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
n8n ↗
Open-source workflow-automation runtime with 400+ pre-built integrations and native LLM nodes. Self-hostable; managed cloud option. Visual workflow editor.
LangGraph ↗
LLM-first agent-orchestration framework from LangChain. State-graph primitive for multi-step agent loops. LangGraph Cloud as managed runtime; LangSmith for observability.
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 |
When to choose which
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).
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.