Who should use the Code Review workflow?
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Development
Practical execution plan for code review with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A finalized production code is ready for publishing, handoff, or integration.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A finalized production code is ready for publishing, handoff, or integration.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use GitLab to inputs, context, and settings are ready so the workflow can move into execution without blockers. Then, you pass the output to Graphite to supporting assets from automate code reviews are prepared and connected to the main workflow. Then, you pass the output to GitLab to supporting assets from automate code review are prepared and connected to the main workflow. Then, you pass the output to AI Code Mentor to a first-pass production code is generated and ready for refinement in the next steps. Then, you pass the output to CodeGen to the production code is improved, validated, and prepared for final delivery. Finally, Conductor (by Melty Labs) is used to a finalized production code is ready for publishing, handoff, or integration.
Automated Code Review
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Automate code reviews
Supporting assets from automate code reviews are prepared and connected to the main workflow.
Automate code review
Supporting assets from automate code review are prepared and connected to the main workflow.
Code Review
A first-pass production code is generated and ready for refinement in the next steps.
Review code quality
The production code is improved, validated, and prepared for final delivery.
Review agent-generated code diffs and merge
A finalized production code is ready for publishing, handoff, or integration.
Prepare inputs and settings through Automated Code Review before running code review.
Automated Code Review sets up the foundation for code review; clean inputs here reduce downstream rework.
Inputs, context, and settings are ready so the workflow can move into execution without blockers.
Use Automate code reviews to build supporting assets that improve code review quality.
Automate code reviews strengthens code review by feeding better supporting material into the pipeline.
Supporting assets from automate code reviews are prepared and connected to the main workflow.
Use Automate code review to build supporting assets that improve code review quality.
Automate code review strengthens code review by feeding better supporting material into the pipeline.
Supporting assets from automate code review are prepared and connected to the main workflow.
Execute code review with Code Review to produce the primary production code.
This is the core step where code review actually happens, so it determines baseline quality for everything after it.
A first-pass production code is generated and ready for refinement in the next steps.
Refine and validate code review output using Review code quality before final delivery.
Review code quality adds quality control so issues are caught before the workflow is finalized.
The production code is improved, validated, and prepared for final delivery.
Package and ship the output through Review agent-generated code diffs and merge so code review reaches end users.
Review agent-generated code diffs and merge is what turns intermediate output into a usable, publishable result for real users.
A finalized production code is ready for publishing, handoff, or integration.
§ Before you start
Teams or solo builders working on development tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
§ Related
A streamlined workflow to prepare data, train a neural network model, and evaluate its performance using AI tools.
Streamlined workflow to automatically refactor existing code, debug errors, and finalize the refactored code for deployment.
End-to-end workflow to orchestrate data pipelines: start by performing predictive analytics to inform the pipeline, then orchestrate the data flow, and finally monitor model performance for ongoing reliability.