This article was originally published on aifoss.dev
---
title: 'Flowise vs n8n vs LangGraph 2026: AI Workflow Orchestration'
description: 'Flowise v3.1.2, n8n v2.21.5, and LangGraph v1.2.0 compared: licenses, setup complexity, AI capabilities, and which runs best for local self-hosted workflows.'
pubDate: 'May 20 2026'
tags: ["flowise", "ai", "nocode", "selfhosted", "llm"]
Three tools sit at the center of every "how do I build local AI workflows" conversation right now: Flowise, n8n, and LangGraph. They look similar at a glance — all three can orchestrate LLM calls, chain tools, and run agents. The actual overlap is much smaller.
Flowise is a visual drag-and-drop canvas that outputs LangChain-backed agent graphs. n8n is a general-purpose automation platform that bolted AI nodes onto an existing integration engine. LangGraph is a Python-first state-machine library for building production-grade agents from code. You'll use one — probably not all three — and picking the wrong one costs real time to undo.
Versions tested: Flowise v3.1.2, n8n v2.21.5, LangGraph v1.2.0. All self-hostable. Not all equally open-source — more on that shortly.
Quick recommendation
Building an AI chatbot, RAG pipeline, or multi-agent workflow without writing a lot of code: Flowise. Agent that needs to reach into Slack, Postgres, Stripe, and a CRM in the same workflow: n8n, but read the license section before committing. Production agent shipping to real users with complex state, retries, and checkpointing: LangGraph.
No single winner exists in the abstract. The split comes down to whether you're prototyping, integrating business systems, or writing production agent infrastructure.
What each tool actually is
Flowise v3.1.2 (Apache 2.0) is a self-hostable Node.js application with a drag-and-drop canvas. You place nodes — LLMs, vector stores, retrievers, tool agents — and connect them visually. The underlying graph compiles to LangChain primitives. Version 3.x introduced Agentflow, which handles multi-agent orchestration with branching and looping. It also added execution tracing with OpenTelemetry support, role-based access control, and Graph RAG capabilities. Roughly 53,000 GitHub stars as of May 2026.
n8n v2.21.5 (Sustainable Use License — not OSI open source) started as a general-purpose workflow automation tool. Think Zapier for developers — 400+ pre-built integrations, a visual editor, webhook triggers. AI capabilities arrived as first-class nodes in the 1.x series and matured through 2.x. n8n's mental model is "automation workflow," not "AI agent." The AI works, but you're fitting LLM calls into a framework built around HTTP triggers and API chaining.
LangGraph v1.2.0 (MIT) is a Python library, not an application. There is no UI. You define agents as directed graphs: each node is a Python function, each edge is a conditional transition. It ships with built-in checkpointing (persistent state across invocations), durable execution (resumes after crashes), per-node timeouts, node-level error handlers, and human-in-the-loop support. The LangGraph Platform adds hosted deployment, but the core library runs anywhere Python runs.
License reality check
Most comparison articles gloss over this. It matters.
- Flowise: Apache 2.0. Build on it, ship products, host it for external customers. No restrictions.
- LangGraph: MIT. Same — unrestricted commercial use.
- n8n: Sustainable Use License. You can self-host for internal business operations. You cannot offer n8n as a hosted service to third parties without an enterprise agreement. Building a product that resells automation-as-a-service on top of n8n requires a separate commercial license. n8n is source-available, not open source by the OSI definition.
For solo developers and internal teams, the n8n license rarely creates problems. If your use case involves offering automated workflows to external customers as a product feature, Flowise or a LangGraph-backed service avoids the gray area entirely.
Installation and hardware
Flowise v3.1.2
Requires Node.js v18.15.0 or v20+.
npm install -g flowise
npx flowise start
# UI at http://localhost:3000
Docker:
docker run -d -p 3000:3000 flowiseai/flowise
Hardware: 2 GB RAM covers development and light testing. Production with multiple concurrent users and agent runs: the official docs recommend at least 4 vCPU / 8 GB RAM. Running local embedding models or heavy document processing pushes that to 8 GB RAM minimum. SQLite is the default; PostgreSQL is recommended for production.
n8n v2.21.5
Node.js 20.19–24.x, or Docker.
docker run -it --rm \
--name n8n \
-p 5678:5678 \
-v ~/.n8n:/home/node/.n8n \
docker.n8n.io/n8nio/n8n
# UI at http://localhost:5678
Hardware minimum: 2 CPU cores, 2 GB RAM for development. Production: 4 vCPU, 8 GB RAM, PostgreSQL over SQLite. n8n's documentation is explicit that SQLite is unsuitable for production — execution history and concurrent workflows tax it quickly.
LangGraph v1.2.0
Python 3.10+ is the only hard requirement.
pip install langgraph
# For the local dev server with hot reload:
pip install "langgraph-cli[inmem]"
langgraph dev # starts at http://localhost:8123
Production self-hosting requires PostgreSQL (checkpoint storage) and Redis (streaming pub/sub). As a pure Python library inside an existing FastAPI or Django app, LangGraph adds near-zero infrastructure — it's just code. The hardware floor is almost entirely determined by your inference backend. If you're running models through Ollama locally, the Ollama review covers the GPU and RAM requirements in detail.
Feature comparison
| Feature | Flowise v3.1.2 | n8n v2.21.5 | LangGraph v1.2.0 |
|---|---|---|---|
| Interface | Visual canvas | Visual canvas | Python code |
| License | Apache 2.0 | Sustainable Use (fair-code) | MIT |
| Multi-agent | Yes (Agentflow) | Via AI nodes | Native (graph nodes) |
| RAG support | Native (100+ vector DBs) | Via integrations | Via LangChain |
| Persistent state | Session-level | Limited | Full (checkpointed) |
| Tool/function calling | Yes | Yes | Yes |
| Human-in-the-loop | Yes | Yes (approval nodes) | Yes (native) |
| Execution tracing | Yes (built-in) | Execution log | Via LangSmith |
| Local LLM support | Yes (Ollama, LM Studio) | Yes (Ollama node) | Yes (any model) |
| External integrations | ~100 tool nodes | 400+ native connectors | Write it yourself |
| Production-ready | Yes | Yes | Yes |
| Min RAM (dev) | 2 GB | 2 GB | ~1 GB |
| Primary language | TypeScript | TypeScript | Python |
Building the same thing in each tool
A concrete example that illustrates the tradeoffs: a RAG chatbot that reads PDFs, chunks them, stores embeddings in a local vector database, and answers questions.
Flowise approach: Drag in a PDF loader node, connect to a text splitter, connect that to a vector store upsert. Add a retrieval QA chain, wire an LLM node and conversation memory. Fifteen to twenty minutes, no code. Flowise auto-generates a REST API endpoint and a chatbot embed snippet. For building a quick document Q&A tool, this is hard to beat.
n8n approach: Use the Vector Store Insert node with a document loader, then a Chat with Vector Store workflow for retrieval. It works, but n8n's RAG tooling is thinner than Flowise's — fewer vector database options, less native control over chunking strategies. The setup requires understanding n8n's workflow trigger model, which adds friction for a chatbot-first use case. For a standalone RAG chatbot, Flowise is faster to working state.
LangGraph approach: Define a state graph in Python. A retrieve node fetches relevant chunks, a generate node calls the LLM with retrieved context, edges handle routing and fallbacks. More verbose, but every detail is explicit — context window management, retrieval scoring thresholds, retry behavior, fallback ch
Top comments (0)