agno-guardrails

name	agno-guardrails
description	Add input and output guardrails to Agno agents. Covers built-in guardrails (PII detection, prompt injection), custom guardrails, output validation, and moderation. Trigger this skill when: importing agno.guardrails, creating guardrail classes, adding pre_hooks or post_hooks, or asking "how do I add safety checks to my agent?"
license	Apache-2.0
metadata	{"version":"1.0.0","author":"agno-team","tags":["guardrails","safety","validation","moderation","agno"]}

Add Guardrails to Agno Agents

Use pre_hooks and post_hooks to validate inputs and outputs. Install with pip install agno.

Quick Start

from agno.agent import Agent
from agno.guardrails.base import BaseGuardrail
from agno.exceptions import InputCheckError
from agno.run.agent import RunInput

class NoSpamGuardrail(BaseGuardrail):
    def check(self, run_input: RunInput):
        content = run_input.input_content_string()
        if content.count("!") > 10:
            raise InputCheckError("Input appears to be spam")

    async def async_check(self, run_input: RunInput):
        self.check(run_input)

agent = Agent(
    model="openai:gpt-4o",
    pre_hooks=[NoSpamGuardrail()],
)

agent.print_response("Hello!", stream=True)  # OK
agent.print_response("BUY NOW!!!!!!!!!!!!", stream=True)  # Blocked

Input Guardrails (pre_hooks)

Run before the agent processes a message. Raise InputCheckError to block:

from agno.guardrails.base import BaseGuardrail
from agno.exceptions import InputCheckError
from agno.run.agent import RunInput

class ContentPolicyGuardrail(BaseGuardrail):
    blocked_topics = ["violence", "illegal"]

    def check(self, run_input: RunInput):
        content = run_input.input_content_string().lower()
        for topic in self.blocked_topics:
            if topic in content:
                raise InputCheckError(f"Blocked topic: {topic}")

    async def async_check(self, run_input: RunInput):
        self.check(run_input)

agent = Agent(
    model="openai:gpt-4o",
    pre_hooks=[ContentPolicyGuardrail()],
)

Output Guardrails (post_hooks)

Run after the agent generates a response. Validate or transform output:

from agno.guardrails.base import BaseOutputGuardrail
from agno.exceptions import OutputCheckError
from agno.run.agent import RunOutput

class NoPIIOutputGuardrail(BaseOutputGuardrail):
    def check(self, run_output: RunOutput):
        import re
        content = str(run_output.output_content_string())
        # Check for email patterns
        if re.search(r'\b[\w.-]+@[\w.-]+\.\w+\b', content):
            raise OutputCheckError("Output contains email addresses")

    async def async_check(self, run_output: RunOutput):
        self.check(run_output)

agent = Agent(
    model="openai:gpt-4o",
    post_hooks=[NoPIIOutputGuardrail()],
)

Built-in Guardrails

PII Detection

from agno.guardrails import PIIDetectionGuardrail

agent = Agent(
    model="openai:gpt-4o",
    pre_hooks=[PIIDetectionGuardrail()],
)

Prompt Injection Detection

from agno.guardrails import PromptInjectionGuardrail

agent = Agent(
    model="openai:gpt-4o",
    pre_hooks=[PromptInjectionGuardrail()],
)

Combining Multiple Guardrails

agent = Agent(
    model="openai:gpt-4o",
    pre_hooks=[
        PIIDetectionGuardrail(),
        PromptInjectionGuardrail(),
        ContentPolicyGuardrail(),
    ],
    post_hooks=[
        NoPIIOutputGuardrail(),
    ],
)

Custom Guardrail with Model-Based Checking

Use an LLM to classify inputs:

from agno.agent import Agent
from agno.guardrails.base import BaseGuardrail
from agno.exceptions import InputCheckError
from agno.run.agent import RunInput

class ToxicityGuardrail(BaseGuardrail):
    def __init__(self):
        self.classifier = Agent(
            model="openai:gpt-4o-mini",
            instructions=[
                "Classify the input as 'safe' or 'toxic'.",
                "Respond with only one word: safe or toxic.",
            ],
        )

    def check(self, run_input: RunInput):
        content = run_input.input_content_string()
        result = self.classifier.run(content)
        if "toxic" in str(result.content).lower():
            raise InputCheckError("Input classified as toxic")

    async def async_check(self, run_input: RunInput):
        content = run_input.input_content_string()
        result = await self.classifier.arun(content)
        if "toxic" in str(result.content).lower():
            raise InputCheckError("Input classified as toxic")

Handling Guardrail Errors

from agno.exceptions import InputCheckError, OutputCheckError

try:
    response = agent.run("Some potentially bad input")
except InputCheckError as e:
    print(f"Input blocked: {e}")
except OutputCheckError as e:
    print(f"Output blocked: {e}")

Anti-Patterns

• Don't forget async_check — both sync and async methods must be implemented
• Don't use expensive guardrails for every message — balance safety with latency
• Don't silently swallow errors — raise InputCheckError or OutputCheckError to block
• Don't put business logic in guardrails — they're for safety and validation only
• Don't rely solely on guardrails — they complement, not replace, proper prompt engineering

Further Reading

For OpenAI moderation integration and advanced patterns, read references/api-patterns.md.