langgraph/examples/multi_agent/multi-agent-collaboration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "39fd1948-b5c3-48c4-b10e-2ae7e8c83334",
   "metadata": {},
   "source": [
    "# Basic Multi-agent Collaboration\n",
    "\n",
    "A single agent can usually operate effectively using a handful of tools within a single domain, but even using powerful models like `gpt-4`, it can be less effective at using many tools. \n",
    "\n",
    "One way to approach complicated tasks is through a \"divide-and-conquer\" approach: create an specialized agent for each task or domain and route tasks to the correct \"expert\".\n",
    "\n",
    "This notebook (inspired by the paper [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://arxiv.org/abs/2308.08155), by Wu, et. al.) shows one way to do this using LangGraph.\n",
    "\n",
    "The resulting graph will look something like the following diagram:\n",
    "\n",
    "![multi_agent diagram](./img/simple_multi_agent_diagram.png)\n",
    "\n",
    "Before we get started, a quick note: this and other multi-agent notebooks are designed to show _how_ you can implement certain design patterns in LangGraph. If the pattern suits your needs, we recommend combining it with some of the other fundamental patterns described elsewhere in the docs for best performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0d7b6dcc-c985-46e2-8457-7e6b0298b950",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install -U langchain langchain_openai langsmith pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "743c19df-6da9-4d1e-b2d2-ea40080b9fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import getpass\n",
    "import os\n",
    "\n",
    "\n",
    "def _set_if_undefined(var: str):\n",
    "    if not os.environ.get(var):\n",
    "        os.environ[var] = getpass(f\"Please provide your {var}\")\n",
    "\n",
    "\n",
    "_set_if_undefined(\"OPENAI_API_KEY\")\n",
    "_set_if_undefined(\"LANGCHAIN_API_KEY\")\n",
    "_set_if_undefined(\"TAVILY_API_KEY\")\n",
    "\n",
    "# Optional, add tracing in LangSmith\n",
    "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
    "os.environ[\"LANGCHAIN_PROJECT\"] = \"Multi-agent Collaboration\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "075c91c3-c249-471d-b259-41975faa83fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5e4344a7-21df-4d54-90d2-9d19b3416ffb",
   "metadata": {},
   "source": [
    "## Create graph utilites\n",
    "\n",
    "The following helper functions will simplify the code when it comes to actually constructing the graph.\n",
    "\n",
    "You can skip ahead if you just want to see what the graph looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4325a10e-38dc-4a98-9004-e1525eaba377",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "from langchain_core.messages import (\n",
    "    AIMessage,\n",
    "    BaseMessage,\n",
    "    ChatMessage,\n",
    "    FunctionMessage,\n",
    "    HumanMessage,\n",
    ")\n",
    "\n",
    "from langgraph.graph import END, StateGraph\n",
    "from langgraph.prebuilt.tool_executor import ToolExecutor, ToolInvocation\n",
    "\n",
    "\n",
    "def add_agent_node(workflow: StateGraph, name: str, llm, tools, system_message: str):\n",
    "    \"\"\"Create an agent and add it to the graph builder.\"\"\"\n",
    "    functions = [format_tool_to_openai_function(t) for t in tools]\n",
    "\n",
    "    prompt = ChatPromptTemplate.from_messages(\n",
    "        [\n",
    "            (\n",
    "                \"system\",\n",
    "                \"You are a helpful AI assistant, collaborating with other assistants.\"\n",
    "                \" Use the provided tools to progress towards answering the question.\"\n",
    "                \" If you are unable to fully answer, that's OK, another assistant with different tools \"\n",
    "                \" will help where you left off. Execute what you can to make progress.\"\n",
    "                \" If you or any of the other assistants have the final answer or deliverable,\"\n",
    "                \" prefix your response with FINAL ANSWER so the team knows to stop.\"\n",
    "                \" You have access to the following tools: {tool_names}.\\n{system_message}\",\n",
    "            ),\n",
    "            MessagesPlaceholder(variable_name=\"messages\"),\n",
    "        ]\n",
    "    ).partial(system_message=system_message)\n",
    "\n",
    "    chain = (\n",
    "        (lambda x: {**x, \"intermediate_steps\": []})\n",
    "        | prompt.partial(tool_names=\", \".join([tool.name for tool in tools]))\n",
    "        | llm.bind_functions(functions)\n",
    "        | (lambda x: _update_state(x, name=name))\n",
    "    )\n",
    "    workflow.add_node(name, chain)\n",
    "\n",
    "\n",
    "def _update_state(ai_message, name: str) -> dict:\n",
    "    \"\"\"This is called after each worker agent is invoked.\n",
    "\n",
    "    It is used to update the global graph state using the agent output.\"\"\"\n",
    "    if isinstance(ai_message, FunctionMessage):\n",
    "        result = ai_message\n",
    "    else:\n",
    "        result = HumanMessage(**ai_message.dict(exclude={\"type\", \"name\"}), name=name)\n",
    "    return {\n",
    "        \"messages\": [result],\n",
    "        # Since we have a strict workflow, we can\n",
    "        # track the sender so we know who to pass to next.\n",
    "        \"sender\": name,\n",
    "    }\n",
    "\n",
    "\n",
    "def call_tool(state, tool_executor):\n",
    "    \"\"\"This runs tools in the graph\n",
    "\n",
    "    It takes in an agent action and calls that tool and returns the result.\"\"\"\n",
    "    messages = state[\"messages\"]\n",
    "    # Based on the continue condition\n",
    "    # we know the last message involves a function call\n",
    "    last_message = messages[-1]\n",
    "    # We construct an ToolInvocation from the function_call\n",
    "    tool_input = json.loads(\n",
    "        last_message.additional_kwargs[\"function_call\"][\"arguments\"]\n",
    "    )\n",
    "    # We can pass single-arg inputs by value\n",
    "    if len(tool_input) == 1 and \"__arg1\" in tool_input:\n",
    "        tool_input = next(iter(tool_input.values()))\n",
    "    tool_name = last_message.additional_kwargs[\"function_call\"][\"name\"]\n",
    "    action = ToolInvocation(\n",
    "        tool=tool_name,\n",
    "        tool_input=tool_input,\n",
    "    )\n",
    "    # We call the tool_executor and get back a response\n",
    "    response = tool_executor.invoke(action)\n",
    "    # We use the response to create a FunctionMessage\n",
    "    function_message = FunctionMessage(\n",
    "        content=f\"{tool_name} response: {str(response)}\", name=action.tool\n",
    "    )\n",
    "    # We return a list, because this will get added to the existing list\n",
    "    return {\"messages\": [function_message]}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b54c0c-0b09-408b-abc5-86308929afb6",
   "metadata": {},
   "source": [
    "## Create graph\n",
    "\n",
    "Now that we've defined our tools and made some helper functions, will create the individual agents below and tell them how to talk to each other using LangGraph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4dce3901-6ad5-4df5-8528-6e865cf96cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "from typing import Annotated, List, Sequence, Tuple, TypedDict, Union\n",
    "\n",
    "from langchain.agents import create_openai_functions_agent\n",
    "from langchain.tools.render import format_tool_to_openai_function\n",
    "from langchain_community.tools.tavily_search import TavilySearchResults\n",
    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
    "from langchain_core.tools import tool\n",
    "from langchain_experimental.utilities import PythonREPL\n",
    "from langchain_openai import ChatOpenAI\n",
    "from typing_extensions import TypedDict\n",
    "\n",
    "\n",
    "# This defines the object that is passed between each node\n",
    "# in the graph. We will create different nodes for each agent and tool\n",
    "class AgentState(TypedDict):\n",
    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
    "    sender: str\n",
    "\n",
    "\n",
    "tavily_tool = TavilySearchResults(max_results=5)\n",
    "\n",
    "# Warning: This executes code locally, which can be unsafe when not sandboxed\n",
    "\n",
    "repl = PythonREPL()\n",
    "\n",
    "\n",
    "@tool\n",
    "def python_repl(\n",
    "    code: Annotated[str, \"The python code to execute to generate your chart.\"]\n",
    "):\n",
    "    \"\"\"Use this to execute python code. If you want to see the output of a value,\n",
    "    you should print it out with `print(...)`. This is visible to the user.\"\"\"\n",
    "    try:\n",
    "        result = repl.run(code)\n",
    "    except BaseException as e:\n",
    "        return f\"Failed to execute. Error: {repr(e)}\"\n",
    "    return f\"Succesfully executed:\\n```python\\n{code}\\n```\\nStdout: {result}\"\n",
    "\n",
    "\n",
    "workflow = StateGraph(AgentState)\n",
    "llm = ChatOpenAI(model=\"gpt-4-1106-preview\")\n",
    "add_agent_node(\n",
    "    workflow,\n",
    "    \"Researcher\",\n",
    "    llm,\n",
    "    [tavily_tool],\n",
    "    system_message=\"You should provide accurate data for the chart generator to use.\",\n",
    ")\n",
    "add_agent_node(\n",
    "    workflow,\n",
    "    \"Chart Generator\",\n",
    "    llm,\n",
    "    [python_repl],\n",
    "    system_message=\"Any charts you display will be visible by the user.\",\n",
    ")\n",
    "\n",
    "# The \"tool executor\" node is called whenever any worker agent\n",
    "#\n",
    "tools = [tavily_tool, python_repl]\n",
    "tool_executor = ToolExecutor(tools)\n",
    "workflow.add_node(\"call_tool\", lambda state: call_tool(state, tool_executor))\n",
    "\n",
    "\n",
    "# Either agent can decide to end\n",
    "def router(state):\n",
    "    # This is the router\n",
    "    messages = state[\"messages\"]\n",
    "    last_message = messages[-1]\n",
    "    if \"function_call\" in last_message.additional_kwargs:\n",
    "        # The previus agent is invoking a tool\n",
    "        return \"call_tool\"\n",
    "    if \"FINAL ANSWER\" in last_message.content:\n",
    "        # Any agent decided the work is done\n",
    "        return \"end\"\n",
    "    return \"continue\"\n",
    "\n",
    "\n",
    "workflow.add_conditional_edges(\n",
    "    \"Researcher\",\n",
    "    router,\n",
    "    {\"continue\": \"Chart Generator\", \"call_tool\": \"call_tool\", \"end\": END},\n",
    ")\n",
    "workflow.add_conditional_edges(\n",
    "    \"Chart Generator\",\n",
    "    router,\n",
    "    {\"continue\": \"Researcher\", \"call_tool\": \"call_tool\", \"end\": END},\n",
    ")\n",
    "# We will assume that any time a tool is called, the researcher will choose what to do next\n",
    "workflow.add_conditional_edges(\n",
    "    \"call_tool\",\n",
    "    # Each agent node updates the 'sender' field\n",
    "    # the tool calling node does not, meaning\n",
    "    # this edge will route back to the original agent\n",
    "    # who invoked the tool\n",
    "    lambda x: x[\"sender\"],\n",
    "    {\n",
    "        \"Researcher\": \"Researcher\",\n",
    "        \"Chart Generator\": \"Chart Generator\",\n",
    "    },\n",
    ")\n",
    "workflow.set_entry_point(\"Researcher\")\n",
    "graph = workflow.compile()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9447e7-9ab6-43eb-8ae6-9b52f8ba8425",
   "metadata": {},
   "source": [
    "## Invoke\n",
    "\n",
    "With the graph created, you can invoke it! Let's have it chart some stats for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "176a99b0-b457-45cf-8901-90facaa852da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Python REPL can execute arbitrary code. Use with caution.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "HumanMessage(content=\"FINAL ANSWER\\n\\nThe complete line graph of the UK's GDP from 2018 to 2022 is displayed above, with the following GDP values for each year:\\n\\n- 2018: 2.16 trillion GBP\\n- 2019: 2.23 trillion GBP\\n- 2020: 1.99 trillion GBP\\n- 2021: 2.23 trillion GBP\\n- 2022: 2.27 trillion GBP\\n\\nThe graph shows the fluctuations in the UK's GDP over the specified period, with a notable dip in 2020 likely due to the economic impact of the COVID-19 pandemic.\", name='Researcher')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = graph.invoke(\n",
    "    {\n",
    "        \"messages\": [\n",
    "            HumanMessage(\n",
    "                content=\"Fetch the UK's GDP over the past 5 years,\"\n",
    "                \" then draw a line graph of it.\"\n",
    "                \" Once you code it up, finish.\"\n",
    "            )\n",
    "        ],\n",
    "    },\n",
    "    # Maximum number of steps to take in the graph\n",
    "    {\"recursion_limit\": 150},\n",
    ")\n",
    "result[\"messages\"][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23cc22c7-33a8-4399-91e4-ac1abf8f0fec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}