mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-08-21 07:02:25 +02:00
docs: clean up and standardize more how-tos (#1959)
This commit is contained in:
Vadym Barda
@@ -13,7 +13,31 @@
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"id": "964686a6-8fed-4360-84d2-958c48186008",
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"metadata": {},
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"source": [
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"A common use case is streaming from an agent is to stream LLM tokens from inside the final node. This guide demonstrates how you can do this.\n",
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"<div class=\"admonition tip\">\n",
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" <p class=\"admonition-title\">Prerequisites</p>\n",
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" <p>\n",
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" This guide assumes familiarity with the following:\n",
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" <ul>\n",
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" <li> \n",
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" <a href=\"https://langchain-ai.github.io/langgraph/concepts/streaming/\">\n",
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" Streaming\n",
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" </a>\n",
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" </li>\n",
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" <li>\n",
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" <a href=\"https://python.langchain.com/docs/concepts/#chat-models/\">\n",
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" Chat Models\n",
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" </a>\n",
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" </li>\n",
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" <li>\n",
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" <a href=\"https://python.langchain.com/docs/concepts/#tools\">\n",
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" Tools\n",
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" </a>\n",
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" </li>\n",
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" </ul>\n",
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" </p>\n",
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"</div> \n",
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"\n",
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"A common use case when streaming from an agent is to stream LLM tokens from inside the final node. This guide demonstrates how you can do this.\n",
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"\n",
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"## Setup\n",
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"\n",
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@@ -33,7 +57,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "c87e4a47-4099-4d1a-907c-a99fa857165a",
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"metadata": {},
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"outputs": [],
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@@ -60,7 +84,7 @@
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" <p style=\"padding-top: 5px;\">\n",
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" 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",
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" </p>\n",
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"</div> "
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"</div>"
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]
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},
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{
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@@ -73,7 +97,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 4,
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"id": "5e62618d-0e0c-483c-acd3-40a26e61894a",
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"metadata": {},
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"outputs": [],
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@@ -120,7 +144,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 5,
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"id": "8c7339d2-1835-4b5a-a99c-a60e150280af",
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"metadata": {},
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"outputs": [],
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@@ -164,28 +188,28 @@
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" return {\"messages\": [response]}\n",
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"\n",
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"\n",
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"workflow = StateGraph(MessagesState)\n",
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"builder = StateGraph(MessagesState)\n",
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"\n",
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"workflow.add_node(\"agent\", call_model)\n",
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"workflow.add_node(\"tools\", tool_node)\n",
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"builder.add_node(\"agent\", call_model)\n",
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"builder.add_node(\"tools\", tool_node)\n",
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"# add a separate final node\n",
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"workflow.add_node(\"final\", call_final_model)\n",
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"builder.add_node(\"final\", call_final_model)\n",
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"\n",
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"workflow.add_edge(START, \"agent\")\n",
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"workflow.add_conditional_edges(\n",
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"builder.add_edge(START, \"agent\")\n",
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"builder.add_conditional_edges(\n",
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" \"agent\",\n",
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" should_continue,\n",
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")\n",
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"\n",
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"workflow.add_edge(\"tools\", \"agent\")\n",
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"workflow.add_edge(\"final\", END)\n",
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"builder.add_edge(\"tools\", \"agent\")\n",
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"builder.add_edge(\"final\", END)\n",
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"\n",
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"app = workflow.compile()"
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"graph = builder.compile()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 6,
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"id": "2ab6d079-ba06-48ba-abe5-e72df24407af",
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"metadata": {},
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"outputs": [
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@@ -203,7 +227,7 @@
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"source": [
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"from IPython.display import display, Image\n",
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"\n",
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"display(Image(app.get_graph().draw_mermaid_png()))"
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"display(Image(graph.get_graph().draw_mermaid_png()))"
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]
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},
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{
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@@ -214,19 +238,6 @@
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"## Stream outputs from the final node"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "84d65cbe-4cfe-44f8-b49e-b37632887c91",
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"from langchain_core._api import LangChainBetaWarning\n",
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"\n",
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"warnings.filterwarnings(\"ignore\", category=LangChainBetaWarning)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5cfaeb64-5506-4546-96c0-4891e6288ad9",
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@@ -260,8 +271,8 @@
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"source": [
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"from langchain_core.messages import HumanMessage\n",
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"\n",
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"inputs = [HumanMessage(content=\"what is the weather in sf\")]\n",
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"async for msg, metadata in app.astream({\"messages\": inputs}, stream_mode=\"messages\"):\n",
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"inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
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"for msg, metadata in graph.stream(inputs, stream_mode=\"messages\"):\n",
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" if (\n",
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" msg.content\n",
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" and not isinstance(msg, HumanMessage)\n",
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@@ -296,13 +307,13 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Well| folks|,| looks| like| we|'ve| got| some| cloudy| skies| in| the| Big| Apple| today|.| So| grab| your| umbrella| just| in| case|,| and| don|'t| let| those| clouds| rain| on| your| parade|!|"
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"Looks| like| we|'ve| got| some| clouds| roll|in|'| in| over| the| Big| Apple| today|,| folks|!| Keep| an| eye| out| for| some| over|cast| skies| in| NYC|.|"
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]
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}
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],
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"source": [
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"inputs = {\"messages\": [(\"human\", \"what's the weather in nyc?\")]}\n",
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"async for event in app.astream_events(inputs, version=\"v2\"):\n",
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"inputs = {\"messages\": [HumanMessage(content=\"what's the weather in nyc?\")]}\n",
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"async for event in graph.astream_events(inputs, version=\"v2\"):\n",
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" kind = event[\"event\"]\n",
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" tags = event.get(\"tags\", [])\n",
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" # filter on the custom tag\n",
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@@ -11,7 +11,31 @@
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"source": [
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"# How to create subgraphs\n",
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"\n",
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"For more complex systems, subgraphs are a useful design principle. Subgraphs allow you to create and manage different states in different parts of your graph. This allows you build things like [multi-agent teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/), where each team can track its own separate state.\n",
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"<div class=\"admonition tip\">\n",
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" <p class=\"admonition-title\">Prerequisites</p>\n",
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" <p>\n",
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" This guide assumes familiarity with the following:\n",
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" <ul>\n",
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" <li> \n",
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" <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/#state\">\n",
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" State\n",
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" </a>\n",
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" </li>\n",
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" <li> \n",
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" <a href=\"https://langchain-ai.github.io/langgraph/concepts/low_level/#reducers\">\n",
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" Reducers\n",
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" </a>\n",
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" </li>\n",
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" <li> \n",
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" <a href=\"https://langchain-ai.github.io/langgraph/concepts/agentic_concepts/#sub-graphs\">\n",
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" Subgraphs\n",
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" </a>\n",
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" </li>\n",
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" </ul>\n",
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" </p>\n",
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"</div> \n",
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"\n",
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"For more complex systems, subgraphs are a useful design principle. Subgraphs allow you to create and manage different states in different parts of your graph. This allows you build things like [multi-agent teams](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/), where each team can track its own separate state. This guide shows how you can add subgraphs to your graph.\n",
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"\n",
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""
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]
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@@ -7,7 +7,7 @@
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"source": [
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"# How to visualize your graph\n",
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"\n",
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"This notebook walks through how to visualize the graphs you create. This works with ANY [Graph](https://langchain-ai.github.io/langgraph/reference/graphs/).\n",
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"This guide walks through how to visualize the graphs you create. This works with ANY [Graph](https://langchain-ai.github.io/langgraph/reference/graphs/).\n",
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"\n",
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"## Setup\n",
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"\n",
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@@ -32,7 +32,7 @@
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"source": [
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"## Set up Graph\n",
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"\n",
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"You can visualize any arbitrary Graph, including StateGraph's and MessageGraph's. Let's have some fun by drawing fractals :)."
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"You can visualize any arbitrary [Graph](https://langchain-ai.github.io/langgraph/reference/graphs/), including [StateGraph](https://langchain-ai.github.io/langgraph/reference/graphs/#langgraph.graph.state.StateGraph). Let's have some fun by drawing fractals :)."
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]
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},
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{
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@@ -109,80 +109,6 @@
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"app = build_fractal_graph(3)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f4fc9378-b141-4b65-b86c-3afba77f7161",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-18T12:18:30.605220Z",
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"start_time": "2024-04-18T12:18:30.587191Z"
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}
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},
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"source": [
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"## Ascii\n",
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"\n",
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"We can easily visualize this graph in ascii"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a0b22e88-7f78-4215-afdd-4eedef9e2b9b",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --quiet grandalf"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ca9b980d-1f0a-4286-9157-a870e3d55134",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-04-19T11:25:37.303260Z",
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"start_time": "2024-04-19T11:25:37.273032Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" +-----------+ \n",
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" | __start__ | \n",
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" +-----------+ \n",
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" * \n",
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" * \n",
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" * \n",
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" .......+------------+******** \n",
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" ................ *****.| entry_node |....... **************** \n",
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" ................ ***********...... +------------+ ****............ **************** \n",
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" ............... ************ ..... . ****** ............. *************** \n",
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" ................ ************ ...... . ****** ............ **************** \n",
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" ................ ******* ...... . ****** ............ **************** \n",
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" ........ +-------------------+ .... . **** ........ ******** \n",
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" . | node_entry_node_B |****** .. . * .. ** \n",
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" ... +-------------------+ ************. . * ... *** \n",
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" . **** *** ... *********** . * ... *** \n",
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" ... **** ** .. ************ . * .. ** \n",
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" . ** ** .. ****** . * .. ** \n",
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"+--------------------------+ +--------------------------+ +--------------------------+ **** +-------------------+ \n",
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"| node_node_entry_node_B_B |........ | node_node_entry_node_B_C | | node_node_entry_node_B_A | ****** ......| node_entry_node_A | \n",
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"+--------------------------+ ...+--------------------------+........ +--------------------------+ ****** ............... +-------------------+ \n",
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" ............... .......... . ******* ............. \n",
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" ............. ......... . ****** ............... \n",
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" ............... ..... . **** ............. \n",
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" .....+---------+........ \n",
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" | __end__ | \n",
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" +---------+ \n"
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]
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}
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],
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"source": [
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"app.get_graph().print_ascii()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "edcd9ad2",
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