Custom Guardrail/Plugins Configuration

Custom Guardrails/Plugins are a way to introduce custom “validation” or “mutations” to the request and response of the LLM. You can implement custom security policies, PII detection, content moderation specific to your use case.

Reference integrations (deployable wrappers)

Before building from the template below, you can deploy production-ready FastAPI wrappers from the integrations-custom-guardrails monorepo. Each wrapper implements the custom guardrail response contract; you deploy the service (TrueFoundry, Docker, Render, or any host with a public HTTPS URL), then register Custom Guardrail configs in AI Gateway → Guardrails pointing at the wrapper endpoints.

These appear on the Integrations → Guardrails page alongside native provider integrations. They use the same Custom Guardrail dashboard flow as the template repository.

Template Repository Overview

The custom guardrails template repository provides a comprehensive FastAPI application with multiple guardrail implementations. It serves as a starting point for building your own custom guardrail server with best practices and example implementations.

Architecture

The template follows a modular architecture:

main.py: FastAPI application with route definitions
guardrail/: Directory containing all guardrail implementations
entities.py: Pydantic models for request/response validation
requirements.txt: Dependencies and libraries

Custom guardrail response contract

The AI Gateway treats your guardrail HTTP status and JSON body as follows:

HTTP 2xx — The guardrail ran to completion. Policy outcome and mutations are expressed only in the JSON body (see fields below). Use 2xx for both allow and deny so the gateway can tell policy failure apart from infrastructure failure.
HTTP non-2xx (4xx/5xx) or network failure — The guardrail did not complete successfully (misconfiguration, auth failure, timeout, crash). Depending on enforcing strategy, the gateway may block or continue the request; this path does not mean “content not allowed.”

JSON body (2xx completion):

Field	Meaning
`verdict`	Optional. `true` = allow, `false` = deny. Preferred explicit signal on 2xx.
`result`	For mutate: full OpenAI-shaped `requestBody` or `responseBody` to apply when `transformed` is `true`. For validate: if `verdict` is omitted, boolean `false` still means deny.
`transformed`	For mutate only. `true` = replace request/response with `result`; `false` = do not replace (even if `result` is present).
`message`	Optional human-readable text for logs/UI; not used for allow/deny decisions.

Why this matters: With enforce_but_ignore_on_error, only non-2xx / runtime errors are candidates to ignore. If the signal is “blocked” with HTTP 400, the gateway may treat that as a runtime error and allow the request—use 2xx + verdict: false instead.

Entities and Data Models

The template defines several Pydantic models that structure the data flow between TrueFoundry AI Gateway and your custom guardrail server.

RequestContext

class SubjectType(str, Enum):
    user = 'user'
    team = 'team'
    serviceaccount = 'serviceaccount'

class Subject(BaseModel):
    subjectId: str
    subjectType: SubjectType
    subjectSlug: Optional[str] = None
    subjectDisplayName: Optional[str] = None

class RequestContext(BaseModel):
    user: Subject
    metadata: Optional[dict[str, str]] = None

RequestContext is a Pydantic model that provides structured contextual information for each request processed by your custom guardrail server. It includes details about the user (as a Subject object) and optional metadata relevant to the request lifecycle. This context is automatically populated by the TrueFoundry AI Gateway and can be leveraged for access control, auditing, or custom logic within your guardrail implementations.

InputGuardrailRequest

class InputGuardrailRequest(BaseModel):
    requestBody: CompletionCreateParams
    context: RequestContext
    config: Optional[dict] = None

InputGuardrailRequest represents the schema for requests sent to the input guardrail endpoint. It encapsulates the original model input (requestBody), which is OpenAI-compatible and follows the schema from the official OpenAI repository, along with configuration options (config) and contextual information (context) about the request.

OutputGuardrailRequest

class OutputGuardrailRequest(BaseModel):
    requestBody: CompletionCreateParams
    responseBody: ChatCompletion
    config: Optional[dict] = None
    context: RequestContext

OutputGuardrailRequest represents the schema for requests sent to the output guardrail endpoint. It encapsulates the original model input (requestBody), the model’s output (responseBody), configuration options (config), and contextual information (context) about the request. Both requestBody and responseBody are OpenAI-compatible and follow the schemas from the official OpenAI repository.

Guardrail response models

from typing import Any, Optional

class ValidateGuardrailResponse(BaseModel):
    verdict: bool
    message: Optional[str] = None

class MutateGuardrailResponse(BaseModel):
    verdict: bool
    transformed: bool
    result: dict[str, Any]

Available Guardrails

The template repository includes five pre-implemented guardrails that demonstrate different validation and transformation techniques.

1. PII Redaction (Presidio)

Info

Endpoint: POST /pii-redaction
Type: Input Guardrail (Mutate)
Technology: Microsoft PresidioDetects and redacts Personally Identifiable Information (PII) from incoming requests using Microsoft’s Presidio library.

Code Snippet

import copy
from entities import InputGuardrailRequest, MutateGuardrailResponse
from presidio_analyzer import AnalyzerEngine
from presidio_anonymizer import AnonymizerEngine

analyzer = AnalyzerEngine()
anonymizer = AnonymizerEngine()

def process_input_guardrail(request: InputGuardrailRequest) -> MutateGuardrailResponse:
    # Work on a full copy so `result` is always a complete OpenAI-shaped requestBody
    body = copy.deepcopy(request.requestBody)
    messages = body.get("messages", [])
    transformed_any = False

    for i, message in enumerate(messages):
        content = message.get("content")
        if not isinstance(content, str):
            continue
        results = analyzer.analyze(text=content, entities=[], language="en")
        if not results:
            continue
        anonymized = anonymizer.anonymize(text=content, analyzer_results=results)
        new_text = anonymized.text
        if new_text != content:
            messages[i]["content"] = new_text
            transformed_any = True

    return MutateGuardrailResponse(
        verdict=True,
        transformed=transformed_any,
        result=body,
    )

Response Behavior

Response Behavior (HTTP 2xx; see Custom guardrail response contract):

transformed: false — No PII redaction applied; gateway keeps the original requestBody (you may still return e.g. { "verdict": true, "transformed": false, "result": <unchanged body> } for clarity).
transformed: true — PII was redacted; result must be the full OpenAI-shaped requestBody to replace the incoming request.
HTTP 4xx/5xx — Processing or dependency failure only; not used for “PII found” policy outcomes.

2. NSFW Filtering (Local Model)

Info

Endpoint: POST /nsfw-filtering
Type: Output Guardrail (Validate)
Technology: Hugging Face Transformers (Unitary toxic classification model)Filters out Not Safe For Work (NSFW) content from model responses using a local toxic classification model.

Code Snippet

from entities import OutputGuardrailRequest, ValidateGuardrailResponse
from transformers import pipeline

classifier = pipeline("text-classification", model="unitary/unbiased-toxic-roberta")

def nsfw_filtering(request: OutputGuardrailRequest) -> ValidateGuardrailResponse:
    for choice in request.responseBody.get("choices", []):
        classification_results = classifier(choice["message"]["content"])
        for result in classification_results:
            if (
                (result['label'] == 'toxicity' and result['score'] > 0.2) or
                (result['label'] == 'sexual_explicit' and result['score'] > 0.2) or
                (result['label'] == 'obscene' and result['score'] > 0.2)
            ):
                return ValidateGuardrailResponse(
                    verdict=False,
                    message="This message is not allowed as it is NSFW",
                )
    return ValidateGuardrailResponse(verdict=True)

Response Behavior

Response Behavior (HTTP status):

HTTP 2xx — Outcome in the JSON body (see Custom guardrail response contract).
- Allow: e.g. { "verdict": true }.
- Deny: e.g. { "verdict": false, "message": "…" } — blocked by policy.
HTTP 4xx/5xx or timeout — Guardrail or dependency failed to run; not “content denied.”

3. Drug Mention Detection (Guardrails AI)

Info

Endpoint: POST /drug-mention
Type: Output Guardrail (Validate)
Technology: Guardrails AIDetects and rejects responses that mention drugs using Guardrails AI’s drug detection capabilities.

Code Snippet

from entities import OutputGuardrailRequest, ValidateGuardrailResponse
from guardrails import Guard
from guardrails.hub import MentionsDrugs

guard = Guard().use(MentionsDrugs, on_fail="exception")

def drug_mention(request: OutputGuardrailRequest) -> ValidateGuardrailResponse:
    try:
        for choice in request.responseBody.get("choices", []):
            guard.validate(choice["message"]["content"])
    except Exception as e:
        return ValidateGuardrailResponse(verdict=False, message=str(e))
    return ValidateGuardrailResponse(verdict=True)

Response Behavior

Response Behavior (HTTP status):

HTTP 2xx — Outcome in the JSON body (see Custom guardrail response contract).
- Allow: { "verdict": true }.
- Deny: { "verdict": false, "message": "…" } — blocked by policy.
HTTP 4xx/5xx or timeout — Guardrail or dependency failed to run; not “content denied.”

4. Web Sanitization (Guardrails AI)

Info

Endpoint: POST /web-sanitization
Type: Input Guardrail (Validate)
Technology: Guardrails AIDetects and rejects requests that contain malicious web content using Guardrails AI’s web sanitization capabilities.

Code Snippet

from entities import InputGuardrailRequest, ValidateGuardrailResponse
from guardrails import Guard
from guardrails_grhub_web_sanitization import WebSanitization

guard = Guard().use(WebSanitization, on_fail="exception")

def web_sanitization(request: InputGuardrailRequest) -> ValidateGuardrailResponse:
    try:
        messages = request.requestBody.get("messages", [])
        for message in messages:
            guard.validate(message["content"])
    except Exception as e:
        return ValidateGuardrailResponse(verdict=False, message=str(e))
    return ValidateGuardrailResponse(verdict=True)

Response Behavior

Response Behavior (HTTP status):

HTTP 2xx — Outcome in the JSON body (see Custom guardrail response contract).
- Allow: { "verdict": true }.
- Deny: { "verdict": false, "message": "…" } — blocked by policy.
HTTP 4xx/5xx or timeout — Guardrail or dependency failed to run; not “content denied.”

5. PII Detection (Guardrails AI)

Info

Endpoint: POST /pii-detection
Type: Input Guardrail (Validate)
Technology: Guardrails AIDetects the presence of Personally Identifiable Information (PII) in incoming requests using Guardrails AI. Unlike the Presidio implementation, this only detects and reports PII without redacting it.

Code Snippet

from entities import InputGuardrailRequest, ValidateGuardrailResponse
from guardrails import Guard
from guardrails.hub import DetectPII

guard = Guard().use(DetectPII, on_fail="exception")

def pii_detection_guardrails_ai(request: InputGuardrailRequest) -> ValidateGuardrailResponse:
    try:
        messages = request.requestBody.get("messages", [])
        for message in messages:
            guard.validate(message["content"])
    except Exception as e:
        return ValidateGuardrailResponse(verdict=False, message=str(e))
    return ValidateGuardrailResponse(verdict=True)

Response Behavior

Response Behavior (HTTP status):

HTTP 2xx — Outcome in the JSON body (see Custom guardrail response contract).
- Allow: { "verdict": true }.
- Deny: { "verdict": false, "message": "…" } — blocked by policy.
HTTP 4xx/5xx or timeout — Guardrail or dependency failed to run; not “content denied.”

Request Examples

Input Guardrail Request

{
  "requestBody": {
    "messages": [
      {
        "role": "user",
        "content": "Hello, my name is John Doe and my email is john.doe@example.com"
      }
    ],
    "model": "gpt-3.5-turbo",
    "temperature": 0.7
  },
  "config": {
    "check_content": true,
    "transform_input": true
  },
  "context": {
    "user": {
      "subjectId": "123",
      "subjectType": "user",
      "subjectSlug": "john_doe@truefoundry.com",
      "subjectDisplayName": "John Doe"
    },
    "metadata": {
      "ip_address": "192.168.1.1",
      "session_id": "abc123"
    }
  }
}

Output Guardrail Request

{
  "requestBody": {
    "messages": [
      {
        "role": "user",
        "content": "Hello"
      }
    ],
    "model": "gpt-3.5-turbo"
  },
  "responseBody": {
    "id": "chatcmpl-123",
    "object": "chat.completion",
    "created": 1677652288,
    "model": "gpt-3.5-turbo",
    "choices": [
      {
        "index": 0,
        "message": {
          "role": "assistant",
          "content": "Hello! How can I help you today?"
        },
        "finish_reason": "stop"
      }
    ]
  },
  "config": {
    "check_content": true
  },
  "context": {
    "user": {
      "subjectId": "123",
      "subjectType": "user",
      "subjectSlug": "john_doe@truefoundry.com",
      "subjectDisplayName": "John Doe"
    },
    "metadata": {
      "ip_address": "192.168.1.1",
      "session_id": "abc123"
    }
  }
}

Running Locally

# Install dependencies
pip install -r requirements.txt

# Run the server
python main.py

# Or using uvicorn directly
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Adding Custom Guardrail Integration

To add Custom Guardrail to your TrueFoundry setup, follow these steps:

Navigate to AI Gateway
- Go to AI Gateway in your TrueFoundry dashboard.
Access Guardrails
- Click on Guardrails.
Add New Guardrails Group
- Click on Add New Guardrails Group.

Navigate to Guardrails section in dashboard — Navigate to Guardrails

Guardrails groups help manage access control and security policies for your LLM applications. Configure rules to prevent harmful content, ensure compliance, and maintain data privacy. For more details, refer to the Collaborator Section.

Fill in the Guardrails Group Form
- Name: Enter a name for your guardrails group.
- Collaborators: Add collaborators who will have access to this group.
- Custom Guardrail Config:
  - Name: Enter a name for the Custom Guardrail configuration.
  - Operation: The operation type to use for the guardrail.
    - Validate: Guardrails that inspect and can block without mutating content. On LLM input validation, the gateway may run these alongside the in-flight model request when applicable; on LLM output and MCP hooks, validation runs synchronously before the response or tool result is released. See Guardrails Overview — Operation Mode.
    - Mutate: Guardrails with this operation can both validate and mutate requests. Mutate guardrails are run sequentially.
  - URL: Enter the URL for the Guardrail Server.
  - Auth Data: Provide authentication data for the Guardrail Server. This data will be sent to the Guardrail Server for authorization.
    - Choose between Custom Basic Auth or Custom Bearer Auth.
  - Headers (Optional): Add any headers required for the Guardrail Server. These will be forwarded as is.
  - Config: Enter the configuration for the Guardrail Server. This is a JSON object that will be sent along with the request.

Custom Guardrail configuration form in dashboard — Fill in the Custom Guardrail Form

How Custom Guardrail Config Relates to Guardrail Requests

When you configure a Custom Guardrail in the TrueFoundry guardrails integration creation form (as described above), the settings you provide—such as the operation type, URL, authentication data, headers, and config—directly influence how the AI Gateway interacts with your guardrail server at runtime. How it works:

Config Propagation:
The Config field you specify in the integration creation form is sent as the config attribute in every guardrail request payload. This allows you to parameterize your guardrail logic (e.g., set thresholds, enable/disable features, or pass secrets) without changing your server code.
Request Structure:
When a request is routed through a guardrail, the AI Gateway constructs a request object (such as InputGuardrailRequest or OutputGuardrailRequest) and sends it to your server. This object includes:
- The original model input (requestBody)
- (For output guardrails) The model’s response (responseBody)
- The config object (from your integration creation form)
- The context (user, metadata, etc.)

Example Payload:

{
  "requestBody": { /* original model input */ },
  "responseBody": { /* model output, for output guardrails */ },
  "config": { /* your custom config from the integration creation form */ },
  "context": { /* user and request metadata */ }
}

Dynamic Behavior:
By updating the Custom Guardrail Config in the integration creation form, you can change the behavior of your guardrail server in real time—no code redeploy required. For example, you might adjust PII detection sensitivity, toggle logging, or update allowed user lists.

Summary Table

Integration Creation Form Field	Sent in Guardrail Request as
Config	`config`
Auth Data, Headers	HTTP headers `customHeaders`
Operation	`validate` or `mutate` (how the gateway interprets the response); combine with URL for the HTTP route your server exposes
URL	Guardrail server endpoint

This tight integration ensures that your guardrail logic remains flexible, maintainable, and easy to update as your requirements evolve.

Example: Sending a Request to Your Guardrail Server

Sample Input Guardrail Request Payload & cURL Example

Integration Form Example Values

Field	Example Value
Operation	`mutate` (guardrail operation); URL path is `/pii-redaction`
URL	`https://my-guardrail-server.example.com/pii-redaction`
Auth Data	`Bearer <token>`
Headers
Config

Sample Output Guardrail Request Payload & cURL Example

Integration Form Example Values

Field	Example Value
Operation	`validate` (guardrail operation); URL path is `/nsfw-filtering`
URL	`https://my-guardrail-server.example.com/nsfw-filtering`
Auth Data	`Bearer <token>`
Headers
Config

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Custom Guardrail/Plugins Configuration

Reference integrations (deployable wrappers)

Template Repository Overview

Architecture

Custom guardrail response contract

Entities and Data Models

RequestContext

InputGuardrailRequest

OutputGuardrailRequest

Guardrail response models

Available Guardrails

Request Examples

Running Locally

Adding Custom Guardrail Integration

How Custom Guardrail Config Relates to Guardrail Requests

Example: Sending a Request to Your Guardrail Server

Sample Input Guardrail Request Payload & cURL Example

Sample Output Guardrail Request Payload & cURL Example

​Reference integrations (deployable wrappers)

​Template Repository Overview

​Architecture

​Custom guardrail response contract

​Entities and Data Models

​RequestContext

​InputGuardrailRequest

​OutputGuardrailRequest

​Guardrail response models

​Available Guardrails

​Request Examples

​Running Locally

​Adding Custom Guardrail Integration

​How Custom Guardrail Config Relates to Guardrail Requests

​Example: Sending a Request to Your Guardrail Server

​Sample Input Guardrail Request Payload & cURL Example

​Sample Output Guardrail Request Payload & cURL Example

Reference integrations (deployable wrappers)

Template Repository Overview

Architecture

Custom guardrail response contract

Entities and Data Models

RequestContext

InputGuardrailRequest

OutputGuardrailRequest

Guardrail response models

Available Guardrails

Request Examples

Running Locally

Adding Custom Guardrail Integration

How Custom Guardrail Config Relates to Guardrail Requests

Example: Sending a Request to Your Guardrail Server

Sample Input Guardrail Request Payload & cURL Example

Sample Output Guardrail Request Payload & cURL Example