\")` input:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "a9f599b5-1a55-406b-a76b-f52b3ca06975",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+ "\n",
+ "[{'text': \"I'll help you with that. Let me first ask the user about their location.\", 'type': 'text'}, {'id': 'toolu_01PewfDABq8kiEkVQHQ9Ggme', 'input': {'question': 'Where are you located?'}, 'name': 'AskHuman', 'type': 'tool_use'}]\n",
+ "Tool Calls:\n",
+ " AskHuman (toolu_01PewfDABq8kiEkVQHQ9Ggme)\n",
+ " Call ID: toolu_01PewfDABq8kiEkVQHQ9Ggme\n",
+ " Args:\n",
+ " question: Where are you located?\n",
+ "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+ "\n",
+ "san francisco\n",
+ "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+ "\n",
+ "[{'text': \"Now I'll search for the weather in San Francisco.\", 'type': 'text'}, {'id': 'toolu_01M7oa7bbUWba21rDyqCA3xB', 'input': {'query': 'current weather san francisco'}, 'name': 'search', 'type': 'tool_use'}]\n",
+ "Tool Calls:\n",
+ " search (toolu_01M7oa7bbUWba21rDyqCA3xB)\n",
+ " Call ID: toolu_01M7oa7bbUWba21rDyqCA3xB\n",
+ " Args:\n",
+ " query: current weather san francisco\n",
+ "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+ "Name: search\n",
+ "\n",
+ "I looked up: current weather san francisco. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\n",
+ "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+ "\n",
+ "[{'text': \"Based on the search results, it's currently sunny in San Francisco. Let me be more specific and search again for more detailed weather information.\", 'type': 'text'}, {'id': 'toolu_012Hcpe3Lovcf4rZJsySpARP', 'input': {'query': 'san francisco temperature today forecast'}, 'name': 'search', 'type': 'tool_use'}]\n",
+ "Tool Calls:\n",
+ " search (toolu_012Hcpe3Lovcf4rZJsySpARP)\n",
+ " Call ID: toolu_012Hcpe3Lovcf4rZJsySpARP\n",
+ " Args:\n",
+ " query: san francisco temperature today forecast\n",
+ "=================================\u001b[1m Tool Message \u001b[0m=================================\n",
+ "Name: search\n",
+ "\n",
+ "I looked up: san francisco temperature today forecast. Result: It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\n",
+ "==================================\u001b[1m Ai Message \u001b[0m==================================\n",
+ "\n",
+ "I apologize, but it seems I'm only able to confirm that it's sunny in San Francisco today. The search results aren't providing detailed temperature information. However, you can be confident that it's a sunny day in San Francisco!\n"
+ ]
+ }
+ ],
+ "source": [
+ "for event in app.stream(\n",
+ " # highlight-next-line\n",
+ " Command(resume=\"san francisco\"),\n",
+ " config,\n",
+ " stream_mode=\"values\",\n",
+ "):\n",
+ " if \"messages\" in event:\n",
+ " event[\"messages\"][-1].pretty_print()"
+ ]
+ }
+ ],
+ "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": "3.11.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/memory/add-summary-conversation-history.ipynb b/examples/memory/add-summary-conversation-history.ipynb
new file mode 100644
index 0000000..4d12266
--- /dev/null
+++ b/examples/memory/add-summary-conversation-history.ipynb
@@ -0,0 +1,585 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
+ "metadata": {},
+ "source": [
+ "# How to add summary of the conversation history\n",
+ "\n",
+ "One of the most common use cases for persistence is to use it to keep track of conversation history. This is great - it makes it easy to continue conversations. As conversations get longer and longer, however, this conversation history can build up and take up more and more of the context window. This can often be undesirable as it leads to more expensive and longer calls to the LLM, and potentially ones that error. One way to work around that is to create a summary of the conversation to date, and use that with the past N messages. This guide will go through an example of how to do that.\n",
+ "\n",
+ "This will involve a few steps:\n",
+ "\n",
+ "- Check if the conversation is too long (can be done by checking number of messages or length of messages)\n",
+ "- If yes, the create summary (will need a prompt for this)\n",
+ "- Then remove all except the last N messages\n",
+ "\n",
+ "A big part of this is deleting old messages. For an in depth guide on how to do that, see [this guide](../delete-messages)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cbd446a-808f-4394-be92-d45ab818953c",
+ "metadata": {},
+ "source": [
+ "## Setup\n",
+ "\n",
+ "First, let's set up the packages we're going to want to use"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture --no-stderr\n",
+ "%pip install --quiet -U langgraph langchain_anthropic"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
+ "metadata": {},
+ "source": [
+ "Next, we need to set API keys for Anthropic (the LLM we will use)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdin",
+ "output_type": "stream",
+ "text": [
+ "ANTHROPIC_API_KEY: ········\n"
+ ]
+ }
+ ],
+ "source": [
+ "import getpass\n",
+ "import os\n",
+ "\n",
+ "\n",
+ "def _set_env(var: str):\n",
+ " if not os.environ.get(var):\n",
+ " os.environ[var] = getpass.getpass(f\"{var}: \")\n",
+ "\n",
+ "\n",
+ "_set_env(\"ANTHROPIC_API_KEY\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
Set up LangSmith for LangGraph development
\n",
+ "
\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 here. \n",
+ "
\n",
+ "
\n",
+ "
Set up LangSmith for LangGraph development
\n",
+ "
\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 here. \n",
+ "
\n",
+ "
\n",
+ "
Set up LangSmith for LangGraph development
\n",
+ "
\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 here. \n",
+ "
\n",
+ "
\n",
- "
Prerequisites
\n",
- "
\n",
- " This guide assumes familiarity with the following:\n",
- "
\n",
- " - \n",
- " \n",
- " Persistence\n",
- " \n",
- "
\n",
- " - \n",
- " \n",
- " Redis\n",
- " \n",
- "
\n",
- "
\n",
- " \n",
- "
\n",
- "
Note
\n",
- "
\n",
- " This is a **reference** implementation. You can implement your own checkpointer using a different database or modify this one as long as it conforms to the BaseCheckpointSaver interface.\n",
- "
\n",
- "
\n",
- "
Set up LangSmith for LangGraph development
\n",
- "
\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 here. \n",
- "
\n",
- "
\n",
+ "
Prerequisites
\n",
+ "
\n",
+ " This guide assumes familiarity with the following:\n",
+ "
\n",
+ " - \n",
+ " \n",
+ " Subgraphs\n",
+ " \n",
+ "
\n",
+ " - \n",
+ " \n",
+ " Persistence\n",
+ " \n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Set up LangSmith for LangGraph development
\n",
+ "
\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 here. \n",
+ "
\n",
+ "
\n",
+ "
Prerequisites
\n",
+ "
\n",
+ " This guide assumes familiarity with the following:\n",
+ "
\n",
+ " - \n",
+ " \n",
+ " Subgraphs\n",
+ " \n",
+ "
\n",
+ " - \n",
+ " \n",
+ " Human-in-the-loop\n",
+ " \n",
+ "
\n",
+ " - \n",
+ " \n",
+ " State\n",
+ " \n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Set up LangSmith for LangGraph development
\n",
+ "
\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 here. \n",
+ "
\n",
+ "