Langflow Review 2026

Low-code visual builder for agentic and RAG applications — drag-and-drop nodes to compose LLMs, vector DBs, tools, and MCP servers into deployable AI apps.

Langflow is an open-source, low-code visual environment for building LLM-powered agents and RAG pipelines. You wire together nodes on a canvas — model providers, prompts, memory, embedders, vector stores, tools, and MCP servers — and Langflow turns the graph into a runnable Python app or a REST endpoint. It's essentially what n8n is to workflows, but for LLM applications. The 1.10 release (mid-2026) leans hard into agent builders: you can create MCP-server nodes right on the canvas, share your Langflow projects as MCP servers other tools can consume, and use a growing library of pre-built tools (SQL, Python REPL, web search, browser, file I/O). It supports every major LLM (OpenAI, Anthropic, Google, Mistral, Groq, Ollama for local models) and every major vector DB (Pinecone, Weaviate, Chroma, Milvus, pgvector, Astra DB). The project is stewarded by DataStax and has 138k+ GitHub stars, 23k+ forks. It's Apache-2 licensed and free to self-host. There's also DataStax Langflow (managed cloud) with hosted flows, team collaboration, and Astra DB integration — pricing is bundled with DataStax's Astra plans rather than published as a standalone SKU. For teams that want to prototype agents visually and then hand a deploy-ready graph to engineering, Langflow has become the default open-source pick.