Google Gemini

Use Gemini 2.5 Flash, Gemini 3 Pro, and other Google models with docker-agent.

Setup

# Set your API key
export GOOGLE_API_KEY="AI..."

Configuration

Inline

agents:
  root:
    model: google/gemini-2.5-flash

Named Model

models:
  gemini:
    provider: google
    model: gemini-2.5-flash
    temperature: 0.5

Available Models

Model	Best For
`gemini-3-pro`	Most capable Gemini model
`gemini-3-flash`	Fast, efficient, good balance
`gemini-2.5-flash`	Fast inference, cost-effective
`gemini-2.5-pro`	Strong reasoning, large context

Thinking Budget

Gemini supports two approaches depending on the model version:

⚠️ Different thinking formats

Gemini 2.5 uses **token-based** budgets (integers). Gemini 3 uses **level-based** budgets (strings like low, high). Make sure you use the right format for your model version.

Gemini 2.5 (Token-based)

models:
  gemini-no-thinking:
    provider: google
    model: gemini-2.5-flash
    thinking_budget: 0 # disable thinking

  gemini-dynamic:
    provider: google
    model: gemini-2.5-flash
    thinking_budget: -1 # dynamic (model decides) — default

  gemini-fixed:
    provider: google
    model: gemini-2.5-flash
    thinking_budget: 8192 # fixed token budget

Gemini 3 (Level-based)

models:
  gemini-3-pro:
    provider: google
    model: gemini-3-pro
    thinking_budget: high # default for Pro: low | high

  gemini-3-flash:
    provider: google
    model: gemini-3-flash
    thinking_budget: medium # default for Flash: minimal | low | medium | high

Built-in Tools (Grounding)

Gemini models support built-in tools that let the model access Google Search and Google Maps directly during generation. Enable them via provider_opts:

models:
  gemini-grounded:
    provider: google
    model: gemini-2.5-flash
    provider_opts:
      google_search: true
      google_maps: true
      code_execution: true

Option	Description
`google_search`	Enables Google Search grounding for up-to-date info
`google_maps`	Enables Google Maps grounding for location queries
`code_execution`	Enables server-side code execution for computations

Vertex AI Model Garden

You can use non-Gemini models (e.g. Claude, Llama) hosted on Google Cloud’s Vertex AI Model Garden through the google provider. When a publisher is specified in provider_opts, requests are routed through Vertex AI’s OpenAI-compatible endpoint instead of the Gemini SDK.

Authentication

Vertex AI uses Google Cloud Application Default Credentials (ADC). Make sure you are authenticated:

gcloud auth application-default login

Configuration

models:
  claude-on-vertex:
    provider: google
    model: claude-sonnet-4-20250514
    provider_opts:
      project: my-gcp-project       # GCP project ID (or set GOOGLE_CLOUD_PROJECT)
      location: us-east5             # GCP region (or set GOOGLE_CLOUD_LOCATION)
      publisher: anthropic           # Model publisher (anthropic, meta, etc.)

Option	Description
`project`	GCP project ID. Falls back to `GOOGLE_CLOUD_PROJECT` env var
`location`	GCP region (e.g. `us-east5`, `us-central1`). Falls back to `GOOGLE_CLOUD_LOCATION`
`publisher`	Model publisher (e.g. `anthropic`, `meta`, `mistral`). Must not be `google`

ℹ️ Gemini models on Vertex AI

Setting publisher: google (or omitting publisher) uses the native Gemini SDK path. The Model Garden endpoint is only used for non-Google publishers.

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