Langfuse Python Sdk Github. The workaround you mentioned is currently the standard approa

The workaround you mentioned is currently the standard approach - you need to create a span with the existing trace context to update tags (1). When to use LiteLLM Python SDK LiteLLM Python SDK Basic usage Responses API Streaming Exception handling Logging Observability - Log LLM Input/Output (Docs) Track Costs, Usage, Latency for streaming LiteLLM Proxy Server (LLM Gateway) 📖 Proxy Endpoints - Swagger Docs Quick Start Proxy - CLI Step 1. [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, VLLM, NVIDIA NIM] - BerriAI/litellm Langfuse Overview Langfuse is an open-source LLM engineering platform (GitHub) that helps teams collaboratively debug, analyze, and iterate on their LLM applications. As a reference, see this example notebook on how to use the OpenTelemetry Python SDK to export traces to Langfuse. Refer to the v3 migration guide for instructions on updating your code. Langfuse is the easiest way to add observability to your AI stack. What is Langfuse? Langfuse is an open-source LLM observability platform that helps you trace, monitor, and debug your LLM applications. It is packaged as a Python library and command line tool that allows builders to configure agents that run on the IBM watsonx Orchestrate platform. Works with any LLM app and model SDKs for Python & JS/TS, native integrations for popular libraries and support for OpenTelemetry Integration overview Request new integration Python SDK JS/TS SDK API Open Telemetry Fully typed SDKs for Python and JavaScript/TypeScript with unified setup, instrumentation, and advanced guidance. This major release brings the power of OpenTelemetry (OTEL) to LLM observability, providing a more robust and standards-compliant foundation for tracing your AI applications.

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