# Langflow — Visual AI Workflow Builder > Low-code visual builder for AI workflows and RAG pipelines. Drag-and-drop components for LLMs, vector stores, tools, and agents with Python extensibility. ## Install Save the content below to `.claude/skills/` or append to your `CLAUDE.md`: ## Quick Use ```bash pip install langflow langflow run ``` Open `http://localhost:7860` — drag, drop, and connect AI components visually. ## What is Langflow? Langflow is a low-code visual builder for AI workflows. It provides a drag-and-drop interface for assembling LLMs, vector stores, embedding models, tools, and agents into complex pipelines — then exports them as Python code or API endpoints. Built on LangChain with full Python extensibility. **Answer-Ready**: Langflow is a low-code visual AI workflow builder with drag-and-drop components for LLMs, vector stores, tools, and agents. Export workflows as Python code or REST APIs. Built on LangChain with 50k+ GitHub stars. **Best for**: Teams prototyping RAG pipelines and AI workflows visually. **Works with**: OpenAI, Anthropic, Google, HuggingFace, Ollama, Pinecone, Weaviate. **Setup time**: Under 3 minutes. ## Core Features ### 1. Visual Flow Editor Drag and drop components: ``` [Input] → [Claude Sonnet] → [Vector Search] → [Output] ↑ [Weaviate DB] ``` Components include: Chat models, embeddings, vector stores, tools, agents, retrievers, text splitters, and custom Python nodes. ### 2. Pre-Built Templates Start from templates: - **Basic RAG**: Upload docs → embed → retrieve → answer - **Multi-Agent Chat**: Multiple specialized agents collaborating - **Data Pipeline**: Ingest → transform → store → query - **Customer Support**: Knowledge base + chat + escalation ### 3. API Export Every flow becomes a REST API: ```bash # After building your flow curl -X POST http://localhost:7860/api/v1/run/your-flow-id \ -H "Content-Type: application/json" \ -d '{"input_value": "What is RAG?"}' ``` ### 4. Custom Components ```python from langflow.custom import Component from langflow.io import MessageTextInput, Output class MyCustomNode(Component): display_name = "Custom Processor" inputs = [MessageTextInput(name="input_text", display_name="Input")] outputs = [Output(display_name="Output", name="output", method="process")] def process(self) -> str: text = self.input_text return text.upper() # Your custom logic ``` ### 5. Multi-Model Support | Provider | Models | |----------|--------| | Anthropic | Claude Sonnet, Opus, Haiku | | OpenAI | GPT-4o, o1 | | Google | Gemini 2.5 | | Local | Ollama, HuggingFace | | Vector DBs | Pinecone, Weaviate, Chroma, Qdrant | ## Deployment Options ```bash # Local langflow run # Docker docker run -p 7860:7860 langflowai/langflow # Cloud # DataStax Langflow (managed) ``` ## FAQ **Q: Is Langflow free?** A: Open-source and free. DataStax offers a managed cloud version. **Q: Do I need to know Python?** A: No for basic flows. Python knowledge helps for custom components and advanced configurations. **Q: How does it compare to n8n or Zapier?** A: Langflow is AI-native — designed for LLM workflows, RAG pipelines, and agents. n8n/Zapier are general automation tools. ## Source & Thanks > Created by [Langflow AI](https://github.com/langflow-ai). Licensed under MIT. > > [langflow-ai/langflow](https://github.com/langflow-ai/langflow) — 50k+ stars ## Quick Start ```bash pip install langflow langflow run ``` Open `localhost:7860` in your browser and drag-and-drop components to build AI workflows. ## What is Langflow? Langflow is a low-code visual AI workflow builder. Drag-and-drop components to compose LLMs, vector stores, tools, and agents — export as Python code or an API. **In one sentence**: Low-code visual AI workflow builder — drag-and-drop components to build RAG pipelines and agent workflows — 50k+ GitHub stars. **For**: Teams prototyping RAG and AI workflows visually. ## Core Features ### 1. Visual Flow Editor Drag to connect LLMs, vector stores, tools, and other components. ### 2. Prebuilt Templates Out-of-the-box RAG, multi-agent chat, data pipelines, and more. ### 3. API Export Every workflow automatically becomes a REST API. ### 4. Custom Components Write custom processing nodes in Python. ## FAQ **Q: Is it free?** A: Open source and free; DataStax offers a hosted cloud version. **Q: Do I need to know Python?** A: Not for basic flows; custom components require it. ## Source & Thanks > [langflow-ai/langflow](https://github.com/langflow-ai/langflow) — 50k+ stars, MIT --- Source: https://tokrepo.com/en/workflows/langflow-visual-ai-workflow-builder-7f8a8ada Author: Agent Toolkit