Skip to main content
To deploy on LangSmith, an application must consist of one or more graphs, a configuration file (langgraph.json), a file that specifies dependencies, and an optional .env file that specifies environment variables. This page explains how a LangSmith application is organized and how to provide the configuration details required for deployment.

​
Key Concepts

To deploy using LangSmith, provide the following information:
  1. A configuration file (langgraph.json) that specifies the dependencies, graphs, and environment variables to use for the application.
  2. The graphs that implement the logic of the application.
  3. A file that specifies dependencies required to run the application.
  4. Environment variables that are required for the application to run.
Framework agnosticYou don’t need to use LangGraph to build your app to deploy on the LangSmith. LangSmith’s deployable unit is a LangGraph graph, but your graph can be a thin adapter around any codebase or framework. This lets you keep your core application logic outside LangGraph while still using LangSmith for deployment, scaling, and observability.

​
File Structure

The following are examples of directory structures for Python and JavaScript applications:
  • Python (requirements.txt)
  • Python (pyproject.toml)
  • JS (package.json)
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 your graph
│   │   └── state.py # state definition of your graph
│   ├── __init__.py
│   └── agent.py # code for constructing your graph
├── .env # environment variables
├── requirements.txt # package dependencies
└── langgraph.json # configuration file for LangGraph
The directory structure of an application can vary depending on the programming language and the package manager used.

​
Configuration File

The langgraph.json file is a JSON file that specifies the dependencies, graphs, environment variables, and other settings required to deploy an application. For details on all supported keys in the JSON file, refer to the LangGraph configuration file reference.
The LangGraph CLI defaults to using the configuration file langgraph.json in the current directory.

​
Examples

  • Python
  • JavaScript
  • The dependencies involve a custom local package and the langchain_openai package.
  • A single graph will be loaded from the file ./your_package/your_file.py with the variable variable.
  • The environment variables are loaded from the .env file.
{
    "dependencies": [
        "langchain_openai",
        "./your_package"
    ],
    "graphs": {
        "my_agent": "./your_package/your_file.py:agent"
    },
    "env": "./.env"
}

​
Dependencies

An application may depend on other Python packages or JavaScript libraries (depending on the programming language in which the application is written). You will generally need to specify the following information for dependencies to be set up correctly:
  1. A file in the directory that specifies the dependencies (e.g., requirements.txt, pyproject.toml, or package.json).
  2. A dependencies key in the configuration file that specifies the dependencies required to run the application.
  3. Any additional binaries or system libraries can be specified using dockerfile_lines key in the LangGraph configuration file.

​
Graphs

Use the graphs key in the configuration file to specify which graphs will be available in the deployed application. You can specify one or more graphs in the configuration file. Each graph is identified by a unique name and a path to either (1) a compiled graph or (2) a function that defines a graph.

​
Environment Variables

If you’re working with a deployed LangGraph application locally, you can configure environment variables in the env key of the configuration file. For a production deployment, you will typically want to configure the environment variables in the deployment environment.
Edit the source of this page on GitHub
⌘I