---
title: Introduction
description: Welcome to HumaLab SDK documentation
---

# HumaLab SDK

Python SDK for HumaLab - A platform for adaptive AI validation.

## Overview

HumaLab SDK provides a comprehensive toolkit for creating, managing, and evaluating AI validation scenarios. It enables researchers and developers to:

- Define adaptive scenarios with probabilistic distributions
- Track metrics and statistics across multiple episodes
- Manage assets and resources for simulation environments
- Upload and analyze results to the HumaLab platform

## Installation

Install HumaLab SDK using pip:

```bash
pip install humalab
```

## Quick Start

Here's a simple example to get you started:

```python
import humalab as hl

# Initialize HumaLab
with hl.init(api_key="your_api_key",
             project="my_project",
             name="my_first_run",
             scenario={
                 "param1": "${uniform(0, 10)}",
                 "param2": "${gaussian(5, 1)}"
             }) as run:

    # Create an episode
    with run.create_episode() as episode:
        # Your validation logic here
        # ...
```

## Key Concepts

### Scenarios

Scenarios define the configuration space for your AI validation tasks using probability distributions. They allow you to create adaptive test cases that explore different parameter combinations.

### Runs

A Run represents a complete validation experiment containing multiple episodes. It tracks all metrics and manages the lifecycle of your validation task.

### Episodes

Episodes are individual instances of a scenario with specific parameter values. Each episode represents one execution of your validation logic.

### Metrics

HumaLab supports various types of metrics for tracking validation results:
- Standard metrics for time-series data
- Scenario statistics for distribution analysis
- Summary metrics for aggregated results
- Code artifacts for version tracking

## Next Steps

- [Scenarios Guide](/docs/scenarios) - Learn how to define adaptive scenarios
- [Runs & Episodes](/docs/runs) - Understand the run lifecycle
- [Metrics](/docs/metrics) - Track and analyze your results
- [API Reference](/docs/api) - Detailed API documentation
