The workflow is the product.
You are wiring many kinds of systems together and AI is one useful node among them. Many teams keep n8n for this glue.
Mobius vs. n8n
n8n is a broad workflow automation platform that can put an AI agent inside a graph. Mobius starts with the agent as the worker and supplies the team backend around it: runtime, state, budgets, integrations, and review.
| Decision point | n8n | Mobius |
|---|---|---|
| Best fit | Broad workflow automation with AI Agent nodes | Reliable AI agents as a managed team backend |
| Core abstraction | Visual workflows that can contain AI Agent nodes | Versioned agents, loops, and sessions are the product objects |
| Agent reliability | Execution history and retries around a workflow graph | Agent runs with checkpoints, guardrail stops, and resume |
| Team operations | Projects, workflow sharing, and credential roles | Team projects centered on agents, runs, data, and review |
| Model choice | Multiple model nodes wired and configured in workflows | One model-agnostic runtime, swap by step without a rebuild |
| Agent state | Attach memory or vector-store sub-nodes to each workflow | Agent memory with recall and ranking across runs |
| Shared data | Workflow data tables; binary storage serves executions | Agent memory, project tables, and durable shared artifacts |
| Cost control | Execution metrics plus model usage tracked separately | Hard run and rolling loop budgets with run-level cost history |
| Integrations | A broad node catalog with workflow credentials | 30+ managed agent integrations with OAuth and events handled |
| Human approval | Approval nodes and forms wired into the workflow | Durable approval gates in the shared run and review UI |
Workflow platform or agent backend.
Both can automate business work. The difference is what the platform is organized around: a graph of many node types, or reliable agents and the operational system they need.
The agent is the product or worker.
Your team needs the runtime, memory, shared data, cost boundaries, and review model to center on the agent itself.
Put the agent at the center.
Start free and give your team one backend for agent runtime, shared data, hard budgets, review, and model choice.