respan-instrumentation-guardrails 0.1.0

Respan instrumentation plugin for Guardrails AI

respan-instrumentation-guardrails

Respan instrumentation plugin for Guardrails AI. It wraps Guardrails Guard execution methods and emits guardrail spans into the Respan tracing pipeline.

Configuration

1. Install

pip install respan-ai respan-instrumentation-guardrails guardrails-ai

guardrails-ai is the Guardrails runtime package. It must be available in your package index for runnable Guardrails applications.

2. Set Environment Variables

Variable	Required	Description
RESPAN_API_KEY	Yes	Your Respan API key. Authenticates both proxy and tracing.
RESPAN_BASE_URL	No	Defaults to https://api.respan.ai/api.

Quickstart

3. Run Script

import os

from dotenv import load_dotenv

load_dotenv()

respan_api_key = os.environ["RESPAN_API_KEY"]
respan_base_url = os.getenv("RESPAN_BASE_URL", "https://api.respan.ai/api")
os.environ["OPENAI_API_KEY"] = respan_api_key
os.environ["OPENAI_BASE_URL"] = respan_base_url

from guardrails import Guard
from pydantic import BaseModel
from respan import Respan
from respan_instrumentation_guardrails import GuardrailsInstrumentor


class SupportReply(BaseModel):
    answer: str
    priority: str


respan = Respan(
    api_key=respan_api_key,
    base_url=respan_base_url,
    instrumentations=[GuardrailsInstrumentor()],
)

guard = Guard.for_pydantic(output_class=SupportReply)
result = guard(
    model="gpt-4o-mini",
    messages=[
        {
            "role": "user",
            "content": "Return JSON with answer and priority for a late shipment.",
        }
    ],
)

print(result.validated_output)
respan.flush()

4. View Dashboard

After running the script, traces appear on your Respan dashboard.

Further Reading

See the Respan example projects for runnable scripts.