Gemini API Tutorial – Create Your First Coding Agent
@MattMacarty
Gemini API Tutorial: Build an AI Coding Agent in Python from Scratch.
Part 1 of 3
Get the Code: https://github.com/mjmacarty/llm-codi...
Want to understand how AI coding tools actually work? In this tutorial, we pull back the curtain and build a functional Python coding agent using the Google Gemini API.
Using Google AI Studio's free account tier, we initialize a Python development environment with 'uv', handle API key security using python-dotenv (.env), and write the core generation loop in under 30 lines of code. The architecture built here is completely modular—meaning you can easily swap out the Google GenAI SDK for OpenAI or Claude with minimal adjustments.
What we cover in this video:
1. Environment Setup: Leveraging modern tools like 'uv' to initialize a repository and manage dependencies seamlessly.
2. Security Best Practices: Storing your Gemini API key in a .env file and configuring .gitignore to prevent accidental leaks.
3. Code Generation Loop: Building the Python script that structures your prompts, initializes the GenAI client, and cleanly outputs executable blocks.
4. Troubleshooting: Real-time debugging when hitting free-tier model limitations.
TIMESTAMPS:
00:00 - Intro to AI Coding Agents
00:35 - How to get a Google AI Studio API Key
01:44 - Creating a Python project with uv
02:37 - Installing dependencies (google-genai, python-dotenv)
02:51 - Project setup in PyCharm IDE
03:20 - Securely storing API keys in a .env file
04:03 - Coding the GenAI Client in main.py
05:12 - Writing the prompt engineering function
06:52 - Troubleshooting API errors & switching models
07:25 - Next steps: Executing and refining code in Part 2
🔗 Resources:
Get more Python and data analytics guides at https://alphabench.com
If you found this walkthrough helpful, please give it a thumbs up and subscribe for Part 2, where we will give the agent the power to execute and self-correct its own code!
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