title	3D guardrails
short_description	3D content you can trust
emoji	🛡️
colorFrom	blue
colorTo	indigo
sdk	docker
app_port	7860
sdk_version	6.1.0
pinned	true
license	apache-2.0
app_file	demo.py
tags	3D guardrails 3D

3D Guardrails API

Run the FastAPI server to scan 3D assets for trust-and-safety risks.

3D Rendering Benchmark

The most expensive part is not 3D rendering, but the LLM analysis. Here is a benchmark of the 3D rendering part running on a CPU:

Prerequisites

• Python 3.11
• For OpenAI: OPENAI_API_KEY exported in your environment
• For Gemini: GEMINI_API_KEY exported in your environment
• For Ollama: Local Ollama server running (default: http://localhost:11434)

Install

uv sync

Start the server

uv run fastapi dev -e dddguardrails.api:app

Gradio Demo

Launch an interactive web interface for testing the 3D guardrails:

uv run python -m dddguardrails.demo

The demo will be available at http://localhost:7860 and provides:

• File upload interface for 3D models
• LLM provider and model selection
• Real-time safety analysis results
• Clear display of risk findings and severity levels

Docker

Run the application using Docker for easy deployment with off-screen rendering support.

Build the Image

docker build -t dddguardrails .

Run Gradio Demo (Default)

# Using docker run
docker run -p 7860:7860 -e APP_MODE=gradio dddguardrails

# Using docker-compose (recommended)
docker-compose -p local up -d --no-deps --build app

Access the Gradio interface at http://localhost:7860

Run FastAPI Service

# Using docker run
docker run -p 8000:8000 -e APP_MODE=fastapi dddguardrails

# Using docker-compose (recommended)
docker-compose up fastapi

Access the FastAPI docs at http://localhost:8000/v1/guardrails/docs

Environment Variables

• APP_MODE: Set to gradio (default) or fastapi
• OPENAI_API_KEY: Your OpenAI API key (optional)
• GEMINI_API_KEY: Your Google Gemini API key (optional)
• OLLAMA_BASE_URL: Ollama server URL (default: http://localhost:11434)

Testing Guidelines

Avoid Change-Detector Tests: This codebase follows best practices for unit testing. Mock tests that verify implementation details rather than behavior are harmful because they break during refactoring and don't provide real confidence in functionality.

For guidance on writing effective tests, see: Testing on the Toilet: Change-Detector Tests Considered Harmful

Endpoint

• POST /v1/guardrails/scan
  • Request format: multipart/form-data
  • Form Fields:
    • file: The 3D asset file. Accepts .glb, .gltf, .fbx, .obj, .stl, .ply.
    • llm_provider: (optional, default: "ollama") LLM provider to use ("openai", "gemini", "ollama").
    • model: (optional) Specific model to use (e.g., "gpt-4o", "gemini-3-flash-preview", "qwen3-vl:235b-cloud").
    • resolution_width: (optional) Width of screenshots for rendering.
    • resolution_height: (optional) Height of screenshots for rendering.
    • risk_categories: (optional) JSON array of custom RiskCategory objects (name and description).
  • Returns: ScanResponse containing detected categories with severity and rationale.

Roadmap

• OpenAI support
• Gemini support
• Ollama support
• Groq support
• User specified provider and models support
• Early exit on the first violation (save tokens)
• Tile rendering for faster violation/non-violation detection
• Docker/docker-compose support with headless rendering
• Improve rendering performance
• Benchmark
• Evals
• Gradio demo
• AWS Bedrock support
• External AI gateways
• Configurable Categories: Allow users to define custom risk categories in their scan requests instead of being limited to the hardcoded ones (weapons, nudity, self-harm, etc.)
• Add multi-modal (image, text, sound, video?)
• Batch API
• Async API
• Streaming API
• Reading content from URLs (presigned URL, internet content, ect)
• Backwards compatible API with OpenAI and AWS Bedrock Guardrails