langgraph_agent/simple_demo.py

"""
Simple LangGraph agent demo connecting to Modal bird classifier MCP
"""

import asyncio
import os
from dotenv import load_dotenv
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI 

load_dotenv()

async def create_bird_agent():
    """Create and return a bird classification agent."""

    # 1. Configure MCP client for Modal bird classifier
    print("[STATUS]: Connecting to Modal MCP server...")

    client = MultiServerMCPClient({
        "bird_classifier": {
            "transport": "streamable_http",
            "url": os.getenv("MODAL_MCP_URL"),
            "headers": {
                "X-API-Key": os.getenv("BIRD_CLASSIFIER_API_KEY")
            }
        }
    })

    # 2. Get tools from MCP server
    print("[STATUS]: Loading MCP tools...")
    tools = await client.get_tools()
    print(f"[LOADED]: {len(tools)} tools - {[t.name for t in tools]}")

    # 3. Create agent with model and tools
    print("[STATUS]: Creating LangGraph agent...")
    agent = create_agent(
        model=ChatOpenAI(
            model="gpt-4o-mini",
            temperature=0
        ),
        tools=tools,
        system_prompt="""
        You are a helpful bird identification assistant.
        When a user provides an image URL, use the classify_from_url tool to identify the bird species.
        Always report the species name and confidence score in a friendly manner."""
    )

    return agent

async def main():
    """Run simple bird classification agent demo"""

    # Create agent
    agent = await create_bird_agent()

    # 4. Test with a bird image URL
    print("\n"+"="*70)
    print("[STATUS]: Testing bird classification...")
    print("="*70+"\n")

    test_url = "https://images.unsplash.com/photo-1445820200644-69f87d946277?w=400&auto=format&fit=crop&q=60&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxzZWFyY2h8MTV8fGJpcmR8ZW58MHx8MHx8fDA%3D"

    result = await agent.ainvoke({
        "messages": [{
            "role": "user",
            "content": f"What bird species is this? {test_url}"
        }]
    })

    # 5. Print results
    print("\n[AGENT RESPONSE]:")
    print(result["messages"][-1].content)

    print("\n[DEMO COMPLETE!]")

if __name__ == "__main__":
    asyncio.run(main())