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.

为什么需要对外包代码进行特殊审查？

外包商通常对您的代码库、架构决策和团队约定了解较少。diffray 确保他们的代码符合您的标准并正确集成——在问题变成技术债务之前捕获它们。

diffray 能将我们的内部编码标准应用于外部代码吗？

是的。您的 Playbook 和自定义规则同样适用于所有 PR。外包商获得与您内部团队相同的反馈，通过审查过程学习您的模式和约定。

它如何帮助解决外部代码的安全问题？

安全代理审查所有代码的漏洞，但特别关注外包代码中常见的模式——硬编码凭据、不安全的依赖项和身份验证绕过。

它能减轻内部审查人员的负担吗？

显著减轻。AI 处理常规质量检查，因此您的内部团队只需要审查架构决策和业务逻辑——而不是格式和基本安全问题。

适用于与外包商合作的团队

信任外部代码
但不盲目信任

外包商帮助你更快交付。但他们的代码需要监督。 diffray 确保外部代码符合你的标准，而不会让你的团队淹没在审查中。

开始免费试用

外包商悖论

你雇佣他们节省时间。审查他们的代码却花时间。

缺少上下文

外包商不了解你的历史、架构决策或不成文规则。

不同的标准

他们从其他项目带来模式。有时是好的，有时与你的不兼容。

安全盲点

外部代码需要额外审查。硬编码凭证、不安全模式、后门。

审查开销

你的团队花更多时间审查外包代码而不是写自己的代码。

无需微管理的质量控制

AI 处理检查清单。你处理战略。

执行你的标准

外包商获得与你的团队相同的反馈。他们通过审查过程学习你的模式。

"这个项目使用依赖注入。参见 AuthService 了解模式。"

安全筛查

对常见外包商安全问题进行额外审查 — 硬编码值、不安全依赖、认证绕过。

"检测到硬编码 API 密钥。根据安全策略使用环境变量。"

架构对齐

捕获外部代码不符合你的架构的情况。防止意外的技术债务。

"这创建了直接 DB 访问。我们使用仓库模式 — 参见 /docs/architecture.md"

减少审查负担

AI 处理检查清单。你的团队专注于业务逻辑和集成问题。

预过滤的 PR：格式、基本安全、约定已检查

清晰的质量关卡

AI + 人工 = 完整覆盖

代码风格和格式自动化

安全漏洞扫描自动化

架构合规自动化

依赖审查自动化

业务逻辑审查人工

集成验证人工

常见问题

自信扩展

使用外包商快速行动。使用 diffray 保持安全。