langgraph/docs/docs/cloud/deployment/graph_rebuild.md

Rebuild Graph at Runtime

You might need to rebuild your graph with a different configuration for a new run. This guide shows how you can do this.

Prerequisites

Make sure to check out this how-to guide on setting up your app for deployment first.

Define graphs

Let's say you have an app with a simple graph that calls an LLM and returns the response to the user. The app file directory looks like the following:

my-app/
|-- requirements.txt
|-- .env
|-- openai_agent.py     # code for your graph

where the graph is defined in openai_agent.py.

No rebuild

In the standard LangGraph API configuration, the server uses the compiled graph instance that's defined at the top level of openai_agent.py, which looks like the following:

from langchain_openai import ChatOpenAI
from langgraph.graph import END, MessageGraph

model = ChatOpenAI(temperature=0)

graph_workflow = MessageGraph()

graph_workflow.add_node("agent", model)
graph_workflow.add_edge("agent", END)
graph_workflow.set_entry_point("agent")

agent = graph_workflow.compile()

To make the server aware of your graph, you need to specify a path to the variable that contains the CompiledStateGraph instance in your LangGraph API configuration (langgraph.json), e.g.:

{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:agent",
    },
    "env": "./.env"
}

Rebuild

To make your graph rebuild on each new run with custom configuration, you need to rewrite openai_agent.py to instead provide a function that takes a config and returns a graph (or compiled graph) instance as follows:

from langchain_openai import ChatOpenAI
from langgraph.graph import END, MessageGraph
from langchain_core.runnables import RunnableConfig

model = ChatOpenAI(temperature=0)

def make_graph(config: RunnableConfig)
    graph_workflow = MessageGraph()

    graph_workflow.add_node("agent", model)
    graph_workflow.add_edge("agent", END)
    graph_workflow.set_entry_point("agent")

    agent = graph_workflow.compile()
    return agent

Finally, you need to specify the path to your graph-making function (make_graph) in langgraph.json:

{
    "dependencies": ["."],
    "graphs": {
        "openai_agent": "./openai_agent.py:make_graph",
    },
    "env": "./.env"
}

See more info on LangGraph API configuration file here