{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to control graph recursion limit\n",
    "\n",
    "You can set the graph recursion limit when invoking or streaming the graph. The recursion limit sets the number of supersteps that the graph is allowed to execute before it raises an error. Read more about the concept of recursion limits [here](https://langchain-ai.github.io/langgraph/concepts/low_level/#recursion-limit). Let's see an example of this in a simple graph with parallel branches to better understand exactly how the recursion limit works.\n",
    "\n",
    "## Setup\n",
    "\n",
    "First, let's install the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stderr\n",
    "%pip install -U langgraph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"admonition tip\">\n",
    "    <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
    "    <p style=\"padding-top: 5px;\">\n",
    "        Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
    "    </p>\n",
    "</div>    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "from typing import Annotated, Any\n",
    "\n",
    "from typing_extensions import TypedDict\n",
    "\n",
    "from langgraph.graph import StateGraph, START, END\n",
    "\n",
    "\n",
    "class State(TypedDict):\n",
    "    # The operator.add reducer fn makes this append-only\n",
    "    aggregate: Annotated[list, operator.add]\n",
    "\n",
    "\n",
    "class ReturnNodeValue:\n",
    "    def __init__(self, node_secret: str):\n",
    "        self._value = node_secret\n",
    "\n",
    "    def __call__(self, state: State) -> Any:\n",
    "        print(f\"Adding {self._value} to {state['aggregate']}\")\n",
    "        return {\"aggregate\": [self._value]}\n",
    "\n",
    "\n",
    "builder = StateGraph(State)\n",
    "builder.add_node(\"a\", ReturnNodeValue(\"I'm A\"))\n",
    "builder.add_edge(START, \"a\")\n",
    "builder.add_node(\"b\", ReturnNodeValue(\"I'm B\"))\n",
    "builder.add_node(\"c\", ReturnNodeValue(\"I'm C\"))\n",
    "builder.add_node(\"d\", ReturnNodeValue(\"I'm D\"))\n",
    "builder.add_edge(\"a\", \"b\")\n",
    "builder.add_edge(\"a\", \"c\")\n",
    "builder.add_edge(\"b\", \"d\")\n",
    "builder.add_edge(\"c\", \"d\")\n",
    "builder.add_edge(\"d\", END)\n",
    "graph = builder.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "display(Image(graph.get_graph().draw_mermaid_png()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, our graph will execute nodes `b` and `c` in parallel (i.e. in a single super-step), which means that if we run this graph it should take exactly 3 steps. We can set the recursion limit to 3 first to check that it raises an error (the recursion limit is inclusive, so if the limit is 3 the graph will raise an error when it reaches step 3) as expected: \n",
    "\n",
    "## Use the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding I'm A to []\n",
      "Adding I'm B to [\"I'm A\"]\n",
      "Adding I'm C to [\"I'm A\"]\n",
      "Adding I'm D to [\"I'm A\", \"I'm B\", \"I'm C\"]\n",
      "Recursion Error\n"
     ]
    }
   ],
   "source": [
    "from langgraph.errors import GraphRecursionError\n",
    "\n",
    "try:\n",
    "    graph.invoke({\"aggregate\": []},{\"recursion_limit\":3})\n",
    "except GraphRecursionError:\n",
    "    print(\"Recursion Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success! The graph raised an error as expected - now let's test setting the recursion limit to 4 and ensure that the graph succeeds in this case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding I'm A to []\n",
      "Adding I'm B to [\"I'm A\"]\n",
      "Adding I'm C to [\"I'm A\"]\n",
      "Adding I'm D to [\"I'm A\", \"I'm B\", \"I'm C\"]\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    graph.invoke({\"aggregate\": []},{\"recursion_limit\":4})\n",
    "except GraphRecursionError:\n",
    "    print(\"Recursion Error\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, just as we expected the graph runs successfully in this case. \n",
    "\n",
    "Setting the correct graph recursion limit is important for avoiding graph runs stuck in long-running loops and thus helps minimize unnecessary costs"
   ]
  }
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