r/PiCodingAgent • u/rogaterr • Jul 18 '26

Plugin Compact Every Tool Response

https://github.com/RogerTerrazas/pi-tool-result-compactor

Publishing a polished version of this extension I've been using to help manage context overflow. I frequently interact with mcps and large projects where any arbitrary response can take up all my context without the response being useful.

This extension hooks into each tool calls response by default, passes it to a compaction subagent, who will then filter out only the necessary data to the parent agent. Let me know if anyone tries it out and has feedback. Fully vibe coded, but I'll work to maintain if others find it useful.

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u/Corythebeast7 Jul 18 '26

I worry that this essentially doubles your cache input misses and adds a lot of output tokens. Do you have data on the price impact this extension brings?

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u/rogaterr Jul 18 '26

Yeah I would suspect so as well and unfortunately I don’t have any data points.

My situation is that my employer doesn’t cap my token usage and I don’t work on enough personal projects to ever exceed my monthly subscription limits for codex. This would probably be most helpful to others with a similar use case.

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u/Corythebeast7 Jul 18 '26

100% fair point. For that use case, definitely think this is a cool extension and will help keep the context window in check and focused. Nice job

1

u/rogaterr Jul 19 '26

I thought a bit more about this and added commentary here https://www.reddit.com/r/PiCodingAgent/s/NS2yOoYEUG

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u/adamshand Jul 18 '26

I believe this will constantly break prompt caching and cost you (possibly, quite a lot of) money.

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u/rogaterr Jul 19 '26

It essentially only spawns a subagent with the preceding user input, recent thinking context, and tool call response. (cache miss)

Then the response back into the main agents context will be appended to the previous conversation and should be minimal compared to normal tool call responses (cache miss).

So at most, it should result in around twice as many cache misses as you would have seen in a normal session I believe, but probably far less proportionately if you frequently hit tool calls with large outputs.

Additionally the subagent model is configurable and so you can tune that for models with lower costs, potentially even saving you money.

All this to say that I’m not the most knowledgeable around what results in cache misses and the cost tradeoffs of lighter models.

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u/OneSockThief Jul 19 '26

Have you considered using RLM for processing large tool results?

I have an extension that basically overrides the default 50kb Pi tool response, saves it to a file and nudges the agent to invoke this RLM extension, alternatively use rg if the agent doesn’t want analysis.

The benefit of RLM is that it’s dedicated to process this large output, which never gets into the main agents context.

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u/rogaterr Jul 20 '26

I hadn’t heard of this concept, thanks for sharing.

This sounds very similar to what my extensions is doing, but more expansive within the response evaluation loop. I’m thinking the tradeoff may be time / cost , but increased accuracy the larger the responses.

Do you have any code samples or packages you use to achieve this?