---
title: "Aporia Guardrails with LiteLLM Gateway"
description: "import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';"
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/litellm-proxy-aporia
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:30:39.342Z
license: CC-BY-4.0
attribution: "Aporia Guardrails with LiteLLM Gateway — Claudary (https://claudary.paisolsolutions.com/skills/litellm-proxy-aporia)"
---

# Aporia Guardrails with LiteLLM Gateway
import Image from '@theme/IdealImage'; import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

## Overview

import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

# Aporia Guardrails with LiteLLM Gateway

In this tutorial we will use LiteLLM AI Gateway with Aporia to detect PII in requests and profanity in responses

## 1. Setup guardrails on Aporia

### Create Aporia Projects

Create two projects on [Aporia](https://guardrails.aporia.com/)

1. Pre LLM API Call - Set all the policies you want to run on pre LLM API call 
2. Post LLM API Call - Set all the policies you want to run post LLM API call


<Image img={require('../../img/aporia_projs.png')} />


### Pre-Call: Detect PII

Add the `PII - Prompt` to your Pre LLM API Call project

<Image img={require('../../img/aporia_pre.png')} />

### Post-Call: Detect Profanity in Responses

Add the `Toxicity - Response` to your Post LLM API Call project

<Image img={require('../../img/aporia_post.png')} />


## 2. Define Guardrails on your LiteLLM config.yaml 

- Define your guardrails under the `guardrails` section and set `pre_call_guardrails` and `post_call_guardrails`
```yaml
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: openai/gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

guardrails:
  - guardrail_name: "aporia-pre-guard"
    litellm_params:
      guardrail: aporia  # supported values: "aporia", "lakera"
      mode: "during_call"
      api_key: os.environ/APORIA_API_KEY_1
      api_base: os.environ/APORIA_API_BASE_1
  - guardrail_name: "aporia-post-guard"
    litellm_params:
      guardrail: aporia  # supported values: "aporia", "lakera"
      mode: "post_call"
      api_key: os.environ/APORIA_API_KEY_2
      api_base: os.environ/APORIA_API_BASE_2
```

### Supported values for `mode`

- `pre_call` Run **before** LLM call, on **input**
- `post_call` Run **after** LLM call, on **input & output**
- `during_call` Run **during** LLM call, on **input** Same as `pre_call` but runs in parallel as LLM call.  Response not returned until guardrail check completes

## 3. Start LiteLLM Gateway 


```shell
litellm --config config.yaml --detailed_debug
```

## 4. Test request 

**[Langchain, OpenAI SDK Usage Examples](../proxy/user_keys#request-format)**

<Tabs>
<TabItem label="Unsuccessful call" value = "not-allowed">

Expect this to fail since since `ishaan@berri.ai` in the request is PII

```shell
curl -i http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \\
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "hi my email is ishaan@berri.ai"}
    ],
    "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```

Expected response on failure

```shell
{
  "error": {
    "message": {
      "error": "Violated guardrail policy",
      "aporia_ai_response": {
        "action": "block",
        "revised_prompt": null,
        "revised_response": "Aporia detected and blocked PII",
        "explain_log": null
      }
    },
    "type": "None",
    "param": "None",
    "code": "400"
  }
}

```

</TabItem>

<TabItem label="Successful Call " value = "allowed">

```shell
curl -i http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer sk-npnwjPQciVRok5yNZgKmFQ" \\
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "hi what is the weather"}
    ],
    "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
  }'
```

</TabItem>


</Tabs>

## 5. Control Guardrails per Project (API Key)

Use this to control what guardrails run per project. In this tutorial we only want the following guardrails to run for 1 project (API Key)
- `guardrails`: ["aporia-pre-guard", "aporia-post-guard"]

**Step 1** Create Key with guardrail settings

<Tabs>
<TabItem value="/key/generate" label="/key/generate">

```shell
curl -X POST 'http://0.0.0.0:4000/key/generate' \\
    -H 'Authorization: Bearer sk-1234' \\
    -H 'Content-Type: application/json' \\
    -d '{
            "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
        }
    }'
```

</TabItem>
<TabItem value="/key/update" label="/key/update">

```shell
curl --location 'http://0.0.0.0:4000/key/update' \\
    --header 'Authorization: Bearer sk-1234' \\
    --header 'Content-Type: application/json' \\
    --data '{
        "key": "sk-jNm1Zar7XfNdZXp49Z1kSQ",
        "guardrails": ["aporia-pre-guard", "aporia-post-guard"]
        }
}'
```

</TabItem>
</Tabs>

**Step 2** Test it with new key

```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \\
    --header 'Authorization: Bearer sk-jNm1Zar7XfNdZXp49Z1kSQ' \\
    --header 'Content-Type: application/json' \\
    --data '{
    "model": "gpt-3.5-turbo",
    "messages": [
        {
        "role": "user",
        "content": "my email is ishaan@berri.ai"
        }
    ]
}'
```

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/litellm-proxy-aporia) · https://claudary.paisolsolutions.com