--- source: ⚠️ Jupyter Notebook title: Observability for LlamaIndex with Langfuse Integration sidebarTitle: LlamaIndex logo: /images/integrations/llamaindex_icon.png description: Discover how to integrate Langfuse with LlamaIndex for enhanced LLM application monitoring, debugging, and tracing. Improve your AI development workflow today. category: Integrations --- # Integrate Langfuse with LlamaIndex This notebook provides a step-by-step guide on integrating **Langfuse** with **LlamaIndex** to achieve observability and debugging for your LLM applications. > **What is LlamaIndex?** [LlamaIndex](https://www.llamaindex.ai/) ([GitHub](https://github.com/run-llama/llama_index)) is an advanced "data framework" tailored for augmenting LLMs with private data. It streamlines the integration of diverse data sources and formats (APIs, PDFs, docs, SQL, etc.) through versatile data connectors and structures data into indices and graphs for LLM compatibility. The platform offers a sophisticated retrieval/query interface for enriching LLM inputs with context-specific outputs. > **What is Langfuse?** [Langfuse](https://langfuse.com) is an open-source AI engineering platform. It offers tracing and monitoring capabilities for LLM applications. Langfuse helps developers debug, analyze, and optimize their AI systems by providing detailed insights and integrating with a wide array of tools and frameworks through native integrations, OpenTelemetry, and dedicated SDKs. ## Getting Started Let's walk through a practical example of using LlamaIndex and integrating it with Langfuse for comprehensive tracing. ### Step 1: Install Dependencies ```python %pip install langfuse openinference-instrumentation-llama-index llama-index-llms-openai llama-index -U ``` ### 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. ```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"); # πŸ‡ͺπŸ‡Ί EU region # Other Langfuse data regions include πŸ‡ΊπŸ‡Έ US: https://us.cloud.langfuse.com, πŸ‡―πŸ‡΅ Japan: https://jp.cloud.langfuse.com and βš•οΈ HIPAA: https://hipaa.cloud.langfuse.com # Your OpenAI key os.environ.setdefault("OPENAI_API_KEY", "sk-proj-..."); ``` 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 langfuse = get_client() # Verify connection if langfuse.auth_check(): print("Langfuse client is authenticated and ready!") else: print("Authentication failed. Please check your credentials and host.") ``` ### Step 3: Initialize LlamaIndex Instrumentation Now, we initialize the [OpenInference LlamaIndex instrumentation](https://docs.arize.com/phoenix/tracing/integrations-tracing/llamaindex). This third-party instrumentation automatically captures LlamaIndex operations and exports OpenTelemetry (OTel) spans to Langfuse. ```python from openinference.instrumentation.llama_index import LlamaIndexInstrumentor # Initialize LlamaIndex instrumentation LlamaIndexInstrumentor().instrument() ``` ### Step 4: Basic LlamaIndex Application Let's create a straightforward LlamaIndex application. In this example, we'll create a simple query engine that can answer questions. This will serve as the foundation for demonstrating Langfuse tracing. ```python from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-4o") with langfuse.start_as_current_observation(as_type="span", name="llama-index-trace"): response = llm.complete("Hello, world!") print(response) langfuse.flush() ``` ### Step 5: 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 LLM calls, retrieval operations, inputs, outputs, and performance metrics. The trace will show the complete flow from query processing through document retrieval to response generation. ![Example Trace in Langfuse](https://langfuse.com/images/cookbook/integration_llama-index/llama-index-example-trace.png) [Example Trace in Langfuse](https://cloud.langfuse.com/project/cloramnkj0002jz088vzn1ja4/traces/12ea412956f99347b0503c1144acd0ec?timestamp=2025-06-05T15:45:52.971Z&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/frameworks/llamaindex.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 .