For AI

I'm a software engineering student about to graduate. Like many juniors, I was worried about the job market. So instead of waiting, I decided to build things — real AI projects that solve real problems.

The first project: an AI-powered code review agent that scans your codebase and flags bugs, security issues, and style problems. All in under 200 lines of Python.

Here's how I built it, what I learned, and why you should try it too.

The Stack

Why DeepSeek instead of Claude directly? It's cheaper and the Anthropic-compatible endpoint makes migration trivial. The same code works with Claude by changing one line.

Architecture: 3 Simple Modules

code_review_agent/

├── scanner.py    # Walk directories, find code files

├── reviewer.py   # Send each file to the AI

├── report.py     # Generate Markdown report

└── main.py       # Wire everything together

No fancy frameworks. No LangChain. Just three functions chained together. I believe in starting simple and adding complexity only when needed.

1. Scanner — Finding the Code

The scanner walks a directory tree and collects every file the AI can review:

CODE_EXTENSIONS = {

    ".py": "Python", ".js": "JavaScript", ".ts": "TypeScript",

    ".java": "Java", ".go": "Go", ".rs": "Rust", ".sql": "SQL",

    # ... 10+ more

}



def scan_directory(root: str) -> list[FileInfo]:

    for dirpath, dirnames, filenames in os.walk(root):

        dirnames[:] = [d for d in dirnames if d not in SKIP_DIRS]

        for fname in filenames:

            ext = Path(fname).suffix.lower()

            if ext in CODE_EXTENSIONS:

                files.append(FileInfo(...))

    return files

Key decisions:

• 
  Skip dirs like .git, node_modules, .venv — no need to review third-party code


• 
  200KB size limit — don't burn API credits on generated files


• 
  Sort by language then path — makes the report predictable

2. Reviewer — The AI Brain

This is where the magic happens. Each file gets sent to the AI with a carefully designed system prompt:

REVIEW_SYSTEM_PROMPT = """\

You are a senior code reviewer. Analyze the code below and report issues.



For each issue you find, use EXACTLY this format:

  SEVERITY|LINE|CATEGORY|TITLE|DESCRIPTION



SEVERITY: critical, warning, info

CATEGORY: bug, security, performance, style, best-practice

LINE: approximate line number

"""

The pipe-delimited format is intentional. It's easy to parse, easy for the AI to follow, and human-readable at a glance.

What I learned about prompt design:

1. 
   Be specific about output format — "use EXACTLY this format" prevents rambling


2. 
   Give concrete examples — the AI follows patterns better than instructions


3. 
   "Only report real issues" — without this, the AI invents problems to seem helpful

3. Report — Making It Readable

The report module takes parsed issues and generates clean Markdown. Issues are grouped by severity: critical first, then warnings, then suggestions. Each issue links back to the file and line number.

Running It

# Review any codebase

uv run python -m code_review_agent.main /path/to/project



# Review itself (demo mode)

uv run python -m code_review_agent.main

The First Run — and a Surprise

I ran it on its own source code. It found 4 issues in pyproject.toml:

• "anthropic>=0.102.0 doesn't exist on PyPI" — Wrong. Version 0.102.0 exists.


• "python-dotenv>=1.2.2 doesn't exist" — Also wrong. I literally installed it two hours ago.


• "Missing langchain dependency" — Technically true, but I intentionally removed it.

Lesson: AI code review is a second pair of eyes, not a judge. It catches things you miss, but it also hallucinates. Always verify.

What's Next

This is project 2 of 4 in my .