# 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 * :snake: **Pythonic API**: Intuitive & minimal syntax that feels natural to Python developers * :nesting\_dolls: **Hierarchical, Conditional Configurations**: Support for nested and swappable configurations * :triangular\_ruler: **Type Safety**: Built-in type hints and validation * :mag: **Schema Exploration**: Inspect parameters, defaults, and active branches with `explore()` * :test\_tube: **Hyperparameter Optimization Built-In**: Native, first-class optuna support > Show your support by giving us a [star](https://github.com/gilad-rubin/hypster)! ⭐ ### How Does it work? {% stepper %} {% step %} **Install Hypster** ```bash uv add hypster ``` Or using pip: ```bash pip install hypster ``` {% endstep %} {% step %} **Define a configuration space** ```python 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} ``` {% endstep %} {% step %} **Explore your configuration** ```python from hypster import explore explore(llm_config) ``` {% endstep %} {% step %} **Instantiate your configuration** ```python from hypster import instantiate cfg = instantiate(llm_config, values={"model_name": "gpt-5", "temperature": 0.7}) ``` {% endstep %} {% step %} **Define an execution function** ```python def generate(prompt: str, model_name: str, temperature: float) -> str: model = llm.get_model(model_name) response = model.prompt(prompt, temperature=temperature) return response ``` {% endstep %} {% step %} **Execute!** ```python generate(prompt="What is Hypster?", **cfg) ``` {% endstep %} {% endstepper %} ## Discover Hypster
Getting StartedHow to create & instantiate Hypster configsGroup 4 (5).pnginstallation
TutorialsStep-by-step guides for ML & Generative AI use-casesGroup 53.pngusage-examples
Best PracticesHow to make the most out of HypsterGroup 26.pngbasic-best-practices
## 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](https://medium.com/@giladrubin/introducing-hypster-a-pythonic-framework-for-managing-configurations-to-build-highly-optimized-ai-5ee004dbd6a5) - A comprehensive introduction to Hypster's core concepts and design philosophy. * [Implementing Modular RAG With Haystack & Hypster](https://towardsdatascience.com/implementing-modular-rag-with-haystack-and-hypster-d2f0ecc88b8f) - A practical guide to building modular, LEGO-like reconfigurable RAG systems. * [5 Pillars for Hyper-Optimized AI Workflows](https://medium.com/@giladrubin/5-pillars-for-a-hyper-optimized-ai-workflow-21fcaefe48ca) - Key principles for designing optimized AI systems. The process behind this article gave rise to hypster's design.