Build and connect AI agents and workflows visually
Create complex AI logic by connecting blocks without writing code, and test and modify the finished flow immediately. Langflow provides a visual editor and an API server, allowing you to convert built workflows into tools for applications using any framework. It supports major LLMs and vector databases out of the box, so you can start without additional configuration.
Combining visual editing and code access
Langflow offers a drag-and-drop visual builder that lets you quickly assemble and iteratively improve workflows. When needed, you can access the Python source directly to customize components. An interactive playground allows you to test the flow step by step, see results immediately, and make fine adjustments. This structure enables you to move from prototyping to production deployment within the same environment.
Deployment and integration methods
Completed workflows can be deployed to an API server or exported as JSON for use in Python applications. Deploying as an MCP server allows MCP clients to invoke the workflow as a tool. It integrates with various observability tools such as LangSmith and LangFuse to track system status. It supports enterprise-level security and scalability, and can be deployed to major cloud environments.
Installation and execution
For local installation, you need Python versions 3.10 through 3.14 and the uv package manager. Run the uv pip install langflow -U command in a new directory to install the latest package. Execute the uv run langflow run command to start the service in your local environment. Desktop apps for Windows and macOS are also available, allowing you to use the tool immediately without setting up a Python environment or installing packages. There is also an option to run it via a Docker container.
Suitable use cases
It is suitable for teams that want to rapidly prototype AI workflows or need a visual interface that allows non-developers to participate. It is useful for developers who want to integrate AI features into existing applications by connecting via standardized APIs or MCP tools rather than writing complex backend code directly. It is a good choice when you need Python-based customization but want to start with visual editing in the initial stages. As an open-source project, it can be freely configured to fit your deployment environment.
