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.

Multi-Agent AI Code Review, das funktioniert

Multi-Agenten-Intelligenz. Kein Ein-Modell-Raten.

GitHub PR URL hier einfügen

Mehr anzeigen →

Kostenlos für Open SourceEinrichtung in wenigen KlicksKeine Kreditkarte

87%

Weniger Fehlalarme

Mehr echte Bugs gefunden

98%

Entwickler-Aktionsrate

Null

Doppelte Kommentare

*Basierend auf internen Tests

Integrates with

GitHub GitLab Bitbucket On-Premise

Security & Compliance

SOC 2 Infrastructure

GDPR Ready

Code Never Stored

Ignorieren Sie Ihren AI Code Reviewer?

Sie sind nicht allein. Das haben uns Entwickler über traditionelle AI-Review-Tools erzählt:

Zu viel Rauschen

"18 Kommentare pro PR. Ich lese vielleicht 2. Die meisten sind nur Stil-Kleinigkeiten, die mich nicht interessieren."

— Mid-Level-Entwickler, 50-Personen-Startup

Übersieht echte Probleme

"Schlägt vor, `e` in `error` umzubenennen, übersieht aber komplett SQL-Injection-Schwachstellen. Jedes. Einzelne. Mal."

— Senior Engineer, Fintech

Null Kontext

"Schlägt Refactoring-Muster vor, die wir vor 3 Monaten bereits abgelehnt haben. Hat keine Erinnerung an unsere Entscheidungen."

— Tech Lead, Series B Unternehmen

Das Problem ist nicht KI.

Das Problem ist Einzelagenten-KI.

Spekulation vs. Investigation

Sehen Sie, wie diffray ein reales Szenario behandelt: eine Änderung der Funktionssignatur in Ihrem PR

Einzelnes LLM

KI-Antwort:

"Dies ändert den Rückgabetyp. Stellen Sie sicher, dass Aufrufer aktualisiert werden."

Generischer Ratschlag

Keine spezifischen Stellen

Überlässt Ihnen die Arbeit

diffray Agent

Agenten-Investigation:

1. Suche nach allen Verwendungen von getUserData()

2. Gefunden: 3 Aufrufstellen mit Typ-Diskrepanzen

3. Testabdeckung geprüft — 2 Tests müssen aktualisiert werden

4. Auswirkung: api/users.ts:47, hooks/useUser.ts:23

Konkrete Dateipfade und Zeilennummern

Gegen tatsächliche Codebase verifiziert

Umsetzbare Erkenntnisse, die Sie jetzt beheben können

Der Unterschied? Investigation, keine Spekulation.

So starten Sie

Sehen Sie, wie einfach es ist, diffray einzurichten und Ihr erstes Multi-Agenten Code Review zu erhalten

Mit GitHub anmelden

Verbinden Sie Ihr GitHub-Konto mit einem Klick. Keine komplexe Einrichtung — einfach autorisieren und los geht's.

GitHub App installieren

Fügen Sie diffray zu Ihrer Organisation oder persönlichen Repositories hinzu. Wählen Sie, welche Repos aktiviert werden — Sie haben die volle Kontrolle.

Repository konfigurieren

Passen Sie Review-Einstellungen an, aktivieren Sie bestimmte Agenten und richten Sie die Coding-Richtlinien Ihres Teams ein.

Intelligente Reviews erhalten

Öffnen Sie einen Pull Request und erleben Sie die Magie. Erhalten Sie fokussiertes, umsetzbares Feedback innerhalb von Minuten.

Real Issues. Real PRs.

Not mock examples — actual findings from production code reviews

diffray detecting duplicate library - moment-timezone when dayjs already exists

Understands Your Project Context

Detected moment-timezone being added when the project already uses dayjs. A linter can't catch this — it requires understanding the existing codebase.

diffray detecting non-transactional Stripe and Prisma updates

Catches Data Consistency Bugs

Found Stripe and database updates that aren't atomic. If one fails, payment data becomes inconsistent. Critical for fintech reliability.

diffray detecting race condition in concurrent promo code application

Finds Concurrency Issues

Concurrent requests can read stale data and overwrite each other. Suggests Prisma transactions with optimistic locking.

diffray detecting missing test coverage for new component

Catches Meaningless Tests

Found tests that exist but don't actually test the component. The test file looks complete, but the assertions are empty.

diffray detecting duplicate formatMoney function

Finds Duplicate Utilities

New formatMoney function duplicates existing formatPrice utility. Suggests reusing what's already there.

diffray detecting duplicate function within same PR

Spots Duplicates Within PRs

getStripePublishableKey defined identically in two files within the same PR. Extract to shared utility.

diffray detecting duplicate type definition across files

Prevents Type Drift

CalPromotionData type defined in both API and component files. Risking type drift as code evolves.

diffray detecting duplicate validation logic

Suggests Shared Schemas

Validation logic reimplements the same checks in multiple places. Suggests using shared Zod schema.

Real findings from cal.com open-source code reviews

So funktioniert diffrays Multi-Agenten-System

Mehrere spezialisierte Phasen arbeiten zusammen, um zu finden, was wirklich wichtig ist

PR Event

Analysis

Specialized Agents

Security

Performance

Bugs

Quality

Dedup

Review

PR Event

Context Analysis

Specialized Agents

Security

Performance

Bugs

Quality

Deduplication

Final Review

Spezialisierte Agenten für jeden Bereich

Dynamische Regelauswahl

Lernt die Muster Ihres Teams

Issue-Deduplizierung

Vollständige Codebase-Awareness

Konfidenz-Scoring filtert Rauschen

Ergebnis: 3 Kommentare, 3 behoben

How diffray compares

See why teams switch to multi-agent AI

Feature	diffray	CodeRabbit	GitHub Copilot	SonarQube
Pricing	$9/dev/mo	$15/dev/mo	$19/user/mo	$150+/year
Multi-Agent AI
False Positive Rate	87% fewer	High	High	Very High
Developer Action Rate	98%	~20%	~15%	~15%
Full Codebase Awareness
Custom Rules
Zero Duplicate Comments
Free for Open Source		Limited		Community

View full comparison

Teams, die zu diffray gewechselt sind

"Wir haben die PR-Review-Zeit von 45 Minuten auf 12 Minuten pro Woche reduziert. Das Team vertraut jetzt tatsächlich dem KI-Feedback."

— Engineering Manager, 35-Personen SaaS-Startup

"CodeRabbit gab uns 20+ Kommentare pro PR. Die meisten haben wir ignoriert. diffray gibt uns 3-4, die immer punktgenau sind."

— Tech Lead, Series B Fintech

"Die codebase-bewusste Analyse ist ein Gamechanger. Sie hat eine doppelte Implementierung erkannt, die uns 2 Tage gekostet hätte."

— CTO, AI-Startup (20 Ingenieure)

Simple, transparent pricing

Pay per developer. Unlimited reviews.

Solo

$10/mo

1 developer

Team

$9/dev/mo

3-10 devs

Growth

$79/mo flat

11-25 devs

Scale

$149/mo flat

26-50 devs

Enterprise

Contact

50+ devs

All plans include:Unlimited reviewsAll AI agentsCustom rules14-day free trial

Free forever for open source

Research-Backed

Built on Proven Research

Our multi-agent approach is grounded in peer-reviewed research from leading institutions

40% improvement

"Multi-agent systems can boost visibility by up to 40% in generative engine responses through coordinated intelligence and cross-validation."

Princeton University & Georgia Tech

GEO: Generative Engine Optimization, 2024

View source

25% shift to AI

"By 2026, traditional search engine volume will decrease by 25% as AI-powered answers increasingly become the primary way users interact with information."

Gartner

AI Search Market Analysis, 2025

View source

60% effectiveness

"Code review is the single most effective technique for finding defects, with an average effectiveness of 60% compared to 25% for unit testing alone."

IEEE Software Engineering

Best Practices in Code Inspection, 2023

View source

87%

Fewer false positives with multi-agent review

vs. single-agent tools

More real bugs detected

cross-validation effect

98%

Developer action rate

vs. 15-20% industry avg

<5min

Review completion time

parallel agent processing

Hören Sie auf, AI Code Reviews zu ignorieren

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Für immer kostenlos für Open SourceJederzeit kündbarKeine VerkaufsgesprächeIhr Code wird nie gespeichert