← AI coding tools
GitHub CodeQL vs Semgrep
Compare semantic GitHub-native code analysis with fast rule-based scanning for secure AI coding workflows and pull request gates.
| Category | GitHub CodeQL | Semgrep |
|---|---|---|
| Best for | Teams using GitHub that want deeper semantic security analysis on critical repositories and AI-generated pull requests. | Teams that need fast static analysis, custom rules, and CI checks before agent-generated changes reach production. |
| Pricing | Free for public repositories; private repository availability depends on GitHub plan and security features | Open-source engine with paid team and enterprise products |
| Rating | ★ 4.5/5 | ★ 4.5/5 |
| Workflow fit | GitHub security review, PR gates, Critical code analysis, Secure SDLC | AI code security, Pull request review, CI gates, Custom policy checks |
| IDE support | GitHub code scanning, Actions, CLI, and extension workflows depending on setup | CLI, CI, GitHub workflows, and editor integrations depending on setup |
| Agent mode | Not a coding agent; use as an automated reviewer and security gate for agent-created changes | Not an autonomous coding agent; best used as a guardrail that reviews agent output |
| Privacy | Privacy depends on GitHub repository settings, code scanning configuration, and enterprise policy | Teams should review hosted scanning, repository access, and rule telemetry settings before rollout |
| MCP angle | CodeQL helps MCP-backed AI coding workflows separate generation from verification: agents propose changes, CodeQL supplies security evidence in GitHub. | Semgrep gives AI coding agents a concrete review gate: code can be generated by an agent, then scanned through least-privilege CI or MCP-backed evidence workflows. |
Verdict
Choose CodeQL for deeper semantic analysis in GitHub security workflows. Choose Semgrep for faster custom policy checks that teams can tune across repos and CI systems.