Langflow ​

Langflow is a visual IDE for building AI agents and RAG pipelines. It combines the approachability of a drag-and-drop builder with the power of full Python extensibility. Components snap together like building blocks — LLMs, retrievers, tools, memory, and now MCP servers — and you can open any component to customize its Python code.

Lf
Langflow
DataStax · langflow.org
TRANSPORT
Streamable HTTP ✓
PLATFORM
Web · Self-hosted · Cloud
MCP VIA
MCP Component

Visual IDE: Drag-and-Drop Meets Python ​

Langflow bridges visual building and code. Start by dragging components — an LLM, a retriever, an MCP server tool — and wiring them together. If you need custom behavior, click into any component and edit its Python implementation. This hybrid approach works for both no-code users and Python developers.

The MCP component connects your agent or RAG pipeline to remote tool servers. The agent reasons about its task, calls MCP tools for external data, and feeds results into the rest of the pipeline — all visible as connected nodes on the canvas.

What Langflow provides:

  • Visual IDE — drag-and-drop component assembly with live preview
  • Python extensibility — edit any component's Python source inline
  • Agent builder — multi-step reasoning with tool selection
  • RAG pipelines — document ingestion, vector search, and retrieval chains
  • API auto-generation — every flow becomes a deployable API endpoint
  • Component marketplace — community-built and shared components
  • DataStax-backed — enterprise support and cloud hosting available

Connecting MCP ​

1. Create a Token ​

In Vinkius Cloud, go to your server → Connection Tokens → Create. Copy the URL.

2. Add MCP Component ​

In the Langflow workspace, drag the MCP Server component from the tool palette. Configure it with your URL:

https://edge.vinkius.com/{TOKEN}/mcp

3. Wire and Deploy ​

Connect the MCP component to your agent or pipeline nodes. Test in the playground, then deploy as an API endpoint.

FAQ ​

Can I customize MCP tool behavior in Langflow? Yes. Click into the MCP component to edit its Python implementation. Add preprocessing, caching, or transformation logic to tool inputs and outputs.

Does Langflow support RAG and MCP together? Yes. Wire MCP tools alongside RAG components. The agent can query your document index and call external MCP tools in the same flow.

Is Langflow suitable for production? Yes. Langflow generates API endpoints from your flows. DataStax offers cloud hosting for enterprise-grade deployments.

Is Langflow free? Open-source under Apache 2.0. Langflow Cloud by DataStax has free and paid tiers.