n8n vs Flowise: AI Workflow Builder Comparison 2026

n8n vs Flowise: TL;DR

Choose n8n if: you need AI as one part of a broader workflow connecting CRMs, databases, webhooks, and third-party APIs.

Choose Flowise if: you're building LLM-first products — RAG chatbots, multi-agent pipelines, or anything where the model is the core logic.

What We're Comparing

Both n8n and Flowise are open-source, self-hostable, visual workflow builders with strong AI support. In 2026, both tools have converged on similar territory — but they started from opposite ends. n8n is a general automation platform that added AI. Flowise was built specifically for LLM workflows from day one. That origin difference shapes everything.

n8n Overview

n8n is a general-purpose workflow automation tool with over 400 integrations. It lets you wire together APIs, databases, messaging platforms, and AI models in a visual node editor, with full JavaScript and Python execution inside nodes.

The AI Agent node (released in 2024, matured in 2025) lets you drop an LLM into any workflow with tool-calling, memory, and structured output support. n8n Cloud runs on a fair-use execution model — you pay per workflow run, not per seat.

Pros:

• 400+ integrations including Salesforce, HubSpot, PostgreSQL, Slack, and Google Workspace
• Full JS/Python inside Function nodes — no sandboxing restrictions
• AI Agent node supports tool-calling with any connected node as a tool
• Active community with 50k+ workflow templates

Cons:

• LLM-specific features (RAG, embeddings, reranking) require more manual wiring
• Debugging complex AI chains is harder than in Flowise's dedicated UI
• Cloud pricing scales with executions — heavy AI workloads get expensive fast

Flowise Overview

Flowise is an open-source LLM orchestration platform built on LangChain. It gives you a drag-and-drop UI to build chains, agents, and RAG pipelines without writing LangChain code directly. Every LangChain abstraction — retrievers, memory, tools, output parsers — is a draggable node.

Flowise 2.x (late 2025) introduced Agentflows: a multi-agent canvas where you visually wire supervisor and worker agents. It also ships with a one-line embed for deploying chatbots to any website.

Pros:

• 50+ native LLM integrations including OpenAI, Anthropic, Ollama, Groq, and Mistral
• First-class RAG: vector store nodes, document loaders, embedding models, and rerankers all built in
• Agentflows for multi-agent orchestration without code
• Chatbot embed in one <script> tag

Cons:

• Poor fit for non-LLM automation — no native CRM, database, or SaaS connectors
• Custom code is limited to JavaScript in Function nodes; no Python
• Smaller integration ecosystem than n8n outside the AI space

Head-to-Head: Key Dimensions

LLM Integration Depth

Flowise wins here. Every LangChain component is a native node — you pick a retriever, attach a reranker, wire in a memory buffer, and you're done. There's no glue code.

n8n's AI Agent node is capable, but it's one node among hundreds. Complex RAG patterns require you to manually chain HTTP Request nodes or use the LangChain community nodes, which adds friction.

Flowise RAG setup:
[PDF Loader] → [Text Splitter] → [Embeddings] → [Vector Store]
                                                      ↓
                                            [Conversational Retrieval QA]
                                                      ↓
                                                  [Chat Model]

n8n equivalent:
[HTTP Request: load doc] → [Code Node: chunk + embed] → [HTTP Request: upsert to Qdrant]
                                                              ↓
                                              [AI Agent Node: query + retrieve]

For LLM work, Flowise requires fewer nodes and less custom code.

Workflow Automation Breadth

n8n wins here by a wide margin. If your workflow touches Salesforce, sends a Slack message, writes to a Google Sheet, then calls an LLM — n8n has native nodes for all of it. Flowise has no CRM, spreadsheet, or communication platform nodes.

# n8n: run locally with Docker
docker run -d \
  --name n8n \
  -p 5678:5678 \
  -v n8n_data:/home/node/.n8n \
  n8nio/n8n

# Flowise: run locally with Docker
docker run -d \
  --name flowise \
  -p 3000:3000 \
  -v flowise_data:/root/.flowise \
  flowiseai/flowise

Custom Code

n8n has a real Code node. You write JavaScript or Python, import npm packages (in cloud), and the output feeds directly into the next node. This makes complex data transformations straightforward.

Flowise's Function node runs sandboxed JavaScript. You can't install packages, and the API surface is limited to what Flowise exposes. For anything beyond string manipulation or basic logic, you need to call an external API.

Self-Hosting

Both tools run cleanly on a $6/mo VPS with Docker. Neither requires a GPU for the orchestration layer — model inference is always delegated to an external provider or a local Ollama instance.

# docker-compose.yml for n8n with PostgreSQL
version: "3.8"
services:
  n8n:
    image: n8nio/n8n
    ports:
      - "5678:5678"
    environment:
      - DB_TYPE=postgresdb
      - DB_POSTGRESDB_HOST=postgres
      - DB_POSTGRESDB_DATABASE=n8n
      - DB_POSTGRESDB_USER=n8n
      - DB_POSTGRESDB_PASSWORD=changeme
      - N8N_ENCRYPTION_KEY=changeme-32-char-key
    volumes:
      - n8n_data:/home/node/.n8n
    depends_on:
      - postgres

  postgres:
    image: postgres:16
    environment:
      POSTGRES_DB: n8n
      POSTGRES_USER: n8n
      POSTGRES_PASSWORD: changeme
    volumes:
      - pg_data:/var/lib/postgresql/data

volumes:
  n8n_data:
  pg_data:

# docker-compose.yml for Flowise
version: "3.8"
services:
  flowise:
    image: flowiseai/flowise
    ports:
      - "3000:3000"
    environment:
      - DATABASE_TYPE=postgres
      - DATABASE_HOST=postgres
      - DATABASE_NAME=flowise
      - DATABASE_USER=flowise
      - DATABASE_PASSWORD=changeme
      - FLOWISE_USERNAME=admin
      - FLOWISE_PASSWORD=changeme
    volumes:
      - flowise_data:/root/.flowise
    depends_on:
      - postgres

  postgres:
    image: postgres:16
    environment:
      POSTGRES_DB: flowise
      POSTGRES_USER: flowise
      POSTGRES_PASSWORD: changeme
    volumes:
      - pg_data:/var/lib/postgresql/data

volumes:
  flowise_data:
  pg_data:

Pricing (Cloud)

For most developers, self-hosting is the better deal on both platforms. Cloud plans make sense for teams that don't want to manage infrastructure.

Developer Experience

n8n's execution log is detailed — every node shows its input and output data. Debugging a 20-node workflow is manageable. However, the AI Agent node doesn't expose intermediate reasoning steps by default. You have to add a logging callback or parse the node output manually to see what the model was thinking.

Flowise shows full chain traces in the UI, including what the retriever returned, what the model received as context, and what it produced. For debugging LLM behavior, this is significantly better.

Which Should You Use?

Pick n8n when:

• Your workflow mixes AI with non-AI systems (CRM updates, email triggers, database writes)
• You need full code execution inside workflows — complex data transformation, custom API auth, or business logic
• Your team already uses n8n for automation and wants to add AI capabilities

Pick Flowise when:

• You're building a RAG chatbot, document Q&A system, or LLM-powered search
• You want multi-agent orchestration without writing LangChain code
• You need to embed a chatbot into a website or app quickly

Use both when: your architecture separates concerns — Flowise handles all LLM logic and exposes an API endpoint, n8n calls that endpoint as one step in a broader business automation. This pattern works well in production.

FAQ

Q: Can n8n replace Flowise for RAG pipelines? A: Technically yes, but it's more work. You'll wire together HTTP Request nodes to call embedding APIs, upsert to a vector store, and handle retrieval manually. Flowise does all of this with native nodes. Use n8n for RAG only if you're already deep in the n8n ecosystem.

Q: Can Flowise replace n8n for general automation? A: No. Flowise has no native connectors for Slack, HubSpot, Google Sheets, or most SaaS tools. If your workflow needs to touch anything outside the LLM stack, you'll need n8n, Zapier, or a custom webhook handler alongside Flowise.

Q: Which is easier to self-host in production? A: Both run well on Docker with a Postgres backend. n8n has more configuration options (queue mode, worker scaling, Redis), which is useful at scale but adds complexity. Flowise is simpler to stand up and maintain for small teams.

Q: Which has better Ollama support? A: Flowise has a native Ollama node — you point it at your Ollama URL and pick a model from a dropdown. n8n requires you to use the HTTP Request node or the community LangChain nodes to call Ollama's API. Flowise is easier here.

Q: Is n8n's fair-code license a problem? A: For internal tools and personal projects, no. For building a product you sell that competes with n8n, the Sustainable Use License restricts embedding. Flowise uses Apache 2.0 — no restrictions. If commercial licensing matters to your use case, Flowise is safer.

Tested with n8n 1.82, Flowise 2.2, Docker 27, Ubuntu 24.04