Chapter 2: Development Environment Setup
A well-sharpened tool makes the work easier. A well-configured development environment will make your Agent development twice as efficient.
Chapter Overview
This chapter walks you through setting up a professional Agent development environment from scratch. From Python virtual environment management, to secure API Key storage, to your first runnable Agent — follow along and you'll have a workbench ready to start developing at any time.
Chapter Goals
After completing this chapter, you will be able to:
- ✅ Manage Python environments and dependencies using
uvorconda - ✅ Install and verify core libraries like LangChain and the OpenAI SDK
- ✅ Securely manage API Keys to prevent key leakage
- ✅ Run your first real Agent!
Chapter Structure
| Section | Content | Estimated Time |
|---|---|---|
| 2.1 Python Environment & Dependency Management | venv, conda, uv comparison and usage | 20 min |
| 2.2 Key Library Installation | LangChain, OpenAI SDK, etc. | 15 min |
| 2.3 API Key Management | .env files, environment variable best practices | 10 min |
| 2.4 Hello Agent! | Your first complete Agent | 30 min |
Prerequisites
- Python 3.10 or higher installed
- Basic command-line experience
- An OpenAI account registered (or another LLM service)
Tool Selection Guide
| Tool | Purpose | Recommendation |
|---|---|---|
uv | Python package management (newest, fastest) | ⭐⭐⭐⭐⭐ Highly recommended |
conda | Environment management (common in data science) | ⭐⭐⭐⭐ Recommended |
venv | Built-in virtual environment | ⭐⭐⭐ Sufficient |
| VS Code | Code editor | ⭐⭐⭐⭐⭐ Recommended |
| Jupyter | Interactive development and experimentation | ⭐⭐⭐⭐ Recommended |
🔗 Learning Path
Prerequisites: Chapter 1: What is an Agent?
Recommended next steps:
- 👉 Chapter 3: LLM Fundamentals — Understand the Agent's core "brain"
- 👉 Chapter 4: Tool Calling — Let Agents "get things done"
Next section: 2.1 Python Environment & Dependency Management