---
title: "Adding a New Guardrail Integration"
url: "/docs/adding_provider/simple_guardrail_tutorial"
canonical_url: "https://docs.litellm.ai/docs/adding_provider/simple_guardrail_tutorial"
type: "docs"
last_updated: "2026-10-08"
summary: "You're going to create a class that checks text before it goes to the LLM or after it comes back. If it violates your rules, you block it."
related:
  - "/docs/adding_provider/generic_guardrail_api"
  - "/docs/adding_provider/adding_guardrail_support"
---
# Adding a New Guardrail Integration

> Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt


You're going to create a class that checks text before it goes to the LLM or after it comes back. If it violates your rules, you block it.

## How It Works

Request with guardrail:

```bash
curl --location 'http://localhost:4000/chat/completions' \
--header "Authorization: Bearer $LITELLM_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "gpt-5.6-terra",
    "messages": [{"role": "user", "content": "How do I hack a system?"}],
    "guardrails": ["my-guardrail"]
}'
```

Your guardrail checks input, then output. If something's wrong, raise an exception.

## Build Your Guardrail

### Create Your Directory

```bash
mkdir -p litellm/proxy/guardrails/guardrail_hooks/my_guardrail
cd litellm/proxy/guardrails/guardrail_hooks/my_guardrail
```

Two files: `my_guardrail.py` (main class) and `__init__.py` (initialization).

### Write the Main Class

`my_guardrail.py`:

Follow from [Custom Guardrail](/docs/proxy/guardrails/custom_guardrail#1-write-a-customguardrail-class) tutorial.

### Create the Init File

`__init__.py`:

```python
from typing import TYPE_CHECKING

from litellm.types.guardrails import SupportedGuardrailIntegrations

from .my_guardrail import MyGuardrail

if TYPE_CHECKING:
    from litellm.types.guardrails import Guardrail, LitellmParams

def initialize_guardrail(litellm_params: "LitellmParams", guardrail: "Guardrail"):
    import litellm
    
    _my_guardrail_callback = MyGuardrail(
        api_base=litellm_params.api_base,
        api_key=litellm_params.api_key,
        guardrail_name=guardrail.get("guardrail_name", ""),
        event_hook=litellm_params.mode,
        default_on=litellm_params.default_on,
    )
    
    litellm.logging_callback_manager.add_litellm_callback(_my_guardrail_callback)
    return _my_guardrail_callback

guardrail_initializer_registry = {
    SupportedGuardrailIntegrations.MY_GUARDRAIL.value: initialize_guardrail,
}

guardrail_class_registry = {
    SupportedGuardrailIntegrations.MY_GUARDRAIL.value: MyGuardrail,
}
```

### Register Your Guardrail Type

Add to `litellm/types/guardrails.py`:

```python
class SupportedGuardrailIntegrations(str, Enum):
    LAKERA = "lakera_prompt_injection"
    APORIA = "aporia"
    BEDROCK = "bedrock_guardrails"
    PRESIDIO = "presidio"
    ZSCALER_AI_GUARD = "zscaler_ai_guard"
    MY_GUARDRAIL = "my_guardrail"
```

## Usage

### Config File

```yaml
model_list:
  - model_name: gpt-5.6-terra
    litellm_params:
      model: gpt-5.6-terra
    api_key: os.environ/OPENAI_API_KEY

guardrails:
    - guardrail_name: my_guardrail
      litellm_params:
        guardrail: my_guardrail
        mode: during_call
        api_key: os.environ/MY_GUARDRAIL_API_KEY
        api_base: https://api.myguardrail.com
```

### Per-Request

```bash
curl --location 'http://localhost:4000/chat/completions' \
--header "Authorization: Bearer $LITELLM_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "gpt-5.6-terra",
    "messages": [{"role": "user", "content": "Test message"}],
    "guardrails": ["my_guardrail"]
}'
```

## Testing

Add unit tests inside `test_litellm/` folder.

## Related pages

- [[BETA] Generic Guardrail API - Integrate Without a PR](https://docs.litellm.ai/docs/adding_provider/generic_guardrail_api.md)
- [Adding Guardrail Support to Endpoints](https://docs.litellm.ai/docs/adding_provider/adding_guardrail_support.md)