langflow-ai/langflow

🔥 Hot★ 148,459PythonAudience · developerComplexity · 3/5ActiveLicenseSetup · hard

Mindmap

mindmap
  root((Langflow))
    What it does
      Visual agent builder
      Deploy as API
      Multi-agent support
    Key features
      Interactive playground
      LLM integrations
      Vector databases
    Use cases
      Prototype workflows
      Share as MCP tool
      Test agent chains
    Tech stack
      Python backend
      React frontend
      Docker support
    Deployment
      Python package
      Desktop app
      Docker container

mindmap root((Langflow)) What it does Visual agent builder Deploy as API Multi-agent support Key features Interactive playground LLM integrations Vector databases Use cases Prototype workflows Share as MCP tool Test agent chains Tech stack Python backend React frontend Docker support Deployment Python package Desktop app Docker container

Things people build with this

USE CASE 1

Build and test multi-step AI workflows visually without writing backend code.

USE CASE 2

Deploy a trained agent as a REST API that other applications can call.

USE CASE 3

Create an MCP server from your workflow so it becomes a reusable tool for other AI systems.

USE CASE 4

Prototype and iterate on LLM chains by connecting components on a canvas and running them step-by-step.

Tech stack

PythonReactDockerLangChainFastAPI

Getting it running

Difficulty · hard Time to first run · 1h+

Requires Docker, multiple services (frontend, backend, potentially database), and LangChain integration setup.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license text.

In plain English

Langflow is a platform for building and deploying AI-powered agents and workflows. The basic problem it tackles is that wiring up large language models, tools, and data sources into a working agent normally takes a lot of custom code, and that experimenting with different combinations is slow. Langflow gives you a visual authoring experience on top of that work so that you can drag and connect components on a canvas, run them, and turn the result into something other applications can call. According to the README, Langflow comes with batteries included and supports all major LLMs, vector databases, and a growing library of AI tools. Highlight features listed include a visual builder interface for quickly getting started and iterating, source-code access in Python so you can customize any component, an interactive playground that lets you test and refine flows with step-by-step control, multi-agent orchestration with conversation management and retrieval, the option to deploy a flow as an API or export it as JSON for Python apps, and the option to deploy a flow as an MCP server so the flow becomes a tool for MCP clients. There are observability integrations with LangSmith and LangFuse, and the README also mentions enterprise-ready security and scalability. You can install Langflow as a Python package via uv (it requires Python 3.10 to 3.13), run it from source with make, or run it in Docker on port 7860. There is also a Langflow Desktop application for Windows and macOS that bundles all dependencies. You would use Langflow when you want to prototype an agent or chain visually, share it as a working API or MCP tool, and avoid managing too much custom infrastructure. The project is open source under the MIT license, written primarily in Python, and lists topics like agents, multi-agent, large language models, and react-flow.

Copy-paste prompts

Prompt 1

Show me how to create a simple retrieval-augmented generation (RAG) workflow in Langflow using a vector database and an LLM.

Prompt 2

How do I export a Langflow workflow as a Python script so I can integrate it into my existing application?

Prompt 3

Walk me through deploying a Langflow agent as an API endpoint that my frontend can call.

Prompt 4

How do I set up multi-agent orchestration in Langflow so multiple agents can collaborate on a task?

Prompt 5

Show me how to use Langflow Desktop to build and test an agent locally without Docker.

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