# Chapter 22 Project Practice: Multimodal Agent

> 🎨 *"The real world speaks more than words — images, audio, and video are all languages an Agent needs to understand."*

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## 🎓 Learning Objectives

After completing this chapter, you will be able to:

- ✅ Understand the capability boundaries and application scenarios of multimodal Agents
- ✅ Master integration patterns for vision understanding and image analysis
- ✅ Integrate speech input/output with an Agent
- ✅ Build a multimodal personal assistant with image and voice capabilities
- ✅ Understand the core architecture and safety practices of Computer Use / GUI Agents
- ✅ Master implementation methods for video understanding and multimodal RAG

## ⏱️ Estimated Learning Time

Approximately **150–180 minutes** (capstone project)

## 💡 Prerequisites

- Complete all chapters from Part I to Part V
- Understand base64 encoding and multipart HTTP requests

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## Chapter Overview

In previous chapters, all the Agents we built only processed text. But in the real world, information exists in many forms — users might send a screenshot asking "how do I fix this error," or say "help me analyze this chart." Multimodal Agents can understand and generate multiple types of content, greatly expanding application scenarios. This chapter will guide you to build a multimodal personal assistant from scratch that can handle text, images, and audio simultaneously.

## Chapter Goals

After completing this chapter, you will be able to:

- ✅ Understand the core capabilities and application scenarios of multimodal models
- ✅ Implement image understanding and analysis with GPT-4.1
- ✅ Implement image generation with DALL-E
- ✅ Integrate speech recognition (STT) and text-to-speech (TTS)
- ✅ Build a complete multimodal personal assistant Agent
- ✅ Understand Computer Use / GUI Agent architecture and safety practices
- ✅ Implement video understanding and multimodal RAG

## Chapter Structure

| Section | Content | Difficulty |
|---------|---------|-----------|
| 22.1 Multimodal Capabilities Overview | Capabilities and application scenarios of multimodal models | ⭐⭐ |
| 22.2 Image Understanding and Generation | GPT-4.1 analyzes images, DALL-E generates images | ⭐⭐⭐ |
| 22.3 Voice Interaction Integration | Speech recognition and text-to-speech | ⭐⭐⭐ |
| 22.4 Practice: Multimodal Personal Assistant | Build a complete multimodal Agent | ⭐⭐⭐⭐ |
| 22.5 Computer Use and GUI Agents | Let Agents operate computers and browsers | ⭐⭐⭐⭐ |
| 22.6 Video Understanding and Multimodal RAG | Video analysis and hybrid image-text retrieval | ⭐⭐⭐⭐ |

## ⏱️ Estimated Learning Time

Approximately **90–120 minutes** (including hands-on exercises)

## 💡 Prerequisites

- Completed Agent development basics from previous chapters
- Familiar with basic OpenAI API usage (including Vision and Audio APIs)
- Python async programming basics (`async/await`)

## 🔗 Learning Path

> **Core Prerequisites**: [Chapter 3: Tool Use / Function Calling](../chapter_tools/README.md), [Chapter 12: LangGraph](../chapter_langgraph/README.md)
> Recommended but not required: [Chapter 17: Agent Evaluation and Optimization](../chapter_evaluation/README.md)
>
> **Related Projects**:
> - 🔨 [Chapter 20: AI Coding Assistant](../chapter_coding_agent/README.md)
> - 📊 [Chapter 21: Data Analysis Agent](../chapter_data_agent/README.md)

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*Next: [22.1 Multimodal Capabilities Overview](./01_multimodal_overview.md)*
