👋Welcome

Hypster is a lightweight configuration framework for managing and optimizing AI & ML workflows

Hypster is in preview and is not ready for production use.

We're working hard to make Hypster stable and feature-complete, but until then, expect to encounter bugs, missing features, and occasional breaking changes.

Key Features

• 🐍 Pythonic API: Intuitive & minimal syntax that feels natural to Python developers

• 🪆 Hierarchical, Conditional Configurations: Support for nested and swappable configurations

• 📐 Type Safety: Built-in type hints and validation

• 🧪 Hyperparameter Optimization Built-In: Native, first-class optuna support

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How Does it work?

1

Install Hypster

uv add hypster

Or using pip:

pip install hypster

2

Define a configuration space

from hypster import HP


def llm_config(hp: HP):
    model_name = hp.select(["gpt-5", "claude-sonnet-4-0", "gemini-2.5-flash"], name="model_name")
    temperature = hp.float(0.0, name="temperature", min=0.0, max=1.0)
    return {"model_name": model_name, "temperature": temperature}

3

Instantiate your configuration

from hypster import instantiate

cfg = instantiate(llm_config, values={"model_name": "gpt-5", "temperature": 0.7})

4

Define an execution function

def generate(prompt: str, model_name: str, temperature: float) -> str:
    model = llm.get_model(model_name)
    response = model.prompt(prompt, temperature=temperature)
    return response

5

Execute!

generate(prompt="What is Hypster?", **cfg)

Discover Hypster

Getting Started

How to create & instantiate Hypster configs

Tutorials

Step-by-step guides for ML & Generative AI use-cases

Best Practices

How to make the most out of Hypster

Why Use Hypster?

In modern AI/ML development, we often need to handle multiple configurations across different scenarios. This is essential because:

1. We don't know in advance which hyperparameters will best optimize our performance metrics and satisfy our constraints.

2. We need to support multiple "modes" for different scenarios. For example:

   1. Local vs. Remote Environments, Development vs. Production Settings

   2. Different App Configurations for specific use-cases and populations

Hypster takes care of these challenges by providing a simple way to define configuration spaces and instantiate them into concrete workflows. This enables you to easily manage and optimize multiple configurations in your codebase.

Additional Reading

• Introducing Hypster - A comprehensive introduction to Hypster's core concepts and design philosophy.

• Implementing Modular RAG With Haystack & Hypster - A practical guide to building modular, LEGO-like reconfigurable RAG systems.

• 5 Pillars for Hyper-Optimized AI Workflows - Key principles for designing optimized AI systems. The process behind this article gave rise to hypster's design.