n8n vs Activepieces: Open-Source Automation Head to Head

Verdicts by Task

AI agent workflowsn8n wins

Native AI nodes and agent patterns are production-proven; Activepieces is still building this muscle.

Mind the n8n 2.0 upgrade — community nodes need 2.0-compatible releases before you migrate.

Strict open-source complianceActivepieces wins

MIT-licensed core passes OSS policy reviews that n8n's fair-code license fails.

Verify the specific pieces you need are in the MIT core, not the enterprise edition.

Production automation at scalen8n wins

Queue mode, workers, and a larger operational knowledge base make scaling predictable.

Activepieces is closing the gap release by release — revisit if their roadmap lands.

Feature Comparison

AI-native
n8n:AI-Powered
Activepieces:AI-Powered
Self-hosting
n8n:Mature: Docker, Kubernetes, queue mode for scale
Activepieces:Solid Docker story; fewer battle-tested scale patterns

n8n wins for production-grade self-hosting

AI capabilities
n8n:Native AI nodes, agent workflows, LangChain integration
Activepieces:AI pieces available but the catalog is thinner

n8n wins decisively on AI workflow depth

Integration library
n8n:500+ nodes plus community catalog (verify 2.0 compatibility)
Activepieces:Growing piece library; easier piece-authoring framework

n8n wins on count; Activepieces wins on contribution ease

License
n8n:Fair-code (Sustainable Use License) — restrictions on commercial hosting
Activepieces:MIT core — genuinely open source

Activepieces wins for teams with strict OSS-license policies

Ease of use
n8n:Developer-leaning; powerful but denser UI
Activepieces:Cleaner non-technical builder experience

Activepieces wins for mixed technical/non-technical teams