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⛓️ Langflow

~ An effortless way to experiment and prototype LangChain pipelines ~

GitHub Contributors GitHub Last Commit GitHub Issues GitHub Pull Requests Github License

Discord Server HuggingFace Spaces

Table of Contents

📦 Installation

Locally

You can install Langflow from pip:

# This installs the package without dependencies for local models
pip install langflow

To use local models (e.g llama-cpp-python) run:

pip install langflow[local]

This will install the following dependencies:

You can still use models from projects like LocalAI

Next, run:

python -m langflow

or

langflow run # or langflow --help

HuggingFace Spaces

You can also check it out on HuggingFace Spaces and run it in your browser! You can even clone it and have your own copy of Langflow to play with.

🖥️ Command Line Interface (CLI)

Langflow provides a command-line interface (CLI) for easy management and configuration.

Usage

You can run the Langflow using the following command:

langflow run [OPTIONS]

Each option is detailed below:

  • --help: Displays all available options.
  • --host: Defines the host to bind the server to. Can be set using the LANGFLOW_HOST environment variable. The default is 127.0.0.1.
  • --workers: Sets the number of worker processes. Can be set using the LANGFLOW_WORKERS environment variable. The default is 1.
  • --timeout: Sets the worker timeout in seconds. The default is 60.
  • --port: Sets the port to listen on. Can be set using the LANGFLOW_PORT environment variable. The default is 7860.
  • --config: Defines the path to the configuration file. The default is config.yaml.
  • --env-file: Specifies the path to the .env file containing environment variables. The default is .env.
  • --log-level: Defines the logging level. Can be set using the LANGFLOW_LOG_LEVEL environment variable. The default is critical.
  • --components-path: Specifies the path to the directory containing custom components. Can be set using the LANGFLOW_COMPONENTS_PATH environment variable. The default is langflow/components.
  • --log-file: Specifies the path to the log file. Can be set using the LANGFLOW_LOG_FILE environment variable. The default is logs/langflow.log.
  • --cache: Selects the type of cache to use. Options are InMemoryCache and SQLiteCache. Can be set using the LANGFLOW_LANGCHAIN_CACHE environment variable. The default is SQLiteCache.
  • --dev/--no-dev: Toggles the development mode. The default is no-dev.
  • --path: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the LANGFLOW_FRONTEND_PATH environment variable.
  • --open-browser/--no-open-browser: Toggles the option to open the browser after starting the server. Can be set using the LANGFLOW_OPEN_BROWSER environment variable. The default is open-browser.
  • --remove-api-keys/--no-remove-api-keys: Toggles the option to remove API keys from the projects saved in the database. Can be set using the LANGFLOW_REMOVE_API_KEYS environment variable. The default is no-remove-api-keys.
  • --install-completion [bash|zsh|fish|powershell|pwsh]: Installs completion for the specified shell.
  • --show-completion [bash|zsh|fish|powershell|pwsh]: Shows completion for the specified shell, allowing you to copy it or customize the installation.

Environment Variables

You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a .env file and loaded using the --env-file option.

A sample .env file named .env.example is included with the project. Copy this file to a new file named .env and replace the example values with your actual settings. If you're setting values in both your OS and the .env file, the .env settings will take precedence.

Deployment

Deploy Langflow on Google Cloud Platform

Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the Langflow in Google Cloud Platform document.

Alternatively, click the "Open in Cloud Shell" button below to launch Google Cloud Shell, clone the Langflow repository, and start an interactive tutorial that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.

Open in Cloud Shell

Deploy on Railway

Deploy on Railway

Deploy on Render

Deploy to Render

🎨 Creating Flows

Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of LangChain components to choose from, including LLMs, prompt serializers, agents, and chains.

Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.

Once you're done, you can export your flow as a JSON file to use with LangChain. To do so, click the "Export" button in the top right corner of the canvas, then in Python, you can load the flow with:

from langflow import load_flow_from_json

flow = load_flow_from_json("path/to/flow.json")
# Now you can use it like any chain
flow("Hey, have you heard of Langflow?")

👋 Contributing

We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.

Join our Discord server to ask questions, make suggestions and showcase your projects! 🦾

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📄 License

Langflow is released under the MIT License. See the LICENSE file for details.