opencomplai-ai
The optional AI intent classification plugin for Opencomplai.
It adds the --ai-intent flag to opencomplai scan, classifying how each AI callsite in
your code is actually used — its decision autonomy, the subjects it acts on, and which EU
AI Act risk tier and Annex III area it maps to.
All inference runs locally — models execute on your machine via ONNX Runtime or llama.cpp. No code or prompts leave your environment.
Prerequisites
opencomplai-ai is a plugin. Install the core engine first:
pip install opencomplai-core # or the opencomplai / opencomplai-cli suite
Install
# Base install — CodeBERT (ONNX) classification, no extra build deps
pip install opencomplai-ai
# Deep install — adds llama.cpp for generative GGUF models
pip install "opencomplai-ai[deep]"
Usage
Once installed alongside the CLI, the --ai-intent flag becomes available on the scan
command:
opencomplai scan --ai-intent
By default only callsites in files with lexical findings are annotated (fast). To analyze every callsite in the repository:
opencomplai scan --ai-intent --ai-deep
Useful flags:
| Flag | Effect |
|---|---|
--ai-intent |
Enable AI intent classification |
--ai-model <id> |
Choose a model (see catalog below) |
--ai-deep |
Annotate every callsite, not just those near lexical findings |
--ai-verbose |
Show all callsite annotations (default: top 10 by risk tier) |
Supported models
The default model (codebert-onnx) runs on the base install. The generative GGUF models
require the [deep] extra. Models are downloaded from the Hugging Face Hub on first use
and cached locally under ~/.opencomplai/.
| Model ID | Runtime | Size | Needs [deep] |
|---|---|---|---|
codebert-onnx (default) |
ONNX Runtime | ~440 MB | no |
qwen2.5-coder-0.5b |
llama.cpp | ~400 MB | yes |
qwen2.5-coder-1.5b (recommended) |
llama.cpp | ~1.0 GB | yes |
smollm2-1.7b |
llama.cpp | ~1.1 GB | yes |
phi-3.5-mini |
llama.cpp | ~2.2 GB | yes |
mistral-7b |
llama.cpp | ~4.1 GB | yes |
opencomplai scan --ai-intent --ai-model qwen2.5-coder-1.5b
Model download flow
On first use of a model, the plugin prompts before downloading and shows a progress bar. The CodeBERT model has no prebuilt ONNX artifact on the Hub, so it is exported from the official PyTorch checkpoint on first run and then cached. Subsequent scans reuse the cached model with no network access.
Documentation
Full AI-intent guide and the model reference at docs.opencomplai.com.
License
AGPL-3.0-only. See LICENSE.
1 maintainer
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