Agent Platform Inference

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.

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION and append the attribution suffix to User-Agent headers:

CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:agent-platform-inference)"
User-Agent: <existing_user_agent> agent-skills/1.0 (skill:agent-platform-inference)

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]

   • Post-Execution Response Grounding (CRITICAL): After receiving explicit user approval and executing the inference call via the SDK, the response returning the generated text MUST explicitly confirm the execution parameters alongside the model's output. Never return a bare model response alone. Always include:

     • Model ID: The exact model ID used (e.g. gemini-2.5-pro or <MODEL_ID>).

     • SDK: The SDK used (e.g. Google GenAI SDK (google-genai) or OpenAI SDK).

     • Project ID: The Google Cloud project ID/number used.

     • Region: The region or endpoint location used (e.g. global or us-central1).

     • Generated Output: The model's complete generated answer.

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:

   Copy

   gcloud auth login
   gcloud auth application-default login

2. Enable API (if not already enabled):

   Copy

   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:

   Copy

   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:

   Copy

   ./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 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:

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

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

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.

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 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.

Alternative: Environment Variables You can set environment variables in your shell instead of updating the code.

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.

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)

# 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. 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 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 for the full list of supported models.

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 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.

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.

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:

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):

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

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)

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.

     • 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, search for the model card (e.g., "Llama 3.3 API Service"), and click Enable. Only then can you make inference requests.