Langflow is an open-source, low-code platform for visually designing, building, and deploying AI-powered agents and RAG workflows. With approximately 146K GitHub stars, it has become the fastest-growing visual AI builder, going from zero to 146K stars in just three years.

low-code visual-builder rag agents langchain python

Langflow provides a drag-and-drop visual interface built on top of the LangChain framework, enabling rapid prototyping of complex AI pipelines without extensive coding. It bridges the gap between LangChain's powerful Python API and accessibility for non-engineers, reducing AI application development time from weeks to hours. Langflow supports RAG workflows, multi-agent orchestration, and deployment as REST APIs, making it suitable for both prototyping and production use cases.

• Visual Drag-and-Drop Builder — Node-based canvas where LangChain primitives (prompts, retrievers, agents) are connected via data flows
• RAG Workflows — Native support for document ingestion, vector store retrieval, and grounded generation
• Multi-Agent Orchestration — Conversational agents with memory management, tool use, and stateful interactions
• MCP Support — Model Context Protocol integration for agentic standards
• REST API Deployment — One-click export of flows as production API endpoints
• LangGraph Integration — Stateful, cyclical workflows with conditional branching
• Component Ecosystem — Bundles for WatsonxAI, Google GenAI, Composio, JigsawStack, Cleanlab AI
• PWA Support — Progressive web app functionality via web app manifests

Langflow's architecture layers a React-based visual frontend on top of a Python/LangChain backend:

Using Langflow's Python API to run a flow programmatically:

import requests
 
LANGFLOW_URL = "http://localhost:7860"
FLOW_ID = "your-flow-id"
 
def run_langflow_agent(query: str) -> str:
    """Execute a Langflow flow via its REST API."""
    endpoint = f"{LANGFLOW_URL}/api/v1/run/{FLOW_ID}"
    payload = {
        "input_value": query,
        "output_type": "chat",
        "input_type": "chat",
        "tweaks": {}
    }
    response = requests.post(endpoint, json=payload)
    response.raise_for_status()
    data = response.json()
    return data["outputs"][0]["outputs"][0]["results"]["message"]["text"]
 
result = run_langflow_agent("What are the benefits of RAG?")
print(result)

Langflow is explicitly built on top of LangChain, serving as its visual companion:

• Exposes LangChain components (agents, chains, memory, RAG, tools) through drag-and-drop
• Depends on LangChain/LangSmith libraries and LangGraph for advanced workflows
• Enables non-engineers to prototype production-ready flows
• LangChain handles underlying execution while Langflow provides the visual abstraction

• Version 1.4.x with frequent iterations on UI/UX and performance
• Dynamic theming, voice mode, multi-model streaming
• PWA support and Langflow Desktop
• DataFrame operations with modern UI and filters
• Bulk actions for flows/files management
• Optimized GenericNode performance for large flows

• GitHub Repository
• Official Website
• Documentation

• LangChain — The framework Langflow is built upon
• n8n — Visual workflow automation platform
• Haystack — Pipeline-based AI framework