[jubilanti]

The fact there is no way to disable auto-compaction and no way to go back in the conversation history to before a compact makes codex a no-go for me on any codebase > 5kloc. Even worse it seems to fire randomly at 10-20% of context left. So really you have 80% of 272k as real usable context. Compaction kills my sessions, it hallucinates and is worse than starting fresh. I've had enough times screaming at my computer when it burns tokens on a large codebase, gets to 15%, auto-compacts, and hallucinates so bad it has to read the entire codebase agin, gets to 15%, auto-compacts....

[volker48]

Have you tried using another harness like Pi? You can easily turn off auto compaction and if you leave it on you can use /tree to go back before the compaction if you like.

[sdesol]

> you can use /tree to go back before the compaction

This is really a killer feature in my opinion. I'm currently working on pi brains extension that is designed to solve the compaction issue I have, which is, I don't know what the AI knows after compaction.

https://gitsense.com/screenshots/pi-inspect-overview.png

The screenshot shot above contains what I call a compaction capsule which contains all the messages/events at the point of compaction. The "Files" section in the right side panel shows me how many files were read/modified since compaction.

After compacting, I can easily tell what Pi use to know and use `/tree` to go back in time.

I still wouldn't compact more than 5 times before starting a new session since compaction messages persists in Pi.

[maherbeg]

You also might like https://github.com/nicobailon/pi-boomerang that lets you effectively do some work, compact that section with a summary and continue on.

[behnamoh]

> The fact there is no way to disable auto-compaction and no way to go back in the conversation history to before a compact makes codex a no-go for me

Same. I've told @tibo many times about these issues but apparently OpenAI's focus is on features that buy them the most users ASAP instead of increasing harness quality.

[mycall]

So the practical workaround is to set model_auto_compact_token_limit very near, but still below, the model’s context window. This postpones compaction rather than disabling it.

config.toml

  model_context_window = 27000
  model_auto_compact_token_limit = 17000
  model_auto_compact_token_limit_scope = "total"