AGNT vs n8n
This is the closest comparison on the list, and the one where the usual local-first argument does not apply — n8n self-hosts too. The real difference is what each system treats as its basic unit of work.
The workflow versus the agent
n8n is a workflow automation tool that can call AI. AGNT is an agent runtime that includes a workflow builder. That sounds like a small distinction and it changes what each is good at.
The workflow is the unit
You design a graph, and the graph is the thing that runs. AI nodes are steps inside it. A mature, well-built tool with a large node catalog and a strong community.
- Self-hostable, source-available
- Large node library and active community
- Excellent for integration and data plumbing
- Strong error handling and retry semantics
- State lives in the execution, not in a worker
The agent is the unit
Workflows exist and matter, but they are one layer. The persistent thing is an agent that carries memory, tools, and budget across runs.
- Agents with typed persistent memory
- Goals that plan, evaluate, and replan autonomously
- Native MCP client for the open tool ecosystem
- Local audit receipts on every execution
- Desktop application, not only a server
Structural comparison
| Property | AGNT | n8n |
|---|---|---|
| Where the work runs | Your own machine, by default | Your server, if you self-host |
| Free to run yourself | Yes — Community Core, unlimited local runs | Yes — fair-code self-hosted |
| Setup effort | Download and open it | Docker, a database and a reverse proxy |
| Runs as a desktop app | Yes — that is the primary form | No — server-first |
| Connector library | 120+ native, plus MCP and any REST API | 400+ nodes, plus community nodes |
| Community node ecosystem | Plugins and a marketplace, younger | Large and established |
| Reasoning between steps | An agent decides, per item | AI nodes, LangChain integration |
| Choose the model per step | Yes, any provider or a local model | Yes |
| Local model support | First-class — Ollama, LM Studio | Supported |
| Reads and writes local files | Yes, on the machine you are sitting at | Yes, on the server |
| Approval gates before an action | Built in, per step | Via wait nodes and forms |
| Audit trail of what ran | A receipt per run, with the reasoning | Execution log per run |
| Licence | Source available | Fair-code (Sustainable Use Licence) |
| Commercial restrictions on self-hosting | None for internal use | Cannot resell it as a service |
| Multi-user and RBAC | Simpler — single-operator focus | Mature on paid tiers |
| Queue mode and horizontal scale | Not the design target | Yes, proven at scale |
| Maintenance burden | None — it is an app that updates itself | You patch, back up and upgrade it |
| Works with no server at all | Yes | No |
| Vendor lock-in | None. Workflows are JSON on your disk | None. Workflows are JSON |
| Best fit | One person or a team automating their own work | A platform team running automation for others |
Across 20 properties: AGNT leads on 5, n8n leads on 4, and 11 are a genuine draw. A mature self-hosted story, a large community node library, and years of production use. If you want a node-wiring tool you host, n8n is the strong default.
Reflects each product's published architecture as of August 2026. Both projects ship frequently — verify current capabilities and licence terms directly before committing to either.
Which one you should actually use
Choose n8n if…
Your work is integration-shaped — move data between systems, transform it, handle failures reliably — and the AI part is one step rather than the point. n8n's catalog is larger, its retry and error semantics are battle-tested, and its community has already solved most integration problems you will hit. If you want a workflow engine, use the tool that is a workflow engine.
Choose AGNT if…
The AI is the point rather than a step. You want a worker that remembers what happened last time, that you can hand an outcome rather than a procedure, and that produces a defensible record of what it did. You also want it on your desktop with local file and shell access, not only on a server.
Honest note on maturity
n8n has been in the market longer and its integration catalog is larger. If the specific connector you need exists there and not here, that is a real reason to pick it — and AGNT's Custom API node, code nodes, and MCP support are the answer only if you are willing to do a little more assembly.
Moving from n8n to AGNT
Nobody writes these, and everybody searches for them. This is the honest version, including the step where the shape of the thing changes.
- Export the workflow from n8nn8n exports clean JSON, and the node model maps closely: trigger, action, and a data shape flowing between them.
- Recreate the triggerMost n8n triggers have a direct AGNT equivalent. Webhook triggers work the same way on both.
- Move credentials into the local vaultThe same OAuth apps and API keys work. They move from your n8n instance to a vault on your machine.
- Replace IF nodes with an agent where the rule got complicatedA chain of IF nodes usually exists because the real rule was a judgement. That is the part an agent replaces — the rest maps one-for-one.
- Keep n8n for what it is better atThis is not all-or-nothing. If you already run n8n in queue mode for high-volume server work, keep it there and use AGNT for the desktop-side work it cannot reach.
- Decommission only once the receipts agreeRun both, compare, then retire the old one.
What it costs at three real usage levels
| Monthly volume | AGNT | n8n |
|---|---|---|
| A few hundred runs a month | $0 — Community Core | Usually inside a free or starter tier |
| Around 10,000 runs a month | $0 — Community Core | Typically $50–$300/mo depending on step count |
| 100,000+ runs a month | $0 — Community Core, plus your own model costs | Typically $500–$1,600/mo, plus AI usage |
AGNT runs on hardware you already own, so the marginal cost of a run is electricity plus whatever the model call costs you directly. Hosted tools meter the run itself. The comparison figures are list prices at the time of writing and are the part of this page most likely to age — check them before quoting them.