agent/_create.py

def agent(payload):

    DEBUG=True

    from agent.memory import Memory
    memory = Memory(payload)

    from agent.jsonencoder import json_parse_chain
    from agent.agent_main import Chain_Main_Agent

    chain_main_agent = Chain_Main_Agent(memory)

    
    from agent.toolset import tool_executor, converted_tools

    from langgraph.prebuilt import ToolInvocation
    import json
    from langchain_core.messages import FunctionMessage


    def call_main_agent(messages):   
        response = chain_main_agent.invoke({"conversation":messages, "thread_id": memory.thread_id})
        
        if DEBUG: print("call_main_agent called");                                        

        return response                                                 

    def use_tool(messages):    
        last_message = messages[-1]                             
        action = ToolInvocation(
            tool=last_message.additional_kwargs["function_call"]["name"],
            tool_input=json.loads(last_message.additional_kwargs["function_call"]["arguments"]),
        )
        response = tool_executor.invoke(action)
        function_message = FunctionMessage(content=str(response), name=action.tool)

        if DEBUG: print("Suggesting Tool to use..."+action.tool);                                                                                

        return function_message

    def render_output(messages):

        import json

        response = json_parse_chain.invoke({"conversation":messages, "thread_id": memory.thread_id})

        if DEBUG: print("Rendering output");   

        from langchain_core.messages import AIMessage                                     

        response = json.dumps(response)
        return AIMessage(content=response)

    from langgraph.graph import MessageGraph, END
    workflow = MessageGraph()

    workflow.add_node("main_agent", call_main_agent)
    workflow.add_node("use_tool", use_tool)
    workflow.add_node("render_output", render_output)

    workflow.set_entry_point("main_agent")

    def should_continue(messages):                                        
        last_message = messages[-1]
        if "function_call" not in last_message.additional_kwargs: return "render_output"
        else: return "continue"


    workflow.add_conditional_edges(
        "main_agent", should_continue, 
        {
            "continue": "use_tool", 
            "render_output":"render_output",
            "end": END
        }
    )
    workflow.add_edge('use_tool', 'main_agent')
    workflow.add_edge('render_output', END)


    app = workflow.compile(checkpointer=memory.checkpoints)
    
    from langchain_core.messages import HumanMessage

    input = payload.get("input") or "Can I earn credit?"
    inputs = [HumanMessage(content=input)]

    response = app.invoke(inputs, {"configurable": {"thread_id": memory.thread_id} } )

    '''
    inputs = [HumanMessage(content="My name is Mark")]
    response = app.invoke(inputs, {"configurable": {"thread_id": memory.thread_id} } )
    print(response[-1].content)

    inputs = [HumanMessage(content="What is my name?")]
    response = app.invoke(inputs, {"configurable": {"thread_id": memory.thread_id} } )
    print(response[-1].content)
    '''
    
    print(response[-1].content)
    return response[-1].content