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Microsoft Foundry Tools (formerly known as Azure AI Services) wrap Azure AI service APIs for agent tool use. These tools live in the langchain-azure-ai package, are exported from langchain_azure_ai.tools, and can be instantiated individually or loaded together with AzureAIServicesToolkit. Use these tools when you want LangChain agents to analyze documents, images, or healthcare text with Azure-managed services.

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Overview

ToolDescription
AzureAIContentUnderstandingToolExtract structured content from documents, images, audio, and video.
AzureAIDocumentIntelligenceToolParse documents into OCR text, tables, and key-value pairs.
AzureAIImageAnalysisToolRun OCR, captions, tagging, object detection, and related image analysis.
AzureAISpeechToTextToolTranscribe audio files to text with language support.
AzureAITextToSpeechToolConvert text to synthesized speech audio with multi-language support.
AzureAITextAnalyticsHealthToolExtract medical entities from healthcare text.

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Features

  • Shared authentication and endpoint handling across all service tools.
  • Support for Azure AI Foundry project endpoints and direct service endpoints.
  • Individual tools for multimodal extraction, document parsing, image analysis, speech-to-text transcription, text-to-speech synthesis, and healthcare text analysis.
  • AzureAIServicesToolkit for loading all service tools at once.
  • Automatic audio source detection (local files and remote URLs) for transcription.
  • Multi-language speech recognition and synthesis with BCP-47 language codes.
  • WAV file generation for synthesized speech output.

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Setup

Install the integration package, configure either an Azure AI Foundry project endpoint or a direct Azure AI Services endpoint, and provide a credential.

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Installation

Install the package with the tools extra: 
pip install -U "langchain-azure-ai[tools]"
This extra installs the service-specific dependencies used by these tools, including azure-ai-documentintelligence, azure-ai-vision-imageanalysis, azure-cognitiveservices-speech, and azure-ai-textanalytics. The base package includes azure-ai-contentunderstanding.

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Credentials

Pass either DefaultAzureCredential() or an API-key string through the credential argument. If you use a Foundry project endpoint, use a Microsoft Entra ID credential such as DefaultAzureCredential().
Initialize credential
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

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Configure endpoints

The service tools support two endpoint styles:
  • An Azure AI Foundry project endpoint via project_endpoint or AZURE_AI_PROJECT_ENDPOINT
  • A direct Azure AI Services endpoint via endpoint or AZURE_AI_INFERENCE_ENDPOINT
If both are available, prefer project_endpoint because it resolves the backing service endpoint automatically for Foundry-based workflows.
Configure endpoint
export AZURE_AI_PROJECT_ENDPOINT="https://<resource>.services.ai.azure.com/api/projects/<project>"

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Instantiate a tool

If AZURE_AI_PROJECT_ENDPOINT is already set, you can usually omit project_endpoint during instantiation.
Initialize tool
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAIContentUnderstandingTool

tool = AzureAIContentUnderstandingTool(
    credential=DefaultAzureCredential(),
)

result = tool.invoke(
    {"source": "https://example.com/invoice.pdf", "source_type": "url"}
)
print(result)

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Use with an agent

Pass one or more tools to create_agent.
Agent with tools
from azure.identity import DefaultAzureCredential
from langchain.agents import create_agent
from langchain.chat_models import init_chat_model
from langchain_azure_ai.tools import (
    AzureAIDocumentIntelligenceTool,
    AzureAIImageAnalysisTool,
)

credential = DefaultAzureCredential()
tools = [
    AzureAIDocumentIntelligenceTool(credential=credential),
    AzureAIImageAnalysisTool(credential=credential),
]

agent = create_agent(
    model=init_chat_model("azure_ai:gpt-4.1", credential=credential),
    tools=tools,
    system_prompt=(
        "You are a document and image analysis assistant. Use tools when the "
        "user asks you to inspect files or images."
    ),
)

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Use the toolkit

Use AzureAIServicesToolkit to get all services tools with a shared credential and endpoint configuration. 
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAIServicesToolkit

toolkit = AzureAIServicesToolkit(credential=DefaultAzureCredential())
tools = toolkit.get_tools()

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Tools

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AzureAIContentUnderstandingTool

AzureAIContentUnderstandingTool extracts structured content from documents, images, audio, and video. It returns markdown-like extracted content and can also surface structured fields from the selected analyzer. The tool defaults to analyzer_id="prebuilt-documentSearch". You can switch analyzers for other modalities, such as prebuilt-audioSearch or prebuilt-videoSearch, and you can provide model_deployments when your analyzer depends on custom model deployment names. 
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAIContentUnderstandingTool

tool = AzureAIContentUnderstandingTool(
    credential=DefaultAzureCredential(),
    analyzer_id="prebuilt-documentSearch",
)

result = tool.invoke(
    {"source": "https://example.com/contract.pdf", "source_type": "url"}
)
print(result)
​
source
str
The input to analyze. Pass a public URL, local file path, or base64-encoded payload.
​
source_type
Literal['url', 'path', 'base64']
default:"url"
Controls how the tool interprets source.
​
analyzer_id
str
default:"prebuilt-documentSearch"
The Content Understanding analyzer to run.
​
model_deployments
dict[str, str] | None
Optional mapping from model names to deployment names when a custom analyzer needs them.

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AzureAIDocumentIntelligenceTool

AzureAIDocumentIntelligenceTool extracts OCR text, tables, and key-value pairs from documents. It is a good fit for invoices, forms, receipts, contracts, and other document-heavy workflows where the agent needs structured output instead of raw text only. The tool defaults to model_id="prebuilt-layout". Its public input schema accepts url, path, and base64 sources. 
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAIDocumentIntelligenceTool

tool = AzureAIDocumentIntelligenceTool(
    credential=DefaultAzureCredential(),
    model_id="prebuilt-layout",
)

result = tool.invoke(
    {"source": "https://example.com/invoice.pdf", "source_type": "url"}
)
print(result)
​
source
str
The document input. Pass a public URL, local file path, or base64-encoded payload.
​
source_type
Literal['url', 'path', 'base64']
default:"url"
Controls how the tool interprets source.
​
model_id
str
default:"prebuilt-layout"
The Document Intelligence model to run.

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AzureAIImageAnalysisTool

AzureAIImageAnalysisTool analyzes images and returns a JSON-formatted summary with captions, OCR text, tags, objects, people, and smart crops when those features are enabled. By default, the tool enables a broad set of visual features, including TAGS, OBJECTS, CAPTION, DENSE_CAPTIONS, READ, SMART_CROPS, and PEOPLE. 
from azure.identity import DefaultAzureCredential
from azure.ai.vision.imageanalysis.models import VisualFeatures
from langchain_azure_ai.tools import AzureAIImageAnalysisTool

tool = AzureAIImageAnalysisTool(
    credential=DefaultAzureCredential(),
    visual_features=[
        VisualFeatures.CAPTION,
        VisualFeatures.READ,
        VisualFeatures.TAGS,
    ],
)

result = tool.invoke(
    {"source": "https://example.com/whiteboard.png", "source_type": "url"}
)
print(result)
​
source
str
The image input. Pass a public URL, local file path, or base64-encoded payload.
​
source_type
Literal['url', 'path', 'base64']
Controls how the tool interprets source.
​
visual_features
list[VisualFeatures] | None
Optional list of image-analysis features to request. If omitted, the tool uses its default feature set.

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AzureAITextAnalyticsHealthTool

AzureAITextAnalyticsHealthTool extracts healthcare entities from medical text. It is useful for clinical notes, patient summaries, intake forms, and research workflows where the agent needs medical entities rather than free-form summarization. The tool accepts a plain-text query and can be configured with optional language and country_hint defaults. 
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAITextAnalyticsHealthTool

tool = AzureAITextAnalyticsHealthTool(
    credential=DefaultAzureCredential(),
    language="en",
    country_hint="us",
)

result = tool.invoke(
    "The patient reports chest pain and was prescribed aspirin after the visit."
)
print(result)
​
query
str
The healthcare text to analyze.
​
language
str | None
Optional default language for the input text.
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country_hint
str | None
Optional country hint used by the underlying Text Analytics client.

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AzureAISpeechToTextTool

AzureAISpeechToTextTool transcribes audio files to text using the Azure AI Speech service. It supports a wide range of audio formats and can handle both local files and remote audio URLs. The tool automatically detects whether the input is a local file path or a remote URL, and handles downloading remote files as needed. It is useful for workflows where the agent needs to convert spoken audio into written text. 
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAISpeechToTextTool

tool = AzureAISpeechToTextTool(
    credential=DefaultAzureCredential(),
    endpoint="https://eastus.api.cognitive.microsoft.com/",
    speech_language="en-US",
)

result = tool.invoke("path/to/audio.wav")
print(result)
​
query
str
Path to a local audio file or a URL pointing to an audio file. Supports WAV, MP3, OGG, FLAC, and other common audio formats.
​
speech_language
str
default:"en-US"
The language of the speech in BCP-47 format (e.g., "en-US", "es-ES", "fr-FR"). Defaults to "en-US".
​
endpoint
str
The Azure AI Speech service endpoint. For example, https://eastus.api.cognitive.microsoft.com/. Can be set via AZURE_AI_INFERENCE_ENDPOINT environment variable or resolved from AZURE_AI_PROJECT_ENDPOINT.
​
credential
str | TokenCredential
The credentials to use. Either a subscription key string or any TokenCredential such as DefaultAzureCredential.

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AzureAITextToSpeechTool

AzureAITextToSpeechTool converts text to spoken audio using the Azure AI Speech service. It synthesizes the provided text and returns a local WAV audio file path containing the synthesized speech. The tool supports multi-language synthesis with BCP-47 language codes and is useful for workflows where the agent needs to generate audio narration, voice-over content, or audio notifications from text. 
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.tools import AzureAITextToSpeechTool

tool = AzureAITextToSpeechTool(
    credential=DefaultAzureCredential(),
    endpoint="https://eastus.api.cognitive.microsoft.com/",
    speech_language="en-US",
)

result = tool.invoke("Hello, this is a test of text to speech synthesis.")
print(result)  # Returns path to generated WAV file
​
query
str
The text to convert to speech.
​
speech_language
str
default:"en-US"
The language of the synthesized speech in BCP-47 format (e.g., "en-US", "es-ES", "fr-FR"). Defaults to "en-US".
​
endpoint
str
The Azure AI Speech service endpoint. For example, https://eastus.api.cognitive.microsoft.com/. Can be set via AZURE_AI_INFERENCE_ENDPOINT environment variable or resolved from AZURE_AI_PROJECT_ENDPOINT.
​
credential
str | TokenCredential
The credentials to use. Either a subscription key string or any TokenCredential such as DefaultAzureCredential.

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API reference

from langchain_azure_ai.tools import (
    AzureAIServicesToolkit,
    AzureAIContentUnderstandingTool,
    AzureAIDocumentIntelligenceTool,
    AzureAIImageAnalysisTool,
    AzureAISpeechToTextTool,
    AzureAITextToSpeechTool,
    AzureAITextAnalyticsHealthTool,
)
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