Google Vertex AI

Route Pullfrog’s model calls through Vertex AI in your own Google Cloud project. Use it when your team already buys through Google Cloud, holds Vertex model-access approvals, or wants Google Cloud’s audit and billing controls around inference.

Vertex AI is bring-your-own-key only. Google Cloud bills inference to your account, and Pullfrog never proxies Vertex traffic.

Setup

Pick Google Vertex AI on the Provider card

In the Pullfrog console, pick the Google Vertex AI tile on the Provider card.

Store a service-account key

Create a Google Cloud service account with the roles/aiplatform.user role and download a JSON key. Click Set up on the tile and paste the whole JSON object as VERTEX_SERVICE_ACCOUNT_JSON.

The Configure Google Vertex AI dialog with the VERTEX_SERVICE_ACCOUNT_JSON field

You can store it as a GitHub Actions secret instead and map it in the workflow’s env: block.

Set the project, location and model in your workflow

Add these to the env: block of .github/workflows/pullfrog.yml. None of them is secret.

- name: Run agent
  uses: pullfrog/pullfrog@v0
  with:
    prompt: ${{ inputs.prompt }}
  env:
    GOOGLE_CLOUD_PROJECT: my-project
    VERTEX_LOCATION: global
    VERTEX_MODEL_ID: <vertex-model-id>
    # only when the key is a GitHub Actions secret
    VERTEX_SERVICE_ACCOUNT_JSON: ${{ secrets.VERTEX_SERVICE_ACCOUNT_JSON }}

Enable the API and the model

Enable the Vertex AI API (aiplatform.googleapis.com) for the project, then enable any gated model in Model Garden.

Variables

Name	Secret	Purpose
`VERTEX_SERVICE_ACCOUNT_JSON`	Yes	The full service-account JSON key, not a file path.
`GOOGLE_CLOUD_PROJECT`	No	Project ID. Optional when the JSON key has a `project_id`.
`VERTEX_LOCATION`	No	Vertex location. Use `global` unless data residency needs a regional endpoint.
`VERTEX_MODEL_ID`	No	Exact Vertex model ID. Pullfrog runs it as written and never upgrades it.

A value stored in the console needs no env: line, because Pullfrog injects it into every run. A value set in the workflow wins over a stored one.

On an account that pays through Pullfrog Router, store the service-account key in the Pullfrog console. Pullfrog decides how to fund a run before the runner starts, when GitHub Actions secrets are not visible to it, so a run whose key exists only there falls back to the Router’s default model.

Model IDs

The model menu has a single Google Vertex AI entry; VERTEX_MODEL_ID names the actual model. Use the ID that Model Garden shows for a Claude, Gemini or partner model your project has enabled. To change models, edit VERTEX_MODEL_ID and commit.

Routing

Pullfrog reads the model ID to choose the agent:

Claude models, whose ID starts with claude-, run on Claude Code. Pullfrog sets CLAUDE_CODE_USE_VERTEX=1 and passes the project and location in Claude Code’s own variable names.
Every other model runs on OpenCode’s google-vertex provider.

Troubleshooting

Symptom	Fix
`VERTEX_MODEL_ID env var is required when the model is set to "vertex/byok"`	The repository uses Google Vertex AI, but the workflow does not set `VERTEX_MODEL_ID`. Add it to `env:`.
”Google Vertex AI model selected but required configuration is missing”	The message lists each missing value: `VERTEX_SERVICE_ACCOUNT_JSON`, `GOOGLE_CLOUD_PROJECT` (when the key has no `project_id`), `VERTEX_LOCATION`, or `VERTEX_MODEL_ID`.
Google authentication error	`VERTEX_SERVICE_ACCOUNT_JSON` must hold the JSON object itself, not a file path. Pullfrog writes it to a credentials file on the runner and sets `GOOGLE_APPLICATION_CREDENTIALS` for the agent.
Permission or model-access error	Check that the service account has `roles/aiplatform.user`, the Vertex AI API is on, and the model is enabled in Model Garden for `VERTEX_LOCATION`.

Get started

Working with Pullfrog

Models and billing

Administration

Reference

Setup

Variables

Model IDs

Routing

Troubleshooting

​Setup

​Variables

​Model IDs

​Routing

​Troubleshooting

Setup

Variables

Model IDs

Routing

Troubleshooting