Claude by Anthropic
A family of foundational AI models
5.0•655 reviews•39K followers
A family of foundational AI models
5.0•655 reviews•39K followers
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale.
This is the 29th launch from Claude by Anthropic. View more
Claude Code Review
Launching today
Claude Code now dispatches a team of agents on every PR to catch bugs that skims miss. Available in research preview for Team and Enterprise. It is an AI-powered multi-agent code review that analyzes every pull request like an expert team. It detects bugs, security issues, and hidden logic flaws in AI-generated code, verifies findings to reduce false positives, and delivers high-signal feedback before code reaches production.
Free Options
Launch Team
Excited to hunt Claude Code Review today! :)
As AI-generated code explodes, code review is becoming the bottleneck. Developers are shipping more code than ever, but PRs often get quick skims instead of deep reviews, letting subtle bugs slip into production.
Claude Code Review tackles this with a team of AI agents reviewing every pull request. Instead of one pass, multiple agents analyze the PR in parallel, verify potential issues, filter false positives, and rank bugs by severity.
What makes it interesting? It is the multi-agent architecture designed for depth over speed. The system scales reviews depending on PR complexity and leaves a high-signal summary plus inline bug comments directly in GitHub.
Key features
Multi-agent PR reviews
Parallel bug detection + verification
Severity-ranked findings
Inline GitHub comments
Review depth scales with PR size
Benefits
Catch bugs humans often miss
Reduce reviewer workload
Higher quality PR reviews
More confidence when shipping AI-generated code
Who it’s for
Engineering teams, AI-heavy dev teams, and organizations managing large volumes of pull requests.
Use cases
Reviewing AI-generated code
Large refactors and complex PRs
Security & logic bug detection
Scaling code reviews across teams
Personally, I think this is a great example of agents solving real developer workflow bottlenecks, not just generating code but improving the quality of what gets shipped.
View details here:
https://claude.com/blog/code-review
https://code.claude.com/docs/en/code-review
What do you think? Share in the comments! :)
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Humans in the Loop
curious how it compares with @Kilo Code, @CodeRabbit and related products in the category
This is honestly the missing piece for teams shipping fast with AI. I've seen so many PRs where the code "works" but has subtle auth bugs or logic holes that a human reviewer would catch on a good day but miss when reviewing 20 PRs.
The IDOR example in the demo is a perfect case. That exact bug pattern shows up constantly in AI-generated code because the model just focuses on making the endpoint functional, not secure. Having agents verify findings before flagging is smart too, cuts down on the noise.
been building with Claude Code for months now and the "quick skim" problem is very real. agents write code fast but the subtle bugs pile up — especially when one agent changes something another agent built two weeks ago. multi-agent review makes a lot of sense here, curious how it handles context across larger PRs where the full picture only emerges from reading multiple files together.
Who has a Team or Enterprise subscription?
Documentation.AI
Seems like Caude killed a lot of code review products from YC. They may have to pivot.
Huge launch, the multi-agent approach for PR reviews makes a lot of sense. Catching logic bugs, security issues, and subtle AI-generated code mistakes before production is exactly where teams need help.
Coincidentally, today I launched something related as well: Blocfeed.
While tools like Claude Code analyze the code itself, Blocfeed focuses on what happens after software reaches real users. Bugs often appear only on specific systems or edge cases where everything works fine on the developer’s machine.
Blocfeed aggregates user feedback and reports to surface:
Bugs that only occur in certain environments
Issues that slip past internal testing
Patterns in what users are complaining about
Feature requests users repeatedly ask for
I can imagine a strong synergy here:
Claude Code → prevents bugs before merge
Blocfeed → detects real-world issues and user needs after release
Congrats on the launch, excited to see where this multi-agent review direction goes. 🚀
So we have AI writing the code, and now a team of AI agents reviewing the code. Are we humans just here to pay the AWS server bills now?Haha. Brilliant launch!