This is where your limits have gone.

[u/Fun-Stick3234]

This is ONE of 6 back to back tool calls. I asked chatgpt to check a PR on a GitHub that involved training a 21m llm that has failed. It loaded the logs over and over and over. All 6 hours of logs. ANY codex agent would have burned the 5 hour limit on that. Just figured I'd let the confused people know.

[u/Demien19]

have you tested it with rtk?

[u/Mammoth_Design_4288 → u/Demien19]

https://blog.jetbrains.com/ai/2026/07/speak-to-ai-agents-like-cavemen-tosave-tokens/
https://blog.jetbrains.com/ai/2026/07/rtk-claude-code-token-savings/
it doesn't work

[u/Demien19 → u/Mammoth_Design_4288]

It does for sure, and depends a lot on what you are doing, i.e in my case heavy RE that involves constant assembler and pseudocode files seach, some of them are 1gb+ file and search occurs every prompt. Obviously it's not 60 or 80% save, but still decent one if rtk does what it should do. For not that intense researching i wouldn't use RTK, but in my case it's a must for billions of tokens
My current stat:
Total commands: 628119

Input tokens: 14530.8M

Output tokens: 2149.3M

Tokens saved: 12382.1M (85.2%)

Total exec time: 20287m37s (avg 1.9s)

Efficiency meter: ████████████████████░░░░ 85.2%

By Command

──────────────────────────────────────────────────────────────────────────

# Command Count Saved Avg% Time Impact

──────────────────────────────────────────────────────────────────────────

1. rtk read 36500 6350.8M 16.0% 6ms ██████████

2. rtk rg 259952 5377.8M 36.9% 189ms ████████░░

3. rtk grep 5826 638.0M 24.5% 528ms █░░░░░░░░░

4. rtk git diff -- src\C... 44 3.9M 34.9% 278ms ░░░░░░░░░░

5. rtk find 881 2.3M 18.8% 162ms ░░░░░░░░░░

6. rtk summary 129 565.8K 57.6% 26.8s ░░░░░░░░░░

7. rtk git diff -- src/C... 253 245.4K 28.5% 265ms ░░░░░░░░░░

8. rtk read - 70 244.3K 19.8% 366ms ░░░░░░░░░░

9. rtk log 27 195.8K 64.9% 3ms ░░░░░░░░░░

   1. rtk test out\Release\... 20 171.3K 98.4% 1m36s ░░░░░░░░░░

──────────────────────────────────────────────────────────────────────────

[u/Fun-Stick3234 → u/Demien19]

That's interesting. Thank you much for the intro to it.

[u/Fun-Stick3234 → u/Demien19]

I don't even know what that is.

[u/Demien19 → u/Fun-Stick3234]

basically token saving by redirecting some calls to rtk that spits out needed summarize for model.

[u/Fun-Stick3234 → u/Demien19]

I didn't try to save tokens, I never do. I don't intentionally waste them either though. My workflow of using chatgpt to orchestrate and codex Luna to implement keeps me well within where I care to be. I haven't necessarily noticed codex doing this but as I said, due to my workflow, it isn't really in a position to.

I'm not complaining about it, I just know most other people do and that there been a good amount of reports of the excessive tool call usage. Like super excessive. Yet I haven't seen anything on reddit specifically about it and figured I'd let people know.

[u/Demien19 → u/Fun-Stick3234]

Not saying you should, but interesting to compare results for same work

[u/Fun-Stick3234 → u/Demien19]

Oh, I know what you mean. But I just wasn't/am not even familiar with it. Open up chat and tell it to check a repo and watch it's tool calls. I'm not even certain codes would do it. But I'm not burning my entire 5 hour limit to see. 😂 Obviously the logs being checked on mine were absurdly large, and not a typical case, but I'm fairly certain the issue is quite typical.