langchain-azure-ai 1.2.2

An integration package to support Azure AI Foundry capabilities in LangChain/LangGraph ecosystem.

langchain-azure-ai

This package contains the LangChain integration for Azure AI Foundry. To learn more about how to use this package, see the LangChain documentation in Azure AI Foundry.

Installation

pip install -U langchain-azure-ai

For using tools, including Azure AI Document Intelligence, Azure AI Text Analytics for Health, or Azure LogicApps, please install the extras tools:

pip install -U langchain-azure-ai[tools]

For using tracing capabilities with OpenTelemetry, you need to add the extras opentelemetry:

pip install -U langchain-azure-ai[opentelemetry]

Auto tracing to Azure Application Insights

For a minimal end-to-end example that auto-injects the Azure tracer into LangGraph callback managers and exports spans to Application Insights, see samples/enable_auto_tracing_appinsights.py.

The sample shows how to:

• set APPLICATION_INSIGHTS_CONNECTION_STRING
• configure an Azure OpenAI chat model for a LangGraph app
• call enable_auto_tracing(..., auto_configure_azure_monitor=True) for a local script or notebook

If you are running inside a hosted Azure AI Foundry agent, keep auto_configure_azure_monitor=False because the host already configures the TracerProvider.

If you are transitioning from Microsoft Foundry classic and you need access to deprecated classes, use [v1] extra.

pip install -U langchain-azure-ai[v1]

Quick Start with langchain-azure-ai

The langchain-azure-ai package uses the Microsoft Foundry family of SDKs and client libraries for Azure to provide first-class support of Microsoft Foundry capabilities in LangChain and LangGraph.

This package includes:

• Microsoft Foundry Models inference
• Microsoft Foundry Tools
• Microsoft Foundry Content Safety
• Microsoft Foundry Agent Service
• Azure AI Search
• Azure AI Services tools
• Cosmos DB

Here's a quick start example to show you how to get started with the Chat Completions model. For more details and tutorials see Get started with LangChain and LangGraph with Foundry.

Microsoft Foundry Models

from langchain_azure_ai.chat_models import AzureAIOpenAIApiChatModel
from langchain_core.messages import HumanMessage, SystemMessage

model = AzureAIOpenAIApiChatModel(
    endpoint="https://{your-resource-name}.services.ai.azure.com/openai/v1",
    credential="your-api-key", #if using Entra ID you can should use DefaultAzureCredential() instead
    model="gpt-5"
)

messages = [
    SystemMessage(
      content="Translate the following from English into Italian"
    ),
    HumanMessage(content="hi!"),
]

model.invoke(messages).pretty_print()

================================== Ai Message ==================================
Ciao!

You can also use builtin tools with them:

from langchain_azure_ai.tools.builtin import ImageGenerationTool

model_with_image_gen = model.bind_tools([ImageGenerationTool(model="gpt-image-1.5", size="1024x1024")])
result = model_with_image_gen.invoke(
    "Generate an image based on the following description: A futuristic cityscape at sunset with flying cars and neon lights."
)

Models in Microsoft Foundry Models are OpenAI-compatible and can be used with the class:

model = AzureAIOpenAIApiChatModel(
    endpoint="https://{your-resource-name}.services.ai.azure.com/openai/v1",
    credential="your-api-key",
    model="Mistral-Large-3"
)

Microsoft Foundry Agent Service

Compose complex graphs by using agents running in the Agent Service:

from azure.identity import DefaultAzureCredential
from langchain_core.messages import AIMessage, HumanMessage
from langchain_azure_ai.agents import AgentServiceFactory
from langchain_azure_ai.utils.agents import pretty_print

factory = AgentServiceFactory(
    project_endpoint="https://{your-resource-name}.services.ai.azure.com/api/projects/{your-project}",
    credential=DefaultAzureCredential()
)

echo_node = factory.get_agent_node(name="my-echo-agent", version="latest")

Agent Service nodes run in Microsoft Foundry but can be added to any graph:

graph.add_node("expert_node", echo_node)

Use the graph as usual:

agent = graph.compile()
messages = [HumanMessage(content="I'm a genius and I love programming!")]
response = agent.invoke({"messages": messages})

pretty_print(response)

================================ Human Message =================================

I'm a genius and I love programming!
================================== Ai Message ==================================
Name: my-echo-agent

You're not a genius and you don't love programming!

Changelog

• 1.2.2:

  • We introduced context_extractor support across content safety middleware classes, so you can control how content is extracted from agent state before safety checks run. #419
  • We introduced context_extractor support for AzureGroundednessMiddleware and added a notebook example for easier adoption. #410
  • We changed the default implementation of init_chat_model("azure_ai:<your-model>") to use the OpenAI Responses API path for improved compatibility with modern LangChain chat model initialization. #409
  • We fixed an AttributeError in AzureAIOpenTelemetryTracer.on_chain_start when chain inputs were not dictionaries. #317
  • We upgraded the requests dependency for this package to include upstream security and maintenance updates. #417
  • We patched multiple high-severity dependency vulnerabilities (including PyJWT, orjson, and tornado) to improve package security posture. #412

• 1.2.1:

  • You can now use context_extractor argument in classes langchain_azure_ai.agents.middleware. to configre how middleware instract extract content from your state.
  • We changed the default implementation of init_chat_model("azure_ai:<your-model>") to use OpenAI Responses API (this is also the default if using langchain>=1.2.3).

• 1.2.0:

  • We now require langchain>=1.2 so our streaming implementation matches the latest version of langchain.
  • We introduced langchain_azure_ai.agents.middleware.content_safety.* namespace which unlocks the power of Azure AI Content Safety with LangChain.
  • We introduced langchain_azure_ai.tools.builtin.* namespace with server-side tools that can be used for models running in Microsoft Foundry.
  • We fixed an issue with duplicated spans generated in OpenTelemetry tracer. #398.
  • We fixed an issue in init_embeddings(provider="azure_ai") where an incorrect kwarg was passed.

• 1.1.0:

  • Creating agents using Foundry Agents V1 has been deprecated in favor of V2. langchain_azure_ai.agents.AgentServiceFactory now using V2 implementation. Namespace langchain_azure_ai.agents.v1.AgentServiceFactory is marked as deprecated and requires the extra v1 to be used.
  • Chat and embedding models using Azure AI Inference SDK has been deprecated in favor of OpenAI-compatible APIs. Namespace langchain_azure_ai.chat_models.inference.AzureAIChatCompletionsModel and langchain_azure_ai.embeddings.inference.AzureAIEmbeddingsModel are marked as deprecated and require the extra v1 to be used.

• 1.0.62:

  • We introduced support for asynchhronous agents operation and tracing using our OpenTelemetry tracer for context to propagate correctly. [#290].(https://github.com/langchain-ai/langchain-azure/pull/290).
  • We introduced support for Bash operations in langchain-azure-dynamic-session. #238.
  • We introduced support for Agent Service V2 in Microsoft Foundry. PR #257.
  • We added a new tool to generate images based on OpenAI-compatible image generation models. PR #325
  • We fixed an issue when on_tool_start ignores enable_content_recording. Now it doesn't. #261.
  • We fixed a problem when uploaded files were not considered by the CodeInterpreterTool for the Agent Service. #256.
  • We fixed an issue when using AzureAIOpenTelemetryTracer on a Mac. #234.

• 1.0.61:

  • This release reverts the code to the state of v1.0.5 while updating the version number to 1.0.61.

• 1.0.5:

  • We fixed an issue with the content type of messages in AzureAIChatCompletionsModel. See [PR #245].
  • We improve metadata generated for AzureAIOpenTelemetryTracer. See [PR ##233].

• 1.0.4:

  • We fixed an issue with dependencies resolution for azure-ai-agents where the incorrect version was picked up. See [PR #221].
  • We fixed an issue with AzureAIOpenTelemetryTracer where spans context was not correctly propagated when called from another service. See [PR #217].
  • We fixed an issue where AzureAIOpenTelemetryTracer where context was deallocated incorrectly, preventing tools like langdev to correctly emit traces. See [Issue #212].
  • We introduced improvements in the order in which environment variables AZURE_AI_* are read.
  • Internal: We improved AzureAIOpenTelemetryTracer test coverage. See PR #239.

• 1.0.2:

  • We updated the AzureAIOpenTelemetryTracer to create a parent trace for multi agent scenarios. Previously, you were required to do this manually, which was unnecesary.

• 1.0.0:

  • We introduce support for LangChain and LangGraph 1.0.

• 0.1.8:

  • We fixed some issues with AzureAIOpenTelemetryTracer, including compliant hierarchy, tool spans under chat, finish reason normalization, conversation id. See [PR #167]
  • We fixed an issue with taking image inputs for declarative agents created with Azure AI Foundry Agents service.
  • We enhanced tool descriptions to improve tool call accuracy.

• 0.1.7:

  • [NEW]: We introduce LangGraph support for declarative agents created in Azure AI Foundry. You can now compose complex graphs in LangGraph and add nodes that take advantage of Azure AI Agent Service. See AgentServiceFactory
  • We fix an issue with the interface of AzureAIEmbeddingsModel #158.
  • We improve the signatures of the tools AzureAIDocumentIntelligenceTool, AzureAIImageAnalysisTool, and AzureAITextAnalyticsHealthTool PR #160.

• 0.1.6:

  • [Breaking change]: Using parameter project_connection_string to create AzureAIEmbeddingsModel and AzureAIChatCompletionsModel is not longer supported. Use project_endpoint instead.
  • [Breaking change]: Class AzureAIInferenceTracer has been removed in favor of AzureAIOpenTelemetryTracer which has a better support for OpenTelemetry and the new semantic conventions for GenAI.
  • Adding the following tools to the package: AzureAIDocumentIntelligenceTool, AzureAIImageAnalysisTool, and AzureAITextAnalyticsHealthTool. You can also use AIServicesToolkit to have access to all the tools in Azure AI Services.

• 0.1.4:

  • Bug fix #91.

• 0.1.3:

  • [Breaking change]: We renamed the parameter model_name in AzureAIEmbeddingsModel and AzureAIChatCompletionsModel to model, which is the parameter expected by the method langchain.chat_models.init_chat_model.
  • We fixed an issue with JSON mode in chat models #81.
  • We fixed the dependencies for NumpPy #70.
  • We fixed an issue when tracing Pyndantic objects in the inputs #65.
  • We made connection_string parameter optional as suggested at #65.

• 0.1.2:

  • Bug fix #35.

• 0.1.1:

  • Adding AzureCosmosDBNoSqlVectorSearch and AzureCosmosDBNoSqlSemanticCache for vector search and full text search.
  • Adding AzureCosmosDBMongoVCoreVectorSearch and AzureCosmosDBMongoVCoreSemanticCache for vector search.
  • You can now create AzureAIEmbeddingsModel and AzureAIChatCompletionsModel clients directly from your AI project's connection string using the parameter project_connection_string. Your default Azure AI Services connection is used to find the model requested. This requires to have azure-ai-projects package installed.
  • Support for native LLM structure outputs. Use with_structured_output(method="json_schema") to use native structured schema support. Use with_structured_output(method="json_mode") to use native JSON outputs capabilities. By default, LangChain uses method="function_calling" which uses tool calling capabilities to generate valid structure JSON payloads. This requires to have azure-ai-inference >= 1.0.0b7.
  • Bug fix #18 and #31.

• 0.1.0:

  • Introduce AzureAIEmbeddingsModel for embedding generation and AzureAIChatCompletionsModel for chat completions generation using the Azure AI Inference API. This client also supports GitHub Models endpoint.
  • Introduce AzureAIOpenTelemetryTracer for tracing with OpenTelemetry and Azure Application Insights.