langgraph-swarm

import os from langchain_openai import ChatOpenAI from langgraph_swarm import AgentSwarm, AgentNode, SwarmGraph from langgraph_swarm.nodes import LLMNode # Set your OpenAI API key os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY", "") if not os.environ["OPENAI_API_KEY"]: print("Warning: OPENAI_API_KEY environment variable not set. Skipping quickstart.") else: llm = ChatOpenAI(model="gpt-4o", temperature=0) # Define the agents (nodes in the swarm) research_agent = AgentNode( name="Researcher", description="Researches given topics and provides factual information.", llm=llm, # Example LLM, replace with actual agent logic if needed tools=[] ) writer_agent = AgentNode( name="Writer", description="Writes creative content based on research.", llm=llm, tools=[] ) # Create a SwarmGraph graph = SwarmGraph() # Add agents to the graph graph.add_agent(research_agent) graph.add_agent(writer_agent) # Define the workflow (how agents interact) graph.add_workflow( entry_point=research_agent.name, edges={research_agent.name: writer_agent.name}, # The writer agent should only activate if research is complete # Add conditional logic or specific messages to trigger in a real scenario exit_point=writer_agent.name ) # Create the AgentSwarm instance swarm = AgentSwarm(graph=graph, llm=llm) # LLM for internal swarm coordination if needed # Invoke the swarm with an initial task task = "Write a short summary about the benefits of multi-agent systems." print(f"\n--- Invoking swarm with task: '{task}' ---\n") result = swarm.invoke({"messages": [("user", task)]}) print("\n--- Swarm execution complete ---\n") print(f"Final result: {result}") # Expected output structure might vary, but should contain the agents' messages. # print(result["messages"][-1].content) # Example access to final message