What it is

Langflow is a visual builder platform for designing, testing, and deploying AI agents and retrieval-augmented generation (RAG) pipelines. It lives in the Python-based generative AI ecosystem and targets developers building intelligent applications. The project provides a low-code interface to assemble components—such as LLMs, vector stores, memory modules, and tools—into executable workflows, while also exposing those workflows as APIs or MCP servers for integration into external applications.

Langflow solves the problem of high-friction prototyping and deployment of AI systems by eliminating boilerplate coding for common patterns like multi-turn conversations, tool calling, and document retrieval. Instead of writing and debugging Python scripts repeatedly, developers use drag-and-drop nodes to iterate quickly, then productionize the same flow via export or direct API invocation.

Key capabilities

• Visual canvas to assemble nodes representing LLMs, retrievers, memory, and custom Python components
• Interactive playground to execute flows step-by-step with real-time inspection of intermediate outputs
• Multi-agent orchestration with conversation state tracking and agent-to-agent messaging
• Export flows as Python code or JSON for embedding into existing applications
• Deploy flows as REST APIs or MCP-compatible servers for external tool consumption
• Observability integrations with LangSmith, LangFuse, and similar telemetry backends
• Full source code access to override or extend any node behavior in Python

Who uses it and how

• AI product teams prototype RAG chatbots and agent workflows visually before shipping to production
• Developers integrate Langflow-built agents into web or mobile apps by calling the auto-generated API endpoints
• Researchers and educators use the interactive playground to debug and demonstrate multi-step AI reasoning
• Enterprise teams deploy Langflow behind authentication layers and connect to internal vector databases and LLM endpoints

Getting started

Install via uv pip install langflow and run with uv run langflow run, or use the standalone Langflow Desktop app for Windows/macOS. Docker deployment uses docker run -p 7860:7860 langflowai/langflow:latest.

When to use it — and when not to

Langflow replaces manual Python scripting for prototyping and iterating on agent/RAG logic, but requires self-hosting infrastructure (e.g., database, storage, SMTP if email notifications needed). It is not a managed service—users must maintain the Langflow instance and its dependencies. For simple, static prompts or minimal deployments, direct LLM API calls may be lighter-weight. Langflow shines when workflows involve dynamic tool selection, memory, or multi-agent coordination.

uv pip install langflow -U

uv run langflow run

make run_cli

docker run -p 7860:7860 langflowai/langflow:latest