> ## Documentation Index
> Fetch the complete documentation index at: https://developer.swytcho.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Swytcho: Unified AI API for Developers — Overview

> Swytcho gives developers a single, OpenAI-compatible API to access multiple AI models — build faster without managing separate integrations.

Swytcho is a unified AI API platform that lets you access the best AI models from a single endpoint. Instead of juggling multiple provider accounts, SDKs, and authentication flows, you write your code once and Swytcho routes your requests to the model of your choice. Whether you're building a customer support chatbot, a code assistant, or a document processing pipeline, Swytcho gives you the flexibility to swap models without rewriting your integration.

<CardGroup cols={2}>
  <Card title="Quickstart" icon="bolt" href="/quickstart">
    Make your first API call in under five minutes. No prior setup required beyond a free Swytcho account.
  </Card>

  <Card title="Authentication" icon="key" href="/authentication">
    Learn how to generate and securely use API keys to authenticate every request you send to Swytcho.
  </Card>

  <Card title="API Reference" icon="code" href="/api-reference/overview">
    Explore every endpoint, parameter, and response schema in the full Swytcho API reference.
  </Card>

  <Card title="Integrations" icon="puzzle-piece" href="/integrations/overview">
    Connect Swytcho to your existing stack — LangChain, LlamaIndex, Vercel AI SDK, and more.
  </Card>
</CardGroup>

## What you can build

Swytcho exposes a clean, OpenAI-compatible interface, so anything you can build with a standard chat completion API works out of the box — and you gain the freedom to switch models as your needs evolve.

* **Text generation** — Generate long-form content, summaries, translations, and structured data from any supported model.
* **Chat completions** — Build multi-turn conversational experiences using the familiar `messages` array format.
* **Streaming** — Stream tokens to your UI in real time by setting `"stream": true` on any completion request.
* **Embeddings** — Create vector representations of text for semantic search, clustering, and retrieval-augmented generation.
* **Function calling** — Define tools your model can invoke, enabling agents that take real-world actions based on user input.