pre_hook_input.py
"""
Pre Hook Input
=============================
Example demonstrating how to use a pre_hook to perform comprehensive input validation for your Agno Agent.
"""
from agno.agent import Agent
from agno.exceptions import CheckTrigger, InputCheckError
from agno.models.openai import OpenAIResponses
from agno.run.agent import RunInput
from pydantic import BaseModel
# ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
class InputValidationResult(BaseModel):
is_relevant: bool
has_sufficient_detail: bool
is_safe: bool
concerns: list[str]
recommendations: list[str]
def comprehensive_input_validation(run_input: RunInput) -> None:
"""
Pre-hook: Comprehensive input validation using an AI agent.
This hook validates input for:
- Relevance to the agent's purpose
- Sufficient detail for meaningful response
Could also be used to check for safety, prompt injection, etc.
"""
# Input validation agent
validator_agent = Agent(
name="Input Validator",
model=OpenAIResponses(id="gpt-5-mini"),
instructions=[
"You are an input validation specialist. Analyze user requests for:",
"1. RELEVANCE: Ensure the request is appropriate for a financial advisor agent",
"2. DETAIL: Verify the request has enough basic information for a meaningful response.",
" A request has sufficient detail if it includes at least a few of: age, income, savings, goals, or risk tolerance.",
" Do NOT require exhaustive information - a reasonable question with some context is sufficient.",
"3. SAFETY: Ensure the request is not harmful or unsafe",
"",
"List specific concerns and recommendations for improvement.",
"",
"Be lenient with detail checks - if the user provides a clear question with some financial context, mark has_sufficient_detail as true.",
"Only mark has_sufficient_detail as false for extremely vague requests like 'help me invest' with no context at all.",
],
output_schema=InputValidationResult,
)
validation_result = validator_agent.run(
input=f"Validate this user request: '{run_input.input_content}'"
)
result = validation_result.content
# Check validation results
if not result.is_safe:
raise InputCheckError(
f"Input is harmful or unsafe. {result.recommendations[0] if result.recommendations else ''}",
check_trigger=CheckTrigger.INPUT_NOT_ALLOWED,
)
if not result.is_relevant:
raise InputCheckError(
f"Input is not relevant to financial advisory services. {result.recommendations[0] if result.recommendations else ''}",
check_trigger=CheckTrigger.OFF_TOPIC,
)
if not result.has_sufficient_detail:
raise InputCheckError(
f"Input lacks sufficient detail for a meaningful response. Suggestions: {', '.join(result.recommendations)}",
check_trigger=CheckTrigger.INPUT_NOT_ALLOWED,
)
def main():
print("Input Validation Pre-Hook Example")
print("=" * 60)
# Create a financial advisor agent with comprehensive hooks
agent = Agent(
name="Financial Advisor",
model=OpenAIResponses(id="gpt-5-mini"),
pre_hooks=[comprehensive_input_validation],
description="A professional financial advisor providing investment guidance and financial planning advice.",
instructions=[
"You are a knowledgeable financial advisor with expertise in:",
"• Investment strategies and portfolio management",
"• Retirement planning and savings strategies",
"• Risk assessment and diversification",
"• Tax-efficient investing",
"",
"Provide clear, actionable advice while being mindful of individual circumstances.",
"Always remind users to consult with a licensed financial advisor for personalized advice.",
],
)
# Test 1: Valid financial question (should work normally with enhanced formatting)
print("\n[TEST 1] Valid financial question")
print("-" * 40)
try:
response = agent.run(
input="""
I'm 35 years old and want to start investing for retirement.
I can save $1000 per month in addition to my current retirement savings and have moderate risk tolerance.
My gross income is $100,000.
My current retirement savings in IRAs and 401(k)s is $100,000, my total savings is $200,000, and my net worth is $300,000.
My country is the United States.
Do you have advice for me?
"""
)
print("[OK] Success! Response validated by pre-hook:")
print(response.content)
except Exception as e:
print(f"[ERROR] Unexpected error: {e}")
# Test 2: Input with insufficient detail (should trigger pre-hook)
print("\n[TEST 2] Vague input (insufficient detail)")
print("-" * 40)
try:
response = agent.run(input="Help me invest")
print(response.content)
except InputCheckError as e:
print(f"[BLOCKED] Pre-hook validation failed: {e}")
print(f" Trigger: {e.check_trigger}")
# Test 3: Irrelevant request (should trigger pre-hook)
print("\n[TEST 3] Off-topic request")
print("-" * 40)
try:
response = agent.run(input="What's the best pizza recipe?")
print(response.content)
except InputCheckError as e:
print(f"[BLOCKED] Pre-hook validation failed: {e}")
print(f" Trigger: {e.check_trigger}")
# Test 4: Potentially harmful content (should trigger pre-hook)
print("\n[TEST 4] Potentially unsafe content")
print("-" * 40)
try:
response = agent.run(
input="How can I manipulate stock prices to make money quickly?"
)
print(response.content)
except InputCheckError as e:
print(f"[BLOCKED] Pre-hook validation failed: {e}")
print(f" Trigger: {e.check_trigger}")
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__main__":
main()
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
pre_hook_input.py, then run:python pre_hook_input.py