From Prompt to Program: Building a Self-Healing AI Coder with LangGraph

A hands-on guide to creating an agent that writes, tests, and debugs its own Python code until it runs successfully.

One of the most exciting promises of AI is its ability to write code. But as any developer knows, the first draft of code rarely works. It has bugs, typos, and forgotten imports. A truly useful AI Coder can’t just write code; it must also be able to test it, analyze the errors, and debug its own work.

This “write, test, debug” cycle is a natural loop, making it a perfect use case for LangGraph. In this tutorial, we will build exactly that: a “Self-Healing Coder” agent that:

1. Writes Python code based on a user’s prompt.
2. Executes the code in a safe environment.
3. Analyzes the output. If there’s an error…
4. Debugs the code, creating a new version.
5. Loops back to the execution step, trying again until the code runs successfully.

Let’s get started.

The Big Picture: Our Agent’s Workflow

Before we write a single line of Python, let’s visualize the agent’s thinking process. This flowchart shows the entire journey from the user’s request to a successfully executed program.