Flowise vs Langflow: Which Should You Self-Host?

Quick Verdict

Flowise is the better choice for most self-hosters who want to build AI agents and chatbots visually. It’s simpler to set up, lighter on resources, and has better documentation. Langflow is more powerful if you need advanced multi-agent orchestration, Python code integration, and the ability to deploy workflows as API endpoints or MCP servers.

Overview

Both are visual drag-and-drop tools for building LLM-powered workflows without writing code. They let you chain together LLMs, vector databases, tools, and data sources into AI agents, chatbots, and RAG pipelines.

Flowise — Apache 2.0 license. 35k+ GitHub stars. Built with Node.js (React frontend). Originally built on LangChain.js, now supports multiple frameworks. Focused on no-code chatbot and agent building.

Langflow — MIT license. 55k+ GitHub stars. Built with Python (React frontend). Created by DataStax. Built on LangChain with deep Python ecosystem integration. Focused on building and deploying AI applications as APIs.

Feature Comparison

Feature	Flowise	Langflow
Visual editor	Drag-and-drop nodes	Drag-and-drop nodes
LLM providers	OpenAI, Anthropic, Ollama, HuggingFace, 20+	OpenAI, Anthropic, Ollama, HuggingFace, 30+
RAG support	Yes (built-in)	Yes (built-in)
Vector DB support	Pinecone, Chroma, Qdrant, Weaviate, Milvus	Pinecone, Chroma, Qdrant, Weaviate, Milvus, Astra DB
Multi-agent workflows	Yes	Yes (advanced)
Custom code nodes	Limited (JS functions)	Yes (Python components)
API deployment	Yes (per-chatflow)	Yes (per-flow, MCP servers)
Chat interface	Built-in embed widget	Built-in playground
Authentication	API key, basic auth	API key, OAuth
Marketplace/templates	Yes (community templates)	Yes (Langflow Store)
LangSmith integration	Via LangChain	Native
Memory/conversation	Yes (multiple stores)	Yes (multiple stores)
Tool calling	Yes	Yes
Streaming	Yes	Yes
Multi-tenant	Limited	Yes
Runtime	Node.js	Python
Docker image	flowiseai/flowise	langflowai/langflow
Default port	3000	7860
License	Apache 2.0	MIT

Installation Complexity

Flowise is straightforward:

services:
  flowise:
    image: flowiseai/flowise:3.0.13
    container_name: flowise
    ports:
      - "3000:3000"
    volumes:
      - flowise_data:/root/.flowise
    environment:
      - FLOWISE_USERNAME=admin
      - FLOWISE_PASSWORD=changeme
      - APIKEY_PATH=/root/.flowise
      - SECRETKEY_PATH=/root/.flowise
    restart: unless-stopped

volumes:
  flowise_data:

Uses SQLite by default (no separate database container needed). Start it and immediately begin building chatflows.

Langflow is similarly simple:

services:
  langflow:
    image: langflowai/langflow:1.7.3
    container_name: langflow
    ports:
      - "7860:7860"
    volumes:
      - langflow_data:/app/langflow
    environment:
      - LANGFLOW_DATABASE_URL=sqlite:////app/langflow/langflow.db
      - LANGFLOW_CONFIG_DIR=/app/langflow
    restart: unless-stopped

volumes:
  langflow_data:

Also uses SQLite by default. Both support PostgreSQL for production.

Setup complexity is comparable. Flowise has a slight edge with better default configuration.

Performance and Resource Usage

Flowise (Node.js):

• Idle RAM: ~150-300 MB
• Under load: Depends on connected LLM backends
• Lightweight — the heaviest work happens on the LLM provider side

Langflow (Python):

• Idle RAM: ~300-600 MB
• Under load: Higher due to Python’s memory overhead
• Includes more built-in components which increase base footprint

Both are primarily frontend orchestration tools — the actual AI inference happens on whatever LLM backend you connect (Ollama, OpenAI, etc.). Flowise is lighter because Node.js has less overhead than Python for web serving.

Community and Support

Flowise: 35k+ stars. Active Discord. Growing community marketplace for shared workflows. Good documentation with video tutorials. Created by Henry Heng.

Langflow: 55k+ stars. Backed by DataStax (commercial entity). Active community. Langflow Store for sharing components. More enterprise-focused documentation. Regular releases.

Langflow has the larger community and corporate backing. Flowise has a more focused, grassroots community.

Use Cases

Choose Flowise If…

• You want the simplest no-code AI chatbot builder
• You’re building customer support bots or RAG chatbots
• You want an embeddable chat widget for your website
• You prefer a lighter resource footprint
• You want to get started quickly without Python knowledge
• You’re building straightforward chatflows (not complex multi-agent systems)

Choose Langflow If…

• You need advanced multi-agent orchestration
• You want to write custom Python components
• You need to deploy AI workflows as REST APIs or MCP servers
• You want LangSmith/LangFuse observability integration
• You need multi-tenant support for different teams
• You’re building complex AI applications beyond chatbots
• You want DataStax/Astra DB integration for vector search

Final Verdict

Flowise is the right choice for building chatbots and simple AI agents. If you want to visually wire together an LLM, a vector database, and some tools to create a chatbot — Flowise does this with minimal fuss. The embedded chat widget makes deployment to websites trivial.

Langflow is the right choice for building AI applications. If you need to deploy flows as API endpoints, write custom Python components, or orchestrate complex multi-agent systems — Langflow’s deeper Python integration and enterprise features make it more capable. The trade-off is higher resource usage and complexity.

For most self-hosters building their first AI chatbot: Flowise. For developers building AI-powered applications: Langflow.

Related

• How to Self-Host Flowise
• How to Self-Host Langflow
• Ollama vs LocalAI
• Self-Hosted ChatGPT Alternatives
• Best Self-Hosted AI Tools
• Docker Compose Basics