langgraph-supervisor

import os from langchain_openai import ChatOpenAI from langgraph_supervisor import create_supervisor from langgraph.prebuilt import create_react_agent from langgraph.graph import END # Set your OpenAI API key from environment variable os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "") # Initialize the LLM for agents and supervisor model = ChatOpenAI(model="gpt-4o") # Define simple tools def add(a: float, b: float) -> float: """Add two numbers.""" return a + b def web_search(query: str) -> str: """Search the web for information.""" # Placeholder for actual web search functionality return f"Found results for '{query}': Example search data." # Create specialized agents math_agent = create_react_agent( model=model, tools=[add], name="math_expert", ) research_agent = create_react_agent( model=model, tools=[web_search], name="research_expert", ) # Create supervisor workflow # The prompt parameter defines the supervisor's role and how to delegate. workflow = create_supervisor( [research_agent, math_agent], model=model, prompt=( "You are a team supervisor managing a research expert and a math expert. " "For research tasks, use research_agent. " "For math tasks, use math_agent." ), ) # To add memory and enable longer conversations, you would typically use a StateGraph and add checkpointing. # For this quickstart, we'll compile and run a single turn. app = workflow.compile() # Example invocation result = app.invoke({ "messages": [ { "role": "user", "content": "What is 10 + 5 and what's the capital of France?" } ] }) print(result["messages"][-1].content)