agent-platform-inference

"""Example of using the GenAI SDK with Gemini on Vertex AI."""

from google import genai
import google.auth

# Get default project ID from environment
_, project_id = google.auth.default()

# Initialize GenAI Client with Vertex AI backend
# Use location="global" for Preview models (Gemini 2.0)
client = genai.Client(enterprise=True, project=project_id, location="us-central1")

response = client.models.generate_content(
    model="gemini-2.5-pro", contents="Why is the sky blue?"
)
print(response.text)

"""Example of using the OpenAI SDK with Gemini on Vertex AI."""

import google.auth
import google.auth.transport.requests
import openai


def get_gcp_access_token():
  creds, _ = google.auth.default()
  # Refresh credentials using a Request transport object to obtain a fresh
  # OAuth access token for the OpenAI client authorization header.
  creds.refresh(google.auth.transport.requests.Request())
  return creds.token


# Get default project ID from environment
_, project_id = google.auth.default()

client = openai.OpenAI(
    base_url=f"https://aiplatform.googleapis.com/v1/projects/{project_id}/locations/us-central1/endpoints/openapi",
    api_key=get_gcp_access_token(),
)

response = client.chat.completions.create(
    model="google/gemini-2.5-pro",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)
print(response.choices[0].message.content)

"""Example of using the Agent Platform SDK with Gemini on Vertex AI."""

import google.auth
import vertexai
from vertexai.generative_models import GenerativeModel

# Get default project ID from environment
_, project_id = google.auth.default()

vertexai.init(project=project_id, location="us-central1")

model = GenerativeModel("gemini-2.5-pro")
response = model.generate_content("Why is the sky blue?")
print(response.text)

"""Example of using the GenAI SDK with OpenMaaS on Vertex AI."""

from google import genai
import google.auth

# Get default project ID from environment
_, project_id = google.auth.default()

client = genai.Client(
    enterprise=True,
    project=project_id,
    location="global",  # OpenMaaS models are often global
)

# Note: For GenAI SDK/Vertex with OpenMaaS, you MUST use the full path:
# `publishers/PUBLISHER/models/MODEL`
response = client.models.generate_content(
    model="publishers/zai-org/models/glm-5-maas",
    contents="Explain quantum computing in simple terms.",
)
print(response.text)

"""Example of using the OpenAI SDK with OpenMaaS on Vertex AI."""

import google.auth
import google.auth.transport.requests
import openai


def get_gcp_access_token():
  creds, _ = google.auth.default()
  creds.refresh(google.auth.transport.requests.Request())
  return creds.token


# Get default project ID from environment
_, project_id = google.auth.default()

client = openai.OpenAI(
    base_url=f"https://aiplatform.googleapis.com/v1/projects/{project_id}/locations/global/endpoints/openapi",
    api_key=get_gcp_access_token(),
)

# NOTE: For OpenMaaS models, you MUST use the format: `publisher/model`
# Example: `deepseek-ai/deepseek-v3.2-maas`, `zai-org/glm-5-maas` etc.

response = client.chat.completions.create(
    model="zai-org/glm-5-maas",
    messages=[{
        "role": "user",
        "content": "Explain quantum computing in simple terms.",
    }],
)

print(response.choices[0].message.content)

"""Example of using the Agent Platform SDK with OpenMaaS on Vertex AI."""

import google.auth
import vertexai
from vertexai.generative_models import GenerativeModel

# Get default project ID from environment
_, project_id = google.auth.default()

vertexai.init(project=project_id, location="global")

# Important: Use the full resource path: `publishers/PUBLISHER/models/MODEL`
model = GenerativeModel("publishers/zai-org/models/glm-5-maas")
response = model.generate_content("Explain quantum computing.")
print(response.text)

google-genai
google-cloud-aiplatform
openai
google-auth

#!/bin/bash
set -e

# Verify against the ambient interpreter, which is the one the skill's scripts
# actually run under. A throwaway venv would verify an environment no real
# invocation ever uses, so it could pass while the real one is broken.
echo "Checking dependencies..."
python3 -c "import vertexai, google.genai, openai" \
  || pip install -q -r scripts/requirements.txt

echo "Running verification tests..."
FAILED=0

# Iterate directly over the files in the scripts directory
for script in scripts/*.py; do
  echo "Running $script..."
  if python3 "$script" > /dev/null 2>&1; then
    echo "  PASS: $script"
  else
    echo "  FAIL: $script"
    python3 "$script" # Run again to show output
    FAILED=1
  fi
done

if [[ $FAILED -eq 0 ]]; then
  echo "All scripts passed verification!"
  exit 0
else
  echo "Some scripts failed verification."
  exit 1
fi

---
name: agent-platform-inference
metadata:
  category: AiAndMachineLearning
description: >-
  Connects to and performs inference with Google Cloud Agent Platform GenAI
  models, including First-Party Gemini models and Third-Party OpenMaaS models
  (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a
  model a question, run a test prompt, execute chat completions, or generate
  code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK,
  OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and
  global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400
  User Validation, or 404 Not Found errors. Don't use for deploying models to
  endpoints or for running model evaluations.
---

# Agent Platform GenAI Inference Skill

This skill provides instructions for authenticating and connecting to Google
Cloud Agent Platform to use Generative AI models. It covers:

*   **First-Party publisher models** (Gemini) — section 2.
*   **Third-Party publisher models** (OpenMaaS: Llama, DeepSeek, Qwen, etc.)
    — section 3.
*   **Custom endpoints** (any model on a numeric `projects/.../endpoints/<id>`
    resource — tuned Gemini models, OSS LLMs self-deployed from Model Garden
    via the `agent-platform-deploy` skill, and legacy custom models) —
    section 4.

## Safety & Confirmation Tiers (CRITICAL)

Before executing any commands or scripts on behalf of the user, you must adhere
to the following safety tiers based on the action requested. (The skill is
read-only; other safety tiers are omitted):

1.  **Tier R: Read-only / Inference (`client.models.generate_content`,
    `client.chat.completions.create`, `client.completions.create`,
    `client.embeddings.create`)**
    *   Requires **interactive confirmation** with 'Yes'/ 'No' options before
        executing model inference on behalf of the user, to prevent unexpected
        cost or quota consumption.
    *   **Required Fields in Confirmation Card**: The confirmation prompt must
        clearly explain the proposed inference execution and explicitly list all
        of the following parameters:
        *   **Project ID**: The Google Cloud project ID or number (e.g.
            `123456789012`, `my-project`).
        *   **Region / Location**: The target region (e.g. `us-central1`,
            `global`).
        *   **Model ID**: The exact model ID (e.g. `gemini-2.5-flash`,
            `deepseek-ai/deepseek-v3.2-maas`).
        *   **SDK**: The SDK choice (e.g. `Google GenAI SDK (google-genai)`,
            `OpenAI SDK`).
        *   **Input Prompt** (or **Input Image** / **Input Media**): The prompt
            text or media URI.
        *   Any additional generation parameters (e.g. `max_output_tokens`,
            `response_schema`) if specified.
        Natural-language paraphrases without explicitly listing these
        parameters are NOT sufficient.
    *   **Same-turn restriction**: Do not execute the inference scripts or
        commands in the same turn as presenting the confirmation prompt. Stop
        and wait for the user's reply; only execute after explicit 'Yes' /
        approval.
    *   **Gold Standard Example**:
        > I will perform model inference with the following parameters. Please
        > confirm this information before I proceed:
        > * **Project ID**: `my-project`
        > * **Region**: `us-central1`
        > * **Model ID**: `gemini-2.5-pro`
        > * **SDK**: Google GenAI SDK (`google-genai`)
        > * **Input Prompt**: "Summarize the plot of Hamlet in 3 sentences"
        >
        > Do you confirm? [Yes/No]

## Phase 0: Environment Setup

**CRITICAL**: Before running any of the Python sample scripts in the `scripts/`
directory (e.g., `scripts/openmaas_openai_sdk.py`), you MUST ensure the
environment is correctly initialized by following these steps:

1.  **Google Cloud Authentication**: Authenticate with your Google Cloud
    credentials and configure active Application Default Credentials (ADC) for
    Agent Platform access:

    ```bash
    gcloud auth login
    gcloud auth application-default login
    ```

2.  **Enable API** (if not already enabled):

    ```bash
    gcloud services enable aiplatform.googleapis.com
    ```

3.  **Python Dependencies**: The scripts import `vertexai` (from
    `google-cloud-aiplatform`), `google-genai`, and `openai`. Do **not** create
    a virtual environment — it starts empty and hides packages the environment
    already provides, forcing a redundant install. Probe, and install only what
    is missing:

    ```bash
    python3 -c "import vertexai, google.genai, openai" \
      || pip install -r scripts/requirements.txt
    ```

    `scripts/requirements.txt` is a fallback for an environment that does not
    already provide these SDKs; do not install it on top of a working
    environment.

4.  **Verify Setup (Optional)**: Run all sample scripts at once to verify the
    environment is working end-to-end:

    ```bash
    ./scripts/verify_all.sh
    ```

5.  **Execution**: Run the scripts with a plain `python3 scripts/...`. There is
    no environment to activate first.



> [!IMPORTANT] **CRITICAL: Model IDs & Availability** * **Gemini Models**: See
> [Gemini Models][gemini-models-docs] for valid Model IDs and Regions. *
> **OpenMaaS Models**: See
> [Use Open Models on Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/maas/use-open-models)
> for Llama, DeepSeek, Qwen, etc. * **Incomplete Lists**: The Model IDs listed
> in this skill are **examples only** and may be incomplete or outdated. *
> **Action**: Always verify the Model ID and Region using the links above before
> generating code.
>
> \[gemini-models-docs]:
> https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/migrate
>

## Parameter Grounding & Clarification Protocol (CRITICAL)

Before preparing code or presenting a Tier R confirmation card, you MUST ensure
all necessary parameters are grounded:

1.  **Missing Model ID, Model Family, or SDK (CRITICAL)**:
    *   If the user has **NOT** specified which model or model family to use
        (e.g., "run a test prompt", "ask a generative AI model to...", "ask
        DeepSeek a question" without model version), or has not specified the
        SDK preference:
    *   **NEVER** guess, volunteer, or default to a model (such as
        `gemini-2.5-flash`, `gemini-2.5-pro`, or `deepseek-v3.2-maas`).
        Proposing a defaulted model in a confirmation card without asking
        violates parameter grounding.
    *   **YOU MUST STOP AND ASK THE USER**: "Which model (or model family,
        such as Gemini, Llama, DeepSeek, or Qwen) and SDK preference (such as
        Google GenAI SDK or OpenAI SDK) would you like to use?" and ask for the
        target region and project ID if not specified.
    *   Only after the user specifies the model (and any missing SDK preference)
        should you proceed to prepare the execution and present the Tier R
        confirmation prompt.

2.  **Missing Project ID or Region**:
    *   If the user's project ID or region is not specified in the prompt or
        conversation context, **ASK** the user for the project ID and region
        (e.g. "Which project ID and region would you like to use?"). Do not
        silently assume a project or region.
    *   **OpenMaaS Locations**: OpenMaaS publisher models are hosted on `global`
        (e.g. `deepseek-ai/deepseek-v3.2-maas`,
        `meta/llama-3.3-70b-instruct-maas`) or regional endpoints such as
        `us-central1` (e.g. `deepseek-ai/deepseek-r1-0528-maas`).
        When configuring inference for OpenMaaS models, use the appropriate
        endpoint:

        *   Global: `https://aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/global/endpoints/openapi`
        *   Regional: `https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/endpoints/openapi`

        and explicitly reflect the region in the confirmation card and final
        response.

3.  **SDK Choice**:
    *   If the user specifies a model but does not specify an SDK, use the
        preferred SDK for that model family (GenAI SDK `google-genai` for
        Gemini, OpenAI SDK `openai` for OpenMaaS).

4.  **Sandbox Execution via Python (CRITICAL)**:
    *   When executing model inference in the sandbox via `run_command`,
        **ALWAYS** run Python code using the official SDKs (e.g., writing and
        running a Python script with `google-genai`, `openai`, or `vertexai`).
        Do not use raw curl commands for final inference execution.

## Workflow Decision Tree

1.  **Model Specified?**
    *   **No** (user omitted model name/family) -> **Ask the user** which model
        or model family, target region, and SDK preference they want to use.
    *   **Underspecified** (e.g., user said "DeepSeek" or "Llama" without
        version) -> **Ask the user** which specific model version they prefer
        (e.g., `deepseek-ai/deepseek-r1-0528-maas`,
        `deepseek-ai/deepseek-v3.2-maas`, `meta/llama-3.3-70b-instruct-maas`).
    *   **Yes** -> Proceed to Step 2.

2.  **Model Family & SDK Selection**:
    *   **Gemini** (e.g., `gemini-2.5-pro`, `gemini-2.5-flash`) -> Preferred:
        **GenAI SDK** (`google-genai`). Proceed to [1. Gemini Models].
    *   **OpenMaaS** (e.g., `deepseek-ai/*`, `meta/llama-*`, `qwen/*`) ->
        Preferred: **OpenAI SDK** (`openai`). Proceed to [2. OpenMaaS Models].
    *   **Custom Endpoint** (numeric endpoint ID
        `projects/.../endpoints/<id>`) -> Proceed to [4. Custom Endpoints].

3.  **Troubleshooting**: Is the user reporting an error (429 Resource Exhausted,
    400 User Validation, 404 Not Found, empty response due to token limits,
    etc.)?
    *   **Yes** -> Proceed to [5. Troubleshooting & Common Error Codes].
    *   **No** -> Present Tier R confirmation prompt with all required fields
        (Project ID, Region, Model ID, SDK, Input Prompt), wait for user
        confirmation, then execute via Python SDK.

## 0.5 Region Availability Check for Publisher Endpoints (Gemini + LoRA base)

> [!NOTE] **Skip this section** if either of these applies:
>
> - The user is calling a custom endpoint (§4) — a tuned Gemini model served on
>   a numeric `projects/.../endpoints/<id>`, a self-deployed OSS LLM (Llama,
>   DeepSeek, Qwen, Gemma, etc.), or a legacy custom model. Those requests hit
>   a specific endpoint resource whose region is fixed at deploy time; if the
>   caller-side region doesn't match, the endpoint lookup returns a clean 404
>   without incurring inference cost. Go to §4.
> - The user is calling an OpenMaaS publisher model (§2) — Llama, DeepSeek,
>   Qwen, etc. served via the global `openapi` base URL. These don't have
>   per-region availability restrictions in the same way first-party Gemini
>   does. Go to §2.
>
> **Apply this section** only if the user is calling a first-party managed
> Gemini model (`gemini-*`, via §1), including fine-tuned LoRA adapters on
> top of Gemini — these route through a publisher endpoint whose regional
> availability actually varies.

Before responding to any inference request that names a specific region for a
first-party managed Gemini model (`gemini-*`) or a fine-tuned Gemini LoRA
adapter (identified by numeric endpoint ID + user-stated base model), you
**MUST** verify the model is actually available in that region by making a
live API call. Do not rely on Google Search, training-corpus knowledge, or
publisher documentation for availability claims — regional availability
changes frequently and grounded text can be stale or wrong.

Probe only the exact model and region the user asked about. Do not probe other
models as a "control" — you cannot infer anything about model A's availability
from model B's status, because a different model may itself be unavailable in
the reference region for unrelated reasons.

For first-party Gemini models, probe with a real `:generateContent` call using
a minimal valid payload:

```bash
curl -sS -o /dev/null -w "%{http_code}\n" \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
    "https://${LOCATION_ID}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION_ID}/publishers/google/${MODEL_ID}:generateContent" \
    -d "{\"contents\":{\"role\":\"user\",\"parts\":{\"text\":\"${PROBE_TEXT:-hi}\"}}}"
```

For inference against a fine-tuned Gemini LoRA adapter, probe the **base
model** in the target region using the same `:generateContent` call above with
`${MODEL_ID}` set to the base (e.g. `gemini-2.5-flash` if the adapter was
tuned on `gemini-2.5-flash`). The LoRA adapter cannot serve in a region where
its base model isn't available.

Interpret the probe result and act:

-   **200** — model is available in that region. Proceed with the SDK setup in
    §1.
-   **404** — model is not available in that region. STOP. Tell the user
    plainly that the model isn't offered in that region and list the regions
    where it is available (from
    [Gemini Models][gemini-models-docs] or `gcloud ai model-garden models list
    --filter="name~$MODEL_NAME"` without `--region`). Do not silently switch
    regions. Do not proceed to write inference code or SDK initialization for
    the unsupported region. Do not run additional "control" probes to
    double-check the 404 — the target-region probe is authoritative.
-   **Any other outcome** (permission denied, quota, transient failure, etc.)
    — do not conclude the model is available or unavailable. Explain the
    underlying cause in plain language (e.g. "your account doesn't have access
    to this project's Vertex AI API — enable it in the console or switch
    projects") and the concrete next action.

## 1. Gemini Models

For Gemini models (e.g., `gemini-2.5-pro`, `gemini-3-flash-preview`), the
**GenAI SDK** (`google-genai`) is the **PREFERRED** method. The legacy
`vertexai` SDK is still supported but GenAI SDK is recommended for new projects.

> [!IMPORTANT]
> **Preview Models (including Gemini 3.1)** are often **ONLY** available in the
> `global` region. Stable models are available in `us-central1` and other
> regions.

### Choosing the Right SDK

*   **Gemini Models**: **GenAI SDK** (`google-genai`) is **PREFERRED**. Use
    OpenAI SDK for compatibility, or Legacy SDK (`vertexai`) if needed.
*   **OpenMaaS Models**: **OpenAI SDK** is **HIGHLY RECOMMENDED**. Use GenAI SDK
    or Legacy SDK if you have specific infrastructure requirements.

### Installation

```bash
pip install google-genai
```

### Python Example (GenAI SDK - Preferred)

See [`scripts/gemini_genai_sdk.py`](scripts/gemini_genai_sdk.py) for the
complete code.

### Alternative: OpenAI SDK (Chat Completions)

Use the standard OpenAI SDK with the Agent Platform endpoint. This is great for
cross-compatibility.

See [`scripts/gemini_openai_sdk.py`](scripts/gemini_openai_sdk.py) for the
complete code.

### Legacy: Agent Platform SDK

The legacy `vertexai` SDK is still widely used but `google-genai` is preferred
for new Gemini projects.

See [`scripts/gemini_vertexai_sdk.py`](scripts/gemini_vertexai_sdk.py) for the
complete code.

**Documentation**:
[Google GenAI SDK](https://github.com/googleapis/python-genai)

**Documentation**:
[Agent Platform Gemini Models](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/google-models)

## 2. OpenMaaS Models (Llama, DeepSeek, Qwen, etc.)

For OpenMaaS (Model-as-a-Service) models, the **HIGHLY RECOMMENDED** approach is
to use the standard **OpenAI SDK** with a specific Vertex AI endpoint.

> [!WARNING] While `GenerativeModel` *can* support some OpenMaaS models, it is
> **discouraged**. Use the OpenAI SDK for best compatibility (especially for
> Chat Completions).

### Installation

```bash
pip install openai google-auth
```

### Authentication for OpenAI SDK

You **MUST** use a Google Cloud OAuth access token as the API key for the OpenAI
SDK.

```python
import subprocess

def get_gcp_access_token():
    return subprocess.check_output(
        ["gcloud", "auth", "print-access-token"]
    ).decode("utf-8").strip()
```

> [!NOTE] Google Cloud access tokens typically expire after 1 hour. The
> `get_gcp_access_token()` function above retrieves a *fresh* token at the time
> it is called. For long-running
> applications, you implement a refresh mechanism. See
> [Refresh the access token](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/migrate/openai/auth-and-credentials?hl=en#refresh_your_credentials)
> for details.

### Configuration (Base URL)



-   **Global Endpoint** (Recommended for most models requiring global
    availability):
    `https://aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/global/endpoints/openapi`
-   **Regional Endpoint**:
    `https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/{REGION}/endpoints/openapi`


### Python Example (OpenMaaS - Chat Completions)

See [`scripts/openmaas_openai_sdk.py`](scripts/openmaas_openai_sdk.py) for the
complete code.

> [!TIP] **Alternative: Environment Variables** You can set environment
> variables in your shell instead of updating the code.
>
> **Alternative: Environment Variables** You can set environment variables in
> your shell instead of updating the code.

```bash
export OPENAI_BASE_URL="https://aiplatform.googleapis.com/v1/projects/YOUR_PROJECT_ID/locations/global/endpoints/openapi"
export OPENAI_API_KEY="$(gcloud auth application-default print-access-token)"
```
> Then initialize the client without arguments: `client = OpenAI()`

### Python Example (OpenMaaS - Completions API)

The following models support the legacy Completions API: `zai-org/glm-5-maas`,
`moonshotai/kimi-k2-thinking-maas`, `minimaxai/minimax-m2-maas`,
`deepseek-ai/deepseek-v3.1-maas`, and `deepseek-ai/deepseek-v3.2-maas`.

```python
response = client.completions.create(
    model="deepseek-ai/deepseek-v3.2-maas",
    prompt="Once upon a time",
    max_tokens=100
)
print(response.choices[0].text)
```

### Python Example (OpenMaaS - Embeddings)

```python
# Verify specific Embedding Model ID on Model Garden (e.g., intfloat/multilingual-e5-small)
response = client.embeddings.create(
    model="intfloat/multilingual-e5-large-maas",
    input="The quick brown fox jumps over the lazy dog",
)
print(response.data[0].embedding)
```

### Alternative: GenAI SDK

The `google-genai` SDK can also access OpenMaaS models via the `vertexai`
backend.

See [`scripts/openmaas_genai_sdk.py`](scripts/openmaas_genai_sdk.py) for the
complete code.

> [!IMPORTANT]
> **Model ID Format**: For GenAI SDK with OpenMaaS, you **MUST** use the full
> path: `publishers/PUBLISHER/models/MODEL` (e.g.,
> `publishers/zai-org/models/glm-5-maas`).

### Legacy: Agent Platform SDK (OpenMaaS)

For OpenMaaS, you can also use `GenerativeModel` (if supported).

See [`scripts/openmaas_vertexai_sdk.py`](scripts/openmaas_vertexai_sdk.py) for
the complete code.

> [!IMPORTANT] **Model ID Format**: For Agent Platform SDK with OpenMaaS, you
> **MUST** use the full path: `publishers/PUBLISHER/models/MODEL`.

### Model Reference & Availability

**Documentation**:
[Use Open Models on Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/maas/use-open-models)

> [!TIP]
> **Self-Deployment for Control**: If you need **dedicated hardware**
> (GPUs/TPUs), **guaranteed capacity**, or **specific regional placement** not
> offered by MaaS, you can **Self-Deploy** these models to Agent Platform
> Endpoints. Search for the model in Model Garden and click "Deploy" to select
> your machine type. See the `agent-platform-deploy` skill for the deployment
> workflow, and **section 4 of this skill** for how to invoke the resulting
> self-deployed endpoint (use `/chat/completions` on the dedicated endpoint
> DNS, NOT the OpenMaaS publisher URL above).

> [!IMPORTANT] **Finding Inference Examples**: The list above is a starting
> point. For the **definitive** inference snippets (especially for Chat
> Completions payload structure): 1. Consult the
> [Use Open Models on Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/maas/use-open-models)
> list. 2. Click the link for your specific model (e.g., "DeepSeek-V3") to visit
> its **Model Garden** page. 3. Look for the **"Sample Code"** or **"Use this
> model"** button on the Model Garden page to get the exact `curl` or Python
> code for that specific model version.

> [!NOTE] This list is **INCOMPLETE**. See
> [Use Open Models on Agent Platform](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/maas/use-open-models)
> for the full list of supported models.

Model Family  | Model ID Examples                              | Location      | Notes
:------------ | :--------------------------------------------- | :------------ | :----
**Llama 4**   | `meta/llama-4-maverick-17b-128e-instruct-maas` | `us-east5`    |
**Llama 4**   | `meta/llama-4-scout-17b-16e-instruct-maas`     | `us-east5`    |
**Llama 3.3** | `meta/llama-3.3-70b-instruct-maas`             | `us-central1` |
**DeepSeek**  | `deepseek-ai/deepseek-v3.2-maas`               | `global`      | Global ONLY
**DeepSeek**  | `deepseek-ai/deepseek-v3.1-maas`               | `us-west2`    | US-West2 ONLY
**DeepSeek**  | `deepseek-ai/deepseek-r1-0528-maas`            | `us-central1` |
**Qwen 3**    | `qwen/qwen3-coder-480b-a35b-instruct-maas`     | `global`      |
**Qwen 3**    | `qwen/qwen3-next-80b-a3b-instruct-maas`        | `global`      |
**Kimi**      | `moonshotai/kimi-k2-thinking-maas`             | `global`      |
**MiniMax**   | `minimaxai/minimax-m2-maas`                    | `global`      |
**GLM**       | `zai-org/glm-4.7-maas`, `zai-org/glm-5-maas`   | `global`      |

## 4. Custom Endpoints (tuned Gemini, self-deployed OSS LLM, legacy custom)

This section covers how to invoke a model on an Agent Platform
**Endpoint** that belongs to your project — i.e., something with a
numeric resource name like
`projects/.../endpoints/5875254126916403200`. This is distinct from
calling the publisher MaaS surfaces in sections 2 and 3 (which hit
`publishers/.../models/...` or `endpoints/openapi`, not your endpoint
ID).

> [!IMPORTANT]
>
> **Publisher MaaS vs your endpoint (don't confuse them).** Section 3's
> OpenMaaS examples (e.g. `meta/llama-3.3-70b-instruct-maas`) hit a
> **shared publisher URL** at
> `/v1/projects/.../locations/.../endpoints/openapi`. This section's
> recipes hit **YOUR** endpoint at `/v1/projects/.../endpoints/<id>`.
> If you have a Llama / Gemma / etc. model deployed via Model Garden
> "Deploy" (NOT the MaaS publisher product), follow this section — not
> section 3.

> [!IMPORTANT]
>
> **Active Endpoint Discovery & Single Source of Truth**:
>
> *   To check if a model or tuned Gemini adapter is deployed and ready for
>     inference, run:
>
>     `gcloud ai endpoints list --project=<PROJECT_ID> --region=<REGION> --format=json`.
>
> *   **`gcloud ai endpoints list` is the ONLY authoritative source of active
>     serving endpoints.**
> *   Do NOT rely on historical `job.tunedModel.endpoint` identifiers from past
>     `gcloud ai tuning-jobs list` records — those record where an endpoint was
>     initially created during tuning, but if the endpoint was subsequently
>     deleted, undeployed, or expired, it is no longer active.
> *   If `gcloud ai endpoints list` returns empty `[]` or the requested tuned
>     model is not deployed on any listed endpoint, report directly to the user
>     that no active endpoint was found in that region and halt. **NEVER**
>     hallucinate that historical/deleted endpoints are ready, and **NEVER**
>     silently substitute the base model without explicit user instruction and a
>     fresh Tier R confirmation prompt.

> [!IMPORTANT]
>
> **Two orthogonal axes determine the call shape:**
>
> **Axis 1 — model family** drives the RPC method and payload:
>
> | Endpoint serves | Method | Payload |
> |---|---|---|
> | A **tuned Gemini model** (output of Gemini tuning — the endpoint is already deployed for you) | `:generateContent` | `contents` / `generationConfig` |
> | A **self-deployed OSS LLM** (Llama, DeepSeek, Qwen, Gemma, Mistral, etc., deployed via Model Garden) | `/chat/completions` | OpenAI-compatible `messages` |
> | A **legacy custom model** (classification, regression, custom-trained, embedding) | `:predict` | `instances` / `parameters` |
>
> Run `gcloud ai endpoints describe <ENDPOINT_ID> --region=<REGION>
> --format=json` and inspect `deployedModels[].model` to decide:
> contains `gemini` → tuned Gemini; matches an OSS publisher
> (`meta/`, `google/gemma-`, `deepseek-ai/`, `qwen/`, ...) → OSS LLM;
> otherwise → likely legacy custom.
>
> **Axis 2 — endpoint type (shared vs dedicated)** drives the URL host:
>
> | `dedicatedEndpointEnabled` | Host |
> |---|---|
> | `false` (default — shared endpoint) | `<REGION>-aiplatform.googleapis.com` |
> | `true` (dedicated endpoint, has its own DNS) | the value of `dedicatedEndpointDns` (format: `<ENDPOINT_ID>.<REGION>-<PROJECT_NUM>.prediction.vertexai.goog`) |
>
> A dedicated endpoint **cannot** be reached via the shared
> `<REGION>-aiplatform.googleapis.com` host (per the
> `Endpoint.dedicated_endpoint_enabled` proto: *"Once you enabled
> dedicated endpoint, you won't be able to send request to the shared
> DNS"*). Always check `dedicatedEndpointDns` in the describe output:
> if it's set, use it as the host; otherwise use the shared host.
>
> **Path is always
> `/v1/projects/.../locations/.../endpoints/<id>/...`** on both hosts.
> Both `/v1/` (GA) and `/v1beta1/` (beta) route to the same backend; the
> recipes in this skill use `/v1/`. The public
> [Gemma deployment notebook](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_deployment_on_vertex.ipynb)
> still uses `/v1beta1/`, which also works.

### 4a. REST recipe — tuned Gemini model

Output of Gemini tuning is always an endpoint that's already deployed for
you, reachable on the shared host with `:generateContent`.

```bash
PROJECT_ID=my-project
ENDPOINT_ID=5875254126916403200
REGION=us-central1
TOKEN=$(gcloud auth application-default print-access-token)

curl -sS -X POST \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  "https://${REGION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${REGION}/endpoints/${ENDPOINT_ID}:generateContent" \
  -d '{
    "contents": [
      {"role": "user", "parts": [{"text": "Hello! Introduce yourself briefly."}]}
    ],
    "generationConfig": {
      "temperature": 0.2
    }
  }'
```

> [!WARNING]
>
> **If you set `maxOutputTokens`, be generous for thinking models.**
> Gemini 2.5 Pro (and other thinking-enabled models) emit "thoughts"
> tokens that count against `maxOutputTokens` BEFORE any user-visible
> text. With a small cap (e.g. 100), the entire budget is consumed by
> thoughts and the response has empty `text` parts but a non-zero
> `usageMetadata.candidatesTokenCount`.
>
> If you don't need to constrain output length, omit `maxOutputTokens`
> entirely and let the model emit as much as it wants. If you do set
> it: `>= 512` for any chat-like use, `>= 1024` for a paragraph of
> output. If you see `finishReason: "MAX_TOKENS"` and no `text`
> content in the response, your cap is too low.

### 4b. REST recipe — self-deployed OSS LLM (Llama, DeepSeek, Qwen, Gemma, etc.)

Self-deployed OSS LLMs may be on a **shared** or **dedicated** endpoint
depending on how the deploy was configured (`dedicated_endpoint_enabled`
at create time). The recipe below handles both cases by checking
`dedicatedEndpointDns` in the describe output.

```bash
PROJECT_ID=my-project
ENDPOINT_ID=5875254126916403200
REGION=us-central1
TOKEN=$(gcloud auth application-default print-access-token)

# Step 1: discover host. dedicatedEndpointDns is empty for shared endpoints.
DEDICATED_DNS=$(gcloud ai endpoints describe "$ENDPOINT_ID" \
  --project="$PROJECT_ID" --region="$REGION" \
  --format="value(dedicatedEndpointDns)")

if [ -n "$DEDICATED_DNS" ]; then
  HOST="$DEDICATED_DNS"
else
  HOST="${REGION}-aiplatform.googleapis.com"
fi

# Step 2: call /chat/completions. Path is identical for both hosts.
curl -sS -X POST \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  "https://${HOST}/v1/projects/${PROJECT_ID}/locations/${REGION}/endpoints/${ENDPOINT_ID}/chat/completions" \
  -d '{
    "messages": [
      {"role": "user", "content": "Hello! Introduce yourself briefly."}
    ]
  }'
```

> [!NOTE]
>
> -   `max_tokens` (NOT `maxOutputTokens`) — this is OpenAI-compatible
>     vocabulary, not Vertex. Omit it entirely to let the model emit as
>     much as it wants; set it explicitly only if you need to cap output.
> -   The `"model"` field in the OpenAI-style payload can be omitted (or
>     set to `""`) for endpoint deployments — the endpoint already
>     determines which model serves the request.
> -   Same endpoint also exposes `/completions` (legacy text completion)
>     and `/embeddings` for embedding models.
> -   **Reasoning models** (DeepSeek-R1, Kimi-K2-Thinking, GLM-5 variants,
>     etc.) emit thinking tokens that count against `max_tokens` BEFORE
>     the final answer — same pathology as Gemini 2.5 Pro in section 4a.
>     If you DO set `max_tokens` and get an empty
>     `choices[0].message.content` or `finish_reason: "length"`, raise it
>     (>= 1024 for chat, >= 2048 for longer thinking chains) or omit it.

Python equivalent (OpenAI SDK) — mirrors the public
[Gemma deployment notebook](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_gemma_deployment_on_vertex.ipynb):

```python
import google.auth
from google.auth.transport.requests import Request
import openai

from google.cloud import aiplatform

PROJECT_ID = "my-project"
ENDPOINT_ID = "5875254126916403200"
REGION = "us-central1"

aiplatform.init(project=PROJECT_ID, location=REGION)
endpoint = aiplatform.Endpoint(
    f"projects/{PROJECT_ID}/locations/{REGION}/endpoints/{ENDPOINT_ID}"
)
endpoint_resource_name = endpoint.resource_name  # full projects/.../endpoints/<id>
dedicated_dns = endpoint.gca_resource.dedicated_endpoint_dns  # empty if shared

host = dedicated_dns if dedicated_dns else f"{REGION}-aiplatform.googleapis.com"
base_url = f"https://{host}/v1/{endpoint_resource_name}"

import subprocess

token = subprocess.check_output(
    ["gcloud", "auth", "print-access-token"]
).decode("utf-8").strip()

client = openai.OpenAI(base_url=base_url, api_key=token)
response = client.chat.completions.create(
    model="",  # endpoint determines the served model
    messages=[{"role": "user", "content": "Hello! Introduce yourself briefly."}],
    # Omit max_tokens to let the model emit as much as it wants. Set it
    # only if you need to cap output length (see notes above).
)
print(response.choices[0].message.content)
```

See also: `agent-platform-deploy` skill section 4 "Verifying Deployment",
which uses the same pattern post-deploy.

### 4c. REST recipe — legacy `:predict` (custom / classification / embedding)

Same host-discovery logic as 4b (shared or dedicated based on
`dedicatedEndpointDns`):

```bash
DEDICATED_DNS=$(gcloud ai endpoints describe "$ENDPOINT_ID" \
  --project="$PROJECT_ID" --region="$REGION" \
  --format="value(dedicatedEndpointDns)")
HOST=${DEDICATED_DNS:-${REGION}-aiplatform.googleapis.com}

curl -sS -X POST \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  "https://${HOST}/v1/projects/${PROJECT_ID}/locations/${REGION}/endpoints/${ENDPOINT_ID}:predict" \
  -d '{
    "instances": [{"key": "value"}],
    "parameters": {}
  }'
```

The exact `instances` shape is model-specific; consult the deployed
model's documentation or the Model Garden card it was deployed from.

### 4d. Python (Vertex AI SDK) — tuned Gemini model

```python
from google import genai
import google.auth

_, project_id = google.auth.default()
client = genai.Client(vertexai=True, project=project_id, location="us-central1")

ENDPOINT_ID = "5875254126916403200"
response = client.models.generate_content(
    model=f"projects/{project_id}/locations/us-central1/endpoints/{ENDPOINT_ID}",
    contents="Hello! Introduce yourself briefly.",
    config={"temperature": 0.2},  # add max_output_tokens only if you need a cap
)
print(response.text)
```

## 5. Troubleshooting & Common Error Codes

### 429: Resource Exhausted

*   **Cause**: OpenMaaS and Gemini models use **Dynamic Shared Quota (DSQ)**.
    Resources are pooled and allocated dynamically based on availability. A 429
    error indicates the shared pool is temporarily exhausted, not necessarily
    that *your* specific project quota is hit (though it can be).
*   **Solution**: Implement strict **exponential backoff and retry** strategies.
*   **High Throughput**: For production workloads requiring high throughput or
    guaranteed capacity, consider **Provisioned Throughput (PT)**.
*   **Important**: Quota increases through normal cloud processes (Cloud
    Console) are **NOT** applicable for DSQ constraints.
*   **Documentation**:
    [Quotas and limits (DSQ)](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/quotas)

### 400: User Validation Error

*   **Cause**: Invalid request format, unsupported parameter, or incorrect Model
    ID.
*   **Action**: Double-check your request payload and parameters. Verify the
    Model ID and Region are correct.
*   **Custom endpoints**: pick the right method + host per section 4's
    decision tables:
    *   Tuned Gemini + you called `:predict` → switch to `:generateContent`
        (section 4a). Error mentions "Required instances format mismatch".
    *   OSS LLM (Llama/DeepSeek/Qwen/Gemma/etc.) + you called
        `:generateContent` or `:predict` → switch to `/chat/completions`
        (section 4b). Error may be 404, 405, or "method not allowed".
    *   Legacy / custom-trained + you called `:generateContent` or
        `/chat/completions` → switch to `:predict` (section 4c).
*   **Dedicated endpoint reached on the shared host (or vice versa)**:
    *   Symptom: DNS resolution failure (`Could not resolve host`) or 404.
    *   Cause: dedicated endpoints don't accept traffic on
        `<REGION>-aiplatform.googleapis.com`, and the dedicated DNS
        (`*.prediction.vertexai.goog`) only exists when
        `dedicatedEndpointEnabled` is true.
    *   Action: re-check `gcloud ai endpoints describe ... --format=json`
        for the `dedicatedEndpointDns` field; use it iff non-empty (per
        the host-discovery snippets in section 4b/4c).

### Empty response text on Gemini deployed endpoints

*   **Cause**: `maxOutputTokens` is set too low. Gemini 2.5 Pro and other
    thinking models emit "thoughts" tokens that count against the budget
    BEFORE any user-visible text. With a small cap (e.g. 100), the entire
    budget is consumed by thoughts and the response has empty `text` parts
    but a non-zero `usageMetadata.candidatesTokenCount` and
    `finishReason: "MAX_TOKENS"`.
*   **Action**: Omit `maxOutputTokens` entirely (let the model emit as much
    as it wants), or raise it to >= 512 for chat-like use, >= 1024 for
    longer output. See section 4 "Custom Endpoints" for details.

### 404: Not Found / Model Not Available

*   **Cause**: The model is not enabled, or not available in the specified
    project or region.
*   **Action**:
    1.  **Check Location Availability**:
        *   **OpenMaaS**: Verify the model is available in your region. See
            [Model Availability by Location](https://docs.cloud.google.com/gemini-enterprise-agent-platform/resources/locations#genai-open-models).
        *   **Gemini**:
            *   **Source of Truth**: Always check
                [Gemini Model Locations](https://docs.cloud.google.com/gemini-enterprise-agent-platform/resources/locations#google-models)
                for the authoritative list.
            *   **Preview Models**: All Preview models (e.g., Gemini 3.1,
                experimental versions) are often **ONLY** available in the
                `us-central1` or `global` regions.
            *   **Stable Models**: (e.g., Gemini 2.5 Pro) Available in
                `us-central1`, `europe-west4`, and many other regions.
            *   **Important**: If you get a 404/400 error, try switching your
                client location to `us-central1` or `global`.
    2.  **Enable Llama Models**: For **Llama 3.3** and **Llama 4**, you **MUST**
        enable the model in Model Garden before use. Go to the
        [Model Garden](https://console.cloud.google.com/agent-platform/model-garden),
        search for the model card (e.g., "Llama 3.3 API Service"), and click
        **Enable**. Only then can you make inference requests.