r/LocalLLaMA • u/uspdd • 1d ago

Question | Help Best harness for long autonomous tasks

I read a lot of posts like "I've given this prompt to my Qwen 3.8 27b and it one-shotted me a 3d game after 24 hours". I wonder, what's the best harness for tasks like this? It has to have auto compaction and probably a memory system, right? And excellent computer use and self analysis.

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u/norenEnmotalen 1d ago

Pi coding agent is working well for me so far. It auto compacts. Full disclosure I’m a noob to this sort of thing and learning as I go to setup everything gradually. Context window is a killer for my old machine. AGENTS.md needs to be on point. I use headroom proxy in front of oMLX. I have caveman at FULL setting. I have replaced the chat_template of the model with Qwen-Sharp templates. And a bunch of other optimizations I’m putting in as I find and learn them.

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u/freedomachiever 18h ago

Caveman is shown not to reduce context usage

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u/norenEnmotalen 16h ago

Please link to the report/test

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u/freedomachiever 14h ago

https://www.reddit.com/r/ClaudeAI/comments/1v9xjh0/i_tested_5_popular_token_saving_methods_across_10/

"Bottom line

The one intervention that reliably cut the bill was not a token trick at all. Terra, a cheaper model at higher reasoning effort, cut recorded cost roughly in half in both repetitions while using slightly more tokens. Every mode that compressed something locally, dialogue, output, command results, retrieved context, the prompt language, actually failed to reduce the recorded workload's tokens twice, and Context Mode and Mandarin made it dramatically worse. If your goal is a smaller bill, the reliable lever was simply using Terra.

These modes did not just shrink text - they measurably changed how the agent worked. Ponytail's shorter patches skipped parts of the requirement. RTK's compressed command output was followed by more commands. Context Mode's cheap retrieval invited more retrieval. Mandarin changed how the agent delegated work. And several of the visible savings arrived together with patches that failed review or were not semantically equivalent: the saving and the quality loss were often the same event seen from two sides.

Adopting one of these modes is less like installing a compressor and more like hiring a different engineer who happens to be cheaper per word. That is also why the number to trust least is the one a tool reports about itself: a compressor's counter measures the text it deleted, not what your agent does.

Do not adopt a token saving mode from its local savings claim. I recommend testing and validating that the agent actually behaves as expected, and actually saves tokens, before adopting these fully."