> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # AzureChatOpenAI integration > Integrate with the AzureChatOpenAI chat model using LangChain Python. You can find information about Azure OpenAI's latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models). For the full set of Microsoft integrations in LangChain (including tools like Azure AI Search, Azure Database for PostgreSQL, and the M365 suite), see the [Microsoft provider page](/oss/python/integrations/providers/microsoft). **Azure OpenAI vs OpenAI** Azure OpenAI refers to OpenAI models hosted on the [Microsoft Azure platform](https://azure.microsoft.com/en-us/products/ai-services/openai-service). Models hosted on Azure come with added enterprise features including support for keyless authentication with Entra ID. **Use `ChatOpenAI` with v1 API (recommended)** Azure OpenAI's [v1 API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/api-version-lifecycle?tabs=python) (Generally Available as of August 2025) allows you to use [`ChatOpenAI`](/oss/python/integrations/chat/openai/#using-with-azure-openai) directly with Azure endpoints. This removes the need for dated `api-version` parameters and provides native support for Microsoft Entra ID authentication with automatic token refresh. We continue to support [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI), which now shares the same underlying base implementation as [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI), which interfaces with OpenAI services directly. This page serves as a quickstart for authenticating and connecting your Azure OpenAI Chat Models to LangChain. **API Reference** For detailed documentation of all features and configuration options, head to the [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) API reference. Visit the [`ChatOpenAI` docs](/oss/python/integrations/chat/openai/) for details on available features. ## Overview ### Integration details | Class | Package | Serializable | JS/TS Support | Downloads | Latest Version | | :------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------- | :----------: | :---------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) | [`langchain-openai`](https://reference.langchain.com/python/langchain-openai/) | beta | ✅ [(npm)](https://js.langchain.com/docs/integrations/chat/openai) | Downloads per month | PyPI - Latest version | ### Model features | [Tool calling](/oss/python/langchain/tools) | [Structured output](/oss/python/langchain/structured-output) | [Image input](/oss/python/langchain/messages#multimodal) | Audio input | Video input | [Token-level streaming](/oss/python/langchain/streaming/) | Native async | [Token usage](/oss/python/langchain/models#token-usage) | [Logprobs](/oss/python/langchain/models#log-probabilities) | | :-----------------------------------------: | :----------------------------------------------------------: | :------------------------------------------------------: | :---------: | :---------: | :-------------------------------------------------------: | :----------: | :-----------------------------------------------------: | :--------------------------------------------------------: | | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ## Setup To access Azure OpenAI models you'll need to [create an Azure account](https://account.azure.com/signup), create a deployment of an Azure OpenAI model, get the name and endpoint for your deployment, and install the `langchain-openai` integration package. ### Installation ```bash pip theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install -U langchain-openai ``` ```bash uv theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} uv add langchain-openai ``` ## Credentials Both [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) and [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) support authenticating to Azure OpenAI with either **Microsoft Entra ID** (recommended) or an **API key**. ### Microsoft Entra ID [Microsoft Entra ID](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/managed-identity) provides keyless authentication with automatic token refresh. Install the `azure-identity` package and create a token provider—the same provider works with both `ChatOpenAI` and `AzureChatOpenAI`: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pip install azure-identity ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from azure.identity import DefaultAzureCredential, get_bearer_token_provider token_provider = get_bearer_token_provider( DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default", ) ``` ### API key Head to the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new\&pivots=programming-language-python) to create your deployment and generate an API key. Set the `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT` environment variables: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import getpass import os if "AZURE_OPENAI_API_KEY" not in os.environ: os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass( "Enter your AzureOpenAI API key: " ) os.environ["AZURE_OPENAI_ENDPOINT"] = "https://YOUR-RESOURCE-NAME.openai.azure.com/" ``` To enable automated tracing of your model calls, set your [LangSmith](/langsmith/observability) API key: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ") os.environ["LANGSMITH_TRACING"] = "true" ``` ## Instantiation ### ChatOpenAI with v1 API Set `base_url` to your Azure endpoint with `/openai/v1/` appended. With the v1 API you can call any model deployed in [Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/) (including OpenAI, Llama, DeepSeek, Mistral, and Phi) through a single interface by pointing `model` at your deployment name. Pass the token provider to `api_key`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure deployment name base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key=token_provider, # callable that handles token refresh ) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure deployment name base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", ) ``` ### AzureChatOpenAI Use [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) when working with traditional Azure OpenAI API versions that require `api_version`. Pass the token provider to `azure_ad_token_provider`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import AzureChatOpenAI llm = AzureChatOpenAI( azure_deployment="gpt-5.4-mini", # or your deployment api_version="2025-04-01-preview", # or your api version azure_ad_token_provider=token_provider, ) ``` Reads `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT` from the environment: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import AzureChatOpenAI llm = AzureChatOpenAI( azure_deployment="gpt-5.4-mini", # or your deployment api_version="2025-04-01-preview", # or your api version temperature=0, max_tokens=None, timeout=None, max_retries=2, # other params... ) ``` ## Invocation ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} messages = [ ( "system", "You are a helpful assistant that translates English to French. Translate the user sentence.", ), ("human", "I love programming."), ] ai_msg = llm.invoke(messages) print(ai_msg.text) ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} J'adore la programmation. ``` ## Tool calling Bind tools to the model using Pydantic classes, dict schemas, LangChain tools, or functions: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field class GetWeather(BaseModel): """Get the current weather in a given location""" location: str = Field(description="The city and state, e.g. San Francisco, CA") llm = ChatOpenAI( model="gpt-5.4-mini", base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", ) llm_with_tools = llm.bind_tools([GetWeather]) ai_msg = llm_with_tools.invoke("What is the weather like in San Francisco?") ai_msg.tool_calls ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [{'name': 'GetWeather', 'args': {'location': 'San Francisco, CA'}, 'id': 'call_jUqhd8wzAIzInTJl72Rla8ht', 'type': 'tool_call'}] ``` For more on binding tools and tool call outputs, head to the [tool calling](/oss/python/langchain/tools) docs. ## Build an agent Use [`create_agent`](https://reference.langchain.com/python/langchain/agents/factory/create_agent) to build an agent with Azure OpenAI and tools: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain.agents import create_agent from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", ) def get_weather(city: str) -> str: """Get weather for a given city.""" return f"It's always sunny in {city}!" agent = create_agent( model=llm, tools=[get_weather], system_prompt="You are a helpful assistant", ) # Stream agent responses stream = agent.stream_events( {"messages": [{"role": "user", "content": "What is the weather in SF?"}]}, version="v3", ) for snapshot in stream.values: print(snapshot["messages"][-1].text) ``` ## Streaming usage metadata OpenAI's Chat Completions API does not stream token usage statistics by default (see the [OpenAI API reference for stream options](https://platform.openai.com/docs/api-reference/completions/create#completions-create-stream_options)). To recover token counts when streaming, set `stream_usage=True` as an initialization parameter or on invocation: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", stream_usage=True, # [!code highlight] ) ``` ## Responses API Azure OpenAI supports the [Responses API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/responses), which provides stateful conversations, built-in server-side tools (code interpreter, image generation, file search, and remote MCP), and structured reasoning summaries. [`ChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/base/ChatOpenAI) automatically routes to the Responses API when you set the `reasoning` parameter, or you can opt in explicitly with `use_responses_api=True`: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure deployment name base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key=token_provider, use_responses_api=True, # [!code highlight] ) # Bind a built-in server-side tool llm_with_tools = llm.bind_tools( # [!code highlight] [{"type": "code_interpreter", "container": {"type": "auto"}}] # [!code highlight] ) # [!code highlight] response = llm_with_tools.invoke( "Use the code interpreter to compute the 25th Fibonacci number." ) print(response.text) ``` ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure deployment name base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key="your-azure-api-key", use_responses_api=True, # [!code highlight] ) # Bind a built-in server-side tool llm_with_tools = llm.bind_tools( # [!code highlight] [{"type": "code_interpreter", "container": {"type": "auto"}}] # [!code highlight] ) # [!code highlight] response = llm_with_tools.invoke( "Use the code interpreter to compute the 25th Fibonacci number." ) print(response.text) ``` For details on built-in tools and how to use them, see the [Azure OpenAI Responses API docs](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/responses). ## Reasoning effort and summary Azure OpenAI [reasoning models](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/reasoning) (for example, `o4-mini`, `gpt-5`) spend extra tokens thinking through a request before producing their final answer. With `ChatOpenAI` on the v1 API, you can configure how much effort the model spends reasoning and optionally request a summary of its chain of thought. ### Reasoning effort Set `reasoning_effort` to `"low"`, `"medium"`, or `"high"`. Higher settings let the model spend more tokens on reasoning, which typically improves quality for complex tasks at the cost of latency: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure reasoning model deployment base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key=token_provider, reasoning_effort="medium", ) response = llm.invoke("Tell me about the bitter lesson.") print(response.text) ``` Reasoning models use tokens for internal reasoning (`reasoning_tokens` in `completion_tokens_details`). These tokens aren't returned in the message content but count toward the output token limit. If you see empty responses, increase `max_tokens` or leave it unset so the model has room for both reasoning and output. ### Reasoning summary When using a reasoning model via the Responses API, you can request a summary of the model's chain of thought by passing a `reasoning` dict. Setting `reasoning` automatically routes `ChatOpenAI` to the Responses API: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import ChatOpenAI reasoning = { "effort": "high", # 'low', 'medium', or 'high' "summary": "auto", # 'auto', 'concise', or 'detailed' } llm = ChatOpenAI( model="gpt-5.4-mini", # your Azure reasoning model deployment base_url="https://YOUR-RESOURCE-NAME.openai.azure.com/openai/v1/", api_key=token_provider, reasoning=reasoning, ) response = llm.invoke("What's the optimal strategy to win at poker?") # Final answer print(response.text) # Reasoning summary blocks for block in response.content_blocks: if block["type"] == "reasoning": print(block["reasoning"]) ``` Attempting to extract raw reasoning tokens through methods other than the reasoning summary parameter isn't supported and may violate Azure's Acceptable Use Policy. Use the `summary` field to access model reasoning. Even when enabled, reasoning summaries aren't guaranteed for every step or request—this is expected behavior. ## Specifying model version (legacy API) This section applies only when using `AzureChatOpenAI` with traditional API versions. The v1 API does not require `api_version` parameters. When using `AzureChatOpenAI`, Azure OpenAI responses contain a `model_name` response metadata property. Unlike native OpenAI responses, it does not contain the specific version of the model (which is set on the deployment in Azure). Pass `model_version` to distinguish between different versions: ```python theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} from langchain_openai import AzureChatOpenAI llm = AzureChatOpenAI( azure_deployment="gpt-5.4-mini", # or your deployment api_version="2025-04-01-preview", # or your api version model_version="0301", ) ``` *** ## API reference For detailed documentation of all features and configuration options, head to the [`AzureChatOpenAI`](https://reference.langchain.com/python/langchain-openai/chat_models/azure/AzureChatOpenAI) API reference. ***
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