If they are literally sending the entire transcript and asking for a summary, wouldn't almost all of the tokens be cache hit, and therefore fairly cheap?
I tested it, and it looks real but more nuanced. It is just for subagents; workflows do not seem affected. I guess many new users prefer workflows (ultracode), so the impact is less than the report suggested.
Moreover it is mostly cached reads, so subagents with short context cost little, while long-context ones cost much more.
In my Claude Code sessions, it looks more like ~8% at most.
They enabled it for everyone in a recent Claude Code update, but you can turn it off (I did).
I am definitely getting frustrated with a lot of the changes they keep making in Claude Code. It's one thing to find settings have changed during your next interactive session, it's another when it changes how overnight unattended sessions behave. I really should be spending more time building my own harness again.
Check your idleCompaction line in /config to make sure it's off -- several times they've toggled new features on by default for me. This one isn't in the VS Code extension so I don't know if it shipped default-on.
Background agents only have 5 minutes' cache TTL, but I'm guessing this doesn't help extend it (not that it would be too useful anyway, especially considering the extra burn).
Also, the claim "15-20% of your tokens" seems unclear; of all tokens, or of tokens spent on background agents? Poorly edited AI writing strikes again!
i think its not the status line at the bottom of the terminal, but actually the few words summary claude generates for each background agents in the agents panel. so if we mostly run one plain session we wont pay for this? i do run claude code with disableAgentView on in my settings. does anyone know if that helps to stop the summary calls or just hides the panel?
Claude Code's (CC) status summaries used 16.4% of our token spend in a week! We have investigated a source of overhead in CC: it's an agent_summary query that calls your current session/subagent model approximately every 30 seconds for a few words in CC’s status line.
Our DRIM system flagged the anomaly in CC's OTEL traces. Grouping requests by query_source showed that over one measured week agent_summary accounted for: 16.4% of total estimated spend (versus 15.7% for the main conversation), and 19.7% of all API requests with approximately 9,000 input tokens per output token.
Most requests reuse the prompt cache, but repeatedly reading a large cached transcript still has a cost. Longer contexts and more concurrent agents increase the overhead.
We couldn’t find a supported setting that disables only these summaries while preserving background agents and forks. Disabling those capabilities also stops the summaries, but changes the workflow we want to keep.
Our workaround is a small Bun preload that intercepts the summary timer without modifying the CC binary. It identifies the timer callback by source-text anchors and leaves other timers alone.
In controlled tests on Linux (from ver 2.1.257 to 2.1.312), the same workload produced five summary calls without the hook and zero with suppression enabled. On our current production setup, agent_summary calls remained at zero with the hook enabled. Background agents and forks continued running. The only visible change was that their generated status summaries stopped updating.
The workaround depends on implementation details. An update can break the callback matching and silently restore the requests. macOS and Windows are untested. If someone could help us with testing there, it would be much appreciated.
I work on Claude Code. You've got the basic mechanism right, but to be super clear, this only applies to background agents that are actively working. They get a summary call about every 30 seconds, and idle background agents don't. These summaries are used for both you and Claude Code to track what each background agent is working on, without reading transcripts.
Your 16.4% sounds like it was for a team running mostly background agents, and doesn't seem like a typical session. (Feel free to send me the session ID at edwinarbus@anthropic.com and we can dig further.)
Last week, across Claude Code users who send us telemetry, agent_summary was about 2% of cost at API list prices and just under 4% of requests. In the terminal, where nearly all of these calls happen, it was about 6% of cost.
That said, we agree that's a lot for a status line. We just rolled out a fix for the most expensive case, summaries that miss the prompt cache (2.1.311+), and right now we're working on a much cheaper mode that sends short notes to a smaller model instead of the full transcript.
If it happens 10 times you have already accepted the same cost as the entire session fresh prefill.
People on HN are either too rich or wayyy too rich
Moreover it is mostly cached reads, so subagents with short context cost little, while long-context ones cost much more.
In my Claude Code sessions, it looks more like ~8% at most.
I'm on pro. Two days ago, 100% 5 hour was using 25% of my weekly. Today, 100% 5 hour is like 13% so something was fixed.
I am definitely getting frustrated with a lot of the changes they keep making in Claude Code. It's one thing to find settings have changed during your next interactive session, it's another when it changes how overnight unattended sessions behave. I really should be spending more time building my own harness again.
Also, the claim "15-20% of your tokens" seems unclear; of all tokens, or of tokens spent on background agents? Poorly edited AI writing strikes again!
Cache rules everything around me.
Similarly to “one language is best,” saying “one harness is best” is shortsighted in the best possible case.
That being said, Claude Code is bloatware.
Our DRIM system flagged the anomaly in CC's OTEL traces. Grouping requests by query_source showed that over one measured week agent_summary accounted for: 16.4% of total estimated spend (versus 15.7% for the main conversation), and 19.7% of all API requests with approximately 9,000 input tokens per output token.
Most requests reuse the prompt cache, but repeatedly reading a large cached transcript still has a cost. Longer contexts and more concurrent agents increase the overhead.
We couldn’t find a supported setting that disables only these summaries while preserving background agents and forks. Disabling those capabilities also stops the summaries, but changes the workflow we want to keep.
Our workaround is a small Bun preload that intercepts the summary timer without modifying the CC binary. It identifies the timer callback by source-text anchors and leaves other timers alone.
In controlled tests on Linux (from ver 2.1.257 to 2.1.312), the same workload produced five summary calls without the hook and zero with suppression enabled. On our current production setup, agent_summary calls remained at zero with the hook enabled. Background agents and forks continued running. The only visible change was that their generated status summaries stopped updating.
The workaround depends on implementation details. An update can break the callback matching and silently restore the requests. macOS and Windows are untested. If someone could help us with testing there, it would be much appreciated.
If you want to try it - the script, full technical explanation, test results, and installation instructions are on GitHub: https://gist.github.com/flamerix-ai/c3905705cc4205e5de3011fa...
Your 16.4% sounds like it was for a team running mostly background agents, and doesn't seem like a typical session. (Feel free to send me the session ID at edwinarbus@anthropic.com and we can dig further.)
Last week, across Claude Code users who send us telemetry, agent_summary was about 2% of cost at API list prices and just under 4% of requests. In the terminal, where nearly all of these calls happen, it was about 6% of cost.
That said, we agree that's a lot for a status line. We just rolled out a fix for the most expensive case, summaries that miss the prompt cache (2.1.311+), and right now we're working on a much cheaper mode that sends short notes to a smaller model instead of the full transcript.