MonkeyPatch – Cheap, fast and predictable LLM functions in Python
Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them. For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior. After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x…
In plain words
MonkeyPatch is a Python tool for building LLM-powered functions that become cheaper and faster with repeated use. Developers can create functions for tasks like PDF classification, data extraction, or synthetic data generation in under five minutes, with outputs that include type safety and guardrails. The tool uses progressive knowledge distillation to automatically fine-tune smaller models in the background, with users typically seeing significant cost and latency improvements after 200-300 function calls.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN, Jack here! I'm one of the creators of MonkeyPatch, an easy tool that helps you build LLM-powered functions and apps that get cheaper and faster the more you use them. For example, if you need to classify PDFs, extract product feedback from tweets, or auto-generate synthetic data, you can spin up an LLM-powered Python function in <5 minutes to power your application. Unlike existing LLM clients, these functions generate well-typed outputs with guardrails to mitigate unexpected behavior. After about 200-300 calls, these functions will begin to get cheaper and faster. We've seen 8-10x reduction in cost and latency in some use-cases! This happens via progressive knowledge distillation - MonkeyPatch incrementally fine-tunes smaller, cheaper models in the background, tests them against the constraints defined by the developer, and retains the smallest model that meets accuracy requirements, which typically has significantly lower costs and latency. As an LLM researcher, I kept getting asked by startups and friends to build specific LLM features that they could embed into their applications. I realized that most developers have to either 1) use existing low-level LLM clients (GPT4/Claude), which can be unreliable, untyped, and pricey, or 2) pore through LangChain documentation for days to build something. We built MonkeyPatch to make it easy for developers to inject LLM-powered functions into their code and create tests to ensure they behave as intended. Our goal is to help developers easily build apps and functions without worrying about reliability, cost, and latency, while following best software engineering practices. We're only available in Python currently but actively working on a Typescript version. The repo has all the instructions you need to get up and running in a few minutes. The world of LLMs is changing by the day and so we're not 100% sure how MonkeyPatch will evolve. For now, I'm just excited to share what we've been working on with the HN community. Would love to know what you guys think! Open-source repo: https://github.com/monkeypatch/monkeypatch.py Sample use-cases: https://github.com/monkeypatch/monkeypatch.py/tree/master/ex... Benchmarks: https://github.com/monkeypatch/monkeypatch.py#scaling-and-fi...
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