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Port another example to langchain
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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_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()])
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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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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "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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async def run_agent():
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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/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":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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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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)
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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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@tool
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def play_song_on_spotify(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)
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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(azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
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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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)
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elif API_HOST == "github":
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model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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elif API_HOST == "ollama":
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model = ChatOpenAI(model=os.environ["OLLAMA_MODEL"], base_url=os.environ.get("OLLAMA_ENDPOINT", "http://localhost:11434/v1"), api_key="none")
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model = model.bind_tools(tools, parallel_tool_calls=False)
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examples/langgraph_mcp_http_graph.py renamed to examples/langgraph_mcp.py

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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(azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default")
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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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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)
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else:
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model = ChatOpenAI(model=os.getenv("GITHUB_MODEL", "gpt-4o"), base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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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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async def setup_agent():
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)
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builder.add_edge("tools", "call_model")
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graph = builder.compile()
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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."})
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hotel_response = await graph.ainvoke(
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{"messages": "Find a hotel in SF for 2 nights starting from 2024-01-01. I need free WiFi and pool."}
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)
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print(hotel_response["messages"][-1].content)
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image_bytes = graph.get_graph().draw_mermaid_png()
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with open("examples/images/langgraph_mcp_http_graph.png", "wb") as f:

‎examples/langgraph_mcp_http.py‎

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