Langfuse Instrumentation Setup

To add Langfuse tracing to your Python LLM pipeline, you instrument your code by mapping LLM calls to generations, tool executions to tools, and overall runs to traces using provided code templates.

Correct LLM observability requires tracing each LLM call as a generation, each tool execution as a tool, and wrapping the entire pipeline execution within a single trace to ensure accurate data capture for debugging.

You instrument an agentic Python application by classifying the pipeline as Agentic, then applying specific code templates to trace multiple LLM calls and tool usages while avoiding common anti-patterns like incorrect trace nesting.

Yes, Langfuse instrumentation supports RAG and Multi-model pipeline classifications, providing specific setup validation and code templates to ensure correct tracing data capture across these varied architectures.

Missing observation types and incorrect trace nesting occur due to common instrumentation anti-patterns, which can be resolved by following best practices for mapping spans, generations, and tools correctly.

Yes, you need to validate your Langfuse environment and SDK installation during the guided setup phase to ensure your Python code correctly captures tracing data before instrumenting complex pipelines.