wehub-resource-sync
92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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"""
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Sample: Host a LangChain / LangGraph agent as a Microsoft Foundry hosted agent.
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This sample shows how to take an agent built with LangGraph and expose it through
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the Microsoft Foundry hosted-agent **Responses** protocol using the
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`langchain_azure_ai.agents.hosting` package. Foundry then manages the runtime,
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sessions, scaling, identity, and protocol endpoints while your agent logic stays
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in LangGraph.
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The Responses API is the recommended API for agent-style development in Foundry:
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it provides OpenAI-compatible chat, streaming, response history, and conversation
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threading in a single API surface.
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Prerequisites
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-------------
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- An Azure subscription and a Microsoft Foundry project.
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- A deployed chat model that supports the Responses API (e.g. gpt-4.1 or gpt-5-mini).
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- Python 3.10+ and the Azure CLI signed in (`az login`).
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- Install the hosting extra:
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pip install -U "langchain-azure-ai[hosting]>=1.2.4" azure-identity
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- Set environment variables:
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FOUNDRY_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
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FOUNDRY_MODEL_NAME=gpt-4.1
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Run locally
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-----------
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python 14-langchain-hosted-agent.py
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Then send a Responses request to the local server:
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curl -sS -H "Content-Type: application/json" \
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-X POST http://localhost:8088/responses \
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-d '{"input":"Give me one tip for testing hosted agents.","stream":false}'
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Deploy to Foundry (Azure Developer CLI)
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---------------------------------------
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azd ext install azure.ai.agents
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azd auth login
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azd ai agent init -m <sample-manifest-url>
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azd ai agent run # runs the container locally (requires Docker)
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azd provision # if a new Foundry project/model is needed
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azd deploy # packages + rolls out to the Foundry hosted runtime
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See: https://learn.microsoft.com/azure/foundry/how-to/develop/langchain-hosted-agents
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"""
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import os
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from azure.ai.projects import AIProjectClient
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from azure.identity import DefaultAzureCredential, get_bearer_token_provider
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from langchain.agents import create_agent
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from langchain_openai import ChatOpenAI
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from langchain_azure_ai.agents.hosting import ResponsesHostServer
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# Scope used to request tokens for the Foundry project's OpenAI-compatible endpoint.
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_AZURE_AI_SCOPE = "https://ai.azure.com/.default"
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def build_chat_model() -> ChatOpenAI:
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"""Create a ChatOpenAI bound to the Foundry project's Responses endpoint."""
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project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"].rstrip("/")
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deployment = os.environ.get("FOUNDRY_MODEL_NAME", "gpt-4.1")
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credential = DefaultAzureCredential()
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project = AIProjectClient(endpoint=project_endpoint, credential=credential)
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# The project's OpenAI-compatible client exposes the Responses-capable base URL.
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openai_client = project.get_openai_client()
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token_provider = get_bearer_token_provider(credential, _AZURE_AI_SCOPE)
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return ChatOpenAI(
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model=deployment,
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base_url=str(openai_client.base_url),
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api_key=token_provider,
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)
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def main() -> None:
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# A minimal LangGraph agent. Add your own tools to the list to give it capabilities.
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graph = create_agent(build_chat_model(), tools=[])
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# ResponsesHostServer exposes the compiled graph over POST /responses and emits
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# Responses API server-sent events (response.created, response.output_text.delta,
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# response.completed) when a request sets "stream": true.
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port = int(os.environ.get("PORT", "8088"))
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ResponsesHostServer(graph).run(port=port)
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if __name__ == "__main__":
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main()
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