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   "id": "53b9dce4-e4ae-4bdb-b752-0f04350a2e3d",
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   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.agents import create_openai_functions_agent\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain import hub\n",
    "from langchain.agents import AgentExecutor, create_openai_functions_agent\n",
    "from langchain_community.chat_models import ChatOpenAI\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "from langchain_core.runnables import RunnablePassthrough, RunnableLambda\n",
    "from permchain.langgraph import Actor, DecisionPoint, Graph\n",
    "\n",
    "tools = [TavilySearchResults(max_results=1)]\n",
    "\n",
    "# Get the prompt to use - you can modify this!\n",
    "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n",
    "\n",
    "# Choose the LLM that will drive the agent\n",
    "llm = ChatOpenAI(model=\"gpt-3.5-turbo-1106\")\n",
    "\n",
    "# Construct the OpenAI Functions agent\n",
    "agent_runnable = create_openai_functions_agent(llm, tools, prompt)\n",
    "\n",
    "from langchain_core.agents import AgentFinish\n",
    "# Define decision-making logic\n",
    "def should_continue(data):\n",
    "    # Logic to decide whether to continue in the loop or exit\n",
    "    if isinstance(data['agent_outcome'], AgentFinish):\n",
    "        return \"exit\"\n",
    "    else:\n",
    "        return \"continue\"\n",
    "    \n",
    "def execute_tools(data):\n",
    "    agent_action = data.pop('agent_outcome')\n",
    "    observation = {t.name: t for t in tools}[agent_action.tool].invoke(agent_action.tool_input)\n",
    "    data['intermediate_steps'].append((agent_action, observation))\n",
    "    return data\n",
    "    \n",
    "    \n",
    "\n",
    "# Define agents\n",
    "agent = RunnablePassthrough.assign(\n",
    "    agent_outcome = agent_runnable\n",
    ")\n",
    "\n",
    "# Define a new graph\n",
    "workflow = Graph()\n",
    "decision_point = DecisionPoint(\"exit?\", should_continue)\n",
    "llm_agent = Actor(\"agent\", agent)\n",
    "tool_actor = Actor(\"tools\", RunnableLambda(execute_tools))\n",
    "\n",
    "# Register actors\n",
    "workflow.register(llm_agent)\n",
    "workflow.register(tool_actor)\n",
    "workflow.register(decision_point)\n",
    "\n",
    "# Define connections with conditional logic\n",
    "workflow.connect(llm_agent, decision_point)\n",
    "workflow.branch(decision_point, tool_actor, condition=\"continue\")\n",
    "workflow.branch(decision_point, None, condition=\"exit\")  # Exit the workflow\n",
    "workflow.connect(tool_actor, llm_agent)\n",
    "\n",
    "# Define entry point and execute the graph\n",
    "workflow.set_entry_point(llm_agent)\n",
    "chain = workflow.compile()\n",
    "\n",
    "chain.invoke({\"input\": \"what is the weather in sf\", \"intermediate_steps\": []})"
   ]
  },
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