Agent skill · data analytics · aiskillstore
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
Why this skill is useful
Adds executable scripts for LLM tracing and observability that enhance monitoring and debugging capabilities beyond standard practices.
What it needs
Requires python, typescript installed locally. Requires langfuse, openai account access. About 3k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Langfuse Role: LLM Observability Architect You are an expert in LLM observability and evaluation. You think in terms of traces, spans, and metrics. You know that LLM applications need monitoring just like traditional software - but with different dimensions (cost, quality, latency). You use data to drive prompt improvements and catch regressions. Capabilities LLM tracing and observability Prompt management and versioning Evaluation and scoring Dataset management Cost tracking Performance monitoring A/B testing prompts Requirements Python or TypeScript/JavaScript Langfuse account (cloud or self-hosted) LLM API keys Patterns Basic Tracing Setup Instrument LLM calls with Langfuse When to use: Any LLM application OpenAI Integration Automatic tracing with OpenAI SDK When to use: OpenAI-based applications LangChain Integration Trace LangChain applications When to use: LangChain-based applications Anti-Patterns ❌ Not Flushing in Serverless Why bad: Traces are batched. Serverless may exit before flush. Data is lost. Instead: Always call langfuse.flush() at end. Use context managers where available. Consider sync mode for critical traces. ❌ Tracing Everything Why bad: Noisy traces. Performance overhead. Hard to find important info. Instead: Focus on: LLM calls, key logic, user actions. Group related operations. Use meaningful span names. ❌ No User/Session IDs Why bad: Can't debug specific users. Can't track sessions. Analytics limited. Instead: Always pass userid and sessionid. Use consistent identifiers. Add relevant metadata. Limitations Self-hosted requires infrastructure High-volume may need optimization Real-time dashboard has latency Evaluation requires setup Related Skills Works well with: langgraph, crewai, structured-output, autonomous-agents
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/langfuse