---
source: ⚠️ Jupyter Notebook
title: Observability for Google Vertex AI with Langfuse
sidebarTitle: Google Vertex AI
logo: /images/integrations/vertexai_icon.png
description: Learn how to integrate Langfuse with Google Vertex AI for comprehensive tracing and debugging of your AI conversations.
category: Integrations
---
# Trace Google Vertex AI Models in Langfuse
This notebook shows how to trace and observe models queried via the Google Vertex API service.
> **What is Google Vertex AI?** [Google Vertex AI](https://cloud.google.com/vertex-ai?hl=en) is Google Cloud’s unified platform for building, deploying, and managing machine learning and generative AI with managed services, SDKs, and APIs. It streamlines everything from data prep and training to tuning and prediction, and provides access to foundation models like Gemini with enterprise-grade security and MLOps tooling.
> **What is Langfuse?** [Langfuse](https://langfuse.com) is an open source platform for LLM observability and monitoring. It helps you trace and monitor your AI applications by capturing metadata, prompt details, token usage, latency, and more.
## Step 1: Install Dependencies
Before you begin, install the necessary packages in your Python environment:
```python
%pip install langfuse google-cloud-aiplatform openinference-instrumentation-vertexai
```
## Step 2: Configure Langfuse SDK
Next, set up your Langfuse API keys. You can get these keys by signing up for a free [Langfuse Cloud](https://langfuse.com/cloud) account or by [self-hosting Langfuse](https://langfuse.com/self-hosting). These environment variables are essential for the Langfuse client to authenticate and send data to your Langfuse project.
Also set your Google Vertex API credentials which uses Application Default Credentials (ADC) from a service account key file.
```python
import os
# Get keys for your project from the project settings page: https://cloud.langfuse.com
os.environ.setdefault("LANGFUSE_PUBLIC_KEY", "pk-lf-...");
os.environ.setdefault("LANGFUSE_SECRET_KEY", "sk-lf-...");
os.environ.setdefault("LANGFUSE_BASE_URL", "https://cloud.langfuse.com"); # 🇪🇺 EU region
# Other Langfuse data regions include 🇺🇸 US: https://us.cloud.langfuse.com, 🇯🇵 Japan: https://jp.cloud.langfuse.com and ⚕️ HIPAA: https://hipaa.cloud.langfuse.com
# Get your Google Vertex API key
os.environ.setdefault("GOOGLE_APPLICATION_CREDENTIALS", "your-service-account-key.json");
```
With the environment variables set, we can now initialize the Langfuse client. `get_client()` initializes the Langfuse client using the credentials provided in the environment variables.
```python
from langfuse import get_client
# Initialise Langfuse client and verify connectivity
langfuse = get_client()
assert langfuse.auth_check(), "Langfuse auth failed - check your keys ✋"
```
## Step 3: OpenTelemetry Instrumentation
Use the [`VertexAIInstrumentor`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-vertexai) library to wrap Google Vertex SDK calls and send OpenTelemetry spans to Langfuse.
```python
from openinference.instrumentation.vertexai import VertexAIInstrumentor
VertexAIInstrumentor().instrument()
```
## Step 4: Run an Example
```python
import vertexai
from vertexai.generative_models import GenerativeModel
# Initialize the SDK (use your project and region)
vertexai.init(project="your-project-id", location="europe-central2")
# Pick a Gemini model available in your region (examples: "gemini-1.5-flash", "gemini-1.5-pro", "gemini-2.5-flash")
model = GenerativeModel("gemini-2.5-flash")
# Single-shot generation
resp = model.generate_content("What is Langfuse?")
print(resp.text)
# (Optional) Streaming
for chunk in model.generate_content("Why is LLM observability important?", stream=True):
print(chunk.text, end="")
```
### View Traces in Langfuse
After executing the application, navigate to your Langfuse Trace Table. You will find detailed traces of the application's execution, providing insights into the agent conversations, LLM calls, inputs, outputs, and performance metrics.
[See trace in the Langfuse UI](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/308aca9bc430ad872d474fc545889ee2?timestamp=2025-07-25T07:35:01.172Z&display=details)
## Interoperability with the Python SDK
You can use this integration together with the Langfuse [SDKs](/docs/observability/sdk/overview) to add additional attributes to the observation.
The [`@observe()` decorator](/docs/observability/sdk/instrumentation#custom-instrumentation) provides a convenient way to automatically wrap your instrumented code and add additional attributes to the observation.
```python
from langfuse import observe, propagate_attributes, get_client
langfuse = get_client()
@observe()
def my_llm_pipeline(input):
# Add additional attributes (user_id, session_id, metadata, version, tags) to all spans created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
tags=["agent", "my-observation"],
metadata={"email": "user@langfuse.com"},
version="1.0.0"
):
# YOUR APPLICATION CODE HERE
result = call_llm(input)
return result
# Run the function
my_llm_pipeline("Hi")
```
Learn more about using the Decorator in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
The [Context Manager](/docs/observability/sdk/instrumentation#custom-instrumentation) allows you to wrap your instrumented code using context managers (with `with` statements), which allows you to add additional attributes to the observation.
```python
from langfuse import get_client, propagate_attributes
langfuse = get_client()
with langfuse.start_as_current_observation(
as_type="span",
name="my-observation",
trace_context={"trace_id": "abcdef1234567890abcdef1234567890"}, # Must be 32 hex chars
) as observation:
# Add additional attributes (user_id, session_id, metadata, version, tags)
# to all observations created within this execution scope
with propagate_attributes(
user_id="user_123",
session_id="session_abc",
metadata={"experiment": "variant_a", "env": "prod"},
version="1.0",
):
# YOUR APPLICATION CODE HERE
result = call_llm("some input")
# Flush events in short-lived applications
langfuse.flush()
```
Learn more about using the Context Manager in the [Langfuse SDK instrumentation docs](/docs/observability/sdk/instrumentation#custom-instrumentation).
## Troubleshooting
No observations appearing
First, enable [debug mode](/docs/observability/sdk/advanced-features#logging--debugging) in the Python SDK:
```bash
export LANGFUSE_DEBUG="True"
```
Then run your application and check the debug logs:
- **OTel observations appear in the logs:** Your application is instrumented correctly but observations are not reaching Langfuse. To resolve this:
1. Call [`langfuse.flush()`](/docs/observability/sdk/instrumentation#client-lifecycle--flushing) at the end of your application to ensure all observations are exported.
2. Verify that you are using the correct API keys and base URL.
- **No OTel spans in the logs:** Your application is not instrumented correctly. Make sure the instrumentation runs before your application code.
Unwanted observations in Langfuse
The Langfuse SDK is based on OpenTelemetry. Other libraries in your application may emit OTel spans that are not relevant to you. These still count toward your [billable units](/docs/administration/billable-units), so you should filter them out. See [Unwanted spans in Langfuse](/faq/all/unwanted-http-database-spans) for details.
Missing attributes
Some attributes may be stored in the metadata object of the observation rather than being mapped to the Langfuse data model. If a mapping or integration does not work as expected, please [raise an issue on GitHub](/issues).
## Next Steps
Once you have instrumented your code, you can manage, evaluate and debug your application:
---
## Agent Instructions
This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/integrations/model-providers/google-vertex-ai.md`.
### Querying these docs
If the answer is not on this page, query the documentation instead of guessing:
- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:
```bash
curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
```
- **Index of every page**: , with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).
### Before writing Langfuse code
- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api `) to read or write traces, prompts, datasets, and scores from the terminal.
Found an error in these docs? Please open an issue at .