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import os
import json
from agents import Agent, OpenAIChatCompletionsModel, Runner, GuardrailFunctionOutput
from pydantic import BaseModel
from openai import AsyncOpenAI
from core.model import get_model_client
class ValidatedOutput(BaseModel):
is_valid: bool
reasoning: str
input_validation_agent = Agent(
name="Guardrail Input Validation Agent",
instructions="""
You are a highly efficient and specialized **Agent** 🌐. Your sole function is to validate the user inputs.
## Core Directives & Priorities
1. You should flag if the user uses unparaliamentary language ONLY.
2. You MUST give reasoning for the same.
## Rules
- If it contains any of these, mark `"is_valid": false` and explain **why** in `"reasoning"`.
- Otherwise, mark `"is_valid": true` with reasoning like "The input follows respectful communication guidelines."
## Output Format (MANDATORY)
* Return a JSON object with the following structure:
{
"is_valid": <boolean>,
"reasoning": <string>
}
""",
model=get_model_client(),
output_type=ValidatedOutput,
)
input_validation_agent.description = "A guardrail agent that validates user input for unparliamentary language."
async def input_validation_guardrail(ctx, agent, input_data):
result = await Runner.run(input_validation_agent, input_data, context=ctx.context)
raw_output = result.final_output
# Handle different return shapes gracefully
if isinstance(raw_output, ValidatedOutput):
final_output = raw_output
print("Parsed ValidatedOutput:", final_output)
else:
final_output = ValidatedOutput(
is_valid=False,
reasoning=f"Unexpected output type: {type(raw_output)}"
)
return GuardrailFunctionOutput(
output_info=final_output,
tripwire_triggered=not final_output.is_valid,
)
__all__ = ["input_validation_agent", "input_validation_guardrail", "ValidatedOutput"]
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