How to Set Up a LangGraph Application for Deployment

A LangGraph application must be configured with a LangGraph API configuration file in order to be deployed to LangGraph Cloud (or to be self-hosted). This how-to guide discusses the basic steps to setup a LangGraph application for deployment using requirements.txt to specify project dependencies. If you prefer using poetry for dependency management, check out this how-to guide on using pyproject.toml for LangGraph Cloud.

The final repo structure will look something like this:

my-app/
|-- requirements.txt    # package dependencies
|-- .env                # environment variables
|-- openai_agent.py     # code for an agent
|-- anthropic_agent.py  # code for another agent
|-- langgraph.json      # configuration file for LangGraph

After each step, an example file directory is provided to demonstrate how code can be organized.

Specify Dependencies

Dependencies can optionally be specified in one of the following files: pyproject.toml, setup.py, or requirements.txt. If none of these files is created, then dependencies can be specified later in the LangGraph API configuration file.

Example requirements.txt file:

langgraph
langchain_openai

Example file directory:

my-app/
|-- requirements.txt    # Python packages required for your graph

Specify Environment Variables

Environment variables can optionally be specified in a file (e.g. .env). See the Environment Variables reference to configure additional variables for a deployment.

Example .env file:

MY_ENV_VAR_1=foo
MY_ENV_VAR_2=bar
OPENAI_API_KEY=key

Example file directory:

my-app/
|-- requirements.txt
|-- .env                # file with environment variables

Define Graphs

Implement your graphs! Graphs can be defined in a single file or multiple files. Make note of the variable names of each CompiledGraph to be included in the LangGraph application. The variable names will be used later when creating the LangGraph API configuration file.

Example openai_agent.py file:

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()

Assign CompiledGraph to Variable

The build process for LangGraph Cloud requires that the CompiledGraph object be assigned to a variable at the top-level of a Python module.

Example file directory:

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

Create LangGraph API Config

Create a LangGraph API configuration file called langgraph.json. See the LangGraph CLI reference for detailed explanations of each key in the JSON object of the configuration file.

Example langgraph.json file:

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

Note that the variable name of the CompiledGraph appears at the end of the value of each subkey in the top-level graphs key (i.e. :<variable_name>).

Example file directory:

my-app/
|-- requirements.txt
|-- .env
|-- openai_agent.py
|-- anthropic_agent.py
|-- langgraph.json      # configuration file for LangGraph

Upload to GitHub

To deploy the LangGraph application to LangGraph Cloud, the code must be uploaded to a GitHub repository.

Next

After you setup your repo, it's time to deploy your app.

Comments