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Develop an application in Langflow

Follow this guide to learn how to build an application using Langflow. You'll learn how to set up a project directory, manage dependencies, configure environment variables, and package your Langflow application in a Docker image.

To deploy your application to Docker or Kubernetes, see Deployment.

Create a project directory​

Create a project directory similar to this one.


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LANGFLOW-APPLICATION/
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├── flows/
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│ ├── flow1.json
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│ └── flow2.json
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├── langflow-config-dir/
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├── docker.env
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├── Dockerfile
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├── README.md

The /flows folder holds the flows you want to host.

The langflow-config-dir is referenced in the Dockerfile as the location for Langflow's configuration files, database, and logs. For more information, see Environment variables.

The docker.env file is copied to the Docker image as a .env file in the container root. This file controls Langflow's behavior, holds secrets, and configures runtime settings like authentication, database storage, API keys, and server configurations.

The Dockerfile controls how your image is built. This file copies your flows and docker.env files to your image.

Package management​

The base Docker image includes the Langflow core dependencies by using langflowai/langflow:latest as the parent image.

If your application requires additional dependencies, create a pyproject.toml file and add the dependencies to the file. For more information, see Install custom dependencies.

To deploy the application with the additional dependencies to Docker, copy the pyproject.toml and uv.lock files to the Docker image by adding the following to the Dockerfile.


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COPY pyproject.toml uv.lock /app/

Environment variables​

The docker.env file is a .env file loaded into your Docker image.

The following example docker.env file defines auto-login behavior and which port to expose. Your environment may vary. For more information, see Environment variables.


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LANGFLOW_AUTO_LOGIN=true
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LANGFLOW_SAVE_DB_IN_CONFIG_DIR=true
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LANGFLOW_BASE_URL=http://0.0.0.0:7860
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OPENAI_API_KEY=sk-...

This example uses Langflow's default SQLite database for storage, and configures no authentication.

To modify Langflow's default memory behavior, see Memory.

To add authentication to your server, see Authentication.

Add flows and components​

Add your flow's .JSON files to the /flows folder.

To export your flows from Langflow, see Flows.

Optionally, add any custom components to a /components folder, and specify the path in your docker.env.

Package your Langflow project in a Docker image​

  1. Add the following commands to your Dockerfile.

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# Use the latest version of langflow
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FROM langflowai/langflow:latest
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# Create accessible folders and set the working directory in the container
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RUN mkdir /app/flows
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RUN mkdir /app/langflow-config-dir
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WORKDIR /app
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# Copy the flows, optional components, and langflow-config-dir folders to the container
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COPY flows /app/flows
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COPY components /app/components
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COPY langflow-config-dir /app/langflow-config-dir
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# copy docker.env file
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COPY docker.env /app/.env
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# Set environment variables
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ENV PYTHONPATH=/app
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ENV LANGFLOW_LOAD_FLOWS_PATH=/app/flows
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ENV LANGFLOW_CONFIG_DIR=/app/langflow-config-dir
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ENV LANGFLOW_COMPONENTS_PATH=/app/components
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ENV LANGFLOW_LOG_ENV=container
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# Command to run the server
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EXPOSE 7860
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CMD ["langflow", "run", "--backend-only", "--env-file","/app/.env","--host", "0.0.0.0", "--port", "7860"]

The environment variables set in the Dockerfile specify resource paths and allow Langflow to access them. Values from docker.env override the values set in the Dockerfile. Additionally, logging behavior is set here with ENV LANGFLOW_LOG_ENV=container for serialized JSON to stdout, for tracking your application's behavior in a containerized environment. For more information on configuring logs, see Logging.

note

Optionally, remove the --backend-only flag from the startup command to start Langflow with the frontend enabled. For more on --backend-only mode and the Langflow Docker image, see Docker.

  1. Save your Dockerfile.
  2. Build the Docker image:

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docker build -t langflow-pokedex:1.2.0 .

  1. Run the Docker container:

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docker run -p 7860:7860 langflow-pokedex:1.2.0

note

For instructions on building and pushing your image to Docker Hub, see Docker.

  1. Confirm the server is serving your flows. Open a .JSON file in your /flows folder and find the file's id value. It's the first value in the flow document.

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"id": "e4167236-938f-4aca-845b-21de3f399858",

  1. Add the file's id value as the flow-id to a POST request to the /run endpoint.

This command also uses a custom session_id value of charizard_test_request. By default, session IDs use the flow-id value. A custom session ID maintains a unique conversation thread, which keeps LLM contexts clean and can make debugging easier. For more information, see Session ID.


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curl --request POST \
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--url 'http://127.0.0.1:7860/api/v1/run/e4167236-938f-4aca-845b-21de3f399858?stream=false' \
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--header 'Content-Type: application/json' \
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--data '{
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"input_value": "Tell me about Charizard please",
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"output_type": "chat",
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"input_type": "chat",
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"session_id": "charizard_test_request"
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}'

If the flow streams the result back to you, your flow is being served, and can be consumed from a front-end application by submitting POST requests to this endpoint.

note

The test application returns a large amount of text, so the example command used ?stream=true. If you prefer, set ?stream=false to use batching. For more information, see the API examples.

Deploy to Docker Hub and Kubernetes​

For instructions on building and pushing your image to Docker Hub, see Docker.

To deploy your application to Kubernetes, see Kubernetes.

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