Comparison
n8n is the right tool when your problem is connecting APIs in a flow. Pinchy is the right tool when your problem is giving a team member an AI colleague. Here's how the two actually differ.
The Core Difference
You draw the workflow: trigger, step 1, step 2, branch, step 3. Each step is a node. An LLM node is one option among many. The structure is fixed; the LLM fills in a blank.
You describe a role: what the agent is for, which tools it can use, which data it can see, who it reports to. The agent decides what to do in each conversation.
Many teams run both. n8n handles scheduled, deterministic integrations. Pinchy handles the interactive, judgment-heavy parts. A Pinchy agent can call an n8n webhook as a tool.
Side by Side
| n8n | Pinchy | |
|---|---|---|
| Primary primitive | Workflow (visual graph) | Agent (role + tools + scope) |
| Who decides the steps | You, at design time | The agent, at runtime |
| Best fit for | Deterministic integrations | Inbox-type work & judgment |
| Self-hosted | Yes | Yes (first-class) |
| Pre-built Docker images | Yes | Yes (GHCR) |
| Fully offline (local models) | Limited | Yes (Ollama, llama.cpp) |
| Role-based access control | Enterprise tier | Basic built-in (Admin/Member) |
| Per-agent permissions | Workflow-level | Per agent, per user, per scope |
| Audit trail | Execution history | Per-row HMAC-signed, with outcomes |
| Chat interface for users | Not the primary mode | Yes, per agent |
| Telegram channels | Triggers & nodes | One bot per agent |
| Approval step (human in loop) | Manual node | Built into agent contract |
| Usage & cost dashboard | Execution metrics | Token & cost by source |
| License | Sustainable-use / Enterprise | AGPL-3.0 open source |
Being Honest
n8n has a huge library of native integrations. If your goal is "move data from A to B on a schedule", n8n gets you there faster.
You can see exactly which step of a workflow ran, with which data. For deterministic pipelines, that transparency is hard to beat.
Cron triggers, polling, batch jobs, nightly reports. n8n is purpose-built for recurring, predictable work.
Where Pinchy Wins
Messy inputs, judgment calls, varied formats — invoices, quote requests, support drafts, applications. The agent decides per message; you don't branch for every edge case.
"Quote drafter", "support assistant", "onboarding guide" — each agent is a role with bounded authority. Easier to reason about than a growing graph of nodes.
Who can talk to the agent, which data it sees, which tools it may use, whether it sends or drafts. These are first-class configuration, not a convention.
Users chat with agents in a web UI or Telegram, one agent at a time. No workflow to launch, no form to fill. The agent asks follow-ups as needed.
Agents draft and ask for confirmation before sending emails, posting to CRM, or taking irreversible actions. Not a special node — part of the agent contract.
Pinchy is built self-hosted-first. Pre-built GHCR images, one-command Docker deploy, local-model support via Ollama. No "cloud is the default" assumption.
Decision Guide
The work is predictable and repeats. You can draw the flow on a whiteboard. The inputs are structured. You mostly want to connect APIs on a schedule or on a webhook.
The work takes judgment. Inputs are messy or conversational. Users want a colleague, not a form. A human should approve some steps. Roles and permissions matter.
You already run n8n for integrations and want an AI layer on top. Let Pinchy agents call n8n webhooks as tools; let n8n workflows trigger Pinchy agents for the judgment step.
Book a call — let's talk about your AI agent needs and how Pinchy can help.
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