OpenAI in your workflows: what changes with GPT models
Why we orchestrate several agent backends instead of one, and where OpenAI naturally fits in an n8n architecture.
We don't bet on a single model provider: each backend has different strengths depending on the task, and our role is to connect the right model to the right workflow, not to impose one single choice.
Where OpenAI fits naturally
In an n8n workflow, an OpenAI node can classify an incoming email, extract structured fields from a document, or draft a first reply — tasks where speed and per-call cost matter as much as raw output quality.
What to keep in mind
A proprietary model called via API means some data passes through the model provider. For sensitive data, we systematically configure the minimal-retention options available on the provider side, and steer toward a self-hosted model (Hermes, GLM) when data structurally shouldn't leave your infrastructure.
Our position
Model choice is a configuration parameter in Trustagents, not a permanent commitment: the same workflow can run on OpenAI, Claude, or a self-hosted model depending on the use case and the sensitivity of the data involved.
A question about your situation?
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