🧰 Frameworks
LangGraph vs n8n: Which Should You Choose?
Both are developer framework agents, but they are built for different people. Here is the honest split.
LangGraph
by LangChain
A low-level orchestration framework for agents that need durable state, loops and human approval mid-run.
n8n
by n8n GmbH
A source-available workflow automation platform with AI agent nodes you can self-host and own outright.
Side by side
LangGraph vs n8n at a glance
| 🕸️ LangGraph | 🔗 n8n | |
|---|---|---|
| Editor score | 4.2★★★★★ | 4.5★★★★★ |
| Vendor | LangChain | n8n GmbH |
| Autonomy | Semi-autonomous | Semi-autonomous |
| Deployment | Self-hosted, Cloud, API | Self-hosted, Cloud, On-prem, API |
| Starting price | Free tier | Free tier |
| Pricing model | Free open-source library; paid managed platform and observability | Free self-hosted (fair-code); cloud priced per workflow execution |
| Free tier | Yes — the framework is MIT-licensed and free | Yes — unlimited self-hosted community edition |
| Integrations | 5 native | 11 native |
| Best for | Engineering teams shipping agents to production | Technical ops and automation engineers |
Decision
Which one should you pick?
Choose LangGraph if…
- Engineering teams shipping agents to production. The strongest fit. Durability, approval gates and observability are exactly what production demands and prototypes ignore.
- Teams that outgrew a higher-level framework. The common migration path — people arrive here when autonomous delegation became too opaque to debug.
- Regulated industries needing approval gates. Native interrupts make a human sign-off a first-class part of the run rather than a bolt-on.
Skip it if: Hardest framework here to learn is a dealbreaker for you.
Choose n8n if…
- Technical ops and automation engineers. The ideal user. Comfortable with APIs, JSON and a bit of JavaScript, and rewarded with almost unlimited flexibility.
- Teams with data residency requirements. Self-hosting means customer data never leaves your infrastructure except for model calls — and you can point those at a local model too.
- High-volume automation on a budget. Self-hosted execution is effectively free at the margin, which beats every per-task competitor at scale.
Skip it if: Steeper learning curve than no-code competitors is a dealbreaker for you.
Trade-offs
Strengths and weaknesses
LangGraph
- The most production-ready agent framework available
- Durable state survives crashes and long pauses
- Explicit graphs are debuggable in a way delegation is not
- Hardest framework here to learn
- Verbose compared with higher-level options
- Observability effectively requires the paid product
n8n
- Genuinely free at unlimited volume if you self-host
- Enormous integration library
- Mix deterministic steps with agent decision points
- Steeper learning curve than no-code competitors
- Self-hosting is real operational work
- Cloud pricing is less compelling than the free tier suggests
FAQ
LangGraph vs n8n FAQ
Is LangGraph better than n8n?
On our scoring n8n edges it at 4.5/5 against 4.2/5, but the gap is smaller than the difference in who they suit. LangGraph is the better choice for engineering teams shipping agents to production; n8n is stronger for technical ops and automation engineers.
Which is cheaper, LangGraph or n8n?
LangGraph: Free tier — free open-source library; paid managed platform and observability. n8n: Free tier — free self-hosted (fair-code); cloud priced per workflow execution. Compare the pricing models rather than the headline numbers; consumption-based plans can overtake a higher flat fee quickly once usage is real.
Can I use LangGraph and n8n together?
Yes, and plenty of teams do. They share 5 integrations, so both can sit on the same data without duplicated plumbing. The usual pattern is to run each on the work it is strongest at rather than forcing one to cover everything.
What are the main differences between LangGraph and n8n?
Three things matter most. Autonomy: LangGraph is semi-autonomous while n8n is semi-autonomous. Deployment: LangGraph runs self-hosted/cloud/api, n8n runs self-hosted/cloud/on-prem/api. Pricing model: free open-source library; paid managed platform and observability versus free self-hosted (fair-code); cloud priced per workflow execution.