AI · alternatives · 2026

24 alternatives to 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…

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… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. MonkeyPatch – Cheap, fast and predictable LLM functions in Python launched in 2023; newer entries below may have overtaken it.

1
LLMs can generate valid JSON 100% of the time▲854
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
2023 · github.com · its alternatives →
2
Magentic – Use LLMs as simple Python functions▲283
This is a Python package that allows you to write function signatures to define LLM queries. This makes it easy to mix regular code with calls to LLMs, which enables you to use the LLM for its creativity and reasoning while also enforcing structure/logic as necessary. LLM output is parsed for you according to the return type annotation of the function, including complex return types such as streaming an array of structured objects. I built this to show that we can think about using LLMs more fluidly than just chains and chats, i.e. more interchangeably with regular code, and to make it…
2023 · github.com · its alternatives →
3
Magic Loops – Combine LLMs and code to create simple automations▲248
Howdy! We built this as an experiment in personal-programming, combining the best of LLMs and code to help automate tasks around you. I personally use it to track the tides and get notified when certain conditions are met, something that pure LLMs had trouble dealing with and pure code was often too brittle for. We created it after getting frustrated with the inability of LLMs to deal with numbers and the various hoops we had to jump through to make ChatGPT output repeatable. At the core, Magic Loops are just a series of "blocks" (JSON) that can be triggered with different inputs (email,…
2023 · magicloops.dev · its alternatives →
4
Speeding up LLM inference 2x times (possibly)▲419
Here's a project I've been working on for the last few months. It's a new (I think) algorithm, that allows to adjust smoothly - and in real time - how many calculations you'd like to do during inference of an LLM model. It seems that it's possible to do just 20-25% of weight multiplications instead of all of them, and still get good inference results. I implemented it to run on M1/M2/M3 GPU. The mmul approximation itself can be pushed to run 2x fast before the quality of output collapses. The inference speed is just a bit faster than Llama.cpp's, because the rest of implementation…
2024 · asciinema.org · its alternatives →
5
My LLM CLI tool can run tools now, from Python code or plugins▲529
LLM 0.26 is out with the biggest new feature since I started the project: support for tools. You can now use the LLM CLI tool—and Python library—to grant LLMs from …
2025 · simonwillison.net · its alternatives →
6
Marvin – build AI functions that use an LLM as a runtime▲127
Hey HN! We're excited to share our new open-source project, Marvin. Marvin is a high-level library for building AI-powered software. We developed it to address the challenges of integrating LLMs into more traditional applications. One of the biggest issues is the fact that LLMs only deal with strings (and conversational strings at that), so using them to process structured data is especially difficult. Marvin introduces a new concept called AI Functions. These look and feel just like regular Python functions: you provide typed inputs, outputs, and docstrings. However, instead of relying on…
2023 · github.com · its alternatives →
7
Modshim – A new alternative to monkey-patching in Python▲109
I've invented a new alternative to forking / vendoring / monkey-patching packages in Python. It's a bit like OverlayFS for Python modules - it allows you write modifications for a target module (lower) in a new module (upper), and have these combined in a new virtual module (mount). It works by rewriting imports using AST transformations, then running both the lower and upper module's code in the new Python module. This prevents polluting the global namespace when monkey-patching, and means if you want to make changes to a third-party package, you don't have to take on the…
2025 · github.com · its alternatives →
8
Leaping – Debug Python tests instantly with an LLM debugger▲120
Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our lives combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We’re currently working on a platform that ingests logs and then automatically reproduces, root causes and ultimately fixes production bugs as they happen. You can see some of our work on this here - https://news.ycombinator.com/item?id=39528087 As we were building the root-cause phase of our automated debugger, we realized that we developed…
2024 · github.com · its alternatives →
9
Why write code if the LLM can just do the thing? (web app experiment)▲436
I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com · its alternatives →
10
Fructose – LLM calls as strongly typed functions▲218
Hi HN! Erik here from Banana (formerly the serverless GPU platform), excited to show you what we’ve been working on next: Fructose Fructose is a python package to call LLMs as strongly typed functions. It uses function type signatures to guide the generation and guarantee a correctly typed output, in whatever basic/complex python datatype requested. By guaranteeing output structure, we believe this will enable more complex applications to be built, interweaving code with LLMs with code. For now, we’ve shipped Fructose as a client-only library simply calling gpt-4 (by default) with json…
2024 · github.com · its alternatives →
11
Code Mode▲144
I’ve been testing a simple idea inspired by Apple/Cloudflare/Anthropic: LLMs are far better at writing a small program than coordinating multiple tool calls. So instead of giving the model 10+ tools, I exposed one: a TypeScript sandbox with access to the same interfaces. The model writes a script → it runs once → done. What changed - +68% reduction in token use - No multi-step drift or retries - Local models (Llama 3.1 8B / Phi-3) became much more reliable Repo: https://github.com/universal-tool-calling-protocol/code-mode Curious whether others have seen…
Nov 2025 · github.com · its alternatives →
12
zodiac, a new approach to monkey patching in python▲39
A monkeypatching library for python. Contribute to colinmarc/zodiac development by creating an account on GitHub.
2012 · github.com · its alternatives →
13
Faster LLM evaluation with Bayesian optimization▲131
Recently I've been working on making LLM evaluations fast by using bayesian optimization to select a sensible subset. Bayesian optimization is used because it’s good for exploration / exploitation of expensive black box (paraphrase, LLM). I would love to hear your thoughts and suggestions on this!
2024 · github.com · its alternatives →
14VL
Verify LLM Generated Code with a Spreadsheet▲83
Hey HN! Been a minute. We launched Mito here last year (https://news.ycombinator.com/item?id=32723766). Mito is a spreadsheet that generates Python code as you edit it. We've spent the past three years trying to lower the startup cost to use Python for data work. In doing so, we’ve been thrust into the middle of many Python transition processes at larger enterprises, and we’ve seen up-close how non-technical folks interact with generated code. The Mito AI chatbot lives inside of the Mito spreadsheet (https://www.trymito.io/>. The obvious benefit of this is that…
2023 · its alternatives →
15
Burr – A framework for building and debugging GenAI apps faster▲94
Hey HN, we're developing Burr (github.com/dagworks-inc/burr), an open-source python framework that makes it easier to build and debug GenAI applications. Burr is a lightweight library that can integrate with your favorite tools and comes with a debugging UI. If you prefer a video introduction, you can watch me build a chatbot here: https://www.youtube.com/watch?v=rEZ4oDN0GdU. Common friction points we’ve seen with GenAI applications include logically modeling application flow, debugging and recreating error cases, and curating data for testing/evaluation (see…
2024 · github.com · its alternatives →
16
LLM Scraper – turn any webpage into structured data▲88
Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
2024 · github.com · its alternatives →
17
Bash4LLM+ – A lightweight, dependency-free Bash wrapper for LLM APIs▲60
Bash4LLM is a single-file Bash wrapper for interacting with LLMs from the terminal. I created it because I wanted something simple that worked without installing Python, Node, or any other runtime. It uses only Bash, curl, and jq. You can send prompts, start a small chat, process files line by line, stream output, and save session metadata in JSON format. I tried to make it safe and predictable: no use of the system /tmp, no use of eval. Groq is supported by default, and other providers can be added with dedicated Bash scripts in the extras/providers/ folder. Example: echo…
Jun 2026 · github.com · its alternatives →
18
Taylor AI▲118
Fine-tune open source LLMs in minutes
2023 · trytaylor.ai · its alternatives →
19
FiddleCube – Generate Q&A to test your LLM▲78
Convert your vector embeddings into a set of questions and their ideal responses. Use this dataset to test your LLM and catch failures caused by prompt or RAG updates. Get started in 3 lines of code: ``` pip3 install fiddlecube ``` ``` from fiddlecube import FiddleCube fc = FiddleCube(api_key="") dataset = fc.generate( [ "The cat did not want to be petted.", "The cat was not happy with the owner's behavior.", ], 10, ) dataset ``` Generate your API key: https://dashboard.fiddlecube.ai/api-key # Ideal QnA datasets for testing, eval and training LLMs Testing, evaluation or…
2024 · github.com · its alternatives →
20LO
Leaping – Open-source debugging with LLMs▲15
Show HN: Leaping - Open-source debugging with LLMs Hi HN! We’re Adrien and Kanav. We met at our previous job, where we spent about a third of our life combating a constant firehose of bugs. In the hope of reducing this pain for others in the future, we’re working on automating debugging. We started by capturing information from running applications to then ‘replay’ relevant sessions later. Our approach for Python involved extensive monkey patching: we’d use OpenTelemetry-style instrumentation to hook into the request/response lifecycle, and capture anything non-deterministic (random,…
2024 · its alternatives →
21
Robust LLM extractor for websites in TypeScript▲72
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
22
Data Bonsai: a Python package to clean your data with LLMs▲47
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
2024 · github.com · its alternatives →
23AT
A tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)▲55
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
24
TurboPilot: Copilot clone runs code completion LLM on your CPU▲37
Hi HN, I spent my easter weekend stuck in the house with COVID and I decided to play with llama.cpp [1] and fauxpilot [2] to see if I could get LLM code assist working on pure CPU. As a proof of concept I'd say I've proven that it's possible. However there's still a lot to do. The auto complete is quite slow at the moment. PRs welcome. [1] https://github.com/ggerganov/llama.cpp [2] https://github.com/fauxpilot/fauxpilot
2023 · github.com · its alternatives →

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