Baz Reviewers

Baz's managed default reviewers analyze change requests for global naming, typing and logic bugs

Default Reviewers use an agentic retrieval and analysis system to process code changes within the context of the entire codebase. Code is divided into manageable chunks using a LangChain-based framework, with tree-sitter handling parsing for supported languages like Python. For embedding and similarity search, Baz relies on Voyage-Code-3, a model optimized for code representation. This setup enables Baz to analyze pull requests while accounting for dependencies and broader repository context, identifying issues such as breaking changes, outdated comments, and log errors.

Code review agents maintained by Baz

Baz automates several steps in the code review process by integrating directly with GitHub. It evaluates outdated comments based on commit metadata and prior comment payloads, determining whether issues have been resolved. Log errors are identified by parsing GitHub Actions logs and attaching detailed comments at the relevant lines. Baz also identifies specific issues like typos, generic variable names, missing test assertions, and type mismatches. These insights are delivered as structured comments, enabling developers to address them directly in the GitHub interface.

The system is designed for efficient processing and scalability. Repository and organization data are stored in a single multi-tenant table, filtered by organization ID, repository name, and file path. Embeddings are stored in a pgvector database, enabling similarity searches to locate relevant code sections. When files are updated, Baz reprocesses only the changed files, ensuring minimal overhead while maintaining up-to-date insights. This approach supports a wide range of use cases and scales to handle large repositories effectively.

FAQ

What do Baz’s default (managed) reviewers do?

They analyze change requests for naming, typing, logic bugs, outdated comments, log errors, etc., using a combination of AI, parsing, and repository context.

How does Baz scale efficiently on large codebases?

Baz divides code into manageable chunks, reprocesses only changed files, stores embeddings in a vector database for similarity search, and filters by organization/repo to maintain performance.

Can I disable some default reviewer checks?

Yes. Organization admins can deactivate specific agents or modify their scope.