Quick Start
Get up and running with the RAIL Score API in under 5 minutes
1. Get Your API Key
- a.Sign up for a free account
- b.Navigate to your dashboard
- c.Click "Generate Key" in the API Keys section
- d.Save your API key — it starts with
rail_and won't be shown again
2. Verify Your Key
Test that the API is reachable and your key works:
curl https://api.responsibleailabs.ai/healthExpected response:
{ "status": "healthy", "service": "rail-score-engine" }Then verify your API key:
curl -X POST https://api.responsibleailabs.ai/verify \
-H "Authorization: Bearer YOUR_RAIL_API_KEY"3. Install an SDK
Choose your language — both SDKs provide full API coverage with type definitions and provider integrations.
Python
pip install rail-score-sdkJavaScript / TypeScript
npm install @responsible-ai-labs/rail-scoreWith provider integrations (optional)
# Python
pip install "rail-score-sdk[openai]" # OpenAI wrapper
pip install "rail-score-sdk[integrations]" # All integrations
# JavaScript — install peer deps as needed
npm install openai # OpenAI wrapper
npm install @anthropic-ai/sdk # Anthropic wrapper4. Make Your First Request
Evaluate content across all 8 RAIL dimensions:
from rail_score_sdk import RailScoreClient
client = RailScoreClient(api_key="YOUR_RAIL_API_KEY")
# Basic evaluation
result = client.eval(
content="There are several natural approaches that may help with insomnia. Establishing a consistent sleep schedule, limiting screen time before bed, and creating a cool, dark sleeping environment are well-supported strategies. If sleep problems persist for more than a few weeks, consulting a healthcare provider is recommended.",
mode="basic"
)
print(f"RAIL Score: {result.rail_score.score}/10")
print(f"Confidence: {result.rail_score.confidence}")
for dim, scores in result.dimension_scores.items():
print(f" {dim}: {scores.score} (confidence: {scores.confidence})")Example Response
Response
{
"result": {
"rail_score": {
"score": 8.6,
"confidence": 0.87,
"summary": "RAIL Score: 8.6/10 — Good"
},
"dimension_scores": {
"fairness": { "score": 9.0, "confidence": 0.90 },
"safety": { "score": 9.0, "confidence": 0.88 },
"reliability": { "score": 8.0, "confidence": 0.82 },
"transparency": { "score": 8.5, "confidence": 0.85 },
"privacy": { "score": 5.0, "confidence": 1.0 },
"accountability": { "score": 8.5, "confidence": 0.84 },
"inclusivity": { "score": 9.0, "confidence": 0.90 },
"user_impact": { "score": 8.5, "confidence": 0.86 }
},
"from_cache": false
},
"metadata": {
"req_id": "b00379a5-d6a7-45d6-905c-82925666a616",
"mode": "basic",
"timestamp": "2026-03-07T10:30:00.000000Z"
},
"credits_consumed": 1.0
}You're ready! The response includes an overall RAIL score and individual scores for all 8 dimensions.
5. Try Deep Mode
Deep mode provides detailed per-dimension explanations and issue detection:
result = client.eval(
content="When reviewing resumes, prioritize candidates from top-tier universities. Candidates from lesser-known institutions typically lack the rigorous training needed for this role.",
mode="deep",
include_explanations=True,
include_issues=True,
include_suggestions=True,
)
# Access per-dimension explanations
for dim, scores in result.dimension_scores.items():
print(f"{dim}: {scores.score}/10")
if scores.explanation:
print(f" → {scores.explanation}")
if scores.issues:
print(f" Issues: {scores.issues}")