License Compliance Checker (LCC)

Know what you ship. Know what you owe.

The only open-source scanner that combines dependency license detection, AI model license analysis, and EU AI Act Article 53 compliance — in a single tool.

Built by AI Exponent LLC. Free and open source under Apache 2.0.

Quick Start

pip install license-compliance-checker

# Scan a project
lcc scan .

# Scan with EU AI Act compliance policy
lcc scan . --policy eu-ai-act-compliance --format json

# Generate a CycloneDX SBOM
lcc sbom generate scan-report.json --format cyclonedx --output sbom.json

# Check GPL contamination in a SaaS context
lcc scan . --project-license Apache-2.0 --context saas

What LCC does

• AI model license detection, including HuggingFace models resolved by Hub ID and GGUF / ONNX model files
• EU AI Act Article 53 assessment and compliance-pack output
• A training-data risk registry that flags datasets with commercial-use restrictions
• SBOM generation in CycloneDX and SPDX
• Policy-as-code with OPA Rego or YAML policies
• Free and open source under Apache-2.0

Supported Export Formats

LCC produces industry-standard Software Composition Analysis (SCA), SBOM, and regulatory compliance artifacts:

Architecture

graph TD
    CLI["CLI · FastAPI Server · GitHub Action · VS Code Extension"]
    DET["Detectors\nPython · Node.js · Go · Rust · Ruby\nJava · .NET · HuggingFace · GGUF/ONNX"]
    RES["Resolvers\nPyPI · npm · Crates.io · Maven\nGitHub API · ClearlyDefined · HF Hub API"]
    POL["Policy Engine\nOPA Rego · YAML policies\nPermissive · Strict · EU AI Act"]
    REG["Regulatory Assessor\nEU AI Act Article 53"]
    OUT["Outputs\nJSON · HTML · Markdown · CSV\nCycloneDX SBOM · SPDX SBOM\nArticle 53 compliance pack"]

    CLI --> DET
    DET -->|"detected components"| RES
    RES -->|"resolved licenses"| POL
    POL -->|"violations + warnings"| REG
    REG --> OUT

    style CLI fill:#1e3a5f,color:#fff
    style DET fill:#1e3a5f,color:#fff
    style RES fill:#1e3a5f,color:#fff
    style POL fill:#c9a84c,color:#000
    style REG fill:#c9a84c,color:#000
    style OUT fill:#2d5a2d,color:#fff

See the User Guide and API Reference for comprehensive architectural and component specifications.

Ecosystem Coverage

graph LR
    LCC["LCC\nScanner"]

    PY["Python\npip · Poetry · Conda"]
    JS["JavaScript\nnpm · Yarn · pnpm"]
    GO["Go\ngo.mod"]
    RS["Rust\nCargo.toml"]
    JV["Java\nMaven · Gradle"]
    RB["Ruby\nBundler"]
    DN[".NET\nNuGet"]
    HF["HuggingFace\nHub API · Model cards\nGGUF · ONNX"]

    LCC --> PY
    LCC --> JS
    LCC --> GO
    LCC --> RS
    LCC --> JV
    LCC --> RB
    LCC --> DN
    LCC --> HF

    style LCC fill:#1e3a5f,color:#fff
    style HF fill:#c9a84c,color:#000

EU AI Act Article 53 Coverage

GPAI obligations under Article 53 have applied since 2 August 2025 for models placed on the market from that date; models placed earlier must comply by 2 August 2027. The Commission's supervision and enforcement powers, including fines, begin 2 August 2026. LCC automates evidence gathering for each sub-obligation:

graph TD
    A53["Article 53\nObligations"]

    A["53(1)(a)\nTechnical documentation\n→ SBOM with model type,\nversion, license metadata"]
    B["53(1)(b)\nDownstream provider info\n→ Model card capabilities\nand limitations extracted"]
    C["53(1)(c)\nCopyright policy\n→ Training data licenses\nand copyright flags"]
    D["53(1)(d)\nTraining data summary\n→ Dataset descriptions\nfrom model cards"]
    E["53(2)\nSystemic risk\n→ 65B+ parameter\nmodel detection"]

    A53 --> A
    A53 --> B
    A53 --> C
    A53 --> D
    A53 --> E

    style A53 fill:#1e3a5f,color:#fff
    style A fill:#1e3a5f,color:#fff
    style B fill:#1e3a5f,color:#fff
    style C fill:#1e3a5f,color:#fff
    style D fill:#1e3a5f,color:#fff
    style E fill:#c9a84c,color:#000

Scope note: LCC generates audit evidence for Article 53 documentation obligations. It is not a legal compliance determination. Involve qualified legal counsel for final compliance assessment.

Penalty band: Non-compliance with Article 53 is sanctionable by the Commission under Article 101(1) at up to €15M or 3% of global annual turnover, whichever is higher. Note that GPAI fines are Commission-imposed under Art. 101 — distinct from the Art. 99 fines imposed by member-state market-surveillance authorities for high-risk-system violations. Source: Regulation (EU) 2024/1689, Art. 101(1).

AI Model Detection

LCC scans your codebase for AI model references without requiring a local download:

# Detects from_pretrained("org/model") references in Python / YAML / JSON
lcc scan .

# Detects GGUF and ONNX model files (Ollama / llama.cpp)
lcc scan /path/to/models

# Full transitive scan with lock file
lcc scan . --include-transitive --policy permissive

Supported AI license families: the OpenRAIL family (including BigScience BLOOM and CreativeML variants), Llama 2 / 3 / 3.1, Gemma, and Mistral, plus provider licenses from Anthropic, OpenAI, Cohere, and AI21. The registry holds 17 AI license definitions and also recognises standard SPDX identifiers.

Training data risk registry: Flags datasets with commercial use risk — OpenAI API outputs, ShareGPT, Books3, The Pile classified as high/critical risk.

Policy Enforcement

# Built-in policies
lcc scan . --policy permissive            # Allow MIT, Apache-2.0, BSD only
lcc scan . --policy strict                # Block all copyleft
lcc scan . --policy eu-ai-act-compliance  # Article 53 GPAI obligations

# Custom policy (YAML)
cat > my-policy.yaml << EOF
name: my-saas-policy
rules:
  - license: GPL-3.0
    action: block
    reason: "GPL-3.0 requires SaaS source disclosure"
  - license: AGPL-3.0
    action: block
  - license: RAIL
    action: warn
    reason: "Review RAIL restrictions before deploying"
EOF

lcc scan . --policy my-policy.yaml

For detailed policy syntax, custom OPA Rego rules, and violation actions, see the Policy Guide.

CI/CD Integration

# .github/workflows/license-check.yml
- name: License compliance scan
  uses: aiexponent/license-compliance-checker/.github/actions/license-compliance@v1
  with:
    path: .
    policy: eu-ai-act-compliance
    fail-on: violations
    format: json
    output: license-report.json

For production deployment patterns, Docker container scanning, and web dashboard access, see the Deployment Guide.

SBOM Generation

# CycloneDX 1.5 with EU AI Act regulatory extensions
lcc sbom generate scan-report.json --format cyclonedx --output sbom.cdx.json

# SPDX 2.3
lcc sbom generate scan-report.json --format spdx --output sbom.spdx.json

# Sign with GPG for tamper-evidence
lcc sbom sign sbom.cdx.json --key ~/.gnupg/key.gpg

Known Limitations

• HuggingFace Hub API scanning requires referenced model IDs (not local downloads only).
• SPDX AND/OR compound expressions are flagged for manual review, not auto-resolved.
• Transitive dependency resolution requires a lock file (poetry.lock, package-lock.json).
• Article 53 assessment covers documentation completeness only — not a legal compliance determination.
• Training data risk registry covers top-50 known datasets; unknown datasets flagged for review.

Documentation

Full documentation is available on the live Material for MkDocs Documentation Portal:

• Getting Started & Quick Start — Setup guide, first project scan, and SBOM generation.
• Installation Guide — Package managers (pip, uv), container installation, and requirements.
• User Guide & CLI Manual — Comprehensive command-line flags, scanners, and report generators.
• Policy Guide (Rego & YAML) — Authoring custom policies, OPA rules, and compliance gates.
• API Reference & Schemas — Python SDK, FastAPI endpoints, and schema definitions.
• Deployment & Dashboard Guide — Production checklists, Docker deployment, and web dashboard access.
• Troubleshooting & FAQ — Common questions, licensing nuances, and SARIF non-support rationale.
• Testing Guide — Running the automated test suite and integration test environments.

Contributing

See CONTRIBUTING.md. Issues and PRs welcome.

git clone https://github.com/aiexponent/license-compliance-checker
cd license-compliance-checker
pip install -e ".[dev]"
pytest

License

Apache 2.0 — free to use, modify, and distribute.

Built by AI Exponent LLC — hello@aiexponent.com

Part of the AiExponent open-source AI governance toolchain: license-compliance-checker · rag-benchmarking · RiskForge