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Merge pull request #26 from Azure-Samples/langchainupdates
Update langchain examples
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‎README.md‎

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@@ -105,10 +105,10 @@ You can run the examples in this repository by executing the scripts in the `exa
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| [langchainv1_tools.py](examples/langchainv1_tools.py) | Uses LangChain v1 to build a weekend planning agent with multiple tools. |
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| [langchainv1_supervisor.py](examples/langchainv1_supervisor.py) | Uses LangChain v1 with a supervisor orchestrating activity and recipe sub-agents. |
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| [langchainv1_quickstart.py](examples/langchainv1_quickstart.py) | Uses LangChain v1 to build an assistant with tool calling, structured output, and memory. Based off official Quickstart docs. |
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| [langchainv1_mcp_github.py](examples/langchainv1_mcp_github.py) | Uses Langchain v1 agent with GitHub MCP server to triage repository issues. |
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| [langchainv1_mcp_http.py](examples/langchainv1_mcp_github.py) | Uses Langchain v1 agent with tools from local MCP HTTP server. |
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| [langgraph_agent.py](examples/langgraph_agent.py) | Builds LangGraph graph for an agent to play songs. |
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| [langgraph_mcp_http.py](examples/langgraph_mcp_http.py) | Uses LangGraph with ReAct agent that uses tools from local MCP HTTP server. |
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| [langgraph_mcp_http_graph.py](examples/langgraph_mcp_http_graph.py) | Builds a custom LangGraph state graph using tools from local MCP HTTP server. |
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| [langgraph_mcp_github.py](examples/langgraph_mcp_github.py) | Uses a LangGraph with agent and GitHub MCP server to triage repository issues. |
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| [langgraph_mcp.py](examples/langgraph_mcp.py) | Builds Langgraph graph that uses tools from MCP HTTP server. |
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### OpenAI and OpenAI-Agents
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‎examples/langchainv1_mcp_github.py‎

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"""LangChain v1 MCP tools example (ported from LangGraph version).
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This script demonstrates how to use LangChain v1 agent syntax with MCP tools
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exposed by the GitHub MCP endpoint. It preserves the Azure OpenAI vs GitHub
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model selection logic from the original LangGraph based example.
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"""
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from __future__ import annotations
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import asyncio
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import logging
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import os
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from pathlib import Path
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import azure.identity
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from dotenv import load_dotenv
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from langchain.agents import create_agent
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from langchain.agents.middleware import AgentMiddleware, AgentState, ModelRequest
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from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain_openai import AzureChatOpenAI, ChatOpenAI
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from rich.logging import RichHandler
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logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
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logger = logging.getLogger("lang_triage")
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST", "github")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(),
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"https://cognitiveservices.azure.com/.default",
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)
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base_model = AzureChatOpenAI(
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azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
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azure_deployment=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT"),
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openai_api_version=os.environ.get("AZURE_OPENAI_VERSION"),
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azure_ad_token_provider=token_provider,
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)
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elif API_HOST == "github":
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base_model = ChatOpenAI(
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model=os.getenv("GITHUB_MODEL", "gpt-4o"),
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base_url="https://models.inference.ai.azure.com",
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api_key=os.environ.get("GITHUB_TOKEN"),
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)
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elif API_HOST == "ollama":
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base_model = ChatOpenAI(
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model=os.environ.get("OLLAMA_MODEL", "llama3.1"),
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base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"),
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api_key="none",
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)
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else:
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base_model = ChatOpenAI(model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"))
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class ToolCallLimitMiddleware(AgentMiddleware):
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def __init__(self, limit) -> None:
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super().__init__()
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self.limit = limit
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def modify_model_request(self, request: ModelRequest, state: AgentState) -> ModelRequest:
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tool_call_count = sum(1 for msg in state["messages"] if isinstance(msg, AIMessage) and msg.tool_calls)
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if tool_call_count >= self.limit:
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logger.info("Tool call limit of %d reached, disabling further tool calls.", self.limit)
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request.tools = []
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return request
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async def main():
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client = MultiServerMCPClient(
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{
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"github": {
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"url": "https://api.githubcopilot.com/mcp/",
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"transport": "streamable_http",
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"headers": {"Authorization": f"Bearer {os.getenv('GITHUB_TOKEN', '')}"},
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}
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}
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)
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tools = await client.get_tools()
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tools = [t for t in tools if t.name in ("list_issues", "search_code", "search_issues", "search_pull_requests")]
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agent = create_agent(base_model, tools, middleware=[ToolCallLimitMiddleware(limit=5)])
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stale_prompt_path = Path(__file__).parent / "staleprompt.md"
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with stale_prompt_path.open("r", encoding="utf-8") as f:
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stale_prompt = f.read()
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user_content = stale_prompt + " Find one issue from Azure-samples azure-search-openai-demo that can be closed."
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async for step in agent.astream({"messages": [HumanMessage(content=user_content)]}, stream_mode="updates"):
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for step_name, step_data in step.items():
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last_message = step_data["messages"][-1]
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if isinstance(last_message, AIMessage) and last_message.tool_calls:
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tool_name = last_message.tool_calls[0]["name"]
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tool_args = last_message.tool_calls[0]["args"]
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logger.info(f"Calling tool '{tool_name}' with args: {tool_args}")
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elif isinstance(last_message, ToolMessage):
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logger.info(f"Got tool result: {step_data['messages'][-1].content[0:200]}...")
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else:
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logger.info(f"Response: {step_data['messages'][-1].content}")
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if __name__ == "__main__":
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logger.setLevel(logging.INFO)
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asyncio.run(main())

‎examples/langchainv1_mcp_http.py‎

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"""LangChain v1 agent + MCP HTTP itinerary server example.
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Prerequisite:
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Start the local MCP server defined in `mcp_server_basic.py` on port 8000:
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python examples/mcp_server_basic.py
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"""
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import asyncio
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import logging
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import os
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import azure.identity
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from dotenv import load_dotenv
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from langchain.agents import create_agent
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from langchain_core.messages import HumanMessage
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from langchain_mcp_adapters.client import MultiServerMCPClient
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from langchain_openai import AzureChatOpenAI, ChatOpenAI
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from rich.logging import RichHandler
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logging.basicConfig(level=logging.WARNING, format="%(message)s", datefmt="[%X]", handlers=[RichHandler()])
20+
logger = logging.getLogger("lang_itinerary")
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST", "github")
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25+
if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
27+
azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
28+
)
29+
base_model = AzureChatOpenAI(
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azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
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azure_deployment=os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT"),
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openai_api_version=os.environ.get("AZURE_OPENAI_VERSION"),
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azure_ad_token_provider=token_provider,
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)
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elif API_HOST == "github":
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base_model = ChatOpenAI(
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model=os.getenv("GITHUB_MODEL", "gpt-4o"),
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base_url="https://models.inference.ai.azure.com",
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api_key=os.environ.get("GITHUB_TOKEN"),
40+
)
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elif API_HOST == "ollama":
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base_model = ChatOpenAI(
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model=os.environ.get("OLLAMA_MODEL", "llama3.1"),
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base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"),
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api_key="none",
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)
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else:
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base_model = ChatOpenAI(model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"))
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async def run_agent():
52+
client = MultiServerMCPClient(
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{
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"itinerary": {
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# Make sure you start your itinerary server on port 8000
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"url": "http://localhost:8000/mcp/",
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"transport": "streamable_http",
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}
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}
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)
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tools = await client.get_tools()
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agent = create_agent(base_model, tools)
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user_query = (
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"Find me a hotel in San Francisco for 2 nights starting from 2026-01-01. "
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"I need a hotel with free WiFi and a pool."
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)
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response = await agent.ainvoke({"messages": [HumanMessage(content=user_query)]})
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final = response["messages"][-1].content
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print(final)
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def main():
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asyncio.run(run_agent())
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if __name__ == "__main__":
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logger.setLevel(logging.INFO)
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main()

‎examples/langchainv1_supervisor.py‎

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@@ -94,9 +94,9 @@ def get_current_date() -> str:
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@tool
97-
def weekend_agent_tool(query: str) -> str:
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"""Invoke the activity planning agent and return its final response as plain text."""
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logger.info("Tool:weekend_agent invoked")
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def plan_weekend(query: str) -> str:
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"""Plan a weekend based on user query and return the final response."""
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logger.info("Tool: plan_weekend invoked")
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response = weekend_agent.invoke({"messages": [HumanMessage(content=query)]})
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final = response["messages"][-1].content
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return final
@@ -159,9 +159,9 @@ def check_fridge() -> list[str]:
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@tool
162-
def meal_agent_tool(query: str) -> str:
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"""Invoke the recipe planning agent and return its final response as plain text."""
164-
logger.info("Tool:meal_agent invoked")
162+
def plan_meal(query: str) -> str:
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"""Plan a meal based on user query and return the final response."""
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logger.info("Tool: plan_meal invoked")
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response = meal_agent.invoke({"messages": [HumanMessage(content=query)]})
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final = response["messages"][-1].content
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return final
@@ -176,7 +176,7 @@ def meal_agent_tool(query: str) -> str:
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"You are a supervisor, managing an activity planning agent and recipe planning agent."
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"Assign work to them as needed in order to answer user's question."
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),
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tools=[weekend_agent_tool, meal_agent_tool],
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tools=[plan_weekend, plan_meal],
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)
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‎examples/langgraph_agent.py‎

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@@ -10,6 +10,33 @@
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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# Setup the client to use either Azure OpenAI or GitHub Models
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST", "github")
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if API_HOST == "azure":
18+
token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
20+
)
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model = AzureChatOpenAI(
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
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openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_ad_token_provider=token_provider,
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)
27+
elif API_HOST == "github":
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model = ChatOpenAI(
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model=os.getenv("GITHUB_MODEL", "gpt-4o"),
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base_url="https://models.inference.ai.azure.com",
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api_key=os.environ["GITHUB_TOKEN"],
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)
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elif API_HOST == "ollama":
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model = ChatOpenAI(
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model=os.environ["OLLAMA_MODEL"],
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base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"),
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api_key="none",
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)
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1441
@tool
1542
def play_song_on_spotify(song: str):
@@ -27,24 +54,6 @@ def play_song_on_apple(song: str):
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tools = [play_song_on_apple, play_song_on_spotify]
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tool_node = ToolNode(tools)
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# Setup the client to use either Azure OpenAI or GitHub Models
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load_dotenv(override=True)
33-
API_HOST = os.getenv("API_HOST", "github")
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35-
if API_HOST == "azure":
36-
token_provider = azure.identity.get_bearer_token_provider(azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
37-
model = AzureChatOpenAI(
38-
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
39-
azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
40-
openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
41-
azure_ad_token_provider=token_provider,
42-
)
43-
elif API_HOST == "github":
44-
model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
45-
elif API_HOST == "ollama":
46-
model = ChatOpenAI(model=os.environ["OLLAMA_MODEL"], base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"), api_key="none")
47-
4857
model = model.bind_tools(tools, parallel_tool_calls=False)
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5059

‎examples/langgraph_mcp_http_graph.py‎ renamed to ‎examples/langgraph_mcp.py‎

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1919
API_HOST = os.getenv("API_HOST", "github")
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2121
if API_HOST == "azure":
22-
token_provider = azure.identity.get_bearer_token_provider(azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
22+
token_provider = azure.identity.get_bearer_token_provider(
23+
azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
24+
)
2325
model = AzureChatOpenAI(
2426
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
2527
azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT"],
2628
openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
2729
azure_ad_token_provider=token_provider,
2830
)
2931
else:
30-
model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
32+
model = ChatOpenAI(
33+
model=os.getenv("GITHUB_MODEL", "gpt-4o"),
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base_url="https://models.inference.ai.azure.com",
35+
api_key=os.environ["GITHUB_TOKEN"],
36+
)
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3339
async def setup_agent():
@@ -56,7 +62,9 @@ def call_model(state: MessagesState):
5662
)
5763
builder.add_edge("tools", "call_model")
5864
graph = builder.compile()
59-
hotel_response = await graph.ainvoke({"messages": "Find me a hotel in San Francisco for 2 nights starting from 2024-01-01. I need a hotel with free WiFi and a pool."})
65+
hotel_response = await graph.ainvoke(
66+
{"messages": "Find a hotel in SF for 2 nights starting from 2024-01-01. I need free WiFi and pool."}
67+
)
6068
print(hotel_response["messages"][-1].content)
6169
image_bytes = graph.get_graph().draw_mermaid_png()
6270
with open("examples/images/langgraph_mcp_http_graph.png", "wb") as f:

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