What makes diffray different from other AI code review tools?

diffray uses multi-agent intelligence instead of single-model AI. Multiple specialized agents work together - Security Agent, Performance Agent, Architecture Agent, and Consistency Agent - each expert in their domain. This coordinated approach reduces false positives by 87% and catches 3x more real bugs compared to traditional single-agent tools like GitHub Copilot or CodeRabbit.

How does multi-agent AI code review work?

Multi-agent AI code review deploys specialized agents that work in parallel, each focused on a specific domain: security vulnerabilities, performance bottlenecks, architectural patterns, and code consistency. Unlike single-model approaches that suffer from context dilution, each agent maintains deep expertise in its area. Research shows this approach improves bug detection by 3x while reducing noise.

Is diffray free for open source projects?

Yes, diffray is completely free forever for open source projects. We support the open source community with full access to our multi-agent code review platform, including all specialized agents, unlimited reviews, and priority support.

What programming languages does diffray support?

diffray supports all major programming languages including TypeScript, JavaScript, Python, Go, Rust, Java, C#, Ruby, PHP, and more. The multi-agent system is language-agnostic and adapts its analysis to language-specific patterns and best practices.

How does diffray integrate with GitHub?

diffray integrates seamlessly with GitHub through a GitHub App. Once installed, it automatically reviews every pull request, posting actionable comments directly on the PR. Setup takes less than 2 minutes with no configuration required. Enterprise teams can also use diffray CLI for local reviews before pushing code.

What is the difference between diffray and CodeRabbit or GitHub Copilot?

While CodeRabbit and GitHub Copilot use single-model AI that can hallucinate and produce false positives, diffray employs multi-agent intelligence where specialized agents cross-validate findings. This results in 87% fewer false positives. Additionally, diffray provides full codebase awareness, custom rule support, and agent memory that learns from your team's patterns.

Can diffray detect security vulnerabilities?

Yes, diffray's Security Agent is specifically trained to detect OWASP Top 10 vulnerabilities, injection attacks, authentication flaws, and sensitive data exposure. It analyzes code in context of your entire codebase, reducing false positives while catching real security issues that static analysis tools miss.

How much does diffray reduce code review time?

According to our customer data, teams using diffray reduce PR review time by 73% on average - from 45 minutes to 12 minutes per week. This is because diffray's multi-agent system produces 87% fewer false positives, so developers spend time on real issues instead of filtering noise.

What is the developer action rate on diffray comments?

diffray achieves a 98% developer action rate on its comments, compared to industry average of 15-20% for traditional AI code review tools. This high engagement is due to our multi-agent approach that eliminates noise and surfaces only actionable findings with confidence scores.

How does diffray handle duplicate comments?

diffray guarantees zero duplicate comments through its intelligent deduplication system. Unlike single-agent tools that often flag the same issue multiple times across a PR, diffray's agents coordinate to consolidate findings and present each issue exactly once with full context.

Does diffray store my code?

No, diffray never stores your source code. Code is processed in memory during the review and immediately discarded. We are SOC 2 compliant and your code is never used for AI training. Enterprise customers can also use our on-premise deployment option for complete data sovereignty.

How does diffray compare to GitHub Copilot code review?

While GitHub Copilot uses a single AI model for code review, diffray employs specialized multi-agent intelligence. Research shows multi-agent systems catch 3x more real bugs while producing 87% fewer false positives. diffray also provides full codebase awareness, custom rules, and agent memory - features not available in Copilot's code review.

diffray 如何预防生产事故？

diffray 分析 PR 中已知会导致事故的模式——竞态条件、缺少错误处理、资源泄漏、不安全的部署。它从常见的故障原因中学习，并在您的代码中标记类似的模式。

它能从我们过去的事故中学习吗？

是的。您可以将过去事故的经验编码为自定义规则。'我们因为 X 发生了故障'变成一条永远捕获类似模式的规则。您的事故历史成为机构知识。

它捕获哪些类型的风险更改？

没有回滚计划的数据库迁移、一次性影响所有用户的配置更改、删除功能标志、身份验证流程的更改、支付处理的修改——任何可能造成广泛影响的内容。

它与事故管理工具集成吗？

diffray 专注于 PR 级别的预防。对于事后分析，经验可以编码为规则，以防止未来 PR 中的类似问题。

适用于厌倦凌晨 3 点告警的团队

在事故发生前
阻止它们

最好的事故是从未发生的事故。diffray 在 PR 阶段捕获风险模式，在它们变成凌晨 3 点告警之前。

开始预防事故

导致宕机的模式

diffray 在它们发布前捕获这些

数据库灾难

大表缺少索引
锁定迁移
大规模 N+1 查询

配置灾难

影响所有用户的功能开关
环境变量拼写错误
缺少回退

性能定时炸弹

内存泄漏
无限循环
缺少分页

静默失败

吞掉的异常
缺少错误处理
损坏的重试逻辑

预防流程

从历史中学习

将过去事故的经验编码为规则。之前导致宕机的问题不会再发布。

模式识别

AI 识别风险模式 — 竞态条件、资源泄漏、不安全的部署。

风险标记

高风险 PR 被标记以进行额外审查。低风险 PR 更快通过。

持续改进

每个事故都成为新规则。您的防御能力会随时间变强。

智能风险评估

不是所有 PR 都需要同样的审查

涉及支付处理高风险

修改认证流程高风险

包含数据库迁移中风险

配置文件更改中风险

新的错误处理低风险

仅 UI 更改低风险

常见问题

睡得更好。
发布更安全。

停止救火。开始预防。

开始免费试用

在事故发生前阻止它们