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Corbulo uses AI models for semantic code analysis: understanding PR intent, finding issues that static analyzers miss, generating architecture reviews, detecting conventions, and creating documentation.

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Model Slots

Corbulo uses four model slots that you can configure independently:

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Cloud Configuration

For cloud-hosted Corbulo, AI models are managed for you. No configuration is needed.

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Per-Project Configuration (BYOK)

Each project can use its own AI model configuration. This is useful when you want to:
  • Bring your own API keys to control costs and billing
  • Choose specific models for cost or capability reasons
  • Route through your own proxy (e.g., OpenRouter)
Configure per-project AI settings in Settings > AI & Analysis. For each model slot, you can set:
Per-project settings override the platform defaults. If you clear a slot’s configuration, it falls back to the platform-managed model.

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Supported Providers

Any OpenAI-compatible API works, including:
  • OpenAI — GPT-4o, GPT-4o-mini, and others
  • Anthropic — Claude Sonnet, Claude Haiku, and others
  • OpenRouter — access to multiple providers through a single API
  • Azure OpenAI — for Azure-hosted deployments
  • AWS Bedrock — via an OpenAI-compatible gateway
  • Self-hosted models — Ollama, vLLM, or any OpenAI-compatible server

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Usage Tracking

Monitor AI token usage per project in Settings > AI & Analysis. The usage view shows total tokens consumed and a cost breakdown by model slot.

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Best Practices

  1. Start with the defaults. The platform-managed models work well for most codebases.
  2. Use smaller models for lightweight tasks. Convention detection and summarization do not need the most capable model.
  3. Use the most capable models for deep analysis. PR analysis and architecture reviews benefit from stronger reasoning.
  4. Monitor usage and adjust. If costs are higher than expected, consider switching the small slot to a cheaper model.