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Langflow

langflow-ai · Workflow automation

GitHub stars

155,410
—Collecting 30d data

Stats updated

155,301 stars on Sep 27 to 155,410 stars on Oct 1, up 109.5 measured star snapshots

What is Langflow?

Langflow represents an AI application as a flow of configurable components. Builders can connect models, prompts, agents, tools, data stores, and custom Python components in the visual editor, test either a component or the complete flow, and then serve the result through an API or MCP interface.

Langflow is an open-source, Python-based platform for building AI agents and workflows in a visual editor. It includes API and MCP servers so a flow can be integrated into applications or used as a tool.

Core capabilities

  • Compose AI application flows from configurable visual components
  • Build agents and use flows or components as agent tools
  • Serve flows through an API or expose them as an MCP server

Components with fast feedback

Each component handles one step and exposes configurable parameters that can be fixed or changed at runtime. The editor joins those components into a flow, while individual runs and the interactive playground help isolate dependencies and inspect application behavior during prototyping.

  • components
  • visual editor
  • playground
  • testing

APIs, MCP, and custom components

A flow can graduate from a prototype into a served application without abandoning its graph. Langflow provides an API, MCP client and server support, built-in integrations, and custom Python components, giving teams several ways to embed the same workflow or extend it with internal logic.

  • API
  • MCP
  • Python
  • custom components

Where it fits

Use cases

  1. 01

    Prototype an AI application visually

    Connect model, prompt, retrieval, input, output, and agent components in the editor, run individual nodes to isolate problems, and use the playground to test the complete application before writing an integration.

  2. 02

    Embed a tested flow in a product

    Run a Langflow server and trigger a reviewed flow through its API so an application can reuse the same orchestration that was assembled and tested in the visual editor.

  3. 03

    Connect agent tools through MCP

    Expose a Langflow project as an MCP server, connect external MCP servers as clients, or turn components and complete flows into tools that an agent can call during a larger workflow.