Codacy is a development tool for code quality and security governance, with the core positioning of enabling development teams to release code safely without reducing delivery speed. It manages code quality, security, and AI coding policies from a single location.
In terms of main capabilities, Codacy embeds security checks and automatic fixes into every prompt step of AI-generated code, covering secret scanning, insecure dependencies (SCA), AI policy violations, and unapproved model calls. It can catch and fix quality and security issues before commit, including code quality violations, error-prone code, infrastructure as code (IaC), code duplication, and untested code (unit test coverage). The platform can also fix CVEs in container images before deployment and fix runtime vulnerabilities in applications and API endpoints before hackers exploit them.
For AI code governance, Codacy provides AI guardrails built into every agent and IDE, so that every line of AI-generated code follows the organization's quality and security standards by default, thereby avoiding a large number of issues when submitting a Pull Request. The platform supports centrally defining and enforcing AI coding policies, capturing AI-specific risks such as unapproved AI models, invisible prompt injection, and vulnerable libraries inherited from outdated training data.
For Pull Request review, Codacy provides accurate, instant AI code review feedback, including directly committable fix suggestions, PR summaries, and automatic false positive detection. The platform also provides auditable reports that track security and compliance posture in real time, including SLA expiration dates and exportable SBOM reports. Daily CVE and malware rescanning, together with a daily-updated CVE database, protects old and new code from insecure libraries and malicious packages.
In terms of security detection, Codacy supports SAST, secrets, and IaC security, and can detect security risks and hardcoded secrets in all application and infrastructure code. Test coverage automation ensures that every line of critical code has test coverage and provides AI with the precise context needed to fill gaps. Runtime testing can dynamically test applications and API endpoints to discover vulnerabilities before threat actors exploit them.
In terms of usage, Codacy can be integrated into every agent, IDE, and Git, supports syncing issues with Jira, and provides key alerts on Slack. Codacy Guardrails can silently scan every line during AI code generation, allowing agents to automatically fix their own issues before users see the code. Users can also turn Codacy scan results into actionable context for AI agents, enabling them to automatically fix issues in legacy codebases with verified precision. In addition, users can adjust policies and obtain code health reports, set AI guardrails to conform to the organization's coding standards, and apply them across agents and IDEs, generating real-time code health reports across teams and projects.
Applicable scenarios include development teams that need unified code standards, improved unit test coverage, and fewer newly introduced critical security issues. The official page presents the LSports case, where its Director of Developer Experience mentioned that repositories were standardized under unified coding standards, unit test coverage across all core projects increased, and there were zero newly introduced critical security issues over more than two years.
In terms of limitations, the official page mentions that full scans complete within a few minutes, a 14-day free trial is available, no credit card is required, and it is free for open source projects. Other details such as specific pricing and platform support scope are not yet provided.
