AI Agents · Lesson

A Code-Explainer Agent

Build an agent that reads source files, asks the LLM for an explanation, and returns Markdown documentation.

A Code-Explainer Agent is a free AI Agents lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AI Agents learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Project Goal

Build an agent that takes a source file (Python, JS, anything) and returns Markdown documentation: purpose, key functions, usage example.

Why Useful?

Generating docs from code is one of the most reliable LLM use cases — code is structured, the task is contained, and the output is read by humans (so minor errors are tolerable).

Architecture

Read source file
Optionally split by class/function
For each chunk, prompt LLM to explain
Combine into a Markdown doc

Step 1: Read the File

import sys

with open('example.py', 'w') as f:
    f.write('print("hello")\n')

path = sys.argv[1] if len(sys.argv) > 1 else 'example.py'
with open(path) as f:
    code = f.read()
print(f'Read {len(code)} characters from {path}')

Step 2: Prompt for Documentation

from openai import OpenAI
oai = OpenAI()

prompt = f'''
You are a senior engineer writing developer-friendly docs.

Given this source file, produce a Markdown document with:
# {path}

## Purpose
(One paragraph)

## Public API
(Each function/class with one-line description)

## Usage Example
(One short, runnable snippet)

Source:
```
{code}
```
'''

response = oai.chat.completions.create(
    model='gpt-4o-mini',
    messages=[{'role': 'user', 'content': prompt}],
    temperature=0.2,
)
print(response.choices[0].message.content)

Handle Long Files

If the file is too long, split by function and explain each separately:

import ast

tree = ast.parse(code)
functions = [node for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)]

for func in functions:
    snippet = ast.unparse(func)
    explain(snippet)

Step 3: Combine Outputs

For multi-chunk runs, stitch the per-function explanations into one doc:

docs = []
for func_name, snippet in functions:
    explanation = explain(snippet)
    docs.append(f'### {func_name}\n\n{explanation}\n')

full_doc = '\n'.join(docs)
open('docs.md', 'w').write(full_doc)

Add a Project-Level Summary

After per-function explanations, ask the LLM for a high-level overview:

summary_prompt = 'Summarise the purpose of this package in 3 sentences, given these function docs:\n\n' + full_doc
summary = ask(summary_prompt)

Multi-Language

Same prompt works for JS, Go, Rust, etc. For better results, add the language to the prompt:

prompt = f'You are documenting {language} code. ...'

Diff-Based Docs

For incremental updates, re-run only on changed files:

import subprocess
changed = subprocess.check_output(['git', 'diff', '--name-only', 'HEAD~1']).decode().splitlines()
for path in changed:
    if path.endswith('.py'):
        regenerate_doc(path)

Use a Tool to Run the Example

Verify the LLM's usage example actually runs — give the agent a Python REPL tool:

def run_python(code):
    try:
        exec(code, {})
        return {'stdout': 'ok', 'stderr': ''}
    except Exception as e:
        return {'stdout': '', 'stderr': str(e)}

tools = [{'name': 'run_python', 'description': 'Execute a Python snippet and return stdout/stderr', 'parameters': {'code': 'str'}}]

broken_example = 'print(1/0)'
result = run_python(broken_example)
if result['stderr']:
    print('Example failed:', result['stderr'])
    fixed_example = 'print(1)'
    result = run_python(fixed_example)
    print('Self-corrected result:', result)
else:
    print('Example ran fine:', result)

When the Agent Hallucinates

The model sometimes invents functions or arguments. Mitigations:

Provide ONLY the file content (no model memory)
Temperature 0
Have a verification step (run the example, lint the snippet)

Productize It

Wrap this as a CLI:

# pip install -e .
# docgen src/myproject/agent.py
# Outputs docs.md

From Tool to CI

Plug into CI: on every PR, regenerate docs for changed files and commit them back. Now your repo is always documented.

Why Read the Whole File?

Why pass the FULL source file to the LLM instead of just the function signatures?

Recap

A 30-line agent that turns code into docs. Easy to extend with tools and verification. A great second project after RAG.

Frequently asked questions

Is the “A Code-Explainer Agent” lesson free?

Yes — the full text of “A Code-Explainer Agent” is free to read here on the web, and the AI Agents course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AI Agents course, upgrade to CoddyKit PRO.

What will I learn in “A Code-Explainer Agent”?

Build an agent that reads source files, asks the LLM for an explanation, and returns Markdown documentation. You practise AI Agents with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start AI Agents?

No prior experience is required. AI Agents on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “A Code-Explainer Agent” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this AI Agents lesson?

Yes. Every AI Agents lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

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