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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.
OO for LLMs
| Date | Stars |
|---|---|
| 2026-07-31 | 912 |
| 2026-08-01 | 912 |
| 2026-08-06 | 912 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<div align="center">
<img src="https://raw.githubusercontent.com/google/langfun/main/docs/_static/logo.svg" width="520px" alt="logo"></img>
</div>
# Langfun
[](https://badge.fury.io/py/langfun)
[](https://codecov.io/gh/google/langfun)
[**Installation**](#install) | [**Getting started**](#hello-langfun) | [**Tutorial**](https://colab.research.google.com/github/google/langfun/blob/main/docs/notebooks/langfun101.ipynb) | [**Discord community**](https://discord.gg/U6wPN9R68k)
## Introduction
Langfun is a [PyGlove](https://github.com/google/pyglove) powered library that
aims to *make language models (LM) fun to work with*. Its central principle is
to enable seamless integration between natural language and programming by
treating language as functions. Through the introduction of *Object-Oriented Prompting*,
Langfun empowers users to prompt LLMs using objects and types, offering enhanced
control and simplifying agent development.
To unlock the magic of Langfun, you can start with
[Langfun 101](https://colab.research.google.com/github/google/langfun/blob/main/docs/notebooks/langfun101.ipynb). Notably, Langfun is compatible with popular LLMs such as Gemini, GPT,
Claude, all without the need for additional fine-tuning.
## Why Langfun?
Langfun is *powerful and scalable*:
* Seamless integration between natural language and computer programs.
* Modular prompts, which allows a natural blend of texts and modalities;
* Efficient for both request-based workflows and batch jobs;
* A powerful eval framework that thrives dimension explosions.
Langfun is *simple and elegant*:
* An intuitive programming model, graspable in 5 minutes;
* Plug-and-play into any Python codebase, making an immediate difference;
* Comprehensive LLMs under a unified API: Gemini, GPT, Claude, Llama3, and more.
* Designed for agile developement: offering intellisense, easy debugging, with minimal overhead;
## Hello, Langfun
```python
import langfun as lf
import pyglove as pg
from IPython import display
class Item(pg.Object):
name: str
color: str
class ImageDescription(pg.Object):
items: list[Item]
image = lf.Image.from_uri('https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Solar_system.jpg/1646px-Solar_system.jpg')
display.display(image)
desc = lf.query(
'Describe objects in {{my_image}} from top to bottom.',
ImageDescription,
lm=lf.llms.Gpt4o(api_key='<your-openai-api-key>'),
my_image=image,
)
print(desc)
```
*Output:*
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/8/83/Solar_system.jpg/1646px-Solar_system.jpg" width="520px" alt="my_image"></img>
```
ImageDescription(
items = [
0 : Item(
name = 'Mercury',
color = 'Gray'
),
1 : Item(
name = 'Venus',
color = 'Yellow'
),
2 : Item(
name = 'Earth',
color = 'Blue and white'
),
3 : Item(
name = 'Moon',
color = 'Gray'
),
4 : Item(
name = 'Mars',
color = 'Red'
),
5 : Item(
name = 'Jupiter',
color = 'Brown and white'
),
6 : Item(
name = 'Saturn',
color = 'Yellowish-brown with rings'
),
7 : Item(
name = 'Uranus',
color = 'Light blue'
),
8 : Item(
name = 'Neptune',
color = 'Dark blue'
)
]
)
```
See [Langfun 101](https://colab.research.google.com/github/google/langfun/blob/main/docs/notebooks/langfun101.ipynb) for more examples.
## Install
Langfun offers a range of features through [Extras](https://packaging.python.org/en/latest/tutorials/installing-packages/#installing-extras), allowing users to install only what they need. The minimal installation of Langfun requires only [PyGlove](https://github.com/google/pyglove), [Jinja2](https://github.com/pallets/jinja/), Excerpt of 5,225 characters
Read on GitHub
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Chengrun Yang
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Matej Aleksandrov · Google · Germany
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8cb5e883882603ca, topic:nlp