qodo-ai

Skip to content Beyond LGTM in the age of AI. Code Reviewwith your rules & standards.for complex codebases.that continuously learns.with #1 precision & recall. Book a Demo Get Started 845.1K 613.4K 10.7K Agentic Issue Finding Focused, accurate reviews Qodo provides context-aware code suggestions that detect critical issues, logic gaps, enforce standards, and accelerate reviews with accurate, actionable insights. LOCAL CODE REVIEW Real-time review while you code Built-in review intelligence for your IDE with guided changes, precise code suggestions, and instant resolution. COMPLIANCE CHECKS Always compliant code Validate pull requests against enterprise security policies, verifies ticket traceability, and enforces organization-specific compliance rules automatically. ISSUE RESOLUTION Resolve issues before commit Fix issues at the source with precise, automated resolutions and verified code updates. living rules system Rules that evolve with your codebase Define, edit and enforce coding standards in one place. Qodo keeps rules consistent, measurable, and up to date as your code and teams change. Use Qodo with any model. Use Qodo with your tools, your workflows, and your AI models. Cleaner code from the start Shift-left reviews that detect bugs, missing tests, and logic issues as you code. Smarter, faster pull requests 15+ agentic workflows that scale reviews to match AI development speed. Consistent code quality Continuous enforcement of coding, security, and compliance policies across the SDLC. State of the art context engineering Used by NVIDIA, Qodo’s context engine provides deep agentic code search and retrieval at enterprise scale Higher Signal Code Review, Proven by Benchmark Data Qodo outperforms other code review tools on F1-score, delivering more accurate, actionable feedback with less noise. Book a demo PR Review Rules System GENERAL 2 min read Qodo Named a Visionary in the 2025 Gartner® Magic Quadrant™ for AI Code Assistants TECHNOLOGY 20 min read The Enterprise Guide for Code Quality Measurement Across the SDLC GENERAL 4 min read Cleaning Up Bugs in the Open-Source Community with Qodo: A Free Initiative in Collaboration with Google Cloud Browse all posts Only necessary code analyzed Data is SSL encrypted SOC2 certification What makes Qodo different from other AI code review tools? Qodo is built for one job – code review at scale – while most AI tools treat it as a side feature of code generation. Where others focus on writing more code, Qodo focuses on making sure the code you ship is correct, consistent, and compliant: Review-first, not copilot-first – Qodo acts as a dedicated AI code review layer, running across your IDE, pull requests, and CLI to continuously review changes, not just suggest snippets. Deep, multi-repo context – Qodo’s context engine understands entire codebases, dependencies, and patterns, allowing review agents to reason about impact across services and repos – not just a single file. Agentic quality workflows – 15+ specialized review agents automate tasks like bug detection, test coverage checks, documentation updates, and changelog maintenance, turning code review into a repeatable quality system. Standards and governance built in – Qodo enforces your coding standards, architecture rules, and compliance policies on every change, so large teams can keep code quality aligned as they scale AI usage. In short: Qodo is where AI code review, code quality, and SDLC governance live together in one platform designed for teams that care as much about how code is reviewed as how fast it’s written. Is Qodo suitable for large, multi-repo, multi-team engineering environments? Yes, Qodo is specifically designed for large, complex engineering organizations: Multi-repo understanding – Qodo’s context engine can index dozens or thousands of repositories, mapping dependencies and shared modules so review agents can see cross-repo impacts. Team- and org-level policies – You can define standards once (security rules, architecture patterns, coding conventions) and apply them consistently across teams, services, and repos. Scalable workflows – Automated review flows run in IDEs and PRs without adding extra clicks for developers, so you can scale AI code review without overwhelming your teams. Mixed seniority support – Qodo helps junior and senior engineers converge on the same quality bar by embedding review best practices directly into the workflow. If you have a lot of services, teams, or legacy + new code living together, Qodo is built for that reality. How does Qodo help us clear our pull request backlog? Qodo reduces PR backlog by pre-reviewing every pull request with AI agents, so human reviewers start with a prioritized list of issues, suggested fixes, and ready-to-merge changes. Instead of reviewers opening a cold PR, Qodo does the groundwork: Automated pre-checks on every PR Qodo’s review agents scan each pull request for bugs, logic gaps, missing tests, risky changes, and security issues, then summarize the most important findings. High-signal, suggested fixes Workflows like /compliance, /improve, /analyze, and /implement turn many findings into concrete suggestions or code changes, so reviewers can approve or tweak instead of starting from scratch. Fewer bad PRs reaching reviewers The same agents run earlier in the IDE, catching issues before they ever become PRs. That means fewer noisy or low-quality pull requests in the queue. Clearer, auto-documented PRs Commands like /describe and /add_docs automatically generate PR descriptions and documentation, making it faster and safer for maintainers to understand and merge changes. Taken together, this turns PR review from a manual slog into a review-ready queue, so teams can work through backlogs faster without lowering the quality bar. What is the ROI or efficiency gain we can expect from using Qodo? Qodo delivers ROI by shortening review cycles, cutting manual review work, and reducing issues that slip into production. In one deployment with a Global Fortune 100 retailer, Qodo saved over 450,000 developer hours in a year, with developers saving around 50 hours per month each. That kind of gain lets teams reinvest time into shipping features faster, improving architecture, and raising the overall bar for code quality. Read the case study here. Can Qodo enforce our organization’s coding standards and compliance rules? Yes. Qodo can turn your coding standards, architecture guidelines, and compliance rules into checks that run automatically on every change. Configurable rulesets – Encode rules for style, patterns, frameworks, security, and compliance (for example, required libraries or disallowed APIs). Automated enforcement – Review agents check each diff in the IDE and PR against these rules and flag or block non-compliant changes. Actionable fixes –  Qodo suggests concrete code changes to bring a diff back in line with your standards. Centralized governance –  Standards can be managed centrally and applied across teams and repos, making it easier to keep large organizations aligned. Continuous learning – Qodo’s feedback improves with each code suggestion you accept, and it learns from past PR history, comments and discussion to continuously adapt to your coding standards. What is AI code review? AI code review uses machine learning to automatically analyze code changes for bugs, security vulnerabilities, performance issues, and coding standards violations. Unlike traditional static analysis tools, AI code review understands context across your codebase, learns your team’s patterns, and delivers actionable feedback directly in pull requests and IDEs. Qodo takes this further with multi-repository context analysis and specialized review agents that detect breaking changes, code duplication, and architectural drift—issues that diff-only tools miss How does AI code review work? AI code review works in three steps: Automated analysis: AI models trained on millions of codebases identify bugs, vulnerabilities, and anti-patterns in your code changes Contextual feedback: The system provides inline comments and code suggestions directly in PRs or IDEs, like a senior engineer would Continuous learning: AI adapts to your team’s standards by analyzing past PRs, accepted suggestions, and review comments Qodo’s Context Engine adds deep codebase understanding—indexing 10 repos or 1000—to catch issues that require full organizational context, not just diff-level analysis. What are the benefits of AI code review? Speed & efficiency: Get instant feedback on every PR, reducing review time by up to 1 hour per pull request (source: monday.com case study) Consistent quality: Automatically enforce coding standards, security policies, and best practices across all teams Early bug detection: Catch critical issues before merge—17% of PRs contain high-severity bugs that slip past manual review Reduced reviewer fatigue: Automate tedious checks so engineers focus on architecture and business logic, not syntax Qodo prevents 800+ potential issues monthly at monday.com while maintaining a 73.8% acceptance rate on code suggestions. Can AI code review enforce our organization's coding standards? Yes. AI code review tools learn and enforce team-specific standards automatically. How it works: Custom rules: Define organization-specific patterns, architecture requirements, and compliance policies Historical learning: AI analyzes past PRs and review comments to understand what “good code” means for your team Automatic enforcement: Standards are validated on every commit—no manual checking required Qodo’s approach: Multi-repo rules enforcement across your entire organization Learns from PR history and accepted suggestions Adapts to different standards for different teams or repos 8% of Qodo’s suggestions focus on aligning code with company best practices This ensures consistency even as your team scales. How does AI code review improve security? AI code review detects security vulnerabilities early—before they reach production. Common vulnerabilities caught: Hardcoded credentials and exposed API keys SQL injection and XSS vulnerabilities Insecure data handling and validation gaps Dependency vulnerabilities and outdated libraries Why it matters: AI-assisted code shows 3x more security vulnerabilities than traditionally developed code (SonarSource), making AI code review essential for validating AI-generated code. Qodo’s security capabilities: OWASP compliance checks Secrets detection before commit Breaking change analysis across repos Compliance validation for enterprise standards Real example: Qodo flagged hardcoded environment variables in a monday.com PR that would have exposed staging credentials—an issue missed in manual review. What programming languages does AI code review support? Most AI code review tools support major programming languages including Python, JavaScript, TypeScript, Java, C++, Go, Ruby, PHP, C#, and more. Qodo supports all major programming languages with no configuration required. The Context Engine and review agents work across your entire stack—whether you’re building with Python microservices, React frontends, or Java backends. Language support extends to: Framework-specific patterns (React, Django, Spring) Multi-language repositories Legacy and modern codebases Infrastructure-as-code (Terraform, Kubernetes YAML) How do I integrate AI code review into my workflow? AI code review integrates directly into your existing development workflow—no context switching required. Integration points: Git platforms: GitHub, GitLab, Bitbucket (automated PR review) IDEs: VS Code, JetBrains (real-time feedback as you code) CLI: Terminal-based review for custom workflows CI/CD pipelines: Automated quality checks before merge Qodo deployment options: Cloud: Quick setup, instant access On-premise: Air-gapped deployment for enterprise security Single-tenant: Dedicated infrastructure within your VPC Setup takes minutes, not weeks. Qodo works where your team already does—IDE, Git, or CLI. How do you build a code review process that scales? A scalable code review process maintains quality as teams and velocity grow—without adding review bottlenecks. Process fundamentals: Define standards: Document what “good code” means for your team Automate checks: Use CI/CD to enforce rules before human review Implement AI code review: Deploy platforms like Qodo to handle routine analysis and maintain consistency Set expectations: Response times, PR size limits, approval criteria Distribute knowledge: Rotate reviewers, share context broadly Measure outcomes: Track review time, bug escape rate, developer satisfaction Common scaling challenges: Review queues grow as teams expand Inconsistent standards across teams Senior engineers become bottlenecks Quality gaps when reviews are rushed Qodo handles 20K PRs daily by automating standard checks and surfacing only high-signal findings. Multi-repo context ensures consistency across teams. Organizational learning from PR history means the system gets smarter over time. The result: faster reviews without sacrificing quality. What are pull request review best practices? Effective PR review balances speed with quality—catching critical issues without becoming a bottleneck. Best practices: Keep PRs small: Under 400 lines when possible Write clear descriptions: Explain the “why,” not just the “what” Review promptly: Within 24 hours to maintain velocity Focus on high-impact issues: Architecture and logic over style Provide context: Link to tickets, explain concerns clearly Automate routine checks: Let tools handle formatting and conventions Use AI code review tools: Leverage platforms like Qodo to automate repetitive checks and surface high-signal findings For AI-generated code: Validate context alignment across the codebase Check for unintended code duplication Verify security and compliance standards Focus human review on architectural decisions Qodo automates routine checks so human reviewers focus on what matters. Qodo’s Context Engine validates AI-generated code against your architecture and existing implementations. Monday.com applies these practices at scale—saving ~1 hour per PR while preventing 800+ issues monthly. Are there open source code review tools? Yes. Open source code review tools offer flexibility for teams that want to self-host and customize their setup. Trade-offs: Require configuration and maintenance Limited context understanding across repos No organizational learning from PR history Missing enterprise features (SSO, air-gapped deployment) Qodo in not an open source tool, but is free for open source projects. The platform automates 15+ pull request review checks—catching style issues, missing documentation, security vulnerabilities, and architectural inconsistencies automatically. This is especially valuable for open source maintainers: 70% of PRs come from one-off contributors unfamiliar with codebase standards. Automated reviews save maintainers 5-10 hours per week, letting them focus on the creative aspects of coding rather than repetitive initial reviews. How much does AI code review cost? AI code review pricing varies by vendor and deployment model. Typical pricing structures: Free tiers: Limited to individual developers or open-source projects Team plans: Per-seat pricing for small to mid-size teams Enterprise: Custom pricing based on seats, repos, and deployment requirements Qodo pricing: Free: For individual developers and open-source projects Team: Scalable per-seat pricing Enterprise: Custom deployment with SSO, air-gapped options, and dedicated support ROI to consider: Monday.com saves ~1 hour per PR with Qodo 800+ issues prevented monthly at scale Reduced security incidents and faster review cycles See the pricing here. See all questions --- Skip to content Qodo is the AI code review platform for the enterprise. Monthly Annually Save 21% Developer 30 PRs Free! AI review tools for individuals $0 $0 Features State-of-the-art PR code review Limited time promo. 30 Free PRs per month IDE plugin for local code review CLI tool for agentic quality workflows 75 credits for the IDE plugin and CLI per user Support Community support via GitHub Get started Teams Unlimited PRs Promo Optimized for collaboration $38 $30 /User per month Features State-of-the-art PR code review 20 PRs/user/month. Limited time only promo: Unlimited PRs. IDE plugin for local code review CLI tool for agentic quality workflows 2500 credits for the IDE plugin and CLI per user Deployment & Support Standard private support No data retention and enhanced privacy Get started Enterprise ALL IN ONE INTELLIGENT REVIEW PLATFORM Contact us Contact us Enterprise Services Features State-of-the-art PR code review IDE plugin for local code review CLI tool for agentic quality workflows Context engine for multi-repo codebase awareness Enterprise dashboard & analytics Enterprise user-admin & portal Enterprise MCP tools for Qodo agents Enterprise SSO Deployment & Support Priority support SaaS (single & multi-tenant options) On-prem & air-gapped deployments Proprietary Qodo models (self-hosted) Let’s Talk Code Integrity world leader Using state-of-the-art fine-tuned models, domain-specific prompts and dedicated UI we’re able to provide measurable and meaningful tests Most loved AI developer tool With an astonishing 4.7 score on VSCode and JetBrains marketplaces, over 40K weekly active users, and 1000 weekly active companies We take privacy seriously Securing your data and information with SOC2 Type II certification, 2-way encryption, secrets obfuscation, and TLS/SSL secure payment How does the credit system work for the IDE/CLI? Most LLM models are charged at one credit per network request to the LLM model ( this typically corresponds to one generated message or one tool use). For premium models, such as Opus, each request to the LLM is counted as five credits. Every network request to LLM models and MCP tool usage costs credits. Most operations cost 1 credit each. The exceptions are premium models: Claude Opus costs 5 credits per request, and Grok 4 costs 4 credits per request. So when asking Qodo Gen to write a function, review code, or help debug an issue, each of those interactions typically uses 1 credit. When working with premium models like Opus or Grok 4 for more complex tasks, each request uses their respective higher credit costs. Credits reset every 30 days based on when the first message was sent in Qodo Gen – not on a calendar schedule. The exact reset date can be checked by clicking the speedometer icon mentioned earlier. Why am I suddenly seeing usage limits? The limit message appears when all credits for the current month have been used. Credits reset every 30 days from the first message sent in Qodo Gen. The remaining balance can be checked by clicking the speedometer icon (“Show quota”)  at the top of Qodo Gen chat. How can I get more credits? Right now, if you run out of credits, you’ll have to wait for your monthly reset. But we’re actively working on giving you more options. We’re developing ways for you to buy additional credit bundles, upgrade to plans with higher monthly allowances, and even pay-as-you-go for usage beyond your plan limits. These features are coming soon, and we’ll make sure to include spending controls so you never get surprised by unexpected charges. In the meantime, if you’re consistently hitting limits and it’s impacting your work, reach out to our support team at support@qodo.ai – we want to help find a solution. Do all my team members need to purchase a license on the Teams plan? Not at all, the license is individual. But in the Qodo Merge, only licensed users will get feedback on their PRs (all other repository members will be able to see the feedback but will not get one for their PRs) What does the "Strict Data Retention Policy" entail? We prioritize the security of our users’ data. Data of our paid subscribers is stored for a brief duration of 48 hours for troubleshooting purposes only and is not utilized to train AI models. How do community support, standard support, and priority support differ? Community support is complimentary and is highly effective as it’s regularly overseen by the very professionals who developed our products. Standard support is managed directly by our dedicated support team. Priority support mirrors the structure of standard support but guarantees a Service Level Agreement (SLA) response time of no more than 2 business days. Are there any limitations to how I can use the service? Users in Free tier can use up to 250 credits per calendar month. Users in the Teams tier can use up to 2500 credits per calendar month. Why do you offer free products? Products under Qodo are available at no cost, with our revenue generated from the Teams and Enterprise plans. Our fundamental principle is to furnish individual developers with all the resources they require, while presenting teams and enterprises with features that are most relevant to their operational scale, such as specialized hosting solutions or tools for preparing pull requests. How do you handle my data? Can I choose to opt-out? We utilize our free-tier user data to enhance our models in order to be able to generate meaningful test suites for our users. Given that we produce tests and text (and not general-purpose code), the risk of code or IP exposure is virtually nonexistent. Nevertheless, we understand and respect privacy considerations, and thus provide a straightforward option for users to opt-out without complications by heading to app.qodo.ai and under account settings set to opt-out. See all questions See Qodo in action Book a Demo --- Skip to content We’re on a mission to make code integrity simple Quality-first AI code review helps busy devs ship high-quality code, faster. Bold enough to lead, wise enough to listen. Good conflict to reach decisions. Move fast, with quality We get it – we’re devs, just like you. We believe code coverage isn’t enough, and that devs need a better solution We believe a dev’s time is better spent creating value We believe in meticulous methodology, creativity & exploring all edges Itamar Friedman Co-Founder & CEO Dedy Kredo Co-Founder & CPO VCs Angels Brian Sack Investor, TLV Partners Daniel Povitsky Co-Founder, Vine Ventures Jenna Zerker Partner, Susa Ventures Yonatan Sela Partner, Square Peg Clara Shih VP AI et Meta, ex CEO AI at Salesforce Liat Zakay Director, Shopify Danny Grander Co-Founder, Snyk Peter Welinder VP Product, OpenAI Eyal Gura Entrepeneur & Investor Nitzan Shapira CEO & Co-Founder, Epsagon Ran Ribenzaft CTO & Co-Founder, Epsagon Yair Geva Head Tech Division, HFN Shlomo Dalezman Founder Adam Jafer Co-Founder, Voi Technology Marcus Krylborn Angel Investor Yoav Zurel CEO, Pontera Yair Cleper Investor, CEO Sivan Metzger DataRobot and Mercury ex-GM Guy Zipori Angel Shemer Schwarz Co-Founder of XIV, EatWith and Octarine Barak Kaufman CEO & Co-Founder, Intello (acquired by SailPoint) Show more OPEN POSITIONS AT QODO --- Skip to content This blog reflects a collaborative effort by Qodo’s research team to design, build and validate the benchmark and this analysis. Anthropic launched Code Review for Claude Code, a multi-agent system that dispatches parallel agents to review pull requests, verify findings, and post inline comments on GitHub. It’s a substantial engineering effort, and we wanted to see how it performs on a rigorous, standardized benchmark. However, according to the Qodo Code Review benchmark (which is being adopted throughout the industry, most recently by NVIDIA in its announcement of Nemotron 3 Super), Qodo outperforms Claude by 12 F1 points! Here’s what we learned: The Benchmark Our research paper “Beyond Surface-Level Bugs: Benchmarking AI Code Review on Scale” introduced the Qodo Code Review Benchmark 1.0, a methodology built around injecting realistic defects into genuine, merged pull requests from production-grade open-source repositories. The benchmark covers 100 PRs with 580 issues across 8 repositories spanning TypeScript, Python, JavaScript, C, C#, Rust, and Swift. Unlike prior benchmarks that backtrack from fix commits and focus narrowly on isolated bugs, our approach evaluates both code correctness and code quality (best-practice and rules enforcement) within full PR review scenarios. The injection-based methodology is repository-agnostic and scalable, as it can be applied to any codebase, open-source or private. Our initial evaluation covered 8 leading AI code review tools. Qodo led the field across all configurations. With the release of Claude Code Review, we added it as the ninth tool under identical conditions. We also applied this same benchmark to the latest generation of open-source models, such as NVIDIA Nemotron-3 Super, which is rapidly closing the gap with proprietary models. This benchmark is designed as a living evaluation, rather than static snapshots. Each run reflects the most current iteration of the tools involved. While we prioritize real-time accuracy over static version numbering, these results represent the latest performance parity in the field Claude Code Review: Setup We configured Claude Code Review using its default settings, exactly as a new customer would. PRs were opened on the same forked repositories, and AGENTS.md rules generated from the codebase and committed to each repo root. Claude Code Review ran automatically on PR submission, and we collected its inline comments for evaluation against our validated ground truth using the same LLM-as-a-judge system applied to all other tools. No tuning, no special configuration. Just a fair, head-to-head comparison. Our evaluation also includes the latest iterations of Qodo. It is important to note that because our system evolves with every run, the absolute numbers for Qodo’s production version (79% precision/ 60% recall) have increased since our original report. We are therefore introducing these as the new baseline for this comparison. To explore the upper limits of AI-assisted review, we tested two distinct Qodo configurations: Qodo (Default): The current production version of our system. Qodo (Extended): An orchestrated multi-agent layer added on top of the default agents. While the Extended mode is currently only research results and not yet available in production, they represent a “quality-first” use case. Imagine a highly sensitive PR where a team is willing to invest extra resources to ensure the most exhaustive review possible. The results are striking: both Qodo configurations and Claude land at identical precision levels. This means the quality of individual findings remains high regardless of the mode. The true differentiation is in recall. Qodo in standard mode already surfaces issues with higher recall than Claude Code Review, but the gap widens substantially when our multi-agent approach orchestrates specialized agents to catch the remaining ground truth. The Orchestrated Multi-Agent Harness The research configurations shown above represent our latest architectural iteration. Rather than running a single review pass, it dispatches multiple agents,each tuned for different issue categories (logical errors, best-practice violations, edge cases, cross-file dependencies) and merges their outputs through a verification and deduplication step. The result is a large recall improvement with no precision degradation. This matters because, as we discussed in our original paper, precision is a dimension that can always be tightened through post-processing and filtering. Recall, however, is an inherently more complex challenge since it depends on the system’s ability to deeply understand the codebase, reason about cross-file interactions, and apply repository-specific standards. You can’t filter your way to finding issues you never detected in the first place. Finally, true multi-agent orchestration requires model diversity. While Anthropic’s system is restricted entirely to the Claude ecosystem, Qodo dynamically leverages a blend of the industry’s best SOTA models crossing OpenAI, Anthropic, and Google. By refusing vendor lock-in, our harness synthesizes the unique analytical strengths of different model families to achieve a depth of review that a single-provider system cannot reach. On Cost Claude Code Review is priced at $15–$25 per review on a token-usage basis. Anthropic positions it as a premium, depth-first product and the engineering behind it reflects that ambition. That said, the cost profile is worth noting. At literally an order of magnitude less than Anthropic’s pricing, Qodo makes scaling your review process painless. You’re catching more issues and running a much deeper analysis without burning through your budget. Higher recall at lower per-review cost is a favorable tradeoff for most engineering organizations. What We Learned Claude Code Review is a capable system. Its precision is on par with the best tools we have evaluated, and its multi-agent architecture clearly represents a step beyond simpler single-pass reviewers. Anthropic’s internal data on comment rates and acceptance rates aligns with what we observed when it flags something, it’s usually right (e.g. high precision). The gap is in coverage. In a benchmark designed to test the full spectrum of code review, not just obvious bugs, but subtle best-practice and rules violations, cross-file issues, and architectural concerns, Qodo’s deeper codebase understanding and multi-agent harness approach translate into catching a significantly broader portion of real issues, at a fraction of the per-review cost. We’ll continue expanding the benchmark as new tools and configurations emerge. The dataset and all evaluated reviews are publicly available for independent verification. The Qodo Code Review Benchmark 1.0 is publicly available in our benchmark GitHub organization. Read the full research paper: “Beyond Surface-Level Bugs: Benchmarking AI Code Review on Scale.” Start to test, review and generate high quality code Get Started AI Code Review Benchmark 2026 READ THE STUDY Share General 5 min Introducing Qodo’s Rule System: Turning Standards into Systems General 4 min Qodo in the NVIDIA GTC Keynote. Here’s Why… General 11 min How Can AI-Powered Test Coverage Detect PR-Level Gaps Before Merge? Browse the blog