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 如何处理 monorepo 的复杂性？

diffray 分析您的 monorepo 结构——无论是 Turborepo、Nx、Lerna 还是 pnpm workspaces——并理解包边界。当您更改共享代码时，它会识别所有受影响的包并标记潜在的破坏性更改。

它能检测跨包的破坏性更改吗？

是的。diffray 跟踪内部 API 和包之间的依赖关系。如果您更改共享库中的函数签名，它会标记所有导入该函数并可能中断的包。

它与 Turborepo 和 Nx 兼容吗？

绝对兼容。diffray 与流行的 monorepo 工具集成，理解它们的依赖图、任务管道和缓存策略。它甚至可以根据更改建议需要测试哪些包。

它如何保持跨包的一致性？

一致性代理在所有包中执行共享约定——命名模式、文件夹结构、依赖版本。即使 monorepo 增长，它也保持内聚性。

适用于 Monorepo 团队

理解 Monorepo 的
代码审查

不再破坏你不知道存在的包。diffray 追踪跨包依赖并在问题级联之前捕获它们。

Turborepo

Lerna

pnpm Workspaces

Yarn Workspaces

Rush

开始免费试用

Monorepo 成长的烦恼

越大越难管理

影响范围盲区

改一个文件，破坏 5 个包。在出问题之前无法知道哪些包依赖共享代码。

标准不一致

每个包发展不同。不同的 lint 规则、不同的模式、不同的质量水平。

审查复杂性

PR 涉及多个包。审查者需要在所有领域都有专业知识，否则问题会遗漏。

依赖地狱

内部包版本漂移。破坏性更改静默传播。集成问题晚期才浮出水面。

理解你的 Monorepo 的 AI

diffray 分析你的工作区结构并提供上下文感知的审查

跨包影响分析

当你更改共享代码时，diffray 映射每个依赖它的包并标记潜在的破坏性更改。

"此更改影响 @app/ui、@app/forms 和 @app/dashboard。考虑对表单验证的影响。"

边界执行

定义哪些包可以从哪些包导入。防止耦合蔓延并保持清晰的架构。

"@app/core 不应从 @app/web 导入。这会产生循环依赖风险。"

一致的标准

在所有包中应用团队约定。无论包的年龄如何，质量标准都相同。

"此包使用默认导出，但团队标准是命名导出。参见 @app/components 的示例。"

智能测试建议

根据更改了解哪些包需要测试。不要浪费 CI 时间测试不受影响的代码。

"对 @shared/utils 的更改影响 12 个包。建议运行测试：@app/auth、@app/payments。"

常见问题

驯服你的 Monorepo

在每个 PR 上获得跨包智能。不再有意外的破坏。