May 20, 2026

Flowise vs n8n vs LangGraph 2026: AI Workflow Orchestration

By AIFoss · 11 min read

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 chains. For a production RAG pipeline where those details are business requirements, the control is the point.

For document-heavy RAG use cases, AnythingLLM is worth comparing against Flowise — it goes even further toward making document ingestion zero-configuration.

Where n8n’s 400 integrations actually win

n8n’s differentiator isn’t AI. It’s everything around AI.

A workflow that: (1) receives a Slack message, (2) queries a Postgres database, (3) calls an internal API, (4) generates a summary with a local LLM, (5) creates a Notion page, and (6) posts back to Slack — n8n handles that without writing any integration code. Flowise can make outbound HTTP calls through tool nodes, but it has no native Salesforce, HubSpot, GitHub, or JIRA connectors. LangGraph can call anything, but you’re writing the client code yourself.

The practical split: if your AI agent is 30% LLM reasoning and 70% business system orchestration, n8n wins on development speed. If your agent is 70% complex reasoning with state management, LangGraph wins on reliability. Flowise lives in the middle — more AI-native than n8n, less code-intensive than LangGraph.

Production self-hosting

Flowise runs as a Node.js process with optional Queue Mode for multi-instance deployments, added in v3.x. PostgreSQL replaces SQLite for production. Observability via Prometheus and OpenTelemetry is built in. Straightforward to deploy on any Linux VPS or container platform, with official guides for AWS, Azure, GCP, and DigitalOcean.

n8n follows a similar pattern — SQLite for development, PostgreSQL for production, worker/main separation for scaling. Enterprise features (SSO, audit logs, advanced RBAC, and source control for workflows) require the Enterprise license, which costs money. The community edition handles most self-hosting scenarios well enough.

LangGraph is the most operationally complex to fully self-host. Managing the LangGraph API server, PostgreSQL, and Redis is real infrastructure work. As a library embedded inside your own application stack, the complexity disappears — you’re just calling Python functions. The LangGraph Platform hosted option handles deployment if you want the managed API without the infrastructure management.

For GPU-heavy workloads — running inference for large models or needing burst capacity — RunPod is worth considering over over-provisioning local hardware. The cloud-vs-local tradeoffs are covered in the Ollama vs vLLM comparison.

When NOT to use each tool

Skip Flowise if:

You need version-controlled, code-reviewable agent logic. JSON canvas exports exist, but diffing agent changes in a pull request is painful compared to Python code.
Your team requires unit tests on individual agent components. Visual builders have a weak test story.
The branching logic is complex enough that code is clearer — deep conditional routing in Agentflow gets unwieldy fast.

Skip n8n if:

You’re building a product or service for external customers. The Sustainable Use License restricts offering n8n as a hosted service to third parties — read it before you architect around it.
LLM orchestration is your primary requirement. n8n’s AI nodes are functional but not LLM-native in the way Flowise is.
You need fine-grained session memory or multi-turn conversation state that persists reliably across workflow executions. n8n’s execution model doesn’t provide this by default.

Skip LangGraph if:

Your team doesn’t write Python regularly. There is no escape hatch to a visual interface.
You need a working prototype in two days. Flowise gets you there faster.
The automation is primarily about connecting SaaS tools. Writing a Stripe or HubSpot integration from scratch when n8n has native nodes is unnecessary work.

The verdict

Flowise is the right default for teams building AI agents, chatbots, and RAG pipelines who want speed-to-working without deep Python investment. The Apache 2.0 license is clean, the canvas covers 80% of real-world agent patterns, and v3.x’s Agentflow makes multi-agent orchestration genuinely accessible.

LangGraph earns its place when agent requirements exceed what a visual builder handles cleanly — complex state machines, production resilience, fine-grained error handling, or when the agent logic needs to live inside an existing Python codebase with proper test coverage and CI.

n8n wins when the workflow is primarily business system orchestration and AI is one component among many. Its 400+ integrations are a real time advantage. Just verify your specific use case fits the Sustainable Use License constraints before building on it.

Most developers starting fresh in 2026: prototype in Flowise, refactor complex agent logic into LangGraph when production requirements demand it.

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