first draft

docs/_scripts/copy_notebooks.py

@@ -56,6 +56,7 @@ _MANUAL = {
         "human_in_the_loop/time-travel.ipynb",
         "human_in_the_loop/edit-graph-state.ipynb",
         "human_in_the_loop/wait-user-input.ipynb",
+        "node-retries.ipynb",
     ],
     "tutorials": [
         "introduction.ipynb",

docs/docs/how-tos/index.md

@@ -68,6 +68,7 @@ These guides show how to use different streaming modes.
 - [How to add runtime configuration to your graph](configuration.ipynb)
 - [How to use a Pydantic model as your state](state-model.ipynb)
 - [How to use a context object in state](state-context-key.ipynb)
+- [How to add node retries](node-retries.ipynb)
 
 ## Prebuilt ReAct Agent
 

docs/mkdocs.yml

@@ -163,6 +163,7 @@ nav:
           - Add runtime configuration: how-tos/configuration.ipynb
           - Use Pydantic model as state: how-tos/state-model.ipynb
           - Use a context object in state: how-tos/state-context-key.ipynb
+          - Add node retries: how-tos/node-retries.ipynb
       - Prebuilt ReAct Agent:
           - Create a ReAct agent: how-tos/create-react-agent.ipynb
           - Add memory to a ReAct agent: how-tos/create-react-agent-memory.ipynb

examples/node-retries.ipynb

@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How to add node retry policies\n",
+    "\n",
+    "There are many use cases where you may wish for your node to have a custom retry policy, for example if you are making HTTP requests, calling an API, etc. \n",
+    "\n",
+    "## Defining your retry policy\n",
+    "\n",
+    "In order to configure the retry policty, you have to pass the `retry` parameter to the `add_node` function. The `retry` parameter takes in a `RetryPolicy` named tuple object. Below we instantiate a `RetryPolicy` object with the default parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langgraph.pregel.types import default_retry_on\n",
+    "from langgraph.pregel import RetryPolicy\n",
+    "\n",
+    "# Note: This is equivalent to writing retry_policy=RetryPolicy() since these are all default values\n",
+    "retry_policy = RetryPolicy(\n",
+    "    initial_interval = 0.5,\n",
+    "    backoff_factor = 2.0,\n",
+    "    max_interval = 128.0,\n",
+    "    max_attempts = 3,\n",
+    "    jitter = True,\n",
+    "    retry_on = default_retry_on\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can then change the default values to work for your custom use case:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RetryPolicy(initial_interval=0.5, backoff_factor=2.0, max_interval=128.0, max_attempts=5, jitter=True, retry_on=<function default_retry_on at 0x1157419e0>)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "retry_policy = retry_policy._replace(max_attempts = 5)\n",
+    "retry_policy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want more information on what each of the parameters does, be sure to read the source code.\n",
+    "\n",
+    "## Passing your retry policy to a node\n",
+    "\n",
+    "Lastly, we can pass `retry_policy` to the `add_node` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import operator\n",
+    "from typing import Annotated, Sequence, TypedDict\n",
+    "\n",
+    "from langchain_anthropic import ChatAnthropic\n",
+    "from langchain_core.messages import BaseMessage\n",
+    "\n",
+    "from langgraph.graph import END, StateGraph, START\n",
+    "\n",
+    "\n",
+    "model = ChatAnthropic(model_name=\"claude-2.1\")\n",
+    "\n",
+    "\n",
+    "class AgentState(TypedDict):\n",
+    "    messages: Annotated[Sequence[BaseMessage], operator.add]\n",
+    "\n",
+    "\n",
+    "def _call_model(state):\n",
+    "    response = model.invoke(state[\"messages\"])\n",
+    "    return {\"messages\": [response]}\n",
+    "\n",
+    "# Define a new graph\n",
+    "workflow = StateGraph(AgentState)\n",
+    "workflow.add_node(\"model\", _call_model,retry=retry_policy)\n",
+    "workflow.add_edge(START, \"model\")\n",
+    "workflow.add_edge(\"model\", END)\n",
+    "\n",
+    "app = workflow.compile()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the `\"model\"` node will be configured to use the custom retry policy that was defined above."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

libs/langgraph/langgraph/graph/state.py

@@ -254,6 +254,7 @@ class StateGraph(Graph):
             action (Optional[RunnableLike]): The action associated with the node. (default: None)
             metadata (Optional[dict[str, Any]]): The metadata associated with the node. (default: None)
             input (Optional[Type[Any]]): The input schema for the node. (default: the graph's input schema)
+            retry (Optional[RetryPolicy]): The policy for retrying the node. (default: None)
         Raises:
             ValueError: If the key is already being used as a state key.