n8n-nodes-gemini3-vertex

n8n community nodes that expose Gemini 3 features for Google Vertex AI.

Nodes

• Google Vertex Chat Model (Gemini 3) — a Chat Model sub-node for Agents
  and Chains. Adds the native Gemini 3 thinking level
  (MINIMAL/LOW/MEDIUM/HIGH), a thinking budget, per-category safety
  settings, and a streaming toggle.
• Google Vertex Gemini 3 — an action node with a Message a Model
  operation. Adds the native thinking level, thought summaries, per-category
  safety settings, Google Search grounding, and structured JSON output.

Both Model and Thinking Level are resource locators — pick from a list
or switch to Expression / ID mode to supply the value dynamically. Safety
settings expose one threshold dropdown per harm category, all at once, so there
is no per-row "add item" clicking.

Installation

In n8n: Settings → Community Nodes → Install and enter
n8n-nodes-gemini3-vertex.

Credentials

Both nodes use the built-in Google API credential — a GCP service account
with the Vertex AI API enabled (email, private key, region).

Model selection

The Model field is a resource locator: From List queries the live Vertex
AI model catalogue (ai.models.list, base models, filtered to Gemini) for the
selected project and region, so it never goes stale — or switch to ID to type
a model name directly.

Leave the Model field empty and the node auto-resolves the latest flash
model at run time — it queries the live catalogue and picks the highest
Gemini version of gemini-<version>-flash (excluding flash-lite and non-chat
flash variants). No hardcoded model, nothing to keep updating.

Feature placement

• The sub-node forwards the native thinking level and thinking budget through
  @langchain/google-vertexai (pinned to 2.1.24, matching the version n8n
  itself ships, to avoid duplicate-@langchain/core runtime conflicts).
• Thought summaries as an independent toggle live on the action node.
  The sub-node's LangChain layer couples thought inclusion to the thinking
  budget rather than exposing a separate switch.

Gotchas with Gemini 3.x

• includeThoughts / thoughtSummary — On Gemini 3.x, the flag is accepted
  by Vertex (the model still thinks; you can see the token count in
  usageMetadata.thoughtsTokenCount), but the response does not include the
  thought text the way Gemini 1.5 / 2.x did. The action node's
  thoughtSummary output field will be empty for 3.x models. Use it with 1.5 /
  2.x if you need the readable reasoning.

• Thinking-by-default models eating your tokens. Gemini 3.5 (and similar
  thinking-by-default flash models) will consume your entire maxOutputTokens
  on reasoning if you don't bound it. If you ask for a short answer with a
  small token budget and get back empty text, set Thinking Level = Minimal
  (or give it more tokens). This caught us in the integration tests — the same
  trap will catch a workflow.

Caveat: grounding & JSON output

Google Search grounding and forced JSON output conflict with how n8n Agents
bind their own tools, so they live on the action node rather than the
sub-node. The two features also cannot be combined with each other (a Gemini
API restriction); the action node validates this and errors clearly.

Development

npm install --ignore-scripts   # eslint-plugin-n8n-nodes-base has a pnpm-only preinstall guard
npm run build
npm test                       # unit tests — mocked, no network

Requires Node.js 20+.

Live integration tests

npm run test:integration exercises both nodes against the real Vertex AI
API and verifies the parameters the nodes send actually take effect in
Google's response. It needs a GCP service-account key:

export GCP_KEY_FILE=/absolute/path/to/service-account.json
# optional overrides:
export GCP_PROJECT_ID=my-project     # defaults to project_id in the key file
export GCP_LOCATION=us-central1      # default
export GEMINI_MODEL=gemini-3.1-pro        # default
npm run test:integration

Without GCP_KEY_FILE the suites skip themselves, so a normal npm test
never makes network calls. These tests make billable API calls.

What is verified against the live response: