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Opencomplai says its compliance rule engine and command-line interface make pass/fail decisions without LLM or machine-learning inference, and that CI checks pull requests for unapproved AI/LLM dependencies. Its optional AI plugin is described as separate from that decision path. These are the project team’s statements about its design, not the result of an independent repository audit.
What Opencomplai keeps out of the decision path
In an engineering article dated 24 September 2026, the Opencomplai Team says its core rule engine, in packages/core, and its CLI are fully deterministic. The team’s stated constraint is that neither LLM nor ML inference participates in the compliance pass/fail path. The team put it this way: “That’s a design constraint, not a line for the landing page, and I don’t just assert it.” Opencomplai’s engineering article
This is Opencomplai’s design choice, not a general claim that every compliance tool must exclude AI. Nor does the article establish that deterministic rules alone guarantee legal compliance or that the implementation has been independently verified.
How CI is meant to enforce the dependency boundary
Opencomplai says its CI scans dependency manifests across the repository on each pull request, looking for unapproved AI/LLM packages. The article names pyproject.toml, package.json, and requirements*.txt as scanned files, and says tests, examples, and fixtures are excluded.
The stated purpose of those exclusions is to allow demos and fixtures without letting an unapproved dependency into code that makes compliance decisions. For example, the team says adding a transformers import to the risk engine should fail the build if the package has not been approved. The article describes the policy and intended outcome; it does not provide independent verification of the scan’s coverage or behavior.
What the optional AI plugin does—and does not do
Opencomplai describes an optional plugin in packages/ai that includes an ONNX/transformers intent classifier. The classifier’s stated task is to classify free-text system descriptions. Opencomplai says the plugin is opt-in and sandboxed to its own package, rather than part of the core pass/fail path.
The team draws a specific boundary around the classifier: it says the tool does not decide whether Article 5 applies. That is a description of the project’s intended role for the feature, not an independently verified behavioral guarantee. The article also mentions an optional [deep] extra for local GGUF models using llama-cpp-python.
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The article gives pip install opencomplai as the installation command and links to the project documentation and repository. For the design rationale and the project’s own account of CI enforcement, see the Opencomplai engineering article; its title and date also appear in the blog index.
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