# Vertex AI SDK Is Dead: Migrate to google-genai Before Your Code Breaks

> Source: <https://byteiota.com/vertex-ai-sdk-is-dead-migrate-to-google-genai-before-your-code-breaks/>
> Published: 2026-06-26 08:12:54+00:00

If your service calls Gemini via `from vertexai.generative_models import GenerativeModel`

, it broke two days ago. Google removed the Vertex AI generative AI modules on June 24 — exactly one year after announcing the deprecation. No grace period, no compatibility shim. The fix is a three-step migration that takes under an hour, and it unlocks Gemini 3.1 Pro as a bonus since that model is not available on the old SDK at all.

## What Was Removed

Five module paths are gone as of June 24, 2026. If your code imports from any of these, you’re getting an `ImportError`

or `AttributeError`

in production right now:

`vertexai.generative_models`

— the big one.`GenerativeModel`

,`Part`

,`Content`

, and`HarmCategory`

all lived here.`vertexai.language_models`

—`TextGenerationModel`

,`ChatModel`

`vertexai.vision_models`

—`ImageGenerationModel`

`vertexai.tuning`

— fine-tuning jobs`vertexai.caching`

— context caching

Run this before anything else to find every affected file:

```
grep -r "vertexai.generative_models" .
```

Every match is a live production failure. Repeat for the other module names above.

## The Fix in Three Steps

The replacement is the [Google Gen AI SDK](https://github.com/googleapis/python-genai) (`google-genai`

). Install it, then swap two patterns in your code.

**Step 1: Install the new SDK**

```
pip install google-genai
```

**Step 2: Replace the old initialization and call pattern.** Here is what your code looks like now (broken):

``` python
from vertexai.generative_models import GenerativeModel
import vertexai

vertexai.init(project="my-project", location="us-central1")
model = GenerativeModel("gemini-2.0-flash")
response = model.generate_content("Summarize this:")
print(response.text)
```

Here is what it should look like after migration:

``` python
from google import genai
from google.genai.types import HttpOptions

client = genai.Client(
    vertexai=True,
    project="my-project",
    location="us-central1",
    http_options=HttpOptions(api_version="v1"),
)
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents="Summarize this:",
)
print(response.text)
```

**Step 3: Update streaming calls** if you use them:

```
for chunk in client.models.generate_content_stream(
    model="gemini-2.0-flash",
    contents="Tell me a story",
):
    print(chunk.text, end="")
```

## What Actually Changed Under the Hood

The old SDK used global module-level state. You called `vertexai.init()`

once and every subsequent `GenerativeModel`

call inherited your project and location silently. That worked until you needed multiple clients, different regions, or proper test isolation — at which point it became a liability.

The new SDK is client-based. All config lives on the `Client`

object. It is explicit, thread-safe, and actually testable. This is the same direction OpenAI took with their Python SDK, and Google is right to follow.

One gotcha worth calling out: `GenerateContentResponse`

objects are now Pydantic models. If you call `.to_dict()`

on a response object, that breaks too. Replace it:

```
# Old (broken)
response_dict = response.to_dict()

# New
response_dict = response.model_dump(mode='json')
```

## You Also Unlock Gemini 3.1 Pro

This migration is not purely defensive. Vertex AI SDK releases after June 2026 do not support new Gemini models at all — they are exclusive to `google-genai`

. [Gemini 3.1 Pro](https://cloud.google.com/blog/products/ai-machine-learning/gemini-3-1-pro-on-gemini-cli-gemini-enterprise-and-vertex-ai), released this month, scored 77.1% on the ARC-AGI-2 benchmark and delivers double the reasoning performance of 3 Pro. You can only reach it through the new SDK:

```
response = client.models.generate_content(
    model="gemini-3-1-pro",
    contents="Your prompt here",
)
```

## Framework Users: Check Your Wrappers

If you’re using LangChain, upgrade to `langchain-google-genai`

4.0.0 — that release migrated the package to the unified google-genai SDK internally. You’ll switch from `ChatVertexAI`

to `ChatGoogleGenerativeAI`

for Gemini calls. One real-world catch: the old wrapper used gRPC for transport. The new one defaults to REST. If you run high-throughput streaming, benchmark before flipping to production — some teams have reported latency differences on real-time workloads.

For Java, the LangChain4j [migration is tracked in issue #4383](https://github.com/langchain4j/langchain4j/issues/4383) — check your wrapper version before assuming it has been handled automatically.

## Verification Checklist

`pip show google-genai`

confirms the package is installed- Searching codebase for
`vertexai.generative_models`

returns zero results `client.models.list()`

returns available models without error- A test
`generate_content()`

call returns`.text`

as expected - Streaming tested with
`generate_content_stream()`

if your app uses it

The migration is not optional and there is no rollback path. But it is also not complex — the API surface maps almost one-to-one, and the code you end up with is meaningfully cleaner. The biggest operational risk is not the migration itself; it is discovering the failure in a production incident rather than a planned deploy. Run the search now, migrate today, and use the new model IDs while you’re at it. Google’s [official migration guide](https://cloud.google.com/vertex-ai/generative-ai/docs/deprecations/genai-vertexai-sdk) covers edge cases including async clients, embeddings, and multimodal content.