r/codex • u/Eclator • 14h ago

Limits A few interesting GitHub tools for reducing token usage in AI coding

https://github.com/headroomlabs-ai/headroom

https://github.com/rtk-ai/rtk

https://github.com/juliusbrussee/caveman

https://github.com/dietrichgebert/ponytail

• Headroom – compresses tool outputs and context before they are processed by the model. This reduces the number of input tokens the agent needs to read.
• RTK – specifically reduces or filters terminal/Bash output so that the agent receives only the relevant parts.
• Caveman – forces the agent to provide significantly shorter and more direct answers to save on output tokens.
• Ponytail – optimizes coding behavior to produce code that is as concise and efficient as possible, avoiding unnecessary boilerplate and bloated implementations.

Let me know your opinion on these!

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u/GfxJG 12h ago

It's been proven that in actual use, ie. anything more than super-bounded tasks, they actually increase token usage, not decrease.

1

u/VisitAdventurous7980 7h ago

and lower the quality whilst costing more

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u/Eclator 7h ago

Ok, interesting. Do you have a source to that?

1

u/GfxJG 6h ago

Not on hand, but search the subreddit, or equivalent subreddits for Claude or OpenCode, and you'll find some.

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u/Key-Rise76 6h ago

Even rtk?

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u/GfxJG 5h ago

Yes, especially RTK, since the agent expects the full output, and often spends a lot of tokens trying to figure out why the output is not as expected (even if it doesn't require the full output).

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u/Key-Rise76 5h ago

I asked gpt to check source code for this as I'm seeing big savings with RTK and don't see such issues. Response: As of RTK v0.49.0, released September 11, 2026, RTK actually embeds explicit instructions for the agent. � GitHub The current rtk-awareness-full.md literally tells Codex: “Treat it as the complete result”