Flowise vs Langflow: Which AI Builder Should You Use in 2026?

At a Glance: Core Differences

Flowise and Langflow share a common ancestor, LangChain, but diverge in language, architecture, and target audience. The table below shows the fundamental differences before going deeper.

The language difference has a practical consequence beyond aesthetics. If your team writes Python for ML work, Langflow integrations are easier to extend. If your team works in JavaScript and deploys to Node.js infrastructure, Flowise components feel native.

Both tools wrap LangChain abstractions, so if you know concepts like chains, retrievers, vector stores, and memory from LangChain documentation, those map directly to nodes in both canvases.

Installation: Docker Setup for Both Tools

Both tools install cleanly via Docker. The commands below get each tool running in under five minutes.

Install Flowise

# Pull the latest Flowise image (Node.js, ~600 MB)
docker run -d \
  -p 3001:3001 \
  --name flowise \
  -v ~/.flowise:/root/.flowise \
  flowiseai/flowise

Access at `http://localhost:3001`. For production with authentication:

docker run -d \
  -p 3001:3001 \
  --name flowise \
  -v ~/.flowise:/root/.flowise \
  -e FLOWISE_USERNAME=admin \
  -e FLOWISE_PASSWORD=your_password_here \
  flowiseai/flowise

For the full Flowise Docker Compose setup with PostgreSQL and reverse proxy, see the Flowise Docker installation guide.

Install Langflow

# Pull the latest Langflow image (Python, ~1.2 GB, first pull takes longer)
docker run -d \
  -p 7860:7860 \
  --name langflow \
  -v langflow-data:/var/lib/langflow \
  langflowai/langflow:latest

Access at `http://localhost:7860`. For a production deployment with an external database:

docker run -d \
  -p 7860:7860 \
  --name langflow \
  -v langflow-data:/var/lib/langflow \
  -e LANGFLOW_DATABASE_URL=postgresql://user:pass@host:5432/langflow \
  -e LANGFLOW_SECRET_KEY=your_secret_key \
  langflowai/langflow:latest

Docker Compose for Langflow with PostgreSQL

version: '3.8'

services:
  langflow:
    image: langflowai/langflow:latest
    container_name: langflow
    restart: unless-stopped
    ports:
      - "7860:7860"
    environment:
      - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@db:5432/langflow
      - LANGFLOW_SECRET_KEY=change_this_secret
    volumes:
      - langflow-data:/var/lib/langflow
    depends_on:
      - db

  db:
    image: postgres:16
    container_name: langflow-db
    environment:
      POSTGRES_USER: langflow
      POSTGRES_PASSWORD: langflow
      POSTGRES_DB: langflow
    volumes:
      - langflow-postgres:/var/lib/postgresql/data

volumes:
  langflow-data:
  langflow-postgres:

Feature-by-Feature Comparison

The table below covers the features that matter most when choosing a builder for a real project.

The most significant functional gap is multi-agent support. Langflow supports agent-to-agent communication where one agent delegates subtasks to a specialist agent. Flowise runs one agent per flow and routes between flows via API. For applications that need a supervisor-agent orchestrating multiple specialist agents, Langflow's architecture fits better.

For RAG chatbots and single-agent automations, both tools are equivalent in capability. The choice comes down to the language your team extends components in.

Which Tool to Choose for Your Use Case

The decision between Flowise and Langflow depends on the type of application, the team's technical background, and whether you need cloud hosting.

If you need to pick one to start with and do not have a specific multi-agent requirement: Flowise is faster to get to a working application. If your project will grow into agentic complexity over time, start with Langflow and avoid migrating later.

Both tools expose a REST API from every flow. This means the underlying tool can be swapped without changing the downstream application that consumes the API, if your requirements shift.

Performance, Memory, and Scaling

At idle, Flowise consumes approximately 200-300 MB RAM. Langflow consumes 400-600 MB RAM at idle due to the Python runtime and loaded dependencies. Under active use with concurrent users both increase significantly based on flow complexity and model response size.

These figures are measured on a 4-core VPS with NVMe storage. They exclude the memory used by the LLM backend (Ollama or remote API calls).

For horizontal scaling, both tools support stateless deployments when configured with an external PostgreSQL database. With SQLite (the default), each container has its own flow storage and cannot share state across instances. Switch to PostgreSQL before scaling beyond one container.

Langflow's Python runtime adds cold-start latency on the first request after a period of inactivity, approximately 500ms-1s for a warm flow versus near-zero for Flowise. For high-frequency, latency-sensitive applications, this matters. For typical chatbot use (one conversation per session), it is negligible.

Community, Documentation, and Support

Both projects are actively maintained. Langflow has more GitHub stars but Flowise has a more cohesive community for beginners because of its smaller surface area.

Flowise's documentation covers common use cases clearly. The integration guides for most vector stores and LLM providers include screenshots and copy-paste configs. For advanced custom component development, the documentation is thinner and the Discord community is the primary resource.

Langflow's documentation is more technically complete but assumes more background knowledge. The Python SDK for custom components is well documented. Langflow Cloud (the Datastax-managed version) has dedicated support tiers.

For troubleshooting production issues, both projects have active GitHub issue trackers. Search the issue tracker before filing a new bug, most common Docker and database connection errors have existing threads with verified fixes.

Both tools have active subreddits: r/LangChain covers Flowise and Langflow questions alongside general LangChain discussion. r/selfhosted covers Docker deployment issues for both.

Troubleshooting

Frequently Asked Questions

docker pull flowiseai/flowise
docker stop flowise && docker rm flowise
docker run -d -p 3001:3001 --name flowise -v ~/.flowise:/root/.flowise flowiseai/flowise

docker pull langflowai/langflow:latest
docker stop langflow && docker rm langflow
docker run -d -p 7860:7860 --name langflow -v langflow-data:/var/lib/langflow langflowai/langflow:latest