LangFlow

Visual builder for LangChain style flows, agents and RAG pipelines

AI Agents & AutomationFree planOverseasβ˜…β˜…β˜…β˜…β˜† 4.0

What is LangFlow?

LangFlow is an open-source visual editor for LLM applications, originally built as a way to assemble LangChain components without writing the wiring by hand. You drag model, prompt, retriever, tool and memory components onto a canvas, connect them, and immediately test the result in a chat pane. Flows can be exported as JSON or as Python code, which means a prototype built visually can later be moved into a codebase. It self-hosts easily and supports local models, making it a common first stop for teams exploring retrieval and agent patterns.

Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.

Key features

  • Visual canvas over LLM, prompt, retriever and tool components
  • Live chat pane for testing a flow as you build it
  • Export a flow as JSON or as runnable Python code
  • Agent and tool-calling components for autonomous steps
  • Local model support through common inference servers
  • Self-hosting with Docker and a Python package

Pros & cons

Strengths

  • Open source and free to self-host on your own hardware
  • Exporting to Python avoids being locked into the canvas
  • Immediate test pane shortens the edit and verify loop

Watch out for

  • Component-heavy graphs become hard to read at scale
  • Tied to the LangChain ecosystem and its pace of change
  • Production deployment is left to the user

Best for & use cases

prototyping rag apps, learning llm patterns, internal assistants and demos

If you're comparing similar products, check the alternatives below, or browse all tools in the AI Agents & Automation category.

FAQ

Do I need to know LangChain?

Not to start. It helps when a flow grows, because the components mirror LangChain concepts and you will want to understand what each one is doing rather than only how it connects.

Can I move a flow into my own codebase?

Yes. Flows export as Python code as well as JSON, which is the usual path from a visual prototype to something you maintain and deploy yourself.

Is it suitable for production?

It can serve production traffic, but scaling, secrets management and monitoring are your responsibility when self-hosting. Most teams use it to design and validate, then deploy in code.