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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Prompt engineering for developers
| Date | Stars |
|---|---|
| 2026-07-31 | 696 |
| 2026-08-03 | 696 |
| 2026-08-06 | 696 |
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# Tanuki <span style="font-family:Papyrus; font-size:2em;">🦝</span> 
Easily build LLM-powered apps that get cheaper and faster over time.
Join us on [Discord](https://discord.gg/uUzX5DYctk)
---
## Contents
<!-- TOC start (generated with https://github.com/derlin/bitdowntoc) -->
* [Introduction](#introduction)
* [Features](#features)
* [Installation and Getting Started](#installation-and-getting-started)
* [How It Works](#how-it-works)
* [Typed Outputs](#typed-outputs)
* [Test-Driven Alignment](#test-driven-alignment)
* [Scaling and Finetuning](#scaling-and-finetuning)
* [Frequently Asked Questions](#frequently-asked-questions)
* [Simple ToDo List App](#simple-todo-list-app)
<!-- TOC end -->
<!-- TOC --><a name="introduction"></a>
## Introduction
Tanuki is a way to easily call an LLM in place of the function body in Python, with the same parameters and output that you would expect from a function implemented by hand.
These LLM-powered functions are well-typed, reliable, stateless, and production-ready to be dropped into your app seamlessly. Rather than endless prompt-wrangling and nasty surprises, these LLM-powered functions and applications behave like traditional functions with proper error handling.
Lastly, the more you use Tanuki functions, the cheaper and faster they gets (up to 9-10x!) through automatic model distillation.
```python
@tanuki.patch
def some_function(input: TypedInput) -> TypedOutput:
"""(Optional) Include the description of how your function will be used."""
@tanuki.align
def test_some_function(example_typed_input: TypedInput,
example_typed_output: TypedOutput):
assert some_function(example_typed_input) == example_typed_output
```
<!-- TOC --><a name="features"></a>
## Features
- **Easy and seamless integration** - Add LLM augmented functions to any workflow within seconds. Decorate a function stub with `@tanuki.patch` and optionally add type hints and docstrings to guide the execution. That’s it.
- **Type aware** - Ensure that the outputs of the LLM adhere to the type constraints of the function (Python Base types, Pydantic classes, Literals, Generics etc) to guard against bugs or unexpected side-effects of using LLMs.
- **Aligned outputs** - LLMs are unreliable, which makes them difficult to use in place of classically programmed functions. Using simple assert statements in a function decorated with `@tanuki.align`, you can align the behaviour of your patched function to what you expect.
- **Lower cost and latency** - Achieve up to 90% lower cost and 80% lower latency with increased usage. The package will take care of model training, MLOps and DataOps efforts to improve LLM capabilities through distillation.
- **Popular model support** - Tanuki supports a wide array of popular models (OpenAI, Amazon Bedrock, Together AI) to carry out the function execution
- **RAG support** - Seamlessly get embedding outputs for downstream RAG (Retrieval Augmented Generation) implementations. Output embeddings can then be easily stored and used for relevant document retrieval to reduce cost & latency and improve performance on long-form content.
- **Batteries included** - No remote dependencies other than OpenAI.
<!-- TOC --><a name="installation-and-getting-started"></a>
## Installation and Getting Started
<!-- TOC --><a name="installation"></a>
### Installation
```
pip install tanuki.py
```
or with Poetry
```
poetry add tanuki.py
```
Set your OpenAI key using:
```
export OPENAI_API_KEY=sk-...
```
<!-- TOC --><a name="getting-started"></a>
### Getting Started
To get started:
1. Create a python function stub decorated with `@tanuki.patch` including type hints and a docstring.
2. (Optional) Create another function decorated with `@tanuki.align` containing normal `assert` statements declaring the expected behaviour of your patched function with different inputs.
3. (OptionaExcerpt of 21,269 characters
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Luke Kuzmish · @loopreturns · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:960ee96024013407, topic:alignment
matched fp:960ee96024013407, topic:llm