---
title: LLM Overview
description: Build LLM-powered applications with type-safe, provider-agnostic abstractions.
---

# LLM Overview

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The `mirascope.llm` module is a Python library for building LLM-powered applications. It provides type-safe, provider-agnostic abstractions that make it easy to call LLMs, use tools, and get structured output.

## Core Concepts

| Concept | Description |
| --- | --- |
| [Messages](/docs/learn/llm/messages) | Understand message types and multimodal content |
| [Models](/docs/learn/llm/models) | Configure and call LLMs across providers |
| [Responses](/docs/learn/llm/responses) | Work with LLM responses and continue conversations |
| [Prompts](/docs/learn/llm/prompts) | Create reusable prompt functions with `@llm.prompt` |
| [Calls](/docs/learn/llm/calls) | Bundle models with prompts using `@llm.call` |

## Features

| Feature | Description |
| --- | --- |
| [Thinking](/docs/learn/llm/thinking) | Use extended reasoning capabilities |
| [Tools](/docs/learn/llm/tools) | Let LLMs call your functions |
| [Structured Output](/docs/learn/llm/structured-output) | Get typed responses with Pydantic models |
| [Streaming](/docs/learn/llm/streaming) | Stream responses in real-time |
| [Async](/docs/learn/llm/async) | Async operations for concurrent calls |

## Advanced Topics

| Topic | Description |
| --- | --- |
| [Agents](/docs/learn/llm/agents) | Build autonomous agent systems |
| [Context](/docs/learn/llm/context) | Manage call context and model resolution |
| [Chaining](/docs/learn/llm/chaining) | Chain prompts for multi-step workflows |
| [Errors](/docs/learn/llm/errors) | Handle errors and edge cases |
| [Reliability](/docs/learn/llm/reliability) | Retries, validation, and resilience |
| [Providers](/docs/learn/llm/providers) | Provider-specific configuration |
| [Local Models](/docs/learn/llm/local-models) | Run models locally |
| [MCP](/docs/learn/llm/mcp) | Model Context Protocol integration |

## Quick Example

```python
from mirascope import llm

@llm.call("openai/gpt-4o-mini")
def recommend_book(genre: str) -> str:
    return f"Recommend a {genre} book"

response = recommend_book("fantasy")
print(response.text())
```

For a complete introduction, see the [Quickstart](/docs/quickstart).
