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 如何帮助框架迁移？

您定义新框架模式的规则——App Router 而不是 Pages Router，服务器组件而不是客户端等。diffray 在每个 PR 上执行这些规则，捕获开发人员出于习惯使用旧模式的情况。

它支持增量迁移吗？

绝对支持。您可以设置规则，在某些目录中允许旧模式，同时在已迁移区域中执行新模式。边界会随着迁移的进展而移动。

它支持哪些迁移？

任何可以表达为代码模式的迁移：React class 到 hooks、REST 到 GraphQL、CommonJS 到 ES modules、Express 到 Fastify、Vue 2 到 Vue 3、Angular 升级等。

如何防止退回到旧模式？

一旦某个区域迁移完成，规则会阻止引入旧模式。已迁移区域中的新代码必须使用新模式——不能后退。

适用于现代化技术栈的团队

框架迁移
无需混乱

当旧模式悄悄回归时，框架迁移就会失败。diffray 在每个 PR 上执行新模式，让您的迁移保持正轨。

React (CRA) → Next.js

Express → Fastify

REST → GraphQL

Class 组件 → Hooks

开始免费试用

迁移为何失败

不是迁移的问题。是维护的问题。

旧习惯难改

开发者因肌肉记忆继续使用旧模式。新代码使用旧约定。

代码库不一致

一半迁移完成，一半还是遗留代码。没有清晰的边界。新功能随机混合使用模式。

退化到旧模式

已迁移的区域被旧模式污染。没有持续警惕，进展会倒退。

知识差距

不是每个人都同样熟悉新框架。代码质量因编写者而异。

持久的迁移

AI 确保新模式保持新

模式执行

将新框架模式定义为规则。AI 在每个 PR 上执行它们，自动捕获旧习惯。

"使用服务器组件进行数据获取。客户端组件仅用于交互。"

边界管理

定义哪些目录已迁移。遗留区域允许旧模式，新区域禁止旧模式。

"此文件在 /app（已迁移）。使用 App Router 模式，而不是 Pages Router。"

迁移指导

AI 解释如何将旧模式转换为新模式。每次审查都是教学时刻。

"用异步服务器组件替换 getServerSideProps。参见迁移指南：/docs/migration.md"

进度跟踪

查看代码库有多少已迁移。庆祝进展，识别落后者。

"73% 的组件已迁移到 hooks。/legacy 中还剩 12 个类组件。"

我们支持的常见迁移

React (CRA)Next.js

Pages Router → App Router

ExpressFastify

中间件 → 插件

RESTGraphQL

端点 → 解析器

Class 组件Hooks

生命周期 → useEffect

CommonJSES Modules

require → import

Vue 2Vue 3

Options API → Composition API

常见问题

现代化。
保持现代。

迁移一次。永远执行。