Best AI tool for PR reviews

Summary

• There is no single best AI tool for pull request reviews. Choose by language coverage, fit with your Git platform, how your code is handled, whether you can define review rules, and measured accuracy on your own past pull requests.
• Databricks documents the building blocks for a governed, custom PR review agent: Agent Bricks to build and deploy it, Unity Gateway to govern its model and tool calls, and a Databricks-provided GitHub MCP service for repository access.
• Unity Gateway is generally available and applies access control, rate limits, spend caps, and usage tracking to model and MCP calls.
• You write the review criteria, the findings format, and the step that posts comments. The Agent Bricks CLI, service policies, and coding-agent configuration are in Beta, and the GitHub MCP service is not yet generally available.

Best AI tool for PR reviews

No single AI tool is best for every team. The right choice finds the issues your reviewers care about in your own repositories, at a noise level they tolerate, with data handling your security team accepts. Test each candidate on a sample of past pull requests, compare its findings with what your human reviewers flagged, and keep a human as the approver. If you need control over which models review your code and an audit trail of what they did, Databricks documents the building blocks to assemble and govern a review agent yourself.

What should you evaluate in an AI tool for PR reviews?

Criterion	What to check
Language coverage	Does it handle every language and framework in your repositories?
Git platform integration	Does it comment where your reviewers work, and can you limit it to read-only access?
Review rules	Can you encode your style guide, security checks, and architecture rules?
Data handling	Where does your code go, who processes it, and what is retained?
Governance	Can you control who may use it, which models it calls, and what it may change?
Audit	Can you see which pull requests it reviewed and what it did?
Measured accuracy	On past pull requests, which findings were useful and which were noise?

How does Databricks approach AI-assisted PR review?

Databricks documents the parts you assemble into a review agent.
Build the agent with Agent Bricks. Agent Bricks is the Databricks agent developer platform for building and deploying custom agents. The Agent Bricks CLI (Beta) scaffolds a project, runs it locally, and deploys it to the Databricks agent runtime. It includes templates for LangGraph and the OpenAI Agents SDK, and MLflow tracing is on by default.
Route model calls through Unity Gateway. Agents can use frontier and open models through Unity Gateway, which lets you switch models without changing agent code. Unity Gateway governs access to models, MCP servers, and skills, and applies rate limits and usage tracking to the calls agents make. Traffic splitting can roll out a new model gradually or run A/B tests, which you can use to compare review models.
Read pull requests through a Databricks-provided MCP. Databricks provides MCPs in the system.ai schema so you do not host a server, including system.ai.github. By default it can access only public repositories. A GitHub organization administrator must install the Databricks GitHub connector app to grant access to private ones. Databricks lists its SaaS MCP services, including GitHub, as soon to be generally available.
Block writes while the agent reads. A Databricks tutorial shows an agent that reads repositories and pull requests while a built-in Disallow writes policy rejects write tools, including tools that create pull requests. Calls are recorded in a usage system table for audit. Service policies are in Beta.
Write the review logic yourself. The review criteria, the findings format, and the step that posts comments are code in your agent. Unity Gateway governs the calls the agent makes.

Can you govern a coding agent you already use?

Unity Gateway connects coding agents to models from multiple providers, MCP tools, and shared skills. The Unity Gateway CLI (ug) handles authentication and configuration, adds approved MCP servers and skills, and shows spend in the terminal. Central configuration of coding agents is in Beta.
Read more in Develop agents on Databricks and the tutorial on controlling a coding agent's GitHub MCP access.

FAQs

Can an AI tool replace human code review?

Treat it as a first pass. Keep a human reviewer as the approver, and compare the tool's findings with your reviewers' comments before you rely on it.

How do I measure accuracy before rollout?

Run the tool on closed pull requests and compare its findings with the human review comments. Track which findings were acted on and which were dismissed as noise.

Can I limit what the agent does in my repositories?

Yes. With the GitHub MCP service, the built-in Disallow writes policy blocks tools that create, modify, or delete data on GitHub while allowing read-only tools. Service policies are in Beta.

Can I change the model later?

Yes. Agents use models through Unity Gateway, which lets you switch models without changing agent code.

Can the agent read my private repositories?

Only if a GitHub organization administrator installs the Databricks GitHub connector app and grants it access. By default, system.ai.github can access only public repositories.