langfuse-observability

Trace LLM calls by integrating the Langfuse SDK into your Python application to capture spans, agentic workflows, and interactions automatically. This provides end-to-end observability for debugging complex LLM application behavior.

Monitor LLM cost and latency per user by tagging traced LLM interactions with user and tenant IDs. This allows you to visualize token usage, cost tracking, and latency metrics for specific users within the Langfuse UI.

Yes, Langfuse observability integrates with FastAPI applications to automatically trace LLM interactions. You can tag these interactions with user and tenant IDs to support multi-tenant isolation and visualize performance metrics in the UI.

Yes, you can manage and version prompts directly from Langfuse within your LLM application. This allows you to retrieve specific prompt versions programmatically during runtime to maintain consistency across deployments.

You can build and run evaluation datasets to score LLM performance using Langfuse's evaluation capabilities. This allows you to systematically assess and quantify your LLM application outputs directly within your observability workflow.

Yes, you need Langfuse SDK and API keys to set up and operate LLM observability. These credentials are required to authenticate your Python application and transmit trace data to the Langfuse platform.