opencomplai-ai 0.1.0

AI-powered intent classification plugin for Opencomplai (EU AI Act)

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.