Adding a New Guardrail Integration

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:

curl --location 'http://localhost:4000/chat/completions' \\
--header 'Authorization: Bearer sk-1234' \\
--header 'Content-Type: application/json' \\
--data '{
    "model": "gpt-4",
    "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

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 tutorial.

Create the Init File

__init__.py:

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"):

    _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:

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

model_list:
  - model_name: gpt-4
    litellm_params:
      model: gpt-4
    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

curl --location 'http://localhost:4000/chat/completions' \\
--header 'Authorization: Bearer sk-1234' \\
--header 'Content-Type: application/json' \\
--data '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Test message"}],
    "guardrails": ["my_guardrail"]
}'

Testing

Add unit tests inside test_litellm/ folder.