Gito

Gito abstracts LLM provider interactions through a unified interface, allowing any LLM (OpenAI, Anthropic, local Ollama, etc.) to be plugged in for code review without changing core logic. The architecture uses a provider adapter pattern where review prompts are sent to the selected LLM backend, which returns structured analysis of code changes. This enables users to swap providers based on cost, latency, or privacy requirements without modifying review workflows.

Gito integrates directly into GitHub Actions workflows as a step that automatically triggers on pull requests, analyzing code changes and posting review comments back to the PR. The integration uses GitHub's REST API to fetch PR diffs, send them to the configured LLM, and write review comments as bot comments on the PR. This enables zero-friction adoption — teams add a single workflow YAML file and reviews run automatically on every PR without manual invocation.

Gito can run as a standalone CLI tool that processes local git repositories or patch files without requiring GitHub Actions or cloud infrastructure. The CLI reads git diffs from the local filesystem, sends them to the configured LLM, and outputs review results to stdout or files. This enables air-gapped environments, on-premise deployments, and local development workflows where code cannot be sent to external services.

Gito can automatically create or link issues in Jira and Linear based on code review findings, mapping review comments to actionable tasks. The integration uses Jira REST API and Linear GraphQL API to create issues with review context (file, line number, severity) and link them back to the PR. This bridges the gap between code review feedback and project management, ensuring review findings don't get lost and are tracked as work items.

Gito can classify code review findings by severity level (critical, major, minor, info) and filter which findings are posted based on configured thresholds. The classification is determined by the LLM's analysis or by post-processing rules that examine the review output. This allows teams to reduce noise by suppressing low-severity findings or focusing only on critical issues, making reviews more actionable.

Gito can analyze code changes across multiple files in a single PR and understand relationships between modified files (imports, dependencies, function calls). The review logic sends the full PR diff to the LLM along with metadata about file relationships, enabling the LLM to detect issues that span multiple files (e.g., breaking API changes, inconsistent refactoring). This is more sophisticated than single-file analysis because it catches architectural issues that wouldn't be visible in isolation.

Gito allows users to define custom review prompts that guide the LLM's analysis toward specific concerns (security, performance, style, etc.). The prompts are stored as templates that can be modified per-repository or per-team, enabling organizations to enforce their own code review standards. The LLM receives the custom prompt along with the code diff, producing feedback aligned with the team's priorities.

Gito can process multiple pull requests or commits in a single CLI invocation, analyzing each one and generating a consolidated report or individual reviews. The batch mode iterates through a list of PRs/commits, sends each to the LLM, and aggregates results. This is useful for backfilling reviews on existing PRs, analyzing a release branch, or generating reports across multiple changes.