Both run AI agents. Choose the operating model.
n8n has a first-party AI Agent node: connect a chat model and some tools, and it decides which tools to call. n8n can attach persistent chat memory to its agent workflows. AGNT exposes agents as separately managed objects with their own memory, assigned tools and budget, alongside a workflow builder.
What changes when you switch
What n8n's AI Agent node already does
It is a root node: attach a chat model and one or more tool sub-nodes, and the agent chooses which to call. If your reasoning fits inside one execution, n8n does that today — you do not need to wire it as IF branches.
Where the scope differs
n8n supports persistent chat history through memory nodes such as Postgres Chat Memory, keyed by session. AGNT organizes typed memory around a managed agent alongside its tools and budget. This is a difference in configuration and operating model, not memory versus no memory. n8n persistent memory documentation.
Receipts and execution logs record different things
n8n logs what each node received and returned, which is excellent for debugging a graph. An AGNT receipt also records the reasoning, the approvals and the cost of a run — including runs that decided to do nothing.
Where n8n is the better choice
A larger node catalog, battle-tested retry and error semantics, queue mode and horizontal scale, and mature multi-user RBAC on paid tiers. If the work is integration-shaped, n8n is the strong default and we say so.
Reviewed 5 September 2026 against n8n's official AI Agent node documentation, which describes the node as an autonomous system that decides which connected tools to call. Both projects ship frequently — verify current capabilities and licence terms directly before choosing either.
Property-by-property table: AGNT vs n8n · or the Zapier alternative