opentelemetry-instrumentation-vertexai
OpenTelemetry Vertex AI instrumentation
Decision gist · record as of 2026-08-14
Yes, if you use VertexAI and already have an OpenTelemetry setup or plan to adopt one. The package is actively maintained, has no known vulnerabilities, and integrates seamlessly with the OpenTelemetry ecosystem. Install friction is low. The privacy control via environment variable is a practical feature for production use. No significant blockers.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; VertexAI library must be installed separately to instrument.
- Low friction: pure Python wheel with four lightweight OpenTelemetry dependencies.
- Actively maintained as of 2026-08-10 with recent release history.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
last release 2026-08-10 (4 days) · last repo commit 2026-08-10 · 7,377 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 7,497,712 downloads/mo, #1,734 on PyPI
Alternatives
Verify before relying
pip install opentelemetry-instrumentation-vertexai
from opentelemetry.instrumentation.vertexai import VertexAIInstrumentor
VertexAIInstrumentor().instrument()- Whether trace export backends (e.g., Jaeger, Datadog, GCP Cloud Trace) are automatically configured or require separate setup.
- Performance overhead when tracing high-volume VertexAI requests.
- Compatibility with specific VertexAI library versions beyond the stated Python range.
What it is and what it does
This package is an OpenTelemetry instrumentation plugin that automatically intercepts calls to the official VertexAI library and records them as distributed traces. When you call VertexAI APIs for prompts, completions, or embeddings, the instrumentation captures the request and response data and exports it as OpenTelemetry spans, which can then be sent to any compatible tracing backend.
By default, the instrumentation logs the full content of prompts, completions, and embeddings into span attributes, giving you visibility into what your LLM application is sending and receiving. This is useful for debugging and evaluating output quality, but you can disable it by setting the TRACELOOP_TRACE_CONTENT environment variable to false if the data contains sensitive user information or if you want to reduce trace size.
Use it for
- Debug LLM application behavior by inspecting prompts and completions in a distributed trace viewer.
- Monitor VertexAI API latency and error rates across your application using OpenTelemetry backends.
- Audit and log all AI API calls for compliance or quality assurance without modifying application code.
- Correlate VertexAI calls with other service traces to understand end-to-end request flow.
- Reduce trace verbosity by disabling content logging while keeping call metadata and performance metrics.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use VertexAI and already have an OpenTelemetry setup or plan to adopt one.
The package is actively maintained, has no known vulnerabilities, and integrates seamlessly with the OpenTelemetry ecosystem. Install friction is low. The privacy control via environment variable is a practical feature for production use. No significant blockers.
Install
opentelemetry-instrumentation-vertexai on PyPI
Before you install
Low friction: pure Python wheel with four lightweight OpenTelemetry dependencies. Actively maintained as of 2026-08-10 with recent release history.
Requires Python 3.10 or later; VertexAI library must be installed separately to instrument.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions.
Quickstart
pip install opentelemetry-instrumentation-vertexai
from opentelemetry.instrumentation.vertexai import VertexAIInstrumentor
VertexAIInstrumentor().instrument()Verify before relying
- Whether trace export backends (e.g., Jaeger, Datadog, GCP Cloud Trace) are automatically configured or require separate setup.
- Performance overhead when tracing high-volume VertexAI requests.
- Compatibility with specific VertexAI library versions beyond the stated Python range.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesopentelemetry-apiopentelemetry-instrumentationopentelemetry-semantic-conventions-aiopentelemetry-semantic-conventions |
| Maintenance | Actively maintained 4 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 7,497,712 / month, #1,734 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: opentelemetry_instrumentation_vertexai-0.62.3-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “vertex ai tracing”
- opentelemetry-instrumentation-vertexaiAutomatically captures traces of VertexAI API calls—prompts,…
- llama-index-llms-vertexIntegrates Google Vertex AI's LLM capabilities with LlamaIndex,…
- langchain-google-vertexaiConnects LangChain applications to Google Cloud's Vertex AI…
Give your agent the search over MCP, or paste the wish link into any chat.
More Monitoring packages
Wraps any iterable to display a real-time progress bar in the terminal or Jupyter notebook, showing iteration count, elapsed time, and estimated time remaining.
Provides generated Python code for OpenTelemetry semantic conventions, enabling standardized attribute naming and constant definitions for instrumentation and telemetry collection.
Install it if you are using OpenTelemetry and want to follow semantic conventions correctly.
Provides the reference implementation of the OpenTelemetry API for collecting and exporting traces, metrics, and logs from Python applications.
Provides the abstract API and interfaces for OpenTelemetry instrumentation in Python, defining how to emit traces, metrics, and logs without tying code to a specific SDK implementation.
Exports OpenTelemetry observability data to an OpenTelemetry Collector using Protobuf-encoded messages over HTTP.
Install it if you are using OpenTelemetry in Python and need to send data to a Collector over HTTP.
Provides automatic instrumentation commands and programmatic APIs to inject distributed tracing into Python applications without code changes, detecting and instrumenting packages used by your program.
Install it if you need distributed tracing without code changes and have compatible instrumented packages in your environment.
See also opentelemetry-instrumentation-openai · openlit · opentelemetry-instrumentation-google-generativeai · opentelemetry-instrumentation-voyageai · opentelemetry-instrumentation-together · literalai · opentelemetry-instrumentation-mistralai · opentelemetry-instrumentation-alephalpha · opentelemetry-instrumentation-groq · opentelemetry-instrumentation-writer