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author=diffray;
description=Multi-agent AI code review that catches real bugs. 87% fewer false positives, 3x more issues found. Free for open source.;
author=diffray;
keywords=AI code review,multi-agent AI,automated code review,GitHub code review,code quality,pull request review,intelligent code analysis,code review tool,developer productivity,diffray,context-aware code review,senior-level code review,zero duplicate comments,codebase-aware AI;

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Text of the page (random words):
diffray multi agent ai code review that actually thinks diffray how it works why multiple agents pricing docs sign in get started multi agent ai code review that works multi agent intelligence not single model guessing start free trial paste any github pr url here thecoryroberts myphotobiz pull 20 calcom cal com pull 25856 diffray diffray pull 3 see more free for open source setup in a few clicks no credit card 87 fewer false positives 3x more real bugs caught 98 developer action rate zero duplicate comments based on internal testing integrates with github gitlab bitbucket on premise security compliance soc 2 infrastructure gdpr ready code never stored are you ignoring your ai code reviewer you re not alone here s what developers told us about traditional ai review tools too much noise 18 comments per pr i read maybe 2 most are just style nitpicks i don t care about mid level dev 50 person startup misses real issues it suggests renaming e to error but completely misses sql injection vulnerabilities every single time senior engineer fintech zero context suggests refactoring patterns we already decided against 3 months ago has no memory of our decisions tech lead series b company the problem isn t ai the problem is single agent ai speculation vs investigation see how diffray handles a real scenario a function signature change in your pr single llm ai response this changes the return type make sure callers are updated generic advice no specific locations leaves work to you diffray agent agent investigation 1 searched for all usages of getuserdata 2 found 3 call sites with type mismatches 3 checked test coverage 2 tests need updates 4 impact api users ts 47 hooks useuser ts 23 concrete file paths and line numbers verified against actual codebase actionable findings you can fix now the difference investigation not speculation how to start see how easy it is to set up diffray and get your first multi agent code review 1 sign in with github connect your github account in one click no complex setup required just authorize and you re ready to go 2 install the github app add diffray to your organization or personal repositories choose which repos to enable you have full control 3 configure your repository customize review settings enable specific agents and set up your team s coding guidelines 4 get intelligent reviews open a pull request and watch the magic happen receive focused actionable feedback within minutes watch 2 min demo real issues real prs not mock examples actual findings from production code reviews 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 catches data consistency bugs found stripe and database updates that aren t atomic if one fails payment data becomes inconsistent critical for fintech reliability finds concurrency issues concurrent requests can read stale data and overwrite each other suggests prisma transactions with optimistic locking catches meaningless tests found tests that exist but don t actually test the component the test file looks complete but the assertions are empty finds duplicate utilities new formatmoney function duplicates existing formatprice utility suggests reusing what s already there spots duplicates within prs getstripepublishablekey defined identically in two files within the same pr extract to shared utility prevents type drift calpromotiondata type defined in both api and component files risking type drift as code evolves 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 how multi agent system works multiple specialized stages working together to find what actually matters pr event analysis specialized agents security performance bugs quality dedup review pr event context analysis specialized agents security performance bugs quality deduplication final review specialized agents for each domain dynamic guideline selection learns your team s patterns issue deduplication full codebase awareness confidence scoring filters noise result 3 comments 3 fixed 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 that switched to diffray we reduced pr review time from 45 minutes to 12 minutes per week the team actually trusts ai feedback now engineering manager 35 person saas startup coderabbit was giving us 20 comments per pr we ignored most diffray gives us 3 4 that are always spot on tech lead series b fintech the codebase aware analysis is a game changer it caught a duplicate implementation that would ve cost us 2 days cto ai startup 20 engineers simple transparent pricing pay per developer unlimited reviews solo 10 mo 1 developer most popular 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 reviews all ai agents custom rules 14 day free trial free forever for open source view all plans 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 3x more real bugs detected cross validation effect 98 developer action rate vs 15 20 industry avg 5min review completion time parallel agent processing stop ignoring ai code reviews try diffray free for 14 days no credit card setup in a few clicks start free trial book a demo free forever for open source cancel anytime no sales calls your code is never stored powered by claude product pricing solutions open source free cli integrations meet the agents security tools resources use cases compare security why multiple agents ai code review playbook glossary faq best ai tools company about us contact documentation blog status community github slack linkedin x rss feed 2026 diffray all rights reserved privacy terms 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