Butter, a muscle memory cache for LLMs
Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https://news.ycombinator.com/item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out:…
In plain words
Butter is an OpenAI-compatible API proxy that caches large language model generations and replays them deterministically when similar requests are made. Designed for developers building agent systems and automation workflows, it features template-aware caching that reuses responses across structurally similar prompts, reducing redundant API calls and improving consistency in repeated tasks.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https://news.ycombinator.com/item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out: https://blog.butter.dev/muscle-mem-as-a-proxy The proxy’s killer feature is being template-aware, meaning it can reuse cache entries across structurally similar requests. Inducing variable structure from context windows is no easy task, which we cover in a technical writeup here: https://blog.butter.dev/template-aware-caching The proxy is currently open-access and free to use so we can quickly discover and work through a slew of edge cases and template-induction errors. There’s much work to be done before it’s technically sound, but we’d love to see you take Butter for a spin and share how it went, where it breaks, if it’s helpful, if we're going down a dead end, etc. Cheers!
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