squall-unified-review

You can automate code review on large pull requests by coordinating multiple AI models and local investigation agents to surface defects with traceable file-level references. The process applies hypothesis-driven analysis to diffs and file sets of any size, automatically selecting the appropriate review depth.

Multi-model code review improves defect detection by orchestrating diverse AI perspectives, such as Gemini, Codex, Grok, and Opus, to generate and test concrete hypotheses. This ensemble approach produces structured consensus and an auditable trail, surfacing design concerns that a single model might miss.

Yes, you can use multiple AI models to review code diffs automatically. The automated review process integrates various models and optional team agents, applying auto-depth selection to coordinate comprehensive analysis across diffs, PRs, and file sets of any size.

The best way to handle code review for complex diffs is to use a hypothesis-driven analysis approach with automatic depth selection. By selecting SWARM or DEEP depth based on code characteristics, the review coordinates multiple models to generate structured synthesis and memorize patterns for future audits.

Multi-model code review integrates with existing CI workflows by applying automated analysis to diffs and PRs. It supports auto-depth selection for file sets of any size, ensuring the review process scales appropriately while maintaining an auditable trail and memory-backed recommendations.

You should use SWARM depth for code review when handling complex file sets where maximum rigor is required. The auto-depth feature selects SWARM based on code characteristics and user intent, coordinating multiple models and optional team agents to ensure comprehensive hypothesis-driven analysis.