Guardrails (gateway)

Route Guardrails’ underlying LLM calls through the Respan gateway to use 250+ models from different providers. Only your RESPAN_API_KEY is needed — no separate provider keys required.

Setup

Install packages

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

Set environment variables

$ export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$ export OPENAI_API_KEY="YOUR_RESPAN_API_KEY"
$ export OPENAI_BASE_URL="https://api.respan.ai/api"

No real OpenAI key needed — the Respan gateway handles provider authentication.

Run your guard

1 import os
2 from typing import Literal
3 
4 os.environ["OPENAI_API_KEY"] = os.environ["RESPAN_API_KEY"]
5 os.environ["OPENAI_BASE_URL"] = "https://api.respan.ai/api"
6 
7 from guardrails import Guard
8 from pydantic import BaseModel, Field
9 
10 
11 class SupportReply(BaseModel):
12     answer: str = Field(description="Customer-facing answer")
13     priority: Literal["low", "medium", "high"] = Field(description="Ticket priority")
14 
15 
16 guard = Guard.for_pydantic(output_class=SupportReply)
17 
18 result = guard(
19     model="gpt-4o-mini",
20     messages=[
21         {
22             "role": "user",
23             "content": "Return JSON with answer and priority for a delayed shipment.",
24         }
25     ],
26 )
27 print(result.validated_output)

Switch models

Change the model parameter on guard(...) to use 250+ models from different providers through the same gateway.

1 result = guard(model="claude-sonnet-4-5", messages=messages)
2 result = guard(model="gemini-2.5-flash", messages=messages)

See the full model list.

$	export RESPAN_API_KEY="YOUR_RESPAN_API_KEY"
$	export OPENAI_API_KEY="YOUR_RESPAN_API_KEY"
$	export OPENAI_BASE_URL="https://api.respan.ai/api"

1	import os
2	from typing import Literal
3
4	os.environ["OPENAI_API_KEY"] = os.environ["RESPAN_API_KEY"]
5	os.environ["OPENAI_BASE_URL"] = "https://api.respan.ai/api"
6
7	from guardrails import Guard
8	from pydantic import BaseModel, Field
9
10
11	class SupportReply(BaseModel):
12	answer: str = Field(description="Customer-facing answer")
13	priority: Literal["low", "medium", "high"] = Field(description="Ticket priority")
14
15
16	guard = Guard.for_pydantic(output_class=SupportReply)
17
18	result = guard(
19	model="gpt-4o-mini",
20	messages=[
21	{
22	"role": "user",
23	"content": "Return JSON with answer and priority for a delayed shipment.",
24	}
25	],
26	)
27	print(result.validated_output)

1	result = guard(model="claude-sonnet-4-5", messages=messages)
2	result = guard(model="gemini-2.5-flash", messages=messages)