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 both First-Party (Gemini) and Third-Party (OpenMaaS) models.

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:

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
```

Enable API (if not already enabled):

bash

gcloud services enable aiplatform.googleapis.com

Virtual Environment: Create and activate a dedicated local virtual environment:
bash
```
python3 -m venv .venv
source .venv/bin/activate
```
Install Dependencies: Install the required SDKs:
bash
```
pip install -r scripts/requirements.txt
```
Verify Setup (Optional): Run all sample scripts at once to verify the environment is working end-to-end:
bash
```
./scripts/verify_all.sh
```
Execution: Advise the user that every time they execute a Python snippet from this skill, they must ensure this virtual environment is activated first.

[!IMPORTANT] CRITICAL: Model IDs & Availability

Gemini Models: See Gemini Models 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Incomplete Lists: The Model IDs listed in this skill are examples only and may be incomplete or outdated.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.

Workflow Decision Tree

Model Family Identification: Has the user specified whether they want to call a Gemini (First-Party) model or an OpenMaaS (Third-Party, e.g. Llama, DeepSeek, Qwen) model?
- No -> Ask the user which model family they want to use. If they provide a specific model name, infer the family from the name.
- Yes -> Proceed to Step 2.
SDK Choice: Which SDK does the user want to use?
- Gemini + GenAI SDK (preferred for Gemini) -> Proceed to [1. Gemini Models].
- Gemini + legacy Vertex AI SDK -> Proceed to [1. Gemini Models].
- OpenMaaS + OpenAI SDK (preferred for OpenMaaS) -> Proceed to [2. OpenMaaS Models].
- OpenMaaS + GenAI SDK -> Proceed to [2. OpenMaaS Models].
- Unsure -> Default to the preferred SDK for the chosen family.
Troubleshooting: Is the user reporting an error (429 Resource Exhausted, 400 User Validation, 404 Not Found, etc.)?
- Yes -> Proceed to [3. Troubleshooting & Common Error Codes].
- No -> Proceed with the SDK choice from Step 2.

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

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

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

for the complete code.

Documentation: Google GenAI SDK

Documentation: Agent Platform Gemini 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 google.auth
from google.auth.transport.requests import Request

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

[!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 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

for the complete code.

[!TIP] 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 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

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

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

[!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.

[!IMPORTANT] Finding Inference Examples: The list above is a starting point. For the definitive inference snippets (especially for Chat Completions payload structure):
Consult the Use Open Models on Agent Platform list.

Click the link for your specific model (e.g., "DeepSeek-V3") to visit its Model Garden page.
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`

3. 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)

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.

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.
  - Gemini: 
    - Source of Truth: Always check Gemini Model Locations 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.

agent-platform-inference

NPX Install

Tags

SKILL.md Content

Agent Platform GenAI Inference Skill

Phase 0: Environment Setup

Workflow Decision Tree

1. Gemini Models

Choosing the Right SDK

Installation

Python Example (GenAI SDK - Preferred)

Alternative: OpenAI SDK (Chat Completions)

Legacy: Agent Platform SDK

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

Installation

Authentication for OpenAI SDK

Configuration (Base URL)

Python Example (OpenMaaS - Chat Completions)

Python Example (OpenMaaS - Completions API)

Python Example (OpenMaaS - Embeddings)

Alternative: GenAI SDK

Legacy: Agent Platform SDK (OpenMaaS)

Model Reference & Availability

3. Troubleshooting & Common Error Codes

429: Resource Exhausted

400: User Validation Error

404: Not Found / Model Not Available