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AI · head to head

LangGraph vs Nango

LangGraph logo

LangGraph

AI

Agent runtime and orchestration framework

From
Free
Rated
-
Full reviewWebsite
Nango logo

Nango

Automation Integration

Open source unified API and OAuth infrastructure for product integrations, licensed under Elastic License 2.0

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: LangGraph steeper learning curve compared to high-level abstractions; Nango the licence is Elastic License 2.0, which is source available rather than OSI open source, and it forbids offering Nango to third parties as a managed service, so anyone planning to resell or embed it in a platform for their own customers has a genuine legal problem.
  • They diverge on capability: LangGraph covers Human-in-the-loop controls, Nango covers Managed OAuth.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which LangGraph and Nango actually diverge.

Attributes where LangGraph and Nango differ
AttributeLangGraphNango
Pricing modelOpen source and free, with optional managed platformPer connection per month
PlatformsPython, JavaScript, WebWeb, Linux, Docker
CategoryAIAutomation Integration

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in LangGraph

  • Human-in-the-loop controls
  • Customizable workflows
  • Memory management
  • Token-by-token streaming
  • Low-level control
  • Multi-agent support

Only in Nango

  • Managed OAuth
  • Pre-built integrations
  • Custom syncs and actions
  • Incremental sync
  • Rate limit and retry handling
  • Webhooks
  • Self-hosting
  • Unified models

What people use each for

The jobs each tool is most often brought in to do.

LangGraph

  • Building production AI agents with auditable workflowsnot Nango
  • Designing multi-agent systems for complex tasksnot Nango
  • Implementing human oversight in autonomous systemsnot Nango
  • Creating reliable agentic applications at scalenot Nango

Nango

  • A SaaS product that needs to ship twenty customer-facing integrations without hiring a team to maintain OAuth and token refresh for eachnot LangGraph
  • A team that needs a niche or internal API integrated, which closed unified API vendors will not build for themnot LangGraph
  • A company with data residency or security constraints that must self-host the integration layer rather than send customer tokens to a vendornot LangGraph
  • An engineering team replacing a homegrown integration service whose main cost is silent token expiry and rate limit failures in productionnot LangGraph

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

LangGraph

  • Steeper learning curve compared to high-level abstractions
  • Requires understanding of graph-based architecture
  • Debugging complex workflows can be challenging
  • Not optimized for simple, one-off use cases

Nango

  • The licence is Elastic License 2.0, which is source available rather than OSI open source, and it forbids offering Nango to third parties as a managed service, so anyone planning to resell or embed it in a platform for their own customers has a genuine legal problem.
  • Pricing is per connection where a connection is one authorised end-user account, so cost scales linearly with your customer base and a product where each user links several services multiplies quickly beyond what a headline plan price suggests.
  • Pre-built integrations vary in depth, and a connection that exists is not the same as a connection that covers the endpoints and objects your feature needs, so each one must be verified before it is designed into a roadmap.
  • Custom syncs are written in TypeScript and run in Nango model, which means integration logic lives in a vendor runtime and migrating away later requires rewriting it rather than lifting it out.
  • Self-hosting removes the vendor from the data path but transfers operational responsibility for a component that holds customer OAuth tokens, and few teams appreciate the security burden that comes with running that themselves.

Pricing, plan by plan

LangGraph

Free
  • Open SourceFree
    • MIT-licensed framework
    • Self-hosted deployment
    • Full API access
  • LangGraph Platform$35/month
    • Managed hosting
    • Enterprise deployment
    • Integrated tooling

Nango

Free
  • FreeFree
    • 10 connections
    • Pre-built integrations
    • Managed OAuth
  • Starter$50/month
    • 20 connections included
    • 1 USD per additional connection
    • Custom syncs and actions
  • Growth$500/month
    • 100 connections included
    • 1 USD per additional connection
    • Higher limits
  • Enterprise$undefined/month
    • Quoted
    • Custom connection volumes
    • Security review and SLA

Which should you pick?

Choose LangGraph if

  • You need human-in-the-loop controls.
  • You want to start without paying.
  • You work on Python, JavaScript, Web.
  • You also want customizable workflows.
LangGraph review

Choose Nango if

  • You need managed oauth.
  • You want to start without paying.
  • You work on Web, Linux, Docker.
  • You also want pre-built integrations.
Nango review

Questions people ask

Is LangGraph or Nango better?
Neither clearly leads. LangGraph starts at Free and Nango at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, LangGraph or Nango?
LangGraph starts at Free and Nango at Free.
Does LangGraph or Nango run on more platforms?
LangGraph runs on Python, JavaScript, Web. Nango runs on Web, Linux, Docker.
Can I use LangGraph for free?
Both have a free tier, so you can try either at no cost before committing.
What is LangGraph best used for?
LangGraph is most often used for building production ai agents with auditable workflows, designing multi-agent systems for complex tasks, implementing human oversight in autonomous systems, creating reliable agentic applications at scale. Of those, building production ai agents with auditable workflows and designing multi-agent systems for complex tasks are not what Nango is typically brought in for.
What can LangGraph do that Nango cannot?
LangGraph covers Human-in-the-loop controls, Customizable workflows, Memory management, Token-by-token streaming. Nango covers Managed OAuth, Pre-built integrations, Custom syncs and actions, Incremental sync.

Answered from the vendors’ own pages

LangGraph: Is LangGraph free to use?

Yes. The core LangGraph framework is MIT-licensed and completely free. You only pay if you use the optional managed LangGraph Platform for hosting.

Source
Nango: Is Nango open source?

It is source available under Elastic License 2.0. You can read, modify and self-host it, but you cannot offer it to third parties as a managed service. That is not the same as an OSI approved open source licence.

LangGraph: What programming languages does LangGraph support?

LangGraph provides first-class support for Python and JavaScript, enabling cross-platform agent development.

Source
Nango: How is it priced?

Per connection per month, where a connection is one authorised end-user account. Free to 10 connections, 50 dollars a month for Starter with 20, 500 for Growth with 100, and one dollar per additional connection.

LangGraph: Can I deploy LangGraph in production?

Yes. LangGraph can be self-hosted on your own infrastructure or deployed through LangGraph Platform with enterprise support and SLA guarantees.

Source
Nango: Can we integrate an API Nango does not support?

Yes. Custom syncs and actions in TypeScript cover any API including internal ones, which is the main advantage over closed unified API products.

Nango: Can we self-host it?

Yes, under the Elastic License 2.0 terms, which permit self-hosting for your own use but not resale as a service.

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