Langflow

What is Langflow?

Langflow is an open source visual builder for LLM-powered apps and AI agents. You drag components — models, prompts, vector stores, tools, and agents — onto a canvas, connect them into a flow, then test it in a built-in playground and deploy it as an API or MCP server. It’s MIT-licensed and built on Python.

What is Langflow best for?

Langflow is best for Python developers and teams who want to prototype RAG pipelines, chatbots, and multi-agent systems fast without wiring all the orchestration code by hand. Because every component exposes its underlying Python source, it suits people who start visually but expect to drop into code as the app gets serious.

What can Langflow do?

• Visual flow canvas — assemble agents and pipelines by connecting nodes, with an interactive playground for step-by-step testing.
• Model-agnostic — works with all major LLMs (OpenAI, Anthropic, local models, and more) and major vector databases.
• Editable Python components — inspect and rewrite the source of any node from inside the interface, or add custom Python components.
• Multi-agent orchestration — build flows where multiple agents coordinate, with conversation and memory management.
• Deploy as an API or MCP server — expose any flow as a REST endpoint, export it as JSON, or serve it to MCP clients.
• Observability hooks — plug into Langfuse or LangSmith to trace and debug runs.
• Langflow Desktop — a one-download desktop app that bundles dependencies for local development.

Where does Langflow fall short?

Self-hosting takes real effort — running it in production means managing infrastructure, scaling, and updates yourself, and setup can take hours rather than minutes. The visual layer adds a learning curve once flows grow complex, and debugging a large graph is harder than reading plain code. Running costs are also dominated by external LLM API usage and any vector database you add, so “free software” does not mean free to operate.

Is Langflow free?

Yes — Langflow is free and open source under the MIT license, with no licensing fees for the self-hosted version. There is also Langflow Cloud, a managed offering with a free tier for development; production cloud usage is paid and priced through sales. Even when self-hosting is free, you still pay for your own hosting, LLM API calls, and any vector database.

FAQ

Is Langflow open source? Yes. Langflow is released under the permissive MIT license, and the full source — including each component’s Python code — is on GitHub. It was originally built by Logspace and is now maintained under DataStax/IBM.

Can I self-host Langflow for free? Yes. The self-hosted version is free to run via Docker, the uv/pip package, or Langflow Desktop. You only pay for your own server, LLM API usage, and optional add-ons like a hosted vector database.

How does Langflow compare to Flowise? Both are open source visual LLM app builders. Flowise is built on a Node.js/TypeScript stack, while Langflow is Python-native and lets you edit each component’s Python directly — a better fit if your team already works in Python.

What do I need to run Langflow? Python 3.10–3.13 and the uv package manager (or Docker). Beyond that you supply API keys for whichever LLMs you use and, optionally, a vector database for RAG.