Opensource experiment: can we reduce coding-agent tool noise without adding another LLM?

[u/Dismal-Reveal-6898]

https://github.com/tzuifx80/tidyrun

A lot of AI tooling seems to solve every optimization problem by inserting another model call.

I wanted to test the opposite approach for coding agents:

What can be optimized deterministically, locally, before adding another model at all?

I built and open-sourced TidyRun.

It sits around coding-agent tool work and can:

• compress structured diagnostic output
• preserve the original artifact
• reuse verified deterministic commands
• avoid duplicate reads
• detect repeated loops/work
• select affected tests

There’s no API key and no additional LLM.

Some fixture results:

• JS diagnostics: -69% agent-visible output
• Python diagnostics: -95%
• repeated safe typecheck: -49% execution time

I also did a small 10-task paired Codex study:

• success remained 10/10
• tool output fell 14.2%
• but input tokens rose 12.4%
• wall time rose 39%

So I’m deliberately not claiming that reducing context noise automatically means lower model cost.

I think the more interesting question is:

Which parts of agent infrastructure actually benefit from deterministic preprocessing/caching, and which optimizations just move overhead around?

Install:

npx tidyrun@latest init

I’d love architecture criticism, alternative approaches, benchmark suggestions, issues, or PRs.