For the previous guide in this series, read CrewAI vs n8n: 6 Key Differences, Pricing, and Which Tool to Use in 2026.
Flowise is an open-source visual platform built specifically for AI agents and LLM workflows, while n8n is a general-purpose workflow automation platform with native AI capabilities. Choosing between them depends on whether the primary goal is building AI chatbots and RAG pipelines, or connecting business apps and automating multi-step processes.
What Are Flowise and n8n?
Flowise is a low-code, open-source AI agent builder co-founded by Henry Heng and Chung Yau Ong in 2023, launched through Y Combinator, and acquired by Workday in August 2025. It wraps LangChain into a drag-and-drop visual canvas where document loaders, vector databases, language models, and memory modules connect as nodes. It has accumulated over 42,000 GitHub stars and is used in production by Fortune 500 companies including Thermo Fisher, Deloitte, and Accenture, according to Pangea. n8n is a fair-code workflow automation platform founded by Jan Oberhauser in Berlin in 2019. It connects over 500 apps, APIs, and services through a visual node-based canvas that also supports inline JavaScript and Python. As of 2025, n8n serves over 3,000 enterprise customers and approximately 230,000 active users worldwide, according to its Series B investor Highland Europe. In October 2025, n8n raised $180 million in a Series C round led by Accel, bringing its valuation to $2.5 billion. It ranked No. 1 in JavaScript Rising Stars 2025, gaining 112,400 GitHub stars in a single year, according to Heise Online.
What Is the Primary Difference Between Flowise and n8n?
The primary difference is focus: Flowise is purpose-built for LLM and AI agent workflows, while n8n is a general-purpose automation platform that also supports AI as one component among hundreds of integrations. Flowise offers 3 distinct builder modes:
- Assistant mode – for beginners building chat agents with uploaded documents
- Chatflow – for single-agent systems and chatbot pipelines
- Agentflow – for multi-agent orchestration with branching, looping, and routing
n8n structures every workflow as a directed graph of trigger and action nodes. Its 2026 AI subsystem includes 70+ LangChain-based nodes for agents, memory, vector stores, and LLM calls, according to StartupOwl. Workflows can combine AI nodes with standard app integrations in a single canvas.
How Do Flowise and n8n Compare on Ease of Use?
Flowise is easier to use for AI-specific workflows, particularly for non-technical users building chatbots or RAG systems, while n8n requires more onboarding time but offers broader workflow control. Both platforms use drag-and-drop interfaces, but their target users differ. Flowise’s Assistant mode enables a beginner to build a document-aware chat agent in minutes, with no SDK knowledge required. Its node library is pre-configured for LLM patterns. AI Agent Store rates Flowise as more accessible specifically for LLM prototyping. n8n’s learning curve is steeper for advanced automations and custom code integration. Users from Zapier describe the first month as the hardest, followed by a second month where productivity exceeds what Zapier allowed, according to G2 and Reddit reviews compiled by StartupOwl. Its Capterra average is 4.9 out of 5 stars.
How Do Flowise and n8n Compare on Integrations?
Flowise supports 100+ integrations focused on LLMs, vector databases, and document sources, while n8n supports 500+ integrations spanning business apps, CRMs, databases, communication tools, and AI services. Flowise integrates with LLM providers and data sources. Examples include OpenAI, Anthropic, Google Gemini, Ollama for local models, Pinecone, Weaviate, Chroma, PDF loaders, web crawlers, and Excel files. n8n integrates across both AI providers and traditional business software. Examples include Slack, HubSpot, Google Sheets, Airtable, GitHub, Stripe, Postgres, and 400+ additional services. Its pricing charges per workflow execution rather than per action step, making multi-step workflows significantly cheaper than per-task platforms like Zapier, according to n8n’s pricing page.
How Do Flowise and n8n Compare on Pricing?
Both platforms are free to self-host, but their cloud pricing and cost structures differ across 4 tiers.
| Plan | Flowise Cloud | n8n Cloud |
|---|---|---|
| Free / Starter | Free plan with 2 flows | Starter from $20/month – 2,500 executions |
| Pro | Tiered cloud plans | Pro from $50/month – 10,000 executions |
| Business | Available | 24 EUR/month – 60 EUR/month tested |
| Enterprise | SSO, RBAC, air-gapped deploy | Custom pricing |
n8n’s self-hosted Community Edition is free with unlimited executions, unlimited workflows, and all integrations. Production infrastructure costs typically exceed $200/month for self-hosted deployments, according to Latenode. Flowise’s self-hosted version is MIT-licensed and free beyond infrastructure costs. Both tools require PostgreSQL for production-grade deployments; Flowise’s default SQLite install handles concurrent writes poorly under load, according to Pangea.
What Are the Best Use Cases for Flowise vs n8n?
Flowise is best for building AI chatbots, RAG pipelines, customer support agents, and document Q&A systems. n8n is best for connecting business apps, automating data pipelines, building DevOps triggers, and embedding AI as one step within a broader process. Flowise use cases include:
- Chat with Documents (PDF, Excel, web pages)
- Customer support bots with memory and tool calling
- Multi-agent systems for research or analysis tasks
- Knowledge bases with vector retrieval
n8n use cases include:
- Slack-to-CRM lead routing with AI classification
- Scheduled data sync between databases and spreadsheets
- Webhook-triggered payment reconciliation
- Security threat intelligence automation (used by Vodafone, saving approximately 2.2 million GBP in operational costs, according to n8n case studies)
- Daily AI-powered content summarizers using vector memory
Voiceflow’s blog notes a common production pattern: teams prototype AI conversations in Flowise, then glue Flowise outputs to business systems using n8n, combining both tools rather than choosing one exclusively.
How Do Flowise and n8n Compare on Flexibility and Code Access?
n8n provides greater flexibility through inline JavaScript and Python code nodes, custom node creation, and a broader API surface. Flowise provides flexibility through its Agentflow SDK and pre-built LLM node library, but is limited to If/Else logic with no native loops or nested workflows. n8n supports custom code inside any workflow step. Developers can write full JavaScript or Python directly in the canvas. Its Sustainable Use License (fair-code) allows free internal use and self-hosting but restricts commercial reselling of n8n as a SaaS product without authorization. Flowise’s Agentflow builder supports multi-agent orchestration with branching and routing. Version 3.1.0, released in March 2026, added an AgentFlow SDK and LangChain v1 migration. Its limitation is the absence of native loops and nested workflows, which constrains highly complex multi-step agentic orchestration.
Flowise vs n8n: Which Should You Choose?
Choose Flowise if the primary goal is building LLM-powered chatbots, document agents, or multi-agent AI systems quickly, with a visual interface and minimal backend code. Choose n8n if the goal is automating business processes across many apps, building data pipelines, or embedding AI as one component within a broader workflow.
| Category | Flowise | n8n |
|---|---|---|
| Founded | 2023 – San Francisco, CA | 2019 – Berlin, Germany |
| Primary focus | AI agents and LLM workflows | General-purpose workflow automation |
| GitHub stars | 42,000+ | 108,000+ |
| Integrations | 100+ (AI-focused) | 500+ (broad) |
| Licensing | MIT open source | Fair-code (Sustainable Use License) |
| Cloud pricing | Free tier available | From $20/month |
| Best for | AI chatbots, RAG, document agents | Business automation, app integration, ETL |
| Acquired / Funded | Acquired by Workday (August 2025) | $2.5B valuation, Series C (October 2025) |
Teams with strong technical resources and broad automation needs fit n8n. Teams focused on AI-first products, chatbot delivery, and rapid LLM prototyping fit Flowise. Many production stacks use both, with Flowise handling AI logic and n8n managing the surrounding business process layer.
Olaitan Oladipo holds a BSc in Sociology from Olabisi Onabanjo University. He is a self-taught automation builder who has spent years inside n8n doing the work that most tutorials skip: debugging OAuth errors at 2am, migrating client automations from Make.com mid-project, fighting reverse proxy misconfigurations on AWS EC2, and figuring out through trial and error what actually holds up in production versus what only looks clean in a demo.
He is not a developer by training and not a SaaS founder. He is the person in the Discord server who actually answers the question instead of linking to the docs.
His writing on n8n Automation Tutorial covers self-hosting, AI agent workflows, tool comparisons, and the security vulnerabilities the automation industry would rather not discuss. He has built AI-assisted invoice approval flows using OpenAI function calling, connected Claude via HTTP Request nodes, and holds considered opinions about Zapier, Make.com, LangChain, and CrewAI that their marketing teams would not appreciate.
He writes for people who are technical enough to follow a tutorial but experienced enough to want the honest version.