Langflow — Agents-OSS

Overview

Useful for sketching an agent visually and for showing non-engineers what a pipeline actually does, then exporting it as an API. Visual editors get unwieldy as branching grows, so treat it as a design and demo surface rather than the final home for complex logic.

What can you do with Langflow?

Build a flow in the browser — uv pip install langflow -U followed by uv run langflow run serves the visual editor at 127.0.0.1:7860, and docker run -p 7860:7860 langflowai/langflow:latest gives the same thing in a container.
Step through a flow while testing — The interactive playground runs a flow with step-by-step control, which is how you find the node where a branch produces the wrong output.
Publish a flow as an API or MCP server — A finished flow can be deployed as an API, exported as JSON to embed in a Python app, or served as an MCP server so MCP clients call it as a tool.
Edit component source in Python — Source code access means a built-in component's Python can be modified directly rather than worked around with adjacent nodes.
Send traces to existing observability — Built-in integrations forward execution traces to LangSmith, LangFuse and other tools, so flows report into the stack you already run.

Documentation

Reproduced from the langflow-ai/langflow README, published under MIT. Read the original ↗

Langflow is a powerful platform for building and deploying AI-powered agents and workflows. It provides developers with both a visual authoring experience and built-in API and MCP servers that turn every workflow into a tool that can be integrated into applications built on any framework or stack. Langflow comes with batteries included and supports all major LLMs, vector databases and a growing library of AI tools.

✨ Highlight features

Visual builder interface to quickly get started and iterate.
Source code access lets you customize any component using Python.
Interactive playground to immediately test and refine your flows with step-by-step control.
Multi-agent orchestration with conversation management and retrieval.
Deploy as an API or export as JSON for Python apps.
Deploy as an MCP server and turn your flows into tools for MCP clients.
Observability with LangSmith, LangFuse and other integrations.
Enterprise-ready security and scalability.

🖥️ Langflow Desktop

Langflow Desktop is the easiest way to get started with Langflow. All dependencies are included, so you don’t need to manage Python environments or install packages manually. Available for Windows and macOS.

📥 Download Langflow Desktop

⚡️ Quickstart

Install locally (recommended)

Requires Python 3.10–3.14 and uv (recommended package manager).

Install

From a fresh directory, run:

uv pip install langflow -U

The latest Langflow package is installed. For more information, see Install and run the Langflow OSS Python package.

Run

To start Langflow, run:

uv run langflow run

Langflow starts at http://127.0.0.1:7860.

That’s it! You’re ready to build with Langflow! 🎉

📦 Other install options

Run from source

If you’ve cloned this repository and want to contribute, run this command from the repository root:

make run_cli

For more information, see DEVELOPMENT.md.

Docker

Start a Langflow container with default settings:

docker run -p 7860:7860 langflowai/langflow:latest

Langflow is available at http://localhost:7860/. For configuration options, see the Docker deployment guide.

🛡️ Security

For security information, see our Security Policy.

🚀 Deployment

Langflow is completely open source and you can deploy it to all major deployment clouds. To learn how to deploy Langflow, see our Langflow deployment guides.

⭐ Stay up-to-date

Star Langflow on GitHub to be instantly notified of new releases.

Star Langflow

👋 Contribute

We welcome contributions from developers of all levels. If you’d like to contribute, please check our contributing guidelines and help make Langflow more accessible.