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Installation

pip install "rail-score-sdk[litellm]" litellm

Usage as guardrail

from rail_score_sdk.integrations import RAILGuardrail
import litellm

# Register RAIL as a guardrail
rail_guardrail = RAILGuardrail(
    api_key="YOUR_RAIL_API_KEY",
    threshold=7.0,
    mode="basic",
    action="block",  # "block" | "warn" | "regenerate"
)

litellm.callbacks = [rail_guardrail]

# Now all LiteLLM calls are automatically scored
response = await litellm.acompletion(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain quantum computing."}],
)

# Access RAIL data from the response metadata
print(response._hidden_params["rail_score"])
print(response._hidden_params["rail_threshold_met"])

Direct usage

from rail_score_sdk.integrations import RAILGuardrail

guardrail = RAILGuardrail(
    api_key="YOUR_RAIL_API_KEY",
    threshold=7.0,
)

result = await guardrail.async_post_call_success_hook(
    data={"messages": [...]},
    response=llm_response,
)
RAILGuardrail implements the LiteLLM CustomLogger interface, so it works with any model supported by LiteLLM, including local models via Ollama.