Langflow is an open-source, low-code platform for building AI agents and retrieval-augmented generation (RAG) applications by connecting components on a visual canvas, written in Python.
Each flow in Langflow is a graph of components such as models, prompts, vector stores and tools. You can test it in a built-in playground and then serve it as an API, which shortens the path from prototype to production for LLM applications.
Key features
- Drag-and-drop canvas for composing LLM workflows
- Agents that can use your components as tools
- Components for major model providers and vector databases
- Interactive playground for testing flows
- Every flow can be served as an API
- MCP support so flows can act as tools for other AI clients
- Custom components written in Python
- Self-hosting, plus a desktop app and a free cloud account option
Who it's for
Langflow fits AI engineers, data scientists and developers who want to prototype and ship agents and RAG pipelines quickly, as well as teams that prefer a visual approach to LLM orchestration.
Common use cases
- Building a chatbot over company documents
- Prototyping multi-agent workflows
- Exposing an AI workflow as an API endpoint
- Creating MCP tools for AI assistants
Pricing
Langflow is free and open source. You can self-host it, and the official site also offers a free cloud account for deploying flows. For enterprise hosting options, check the official site.
Alternatives
- Dify: open-source LLM app platform with visual workflows
- n8n: workflow automation with AI nodes
- Flowise: open-source drag-and-drop LLM builder
FAQ
Is Langflow free?
Yes. Langflow is free and open source, and a free cloud account is available.
What is Langflow used for?
Langflow is used to visually build AI agents, RAG apps and LLM workflows and deploy them as APIs.
Langflow vs Flowise: which is better?
Both are visual LLM builders. Langflow is Python-based with strong agent and MCP support, while Flowise is built on Node.js; the choice usually depends on your stack.
Langflow vs Dify: what's the difference?
Dify is a full LLM app platform with built-in app publishing and monitoring, while Langflow focuses on flexible, component-based flow building.
Can I self-host Langflow?
Yes. Langflow can be installed with pip or Docker and run on your own infrastructure.
Does Langflow support MCP?
Yes. Langflow supports the Model Context Protocol so flows can be used as tools by MCP clients.