Certified runtime profiles
Define the runtime your team trusts: OS, Python, CUDA, ROS2, TensorRT, package versions, and forbidden dependencies.
$ rtseal check --profile runtime.profile.yaml scanning Dockerfile, requirements.txt, package.xml profile python=3.10 · cuda=12.1 · ros=humble passed imports, CUDA probe, ROS2 launch smoke blocked numpy 2.x drift detected in inference path sealed runtime-report.md · policy-report.json
RuntimeSeal focuses on the environment layer that usually fails after merge: dependencies, containers, GPU stacks, launch paths, and CI images.
A visual gate for the pieces that actually decide whether generated code can run in production.
RuntimeSeal is for teams where a small dependency change can break the entire system: robotics, AI inference, GPU stacks, industrial systems, and complex platform engineering.
Define the runtime your team trusts: OS, Python, CUDA, ROS2, TensorRT, package versions, and forbidden dependencies.
Run checks in GitHub Actions, GitLab CI, or your own build pipeline to block risky changes before they merge.
Go beyond static rules with import checks, CUDA availability, command checks, and minimal runtime dry-runs.
RuntimeSeal does not just say pass or fail. It creates a release artifact your team can review, archive, and attach to MR or CI output.
Start with a CLI and CI gate. Add Agent hooks and MCP integration later when your workflow is ready.
Collect runtime, image, and dependency facts.
Compare against certified profiles and policies.
Run imports, commands, CUDA, ROS2, and toolchain probes.
Produce a report and gate the release decision.
RuntimeSeal is positioned as the runtime safety layer for AI coding workflows, not just another linter.