Google Gemini

AI & modelsAPI keyConnection test available

Gemini models via the Google Generative Language API.

OCNORA ships 0 typed Google Gemini operations and 4 triggers. Each node has a schema-driven form, input and output tables beside its settings, and is pre-flighted by Workflow Health before your first real run.

Before you connect Google Gemini

Authentication
API key
Credential
{"client_id", "client_secret", "refresh_token"}
Connection test
Available: the stored credential can be checked against the provider when you add it. This is not a record that a test was run or passed.

What you can do with Google Gemini

Operations, grouped by what they do to your data, plus 4 triggers that start a run.

Other operations 9

  • AI Agent
  • AI Classify
  • AI Embed
  • AI Extract
  • AI Generate
  • AI Guard
  • and 3 more

Nodes that use your Google Gemini connection

Beyond its own nodes, these generic nodes accept your Google Gemini credential. Each chooses the provider from the connection you select, so the same node also works with the other providers shown.

  • AI Agent
    ai.agent
    Tool-using LLM agent: model + memory + tools, with a step-by-step trace.
  • AI Classify
    ai.classify
    Pick one label from a fixed list, with a confidence score.
  • AI Embed
    ai.embed
    Turn text into an embedding vector with an OpenAI or Google model.
    Also works withOpenAI
  • AI Extract
    ai.extract
    Pull structured fields out of text against a JSON Schema, with optional human review.
  • AI Generate
    ai.generate
    Fill a {{var}} prompt template from the input and generate free text.
  • AI Guard
    ai.guard
    Check text for PII, secrets, prompt injection, topics, length or language.
  • LLM Call
    ai.llm
    Free-form prompt to any configured LLM provider, optionally JSON-schema constrained.
  • AI Memory
    ai.memory
    Read, extend, clear or summarise an ai.agent conversation history.
  • AI Transform
    ai.transform
    Summarize, translate, paraphrase or clean text with a curated prompt.

A sample Google Gemini workflow

  1. 1
    New Reviewtrigger.google_business_profile_review
    Start a run for every new or edited review on a location (polled).
  2. 2
    AI Agentai.agent
    Tool-using LLM agent: model + memory + tools, with a step-by-step trace.

An outline built from this provider’s node contracts, not a recorded run. Each step’s output fields are available to the next in the field picker.

Google Gemini questions

How does OCNORA authenticate with Google Gemini?
With a stored API key credential, shaped {"client_id", "client_secret", "refresh_token"}. A connection test can check it when you add it.
What can I automate with Google Gemini?
0 typed operations and 4 triggers, each with a reference page listing its config fields and the shapes it emits. The credential also drives 9 generic nodes.
What if the Google Gemini operation I need is not listed?
Where Google Gemini has an HTTP API, call it from the HTTP node with the same credential, or describe the step to the AI builder, which asks our team for a typed node when one is missing.
Are there templates that use Google Gemini?
Not yet. Start from a blank workflow, or describe the workflow to Build with AI.
How are Google Gemini calls controlled?
Every call and every run step is audited, runs count against the tenant’s token and USD budgets, and Workflow Health pre-flights the workflow when you save it.

See your first Google Gemini workflow run.

Book a technical demo. We add the connection, pick a template or describe the workflow to Build with AI, and Workflow Health checks it before it runs.