AI Code Review for Open-Source Maintainers

Review community contributions at scale. Consistent standards, institutional memory, and security scanning for every PR.

Last updated: 2025-04-11

What problems do Open-Source Maintainerss face in code review?

How does Argus fit your workflow?

Which Argus features matter most for your team?

Multi-pass specialist pipeline

Catches convention violations, security concerns, and architectural issues in one automated pass — maintainers review the exceptions, not the baseline

Pattern learning

Learns the project's style and architecture conventions from past reviews — consistent enforcement without writing a linter config

Institutional memory

Captures past design decisions and rejected approaches — 'we discussed this pattern in #342 and chose a different approach'

BYOK (Bring Your Own Key)

Use your own LLM provider key — no per-seat costs, compatible with any OpenAI-compatible API

Open-source maintainers using AI-assisted review handle 3.2× more PRs per month with equivalent quality vs. manual-only reviewGitHub Octoverse contributor trends, 2024

Don't let another risky PR merge without the review your team needs.

Free for up to 3 repos. Institutional memory starts on your first PR.

Install Argus on GitHub