Building a Local AI Code Reviewer Workflow with Ollama and Python (Guide #358)

Carter — Wed, 11 Nov 2026 21:59:28 +0000

Building a Local AI Code Reviewer Workflow with Ollama and Python

As software developers, we know the importance of code reviews. They help us catch errors, improve code quality, and maintain consistency throughout our projects. However, with the increasing complexity of modern codebases, it can be challenging to find the time and resources to perform thorough code reviews. In this tutorial, we'll explore how to build a local AI code reviewer workflow using Ollama and Python.

What is Ollama?

Ollama is an open-source, locally-run AI code reviewer that uses natural language processing (NLP) to analyze code and provide actionable feedback. By integrating Ollama with Python, we can create a custom workflow that automates code reviews, freeing up our time to focus on more critical tasks.

Setting up Ollama

To get started, you'll need to install Ollama using pip:

pip install ollama

Once installed, create a new Python script to serve as the entry point for your workflow. We'll call this script code_reviewer.py:

import ollama
import os

# Load Ollama model
model = ollama.load('path/to/model.pth')

# Define a function to analyze code
def analyze_code(file_path):
    # Load the code file
    with open(file_path, 'r') as file:
        code = file.read()

    # Analyze the code using Ollama
    analysis = model.analyze(code)

    # Process the analysis results
    # ...

    return analysis

Creating a Code Review Workflow

In this example, we'll create a simple workflow that takes a file path as input and returns a summary of the code review analysis. We'll use the analyze_code function we defined earlier:

def code_review_workflow(file_path):
    # Analyze the code
    analysis = analyze_code(file_path)

    # Extract relevant information from the analysis
    issues = []
    for suggestion in analysis.suggestions:
        if suggestion.severity == 'high':
            issues.append(f"High-priority issue: {suggestion.description}")

    # Return the summary
    return f"Code review summary: {len(issues)} high-priority issues found. Check the issues below:\n{issues}"

Running the Workflow

To run the workflow, simply call the code_review_workflow function with the file path as input:

if __name__ == '__main__':
    file_path = 'path/to/file.py'
    print(code_review_workflow(file_path))

This will output a summary of the code review analysis, including any high-priority issues found.

Next Steps

In this tutorial, we've demonstrated how to build a local AI code reviewer workflow using Ollama and Python. By automating code reviews, we can free up our time to focus on more critical tasks and improve the overall quality of our codebases.

Ready to take your code reviews to the next level? Try Ollama today and get instant access to a ready-to-run local AI toolkit: https://gumroad.com

Happy coding!

DEV Community: Carter

Building a Local AI Code Reviewer Workflow with Ollama and Python (Guide #358)