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Mibyan supports Gemini models on Google Cloud Vertex AI through Vertex’s OpenAI-compatible endpoint. Unlike the Google AI Studio provider (which uses a static API key against generativelanguage.googleapis.com), Vertex gives you enterprise-grade rate limits and GCP billing/credits, and is the right choice when you want Gemini usage to draw on your Google Cloud account rather than an AI Studio key.
Vertex authenticates with OAuth2, not an API keyVertex has no static API key for the standard endpoint. Every request needs a short-lived OAuth2 access token (≈1 hour TTL) minted from either a service-account JSON or Application Default Credentials (ADC). Mibyan mints and auto-refreshes these tokens for you — you never paste a token by hand. This is why pasting a temporary token into a custom provider’s api_key field does not work: it expires mid-session.

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Prerequisites

  • A Google Cloud project with the Vertex AI API enabled and billing active.
  • Credentials, one of:
    • a service-account JSON key file with the roles/aiplatform.user role, or
    • Application Default Credentials via gcloud auth application-default login (or the metadata server when running on a GCP VM).
  • google-auth — installed automatically the first time you select Vertex (lazy install). Run mibyan setup to repair a managed install if that fails.

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Quick Start

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Configuration

Vertex splits its settings by sensitivity:
  • The credential path is a pointer to a secret and lives in ~/.mibyan/.env.
  • Project ID and region are non-secret routing settings and live in ~/.mibyan/config.yaml.
~/.mibyan/.env:
~/.mibyan/config.yaml:
Environment variables win over config.yamlVERTEX_PROJECT_ID and VERTEX_REGION override the vertex.project_id / vertex.region values in config.yaml. Use them for per-shell overrides; keep the durable settings in config.yaml.

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How authentication works

  1. Mibyan resolves credentials in this order: VERTEX_CREDENTIALS_PATH → GOOGLE_APPLICATION_CREDENTIALS → ADC.
  2. It mints an OAuth2 access token (cloud-platform scope) and caches it, refreshing when the token is within 5 minutes of expiry.
  3. The token is handed to a standard OpenAI client pointed at the Vertex endpoint:
    Regional locations use a {region}-aiplatform.googleapis.com host instead.
  4. If a session runs longer than the token lifetime and a request returns 401, Mibyan re-mints the token and retries automatically. On a long-running gateway, if ADC’s refresh token has itself expired, Mibyan falls back to the service-account JSON when one is configured.

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Available Models

Vertex requires the google/ vendor prefix on model IDs. The mibyan model picker offers:
global region for Gemini 3.xThe Gemini 3.x preview models are served through the global endpoint. Regional endpoints (us-central1, etc.) may 404 them. Leave region: global unless you have a specific reason to pin a region.

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Switching Models Mid-Session

/model switches among already-configured providers and models; it does not collect new credentials. Configure Vertex with mibyan model first.

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Reasoning / Thinking

Vertex exposes Gemini’s thinking budget through the OpenAI-compatible surface. Mibyan maps its reasoning-effort setting onto extra_body.google.thinking_config automatically, so reasoning_effort works the same way it does on other Gemini surfaces.

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Diagnostics

The doctor reports whether Vertex credentials can be resolved (service-account path or ADC) and whether the provider is configured.

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Troubleshooting

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”Vertex AI credentials could not be resolved”

Mibyan found neither a service-account JSON nor working ADC. Either set VERTEX_CREDENTIALS_PATH in ~/.mibyan/.env, or run gcloud auth application-default login. If your project isn’t embedded in the credentials, set vertex.project_id in config.yaml.

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google-auth not installed

Mibyan lazy-installs it the first time you select the Vertex provider. If that fails, run mibyan setup to repair the managed install.

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404 on Gemini 3.x models

You are probably on a regional endpoint. Set region: global in the vertex: section of config.yaml (or unset VERTEX_REGION).

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403 / permission denied

The service account (or your ADC identity) needs the roles/aiplatform.user role on the project, and the Vertex AI API must be enabled for that project.