Butter – A Behavior Cache for LLMs
Hi HN! I'm Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs. - It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url - The cache is template-aware, meaning lookups can treat dynamic content (names, addresses, etc.) as variables You can see it in action in this demo where it memorizes tic-tac-toe games: https://www.youtube.com/watch?v=PWbyeZwPjuY Why we built this: before Butter, we were Pig.dev (YC W25), where we…
Butter – A Behavior Cache for LLMs launched on October 28, 2025 with 50 votes, #412 of 8,178 launches that month and more than 90% of that year's launches.
In plain words
Butter is an LLM proxy that caches and replays responses to make agent systems deterministic and consistent across runs. It functions as a chat completions-compatible endpoint that integrates into existing agents via custom base URL, with template-aware caching that treats dynamic content like names and addresses as variables. Built for developers creating automation systems that require predictable behavior.
written from the facts on this page · September 2026
From the sources
Butter is building bVisor, an incredibly lightweight sandbox runtime for AI agents.
Butter is building bVisor , an incredibly lightweight sandbox runtime for AI agents. It embeds directly into your local process, providing a safe bash environment without the overhead of VMs or remotefrom butter.dev
In the maker’s words, at launch
Hi HN! I'm Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs. - It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url - The cache is template-aware…
Does a similar job
all alternatives →- BAButter, a muscle memory cache for LLMs2025 · docs.butter.dev · ▲23
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:…
An LLM response cache that's aware of dynamic dataJan 2026 · blog.butter.dev · ▲17Raymond here from Butter.dev, an LLM response cache built as a chat-completions proxy. Today we're launching a key feature for the platform: the ability to generalize on dynamic, templated inputs. Caching at the HTTP request level has the obvious problem of generalizability. Nearly no request is identical, due to templated variables (like names) and metadata (like timestamps), so exact-match cache lookups rarely hit. We solve this at Butter by using LLMs to detect dynamic content in requests and derive their inter-relationships, allowing the cache entry to be stored as a template + variables…
Blast – Fast, multi-threaded serving engine for web browsing AI agents2025 · github.com · ▲145Hi HN! BLAST is a high-performance serving engine for browser-augmented LLMs, designed to make deploying web-browsing AI easy, fast, and cost-manageable. The goal with BLAST is to ultimately achieve google search level latencies for tasks that currently require a lot of typing and clicking around inside a browser. We're starting off with automatic parallelism, prefix caching, budgeting (memory and LLM cost), and an OpenAI-Compatible API but have a ton of ideas in the pipe! Website & Docs: https://blastproject.org/ https://docs.blastproject.org/ MIT-Licensed…
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