Microsoft Foundry middleware integration

Middleware specifically designed for Microsoft Foundry and Azure AI Content Safety. Learn more about middleware.

These middleware classes live in the langchain-azure-ai package and are exported from langchain_azure_ai.agents.middleware.

Overview

Features

• Text moderation for harmful content and custom blocklists.
• Image moderation for data URLs and public HTTP(S) image inputs.
• Prompt injection detection with Prompt Shield.
• Protected material detection for text and code.
• Groundedness evaluation for generated answers against retrieved context.
• Custom context_extractor hooks to adapt screening and evaluation to your agent state.

Setup

To use the Azure AI Content Safety middleware, install the integration package, configure either an Azure AI Foundry project endpoint or an Azure Content Safety endpoint, and provide a credential.

Installation

Install the package:

pip install -U langchain-azure-ai

uv add langchain-azure-ai

Credentials

For authentication, pass either DefaultAzureCredential() or an API-key string through the credential argument. Using a Foundry Project requires the use of Microsoft Entra ID for authentication.

from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

Instantiation

The middleware supports two endpoint styles:

• An Azure Content Safety resource endpoint via AZURE_CONTENT_SAFETY_ENDPOINT
• An Azure AI Foundry project endpoint via AZURE_AI_PROJECT_ENDPOINT

If both are available, prefer project_endpoint because it gives better defaults for Azure AI Foundry-based workflows. In most setups, you can set the environment variable once and omit endpoint or project_endpoint from each middleware instantiation.

import os

os.environ["AZURE_AI_PROJECT_ENDPOINT"] = "https://<resource>.services.ai.azure.com/api/projects/<project>"

Import and configure your middleware from langchain_azure_ai.agents.middleware.

from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.middleware import AzureContentModerationMiddleware

middleware = AzureContentModerationMiddleware(
    project_endpoint="https://<resource>.services.ai.azure.com/api/projects/<project>",
    credential=DefaultAzureCredential(),
    categories=["Hate", "Violence"],
    exit_behavior="error",
)

Use with an agent

Pass middleware to create_agent in order. You can combine Azure AI middleware with built-in middleware.

from azure.identity import DefaultAzureCredential
from langchain.agents import create_agent
from langchain_azure_ai.agents.middleware import AzureContentModerationMiddleware

agent = create_agent(
    model="azure_ai:gpt-5.5",
    middleware=[
        AzureContentModerationMiddleware(
            project_endpoint="https://<resource>.services.ai.azure.com/api/projects/<project>",
            credential=DefaultAzureCredential(),
            categories=["Hate", "Violence"],
            exit_behavior="error",
        )
    ],
)

Azure AI Content Safety

Text moderation

Use AzureContentModerationMiddleware to screen the last HumanMessage before the agent runs and the last AIMessage after the agent runs. This middleware uses Azure AI Content Safety harm detection and can also check custom blocklists configured in your resource.

Text moderation is useful for the following:

• Blocking harmful user input before a model call
• Screening model output before it reaches end users
• Enforcing custom blocklists in regulated or enterprise deployments
• Composing multiple moderation passes with different category and direction settings

from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.middleware import AzureContentModerationMiddleware

middleware = AzureContentModerationMiddleware(
    project_endpoint="https://<resource>.services.ai.azure.com/api/projects/<project>",
    credential=DefaultAzureCredential(),
    categories=["Hate", "SelfHarm", "Sexual", "Violence"],
    severity_threshold=4,
    exit_behavior="error",
    apply_to_input=True,
    apply_to_output=True,
)

Image moderation

Use AzureContentModerationForImagesMiddleware when your agent handles visual content. It extracts images from the latest input or output message and screens them with the Azure AI Content Safety image analysis API.

This middleware supports:

• Base64 data URLs such as data:image/png;base64,...
• Public HTTP(S) image URLs

from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.middleware import (
    AzureContentModerationForImagesMiddleware,
)

middleware = AzureContentModerationForImagesMiddleware(
    endpoint="https://<resource>.cognitiveservices.azure.com/",
    credential=DefaultAzureCredential(),
    categories=["Hate", "SelfHarm", "Sexual", "Violence"],
    severity_threshold=4,
    exit_behavior="error",
    apply_to_input=True,
    apply_to_output=False,
)

Prompt shield

Use AzurePromptShieldMiddleware to detect prompt injection in user prompts and optional supporting documents. By default it screens input only, because prompt injection is usually an input-side attack, but you can also enable output screening.

from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.middleware import AzurePromptShieldMiddleware

middleware = AzurePromptShieldMiddleware(
    project_endpoint="https://<resource>.services.ai.azure.com/api/projects/<project>",
    credential=DefaultAzureCredential(),
    exit_behavior="continue",
    apply_to_input=True,
    apply_to_output=False,
)

Protected material

Use AzureProtectedMaterialMiddleware to detect protected content such as copyrighted text or code. This middleware can screen both the latest user input and the latest model output.

from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.middleware import AzureProtectedMaterialMiddleware

middleware = AzureProtectedMaterialMiddleware(
    endpoint="https://<resource>.cognitiveservices.azure.com/",
    credential=DefaultAzureCredential(),
    type="code",
    exit_behavior="replace",
    apply_to_input=False,
    apply_to_output=True,
    violation_message="Protected material detected. Please provide a higher-level summary instead.",
)

Groundedness

Use AzureGroundednessMiddleware to evaluate whether a model response is grounded in the context available to the agent. Unlike the other middleware classes on this page, groundedness runs after model generation and inspects the generated answer against supporting sources.

By default, groundedness collects sources from the current conversation, including system content, tool outputs, and relevant annotations attached to model responses.

from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.middleware import AzureGroundednessMiddleware

middleware = AzureGroundednessMiddleware(
    project_endpoint="https://<resource>.services.ai.azure.com/api/projects/<project>",
    credential=DefaultAzureCredential(),
    domain="Generic",
    task="QnA",
    exit_behavior="continue",
)

API reference

For the full public API, see the middleware exports in langchain_azure_ai.agents.middleware and the underlying Content Safety middleware package in langchain_azure_ai.agents.middleware.content_safety.