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Documentation Index

Fetch the complete documentation index at: https://docs.responsibleailabs.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Installation

pip install rail-score-sdk
Install with optional extras for provider integrations: 
pip install "rail-score-sdk[openai]"       # OpenAI wrapper
pip install "rail-score-sdk[anthropic]"    # Anthropic wrapper
pip install "rail-score-sdk[google]"       # Gemini wrapper
pip install "rail-score-sdk[litellm]"      # LiteLLM wrapper
pip install "rail-score-sdk[integrations]" # All LLM providers
pip install "rail-score-sdk[agents]"       # CrewAI, LangGraph, AutoGen
pip install "rail-score-sdk[telemetry]"    # OpenTelemetry support
pip install "rail-score-sdk[dev]"          # Development tools

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Sync client

from rail_score_sdk import RailScoreClient

client = RailScoreClient(api_key="YOUR_RAIL_API_KEY")

result = client.eval(content="Your AI-generated text here", mode="basic")
print(f"RAIL Score: {result.rail_score.score}/10")
RailScoreClient returns typed dataclass objects. Access scores as result.rail_score.score, result.dimension_scores["fairness"].score, etc.

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Async client

import asyncio
from rail_score_sdk import AsyncRAILClient

async def main():
    client = AsyncRAILClient(api_key="YOUR_RAIL_API_KEY")
    result = await client.eval(content="Your text here", mode="basic")
    print(result["rail_score"]["score"])  # Returns raw dicts

asyncio.run(main())
AsyncRAILClient returns raw dictionaries rather than dataclasses.

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Key classes

ClassPurpose
RailScoreClientSync client - all core methods
AsyncRAILClientAsync client - all core methods
RAILSessionTrack quality across a conversation
PolicyDeclarative rules for score enforcement
RuleIndividual policy rule
RAILMiddlewareWrap any async LLM function

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Error handling

from rail_score_sdk import (
    RailScoreClient,
    AuthenticationError,
    InsufficientCreditsError,
    RateLimitError,
    ContentTooHarmfulError,
)

client = RailScoreClient(api_key="YOUR_RAIL_API_KEY")

try:
    result = client.eval(content="...", mode="deep")
except AuthenticationError:
    print("Check your API key")
except InsufficientCreditsError as e:
    print(f"Need {e.required} credits, have {e.balance}")
except RateLimitError:
    print("Slow down requests")
except ContentTooHarmfulError:
    print("Content blocked at safety layer")

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What’s next

Evaluation

Sync and async eval examples.

Safe Regeneration

Auto-fix below-threshold content.

Sessions & Policy

Track quality across conversations.

Integrations

Provider wrappers for OpenAI, Gemini, Anthropic.