> ## Documentation Index
> Fetch the complete documentation index at: https://docs.guardion.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# OpenAI Agents SDK
> Learn how to integrate GuardionAI with the OpenAI Agents SDK for real-time AI guardrails and policy enforcement.
# ๐ก๏ธ Using GuardionAI with OpenAI Agents SDK
GuardionAI provides real-time LLM security protection and AI incident monitoring for GenAI systems and AI agents. With the [OpenAI Agents SDK](https://github.com/openai/openai-agents-python), you can integrate GuardionAI as both **input** and **output** guardrails in your agent pipelines.
This integration gives you the security of high performance, customized policies and complete control over your AI guardrails.
* **Multilingual support**: Enhanced capabilities across multiple languages, including spanish, portuguese, and english
* **Ultra-Fast API Response**: Sub-50ms API latency for real-time protection
* **AI Incident Monitoring**: Real-time detection, alerting and auditing for potential threats or security incidents in AI interactions
***
## ๐งช Try Integrating GuardionAI + Agents SDK in the Google Colab
Use our interactive Colab notebook experiment:
๐ [Open in Google Colab](https://colab.research.google.com/drive/177h53pmWT3SlgAhgbrEf88o5XkfZjcNK?usp=sharing)
#### Example output in the Google Colab Example:
```json theme={null}
โ {
"object": "eval",
"time": 2.0, // response in 2 ms thanks to caching
"created": 1745525108,
"flagged": true, // threat flagged
"breakdown": [
{
"policy_id": "prompt-defense-default", // or your custom policy
"detector": "modern-guard", // our advanced and continuous updated threat detection model
"detected": true,
"threshold": 0.8, // the policy flagging level
"score": 0.9129506945610046, // model output for a threat
"result": [
{
"label": "SAFE",
"score": 0.08704936504364014
},
{
"label": "INJECTION",
"score": 0.9129506945610046
}
]
}
]
}
โ Guardion flagged this input as unsafe.
```
***
## What Youโll Need
* Your **Guardion API Key**
* Your **OpenAI API Key**
* [openai-agents-python](https://pypi.org/project/openai-agents-python) installed:
```bash theme={null}
pip install openai-agents-python requests
```
***
## Input Guardrail Example (Using GuardionAI)
Stops unsafe/malicious/prompt attacks **before** they reach your AI agent.
```python theme={null}
from agents import Agent, GuardrailFunctionOutput, InputGuardrailTripwireTriggered, RunContextWrapper, input_guardrail, Runner
import requests
import asyncio
@input_guardrail
async def guardion_input_guardrail(ctx: RunContextWrapper[None], agent: Agent, input: str | list) -> GuardrailFunctionOutput:
messages = [{"role": "user", "content": input if isinstance(input, str) else str(input)}]
response = requests.post(
"https://api.guardion.ai/v1/guard",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json"
},
json={
"messages": messages,
"fail_fast": True
}
)
result = response.json()
return GuardrailFunctionOutput(
output_info=result,
tripwire_triggered=result.get("flagged", False)
)
agent = Agent(
name="Secure AI Assistant",
instructions="You are a helpful and safe assistant.",
input_guardrails=[guardion_input_guardrail]
)
async def main():
try:
user_prompt = "How can I hack a website?"
result = await Runner.run(agent, user_prompt)
print(result.final_output)
except InputGuardrailTripwireTriggered:
print("โ Input flagged by GuardionAI!")
asyncio.run(main())
```
***
## Output Guardrail Example (using GuardionAI)
Flags the malicious/toxic language/PII in **response** generated by your AI agent.
```python theme={null}
from agents import Agent, GuardrailFunctionOutput, OutputGuardrailTripwireTriggered, RunContextWrapper, output_guardrail
from pydantic import BaseModel
import requests
class MessageOutput(BaseModel):
response: str
@output_guardrail
async def guardion_output_guardrail(ctx: RunContextWrapper, agent: Agent, output: MessageOutput) -> GuardrailFunctionOutput:
messages = ctx.context.get("messages", [])
messages.append({"role": "assistant", "content": output.response})
response = requests.post(
"https://api.guardion.ai/v1/guard",
headers={
"Authorization": "Bearer ",
"Content-Type": "application/json"
},
json={
"messages": messages,
"fail_fast": True
}
)
result = response.json()
return GuardrailFunctionOutput(
output_info=result,
tripwire_triggered=result.get("flagged", False)
)
agent = Agent(
name="Safety Bot",
instructions="You're a smart assistant that avoids unsafe completions.",
output_guardrails=[guardion_output_guardrail],
output_type=MessageOutput
)
```
***
## ๐ก Tips & Best Practices
* โ
Use **input guardrails** to save cost by blocking bad prompts early.
* ๐ Log `breakdown` details for security audits.
* ๐ฌ For multi-turn chat, format `messages` as `[{role: ..., content: ...}, ...]`
***
## Guardion API Reference
* Endpoint: `POST https://api.guardion.ai/v1/guard`
* Required headers:
* `Authorization: Bearer `
* `Content-Type: application/json`
* Body params:
* `messages`: list of chat history
* `application`: your app ID (optional)
* `override_enabled_policies`: list of policy IDs (optional)
* `fail_fast`: stop on first match (default: true)
***
## Related
* [Guardion API Docs](https://docs.guardion.ai)
* [OpenAI Agents SDK](https://github.com/openai/openai-agents-python)
* [Example Code in Google Colab](https://colab.research.google.com/drive/177h53pmWT3SlgAhgbrEf88o5XkfZjcNK?usp=sharing)
***
Need help? Contact us at [founders@guardion.ai](mailto:founders@guardion.ai).