n8n vs LangGraph: Visual Automation or Code-First Agents?

n8n vs LangGraph at a Glance

The Core Difference

n8n is the body moving things around: pull data from Salesforce, ask an LLM to summarize it, push the result to Slack. Every step is predetermined.

LangGraph is the brain making decisions: the AI agent decides what data it needs, when to loop back, when to ask for human input, and how to recover from errors. Every path is dynamic.

When to Choose n8n

Pick n8n if:

• You need rapid prototyping. Hours matter more than perfection. n8n ships in 2-3 hours; LangGraph takes 2-3 days.
• Integrations are the core job. Slack to HubSpot to GitHub to your database. n8n's 1,500+ connectors are ready to go.
• Your team has non-technical people. Product managers, ops staff, marketers. They can build n8n workflows; they cannot write Python.
• Workflows are mostly linear. Step A to Step B to Step C. No complex branching or multi-agent loops.
• Cost is tight. n8n starts at $1,600/mo (SaaS) or free (self-hosted). LangSmith is $39/seat + infrastructure.

Examples where n8n wins:

• Sync customer data from Stripe to a CSV, send summary emails.
• Monitor GitHub issues, extract features using an LLM, post to Slack daily.
• Ingest form submissions, classify urgency with AI, route to the right Jira epic.
• Back up your Notion database to Google Drive every night.

When to Choose LangGraph

Pick LangGraph if:

• The AI needs to think. Multi-step reasoning, tool selection, error recovery. The agent decides the path.
• Loops and state matter. The agent needs to retry, re-plan, or hold context across hours or days.
• You're building multi-agent systems. Agents talking to each other, delegating work, reaching consensus.
• You have engineering resources. Python developers who can write async code and debug AI behavior.
• Full control is non-negotiable. No SaaS limitations. You own the code, the model, the inference.

Examples where LangGraph wins:

• A customer support agent that gathers context, routes to specialists, waits for responses, and follows up.
• A code-review agent that reads PRs, asks clarifying questions, suggests tests, and learns from feedback.
• A research assistant that searches, synthesizes across sources, spots contradictions, and marks gaps.
• A compliance auditor that examines documents, asks follow-up questions, and builds a risk profile over multiple interactions.

The Honest Truth

If you're building 80% of use cases (data sync, routine alerts, form processing, scheduled tasks), n8n wins. It's faster, cheaper, and doesn't require an engineer.

If you're building the other 20% (complex reasoning, long-running agents, dynamic decision-making), LangGraph is the only choice.

Most teams with mature AI use both. n8n runs the business workflows. LangGraph powers the intelligent agents.

Using n8n and LangGraph Together

The best teams don't choose. They use both.

n8n orchestrates your business workflows: pull from CRM, send to Slack, write to database. LangGraph runs your intelligent agents: research, reasoning, multi-turn conversations, human loops.

Where they meet: n8n calls your LangGraph agent via API. The agent thinks, decides, and returns a result. n8n takes that result and moves it into your business systems.

A Real Example

Lead qualification workflow:

1. n8n pulls a new lead from Salesforce (integration handled by n8n).
2. n8n calls a LangGraph agent with the lead data (API call).
3. The LangGraph agent asks clarifying questions, reads the company website, assesses fit, and scores the lead (agent logic).
4. n8n receives the score and recommendation back.
5. n8n routes the lead to the right sales team and sends a Slack notification (orchestration handled by n8n).

Why this works: n8n is the body. LangGraph is the brain. Neither is diminished; both do what they're best at.

This is only possible because both n8n and LangGraph can be self-hosted or deployed on managed infrastructure without vendor lock-in. You own both pieces.