---
title: What is Langflow?
slug: /
---

import Cards from "@site/src/components/Cards";
import Card from "@site/src/components/Card";
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Langflow is an open-source, Python-based, customizable framework for building AI applications.
It supports important AI functionality like agents and the Model Context Protocol (MCP), and it doesn't require you to use specific large language models (LLMs) or vector stores.

The visual editor simplifies prototyping of application workflows, enabling developers to quickly turn their ideas into powerful, real-world solutions.

:::tip Try it
Build and run your first flow in minutes: [Install Langflow](/get-started-installation), and then try the [Quickstart](/get-started-quickstart).
:::

<Cards cols={3}>
  <Card icon={<Icon name="Rocket" size={20} />} title="Quickstart" href="/get-started-quickstart">
    Build and run your first flow in minutes.
  </Card>
  <Card icon={<Icon name="Blocks" size={20} />} title="Components" href="/concepts-components">
    Explore all available components and integrations.
  </Card>
  <Card icon={<Icon name="Cloud" size={20} />} title="Deploy" href="/deployment-overview">
    Deploy Langflow to production environments.
  </Card>
</Cards>

## Application development and prototyping

Langflow can help you develop a wide variety of AI applications, such as chatbots, document analysis systems, content generators, and agentic applications.

Langflow includes several pre-built templates that are ready to use or customize to your needs.

### Create flows in minutes

The primary purpose of Langflow is to create and serve flows, which are functional representations of application workflows.

To [build a flow](/concepts-flows), you connect and configure component nodes. Each component is a single step in the workflow.

With Langflow's [visual editor](/concepts-overview), you can drag and drop components to quickly build and test a functional AI application workflow.
For example, you could build a chatbot flow for an e-commerce store that uses an LLM and a product data store to allow customers to ask questions about the store's products.

![Basic Prompting flow in Langflow](/img/workspace-basic-prompting.png)

### Test flows in real-time

You can use the [**Playground**](/concepts-playground) to test flows without having to build your entire application stack.
You can interact with your flows and get real-time feedback about flow logic and response generation.

You can also run individual components to test dependencies in isolation.

### Run and serve flows

You can use your flows as prototypes for more formal application development, or you can use the Langflow API to embed your flows into your application code.

For more extensive development, you can build Langflow as a dependency or deploy a Langflow server to serve flows over the public internet.

<Cards cols={2}>
  <Card icon={<Icon name="Zap" size={20} />} title="Trigger flows with the API" href="/concepts-publish">
    Use the Langflow API to run flows from your application code.
  </Card>
  <Card icon={<Icon name="Container" size={20} />} title="Containerize your app" href="/develop-application">
    Build and deploy Langflow as a containerized application.
  </Card>
</Cards>

## Endless modifications and integrations

Langflow provides [components](/concepts-components) that support many services, tools, and functionality that are required for AI applications.

Some components are generalized, such as inputs, outputs, and data stores.
Others are specialized, such as agents, language models, and embedding providers.

All components offer parameters that you can set to fixed or variable values. You can also use tweaks to temporarily override flow settings at runtime.

### Agent and MCP support

<Cards cols={2}>
  <Card icon={<Icon name="Bot" size={20} />} title="Langflow Agents" href="/agents">
    Build and configure AI agents with Langflow.
  </Card>
  <Card icon={<Icon name="Wrench" size={20} />} title="Agent tools" href="/agents-tools">
    Use components and flows as agent tools.
  </Card>
  <Card icon={<Icon name="Plug" size={20} />} title="MCP server" href="/mcp-server">
    Expose Langflow as an MCP server.
  </Card>
  <Card icon={<Icon name="Cable" size={20} />} title="MCP client" href="/mcp-client">
    Connect Langflow to external MCP servers.
  </Card>
</Cards>

### Extensibility

In addition to the core components, Langflow supports custom components.

You can use custom components developed by others, and you can develop your own custom components for personal use or to share with other Langflow users.

<Cards cols={3}>
  <Card icon={<Icon name="GitPullRequest" size={20} />} title="Contribute" href="/contributing-how-to-contribute">
    Help build Langflow.
  </Card>
  <Card icon={<Icon name="Code" size={20} />} title="Custom components" href="/components-custom-components">
    Create your own Python components.
  </Card>
  <Card icon={<Icon name="HelpCircle" size={20} />} title="Get help" href="/contributing-github-issues">
    Request enhancements and report issues.
  </Card>
</Cards>

## Next steps

* [Install Langflow](/get-started-installation)
* [Quickstart](/get-started-quickstart)
