totaling 196M tokens — most of it wasted by an idle agent. The cause: a forgotten agent called "main" was being poked by OpenClaw's heartbeat every 30 minutes, loading a 225k-token session history just to reply "HEARTBEAT_OK".

The Leak: cacheRead Dominance

Two numbers stood out from the transcript analysis:

• 95% of tokens were cacheRead — the model re-reading old conversation history instead of doing new work.
• 56% of all tokens came from a single agent named "main" that wasn't even in use anymore.

The heartbeat ran 48 times daily, each time loading a months-old session. Even an empty HEARTBEAT.md — intended to disable the heartbeat — failed to stop it in that build.

The Fix: Two Steps

1. Clear the bloated session. Wipe the session file for the idle agent. The next heartbeat starts from a blank slate. Only remove junk sessions, keep real DM/chat sessions.
2. Prevent refilling. A one-time clear won't stick because the heartbeat keeps adding to the session. Use config changes:
   • Set heartbeat every: "0m" to turn off heartbeat entirely if the agent does nothing, or
   • Set isolatedSession: true and lightContext: true so each heartbeat runs on a fresh, tiny context (~2-5k tokens) instead of the full history (~100k+).

Bonus: other agents were reusing one ever-growing session because a session ID was not actually starting fresh in that build. Reset session between jobs to keep each run clean.

Takeaways

• Idle agents are not free — a heartbeat on a fat session can cost more than real work.
• If most tokens are cacheRead, you're paying to re-read history, not for new work.
• Verify that "off" is actually off — empty HEARTBEAT.md didn't stop the heartbeat in the reported build.
• Read the per-turn transcripts; token usage is recorded there per input/output/cacheRead.

The user cut their token usage by more than half with these config changes.