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Google ADK defines and runs the agent, Gemma supplies the model, and Docker Model Runner serves models locally. There is an important compatibility caveat: Docker’s published ADK example uses Gemma 3 4B, while Google’s Gemma 4 guide shows Gemini API and self-hosted vLLM routes. The official material cited here does not establish a complete, tested Gemma 4 + ADK + Docker Model Runner setup. You can use Docker’s example as a starting point, but treat a Gemma 4 connection as an integration to validate—not a guaranteed recipe.
What each part does
- ADK (Agent Development Kit) is the framework for defining and running the agent, including its instructions and tools.
- Gemma 4 is the model the agent sends requests to; it is not the agent framework or the serving runtime.
- Docker Model Runner manages and serves models locally, exposing an API that an application can connect to.
For an agent that uses tools, ordinary text generation is not enough: the model-serving endpoint must support compatible tool or function calling, and the relevant parsers must be enabled. Google’s ADK Gemma guide calls out tool/function-calling and reasoning-parser support for serving endpoints. Google ADK: Gemma models
Does Docker’s ADK sample use Gemma 4?
No. Docker’s published ADK Compose tutorial uses ai/gemma3:4B-Q4_0 with a context size of 10,000. It is a useful example of the local ADK pattern, but it does not show Gemma 4 or prove that Gemma 4 will work identically. Docker’s ADK tutorial
Docker states that this particular Gemma 3 sample requires 3.5 GB of VRAM and 2.31 GB of storage. Those figures apply to that sample only; they are not Gemma 4 specifications or a general minimum for running local agents.
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Choose a serving route before wiring up the agent
| Route | What the documentation establishes | Trade-off to consider |
|---|---|---|
| Docker Model Runner locally | Docker documents enabling Model Runner, managing models with the docker model CLI, and API compatibility options. Docker’s ADK sample demonstrates this route with Gemma 3 4B. Docker Model Runner documentation |
Local serving keeps the model runtime on your machine, but the exact Gemma 4 model identifier, API behavior, and tool-call compatibility need validation. |
| Gemini API | Google’s ADK Gemma guide includes Gemma 4 integration examples using the Gemini API. Google ADK: Gemma models | This is a documented Gemma 4 integration route, but it is not local model serving through Docker Model Runner. |
| Self-hosted vLLM endpoint | Google’s guide describes Gemma 4 with an OpenAI-compatible vLLM endpoint, using LiteLLM in Python or LangChain4j in Java, and notes the need for compatible tool/function calling and reasoning parsers. Google ADK: Gemma models | This is a documented self-hosted route, but it is a different serving setup from Docker Model Runner. |
Google’s local-model guidance emphasizes that model variant and execution framework depend on available hardware and workflow preferences; it does not prescribe one universal local stack. Google Gemma: Run Gemma models
Use Docker’s verified Gemma 3 example as a baseline
- Enable Model Runner. Follow Docker’s current setup instructions for Docker Desktop or Docker Engine. The available settings and runtime details can vary, so use the setup guide for your installed version. Docker Model Runner setup
- Start from Docker’s ADK Compose sample. Its model reference is
ai/gemma3:4B-Q4_0, with context size 10,000. Keep this model choice if your goal is to reproduce the example rather than test Gemma 4. Docker’s ADK tutorial - Run and inspect the sample. Use the commands and configuration in Docker’s tutorial for its Compose project. The sample gives you a known Gemma 3 baseline for checking the local serving and ADK connection.
- Verify agent behavior. Check that the model responds through the endpoint and, separately, that an ADK tool call is emitted and handled. A successful chat response does not establish tool-call compatibility.
What changes when you substitute Gemma 4?
Google documents Gemma 4 with Gemini API or a self-hosted vLLM endpoint through ADK; the reviewed official material does not provide an equivalent end-to-end Gemma 4 recipe for Docker Model Runner. If you try Gemma 4 locally through Model Runner, treat these as integration checks, not steps guaranteed by Docker’s Gemma 3 tutorial:
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- Confirm that your runtime can serve the chosen Gemma 4 variant. Use the model identifier and format supported by your installed Docker Model Runner version; do not assume the Gemma 3 identifier or configuration transfers.
- Connect ADK to the endpoint using a supported connector. Verify the API base URL, request format, and model name expected by both ADK and the serving runtime. Google’s documented Gemma 4 examples describe Gemini API and vLLM routes, not a Model Runner-specific connector configuration. Google ADK: Gemma models
- Test tool calls and reasoning behavior. Confirm that the endpoint supports the compatible function/tool-call format and that any required parsers are enabled. Do not infer this from a successful plain-text response.
- Check the full agent workflow. Exercise a real tool call from the ADK agent, inspect the returned result, and verify that the model can use it in a follow-up response.
Hardware is another version- and model-dependent part of this test. A Docker issue describes one user’s failed attempt to load Gemma 4 12B because available GPU memory was insufficient; this is an individual report, not a general minimum specification. Docker Model Runner issue tracker
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A separate issue reports a Gemma 4 E2B reasoning-budget behavior on macOS. An open issue is evidence of a reported case, not proof of a universal defect or its current resolution. Docker Model Runner issue tracker
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Pick the right ADK authoring mode
ADK supports code-based agents and Agent Config, but do not treat their model support as interchangeable. Agent Config documentation lists its own limitations, including model support restrictions. Check those constraints if you are using Agent Config; they do not automatically describe every code-based ADK agent. Google ADK: Agent Config
For a code-based agent, choose the language and model connector that match the serving route. Google’s Gemma 4 material describes LiteLLM for Python or LangChain4j for Java with the documented vLLM route; it does not establish a ready-made Docker Model Runner configuration for those examples.
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How to decide whether the local Gemma 4 setup is ready
- Model loads: Docker Model Runner can pull and serve the precise Gemma 4 variant you intend to use on your machine.
- ADK reaches the endpoint: The model identifier, API base URL, and request format align across ADK and the runtime.
- Tools work end to end: The endpoint returns compatible tool calls, ADK executes them, and the model can incorporate their results.
- Resources are adequate: The chosen model runs within your available compute and memory; the Gemma 3 sample’s published figures cannot establish Gemma 4 requirements.
- Authoring mode fits: You have checked Agent Config’s documented restrictions if using it, rather than assuming code-agent support applies.
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