base

Synthetic tool name used for MCP approval request tool calls.

Extended AgentState that carries per-invocation agent context.

By storing conversation IDs and pending-call type in the graph state (rather than on the node instance), the node becomes thread-safe: concurrent invocations each operate on their own copy of the state, and the graph's checkpointer can persist / restore it across interrupts.

Fields

azure_ai_agents_conversation_id : str | None The Responses-API conversation ID. Created lazily on the first HumanMessage and reused for subsequent turns. azure_ai_agents_previous_response_id : str | None The ID of the most recent Response object, used to chain tool-call outputs within a single turn. azure_ai_agents_pending_type : str | None Indicates whether the last response left unresolved calls: "function_call", "mcp_approval", or None.

A tool that interacts with Azure AI Foundry Agent Service V2.

Use this class to wrap tools from Azure AI Foundry for use with PromptBasedAgentNodeV2.

Example:

from langchain_azure_ai.agents.prebuilt.tools_v2 import AgentServiceBaseToolV2
from azure.ai.projects.models import CodeInterpreterTool

code_interpreter_tool = AgentServiceBaseTool(tool=CodeInterpreterTool())

Some tools require extra HTTP headers when calling the Responses API. For example, ImageGenTool requires an x-ms-oai-image-generation-deployment header:

from azure.ai.projects.models import ImageGenTool

image_tool = AgentServiceBaseToolV2(
    tool=ImageGenTool(model="gpt-image-1", quality="low", size="1024x1024"),
    extra_headers={
        "x-ms-oai-image-generation-deployment": "gpt-image-1",
    },
)

All extra_headers from every tool are merged and sent with each responses.create() call made by the agent node.

A LangGraph node for an existing Azure AI Foundry agent (V2 Responses API).

This node wraps a prompt-based agent that has already been created in Azure AI Foundry. It handles building requests and processing responses using the V2 Responses/Conversations API.

Use :meth:~langchain_azure_ai.agents.v2.AgentServiceFactory.create_prompt_agent_node to create an agent and obtain a node in a single step, or instantiate this class directly to reference an existing agent by name.

Example:

from azure.ai.projects import AIProjectClient
from azure.identity import DefaultAzureCredential
from langchain_azure_ai.agents.prebuilt import ResponsesAgentNode

client = AIProjectClient(
    endpoint="https://resource.services.ai.azure.com/api/projects/demo-project",
    credential=DefaultAzureCredential(),
)

coder = ResponsesAgentNode(
    client=client,
    name="code-interpreter-agent",
    version="latest",
)