---
title: Building Applications
description: Comprehensive guides for building AI applications with Llama Stack
sidebar_label: Overview
sidebar_position: 5
---

# AI Application Examples

Llama Stack provides all the building blocks needed to create sophisticated AI applications.

## Getting Started

The best way to get started is to look at this comprehensive notebook which walks through the various APIs (from basic inference, to RAG agents) and how to use them.

**📓 [Building AI Applications Notebook](https://github.com/meta-llama/llama-stack/blob/main/docs/getting_started.ipynb)**

## Core Topics

Here are the key topics that will help you build effective AI applications:

### 🤖 **Agent Development**
- **[Agent Framework](./agent.mdx)** - Understand the components and design patterns of the Llama Stack agent framework
- **[Agent Execution Loop](./agent_execution_loop.mdx)** - How agents process information, make decisions, and execute actions
- **[Agents vs Responses API](./responses_vs_agents.mdx)** - Learn when to use each API for different use cases

### 📚 **Knowledge Integration**
- **[RAG (Retrieval-Augmented Generation)](./rag.mdx)** - Enhance your agents with external knowledge through retrieval mechanisms

### 🛠️ **Capabilities & Extensions**
- **[Tools](./tools.mdx)** - Extend your agents' capabilities by integrating with external tools and APIs

### 📊 **Quality & Monitoring**
- **[Evaluations](./evals.mdx)** - Evaluate your agents' effectiveness and identify areas for improvement
- **[Telemetry](./telemetry.mdx)** - Monitor and analyze your agents' performance and behavior
- **[Safety](./safety.mdx)** - Implement guardrails and safety measures to ensure responsible AI behavior

## Application Patterns

### 🤖 **Conversational Agents**
Build intelligent chatbots and assistants that can:
- Maintain context across conversations
- Access external knowledge bases
- Execute actions through tool integrations
- Apply safety filters and guardrails

### 📖 **RAG Applications**
Create knowledge-augmented applications that:
- Retrieve relevant information from documents
- Generate contextually accurate responses
- Handle large knowledge bases efficiently
- Provide source attribution

### 🔧 **Tool-Enhanced Systems**
Develop applications that can:
- Search the web for real-time information
- Interact with databases and APIs
- Perform calculations and analysis
- Execute complex multi-step workflows

### 🛡️ **Enterprise Applications**
Build production-ready systems with:
- Comprehensive safety measures
- Performance monitoring and analytics
- Scalable deployment configurations
- Evaluation and quality assurance

## Next Steps

1. **📖 Start with the Notebook** - Work through the complete tutorial
2. **🎯 Choose Your Pattern** - Pick the application type that matches your needs
3. **🏗️ Build Your Foundation** - Set up your [providers](/docs/providers/) and [distributions](/docs/distributions/)
4. **🚀 Deploy & Monitor** - Use our [deployment guides](/docs/deploying/) for production

## Related Resources

- **[Getting Started](/docs/getting_started/quickstart)** - Basic setup and concepts
- **[Providers](/docs/providers/)** - Available AI service providers
- **[Distributions](/docs/distributions/)** - Pre-configured deployment packages
- **[API Reference](/docs/api/llama-stack-specification)** - Complete API documentation
