Agentic coding to generate confidence, not just code.
Qodo is a multi-agent code integrity platform that enhances code quality with AI-powered tools across the software development process. Qodo’s agents for code testing, review, coverage, writing and more, enable developers to leverage AI to address complex, enterprise coding challenges. Qodo offers flexible deployment options, integrates seamlessly into developers' preferred IDEs and Git platforms, and supports all programming languages and testing frameworks.
Qodo's Rules System auto-discovers and enforces organizational coding standards, security rules, and compliance policies across all repositories and teams. The living rules system continuously learns from codebase changes and team patterns, ensuring consistency without manual policy maintenance across distributed teams.
Qodo CLI allows teams to build and deploy custom review agents for any SDLC workflow—from test coverage validation to documentation generation to production issue triage. Agents can be triggered in CI/CD, via webhooks, or as MCP servers, enabling flexible automation across the entire development lifecycle.
Qodo automates pre-review of every PR with AI agents that detect bugs, logic gaps, missing tests, and security issues before human reviewers see them. This prioritizes findings by severity and generates suggested fixes, allowing teams to work through backlogs faster. Evidence shows one Fortune 100 retailer saved 450,000 developer hours annually (50 hours/developer/month).
Qodo provides shift-left code review through IDE plugins for local code review and 15+ specialized agentic workflows that detect bugs, missing tests, logic issues, and security vulnerabilities as developers code. With F1 score of 64.3% on Code Review Bench, Qodo catches real problems at nearly 2x the rate of competitors.
Qodo's Context Engine indexes entire multi-repo codebases, maps dependencies, and builds deep understanding of code structure, PR history, and business requirements. This enables review agents to reason about cross-repo impacts and provide context-aware suggestions that account for organizational patterns and tribal knowledge.