# How to Set Up a LangGraph Application for Deployment

A LangGraph application must be configured with a [LangGraph API configuration file](../reference/cli.md#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. 

This walkthrough is based on [this repository](https://github.com/langchain-ai/langgraph-example), which you can play around with to learn more about how to setup your LangGraph application for deployment.

!!! tip "Setup with pyproject.toml"
    If you prefer using poetry for dependency management, check out [this how-to guide](./setup_pyproject.md) on using `pyproject.toml` for LangGraph Cloud.

!!! tip "Setup with a Monorepo"
    If you are interested in deploying a graph located inside a monorepo, take a look at [this](https://github.com/langchain-ai/langgraph-example-monorepo) repository for an example of how to do so.


The final repo structure will look something like this:

```bash
my-app/
├── my_agent # all project code lies within here
│   ├── utils # utilities for your graph
│   │   ├── __init__.py
│   │   ├── tools.py # tools for your graph
│   │   ├── nodes.py # node functions for you graph
│   │   └── state.py # state definition of your graph
│   ├── requirements.txt # package dependencies
│   ├── __init__.py
│   └── agent.py # code for constructing your graph
├── .env # environment variables
└── 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](#create-langgraph-api-config).

The dependencies below will be included in the image, you can also use them in your code, as long as with a compatible version range:
```
langgraph>=0.2.0,<0.3.0
langchain-core>=0.2.27,<0.3.0
langsmith>=0.1.63
orjson>=3.10.1
httpx>=0.27.0
tenacity>=8.3.0
uvicorn>=0.29.0
sse-starlette>=2.1.0
uvloop>=0.19.0
httptools>=0.6.1
jsonschema-rs>=0.18.0
croniter>=1.0.1
structlog>=24.4.0
```

Example `requirements.txt` file:
```
langgraph
langchain_anthropic
tavily-python
langchain_community
langchain_openai

```

Example file directory:
```bash
my-app/
├── my_agent # all project code lies within here
│   └── requirements.txt # package dependencies
```

## Specify Environment Variables

Environment variables can optionally be specified in a file (e.g. `.env`). See the [Environment Variables reference](../reference/env_var.md) 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:

```bash
my-app/
├── my_agent # all project code lies within here
│   └── requirements.txt # package dependencies
└── .env # 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][compiledgraph] to be included in the LangGraph application. The variable names will be used later when creating the [LangGraph API configuration file](../reference/cli.md#configuration-file).

Example `agent.py` file, which shows how to import from other modules you define (code for the modules is not shown here, please see [this repo](https://github.com/langchain-ai/langgraph-example) to see their implementation):


```python
# my_agent/agent.py
from typing import TypedDict, Literal

from langgraph.graph import StateGraph, END
from my_agent.utils.nodes import call_model, should_continue, tool_node # import nodes
from my_agent.utils.state import AgentState # import state

# Define the config
class GraphConfig(TypedDict):
    model_name: Literal["anthropic", "openai"]

workflow = StateGraph(AgentState, config_schema=GraphConfig)
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
workflow.set_entry_point("agent")
workflow.add_conditional_edges(
    "agent",
    should_continue,
    {
        "continue": "action",
        "end": END,
    },
)
workflow.add_edge("action", "agent")

graph = workflow.compile()
```

!!! warning "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 (alternatively, you can provide [a function that creates a graph](./graph_rebuild.md)).

Example file directory:
```bash
my-app/
├── my_agent # all project code lies within here
│   ├── utils # utilities for your graph
│   │   ├── __init__.py
│   │   ├── tools.py # tools for your graph
│   │   ├── nodes.py # node functions for you graph
│   │   └── state.py # state definition of your graph
│   ├── requirements.txt # package dependencies
│   ├── __init__.py
│   └── agent.py # code for constructing your graph
└── .env # environment variables
```

## Create LangGraph API Config

Create a [LangGraph API configuration file](../reference/cli.md#configuration-file) called `langgraph.json`. See the [LangGraph CLI reference](../reference/cli.md#configuration-file) for detailed explanations of each key in the JSON object of the configuration file.

Example `langgraph.json` file:
```json
{
  "dependencies": ["./my_agent"],
  "graphs": {
    "agent": "./my_agent/agent.py:graph"
  },
  "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>`).

!!! warning "Configuration Location"
    The LangGraph API configuration file must be placed in a directory that is at the same level or higher than the Python files that contain compiled graphs and associated dependencies.

Example file directory:

```bash
my-app/
├── my_agent # all project code lies within here
│   ├── utils # utilities for your graph
│   │   ├── __init__.py
│   │   ├── tools.py # tools for your graph
│   │   ├── nodes.py # node functions for you graph
│   │   └── state.py # state definition of your graph
│   ├── requirements.txt # package dependencies
│   ├── __init__.py
│   └── agent.py # code for constructing your graph
├── .env # environment variables
└── langgraph.json # configuration file for LangGraph
```

## Next

After you setup your project and place it in a github repo, it's time to [deploy your app](./cloud.md).
