Vendor head-to-head · 1 May 2026

n8n vs LangGraph: agent orchestration runtimes compared

n8n and LangGraph occupy adjacent categories that procurement teams often confuse. n8n is a workflow-automation runtime that has added LLM-orchestration nodes; LangGraph is an LLM-first agent-orchestration runtime that has added workflow primitives. The feature matrices look similar; the operating models differ in ways that matter at production scale. This comparison is for platform engineers and architecture leads selecting an agent-orchestration runtime in 2026. The decision often hinges on whether the team has more workflow-engineering muscle or more LLM-engineering muscle, and on whether the deployment leans toward general-purpose automation with LLM nodes or toward LLM-loop-heavy agent behaviour with conditional tool use.

Who this is for

· Platform engineers selecting an agent-orchestration runtime for production
· Solo founders + small businesses choosing between general-purpose automation and LLM-first agent platforms
· Architecture leads evaluating self-hosted vs managed orchestration tradeoffs

Side A

n8n ↗

Open-source workflow-automation runtime with 400+ pre-built integrations and native LLM nodes. Self-hostable; managed cloud option. Visual workflow editor.

PricingFree self-hosted; cloud from $24/month (Starter, 5k executions)· as of 1 May 2026source ↗

Side B

LangGraph ↗

LLM-first agent-orchestration framework from LangChain. State-graph primitive for multi-step agent loops. LangGraph Cloud as managed runtime; LangSmith for observability.

PricingFree open-source; LangGraph Cloud from $39/seat/month (Plus); LangSmith Plus from $39/user/month· as of 1 May 2026source ↗

Feature matrix

Dimension	n8n	LangGraph
Mental modelsource ↗	Workflow with LLM nodes (LLM is one node in a deterministic graph)	Agent loop with state graph (LLM controls graph transitions)
Best forsource ↗	Workflows with predictable structure that include LLM steps (extract from email → classify with LLM → write to CRM)	Agent loops where the LLM decides the next step, including tool selection and branching (research agent, customer-service agent)
Pre-built integrationssource ↗	400+ pre-built nodes (Slack, Google Workspace, Salesforce, HubSpot, Notion, etc.)	MCP support; LangChain ecosystem of tools; custom-tool development is the primary integration path
Editorsource ↗	Visual node-graph editor (low-code)	Code-first (Python / TypeScript); LangGraph Studio for visual debugging
State persistencesource ↗	Per-execution; PostgreSQL backing store; built-in execution history	Checkpointer (Postgres, Redis, SQLite); thread-aware persistence with rollback
Multi-agent / sub-agent supportsource ↗	Sub-workflow nodes; AI Agent node with tool sub-workflows	Multi-agent supervisor + worker patterns native; subgraphs as agent abstractions
Self-hosted deploymentsource ↗	Yes — Docker / Docker Compose / Kubernetes; SQLite or PostgreSQL	Yes — open-source LangGraph; LangGraph Server self-hosted (Helm chart)
Managed cloud optionsource ↗	n8n Cloud (Starter, Pro, Enterprise tiers)	LangGraph Cloud (Plus, Enterprise tiers)
Observabilitysource ↗	Built-in execution history; integration with external observability via webhooks/log shipping	LangSmith (deeply integrated trace, eval, monitoring); OpenTelemetry export
Licensingsource ↗	Sustainable Use License (source-available with restrictions on commercial hosting)	MIT (open-source); managed cloud / LangSmith are commercial products

What our claim ledger says about each

AM-137· Holding · last review 5 May 2026 · next +58dAgent evaluation in production resolves on three operational components that determine whether the chosen evaluation platform produces useful signal: eval-set design across three layers (50-200 calibration prompts, 30-100 edge-case prompts, 10-50 production-sampled prompts per week), drift detection across three signal classes (output-distribution, score-distribution, tool-use distribution), and a regression-budget framework that forces binary ship/hold decisions (defensible default 5% absolute decline on calibration set, 10% on edge-case set, per release window). The procurement decision (which platform to buy, covered at AM-122) is the easier half; the operational discipline is what most enterprises under-invest in even after buying a platform.

When to choose which

Choose n8n

Pick n8n when the team has more workflow-engineering muscle than LLM-engineering muscle, when the deployment is mostly deterministic flows with occasional LLM steps, when the 400+ pre-built integrations cover the in-house systems, or when self-hosted is required for data-residency or sovereignty reasons. Strong default for solo founders and small businesses (the sibling /operators/ register has dedicated coverage).

Choose LangGraph

Pick LangGraph when the deployment is LLM-loop-heavy (the agent decides the next step rather than following a fixed graph), when the team is comfortable in Python/TypeScript, when LangSmith's deep eval/trace tooling is part of the production observability story, or when multi-agent supervisor patterns are the architecture. Stronger fit for engineering teams shipping custom-built agents rather than connecting pre-built nodes.

Related decisions

Adjacent procurement decisions in the same cluster. Use the buyer's-guide structure: pick the deployment shape first, then walk the comparison matrix.

LangChain vs LangGraph: when to use which
LangChain (the framework) vs LangGraph (the runtime). What each is built for, when to pick which, and how they relate inside the LangChain ecosystem.
MCP vs A2A: agent-protocol comparison
Model Context Protocol (MCP) vs Agent-to-Agent (A2A) protocol. What each standardises, who maintains them, how they fit in 2026 agent stacks.
Cursor vs GitHub Copilot: AI coding tools compared
AI-paired developer tooling: Cursor (agentic editor) vs GitHub Copilot (suggest + agent). Feature matrix, pricing, governance for 2026 procurement.