--- source: ⚠️ Jupyter Notebook title: Observability for Google Gemini Models with Langfuse Integration seoTitle: Google Gemini Observability with Langfuse sidebarTitle: Google Gemini logo: /images/integrations/google_gemini_icon.svg description: Learn how to integrate Langfuse with the Google GenAI SDK for comprehensive tracing and debugging of your AI conversations. category: Integrations --- # Trace Google Gemini Models in Langfuse This notebook shows how to trace and observe Google Gemini models with Langfuse and the Google GenAI SDK. > **What is Google Gemini?** [Google Gemini](https://ai.google.dev/gemini-api/docs/libraries) is Google’s family of multimodal generative models (text, images, audio, video, code) available through the Gemini API and Vertex AI, with tiers like Flash and Pro for different speed/quality needs. > **What is the Google GenAI SDK?** The [Google GenAI SDK](https://cloud.google.com/vertex-ai/generative-ai/docs/sdks/overview) is a unified client library (Python/JavaScript) that simplifies calling Gemini—handling auth (API key or ADC), streaming, tool/function calling, and safety—so you can integrate models in a few lines. > **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 google-genai openai langfuse openinference-instrumentation-google-genai ``` ## 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 # Your Google Gemini API key os.environ.setdefault("GOOGLE_API_KEY", "***"); ``` 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 [`GoogleGenAIInstrumentor`](https://github.com/Arize-ai/openinference/tree/main/python/instrumentation/openinference-instrumentation-google-genai) library to wrap [Google GenAI SDK](https://ai.google.dev/gemini-api/docs/libraries) calls and send OpenTelemetry spans to Langfuse. ```python from openinference.instrumentation.google_genai import GoogleGenAIInstrumentor GoogleGenAIInstrumentor().instrument() ``` ## Step 4: Run an Example ```python from google import genai client = genai.Client() response = client.models.generate_content( model="gemini-2.5-flash", contents="What is Langfuse?", ) print(response.text) ``` ```python # Streaming Example for chunk in client.models.generate_content_stream( model="gemini-2.5-flash", contents="What is Langfuse?", ): print(chunk.text, end="", flush=True) print() # newline after streaming ``` ### 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. ![Langfuse Trace](https://langfuse.com/images/cookbook/integration_gemini/gemini-trace.png) [View trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/9f2f0fe0228fd81a9fe75882934b384a?timestamp=2025-08-01T13%3A22%3A00.147Z&display=details&observation=b7a63ca7e1d083bc) ## 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: - [Manage prompts in Langfuse](/docs/prompts/get-started) - [Add evaluation scores](/docs/evaluation/features/evaluation-methods/custom-scores) - [Run LLM-as-a-judge Evaluators](/docs/scores/model-based-evals) - [Create datasets](/docs/datasets/overview) - [Create custom dashboards](/docs/analytics/custom-dashboards) - [Test queries in the Playground](/docs/playground) --- ## 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-gemini.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 .