open-code-review

AI code slop draining you or your engineering team? I've been there. That's why my team and I created Open Code Review (OCR), a free multi-agent code review tool that sits atop your existing agentic tools to give you high quality, structured code review designed to match code review on high performing engineering teams. My teams and I have been dogfooding this for about a year now and we've seen as much as 70% time savings using OCR to ensure that human code review is only reached once code is truly polished and ready for real human eyes. Save your code quality (and money) and try Open Code Review today. Just dropped V2 with a number of stabilizations and enhancements. More to follow. Link to get started here: https://github.com/spencermarx/open-code-review Would love feedback, contributors, and really just to know that the tool is provid

You hit the nail on the head. Completely agree. This framing... > Otherwise you can end up with persuasive agreement rather than real verification. ... is very familiar and one of the inspirational elements that led to https://github.com/spencermarx/open-code-review/tree/main (not trying to shamelessly plug, just your articulation was right on the money). It's definitely the holistic set of static analysis and a robust automated test pyramid, then agentic analysis (ideally differentiated, and redundant with discourse -- like Open Code Review), that is proving to be effective at ensuring quality code. That said, and a bit of a premature callout to the other project I mentioned, I think structured knowledge graphs to describe and commit to software intent (beyond what nested AGENT/CLAUDE/rule files and free form docs give you) are going to

Great take. As it happens, I'm working with a number of other engineers on a solution that solves for this. However to dispel any false hope, static analysis + agentic development can only go so far. It's the holistic combo of these tools plus Agentic AI as both an implementation AND a feedback mechanism that unlock higher quality. If you're looking to see what that feedback mechanism might look like in action, you might like checking out one of the other projects I've worked on which pre-dates Orc: https://github.com/spencermarx/open-code-review Love where your head is at though! DEFINITELY an important problem we've got to solve here.

I got tired of AI code review tools that just run a single LLM pass over a diff and call it a review. So I built something different. Open Code Review spins up multiple AI reviewers from your CLI, each with a different focus. They review independently, then debate each other’s findings, then synthesize into a single review. You get a local dashboard that spins up out of your repo to navigate everything. The debate step is what makes it work. A single-pass reviewer hallucinates issues and misses real ones. When agents argue with each other, the false positives get killed and the real problems get surfaced with better reasoning. My team’s senior engineers have been consistently surprised by what it catches. Latest release adds GitHub integration — post the synthesized review directly to PRs. You can post the raw synthesis or have it ghostwr

Been working with spec-driven development since it first emerged (OpenSpec fan), and kept running into the same gap: specs get written, AI writes the code, but validating the implementation against the spec is still a manual slog. So I put together a tool that runs multi-agent code reviews inside agentic AI environments (Claude Code, Cursor, Windsurf, etc). The idea is simple – instead of one reviewer, you get a few different perspectives, modeled after common engineering teams: someone thinking architecturally, someone focused on code quality, another with security expertise, a QA engineer paying close attention to testing. They review changes independently, then discuss before synthesizing feedback. You can also configure custom reviewers and adjust redundancy if you want multiple sub-agents for a given role. I originally built it to co