Trace LLM applications and manage prompts with Langfuse SDKs.
npx skills add https://github.com/melikhanmutlu/web_ar --skill langfuse-melikhanmutlu
Please help me install this Agent Skill. Skill: langfuse Source: https://github.com/melikhanmutlu/web_ar/tree/main/skills-extra/langfuse Command: npx skills add https://github.com/melikhanmutlu/web_ar --skill langfuse-melikhanmutlu
Langfuse provides end-to-end observability for LLM-powered applications, including tracing, prompt versioning, evaluation, and dataset management, enabling teams to debug and improve model behavior.
Initialize Langfuse tracing in your application and log a sample generation to verify setup.
High-intent search queries and answers about installing and using this skill.
LLM tracing and observability can be added by initializing the client SDK in your Python or TypeScript/JavaScript environment. This allows you to monitor latency, costs, and quality by logging generations directly to your Langfuse account.
LLM prompt management is the process of tracking and organizing prompt changes over time. Versioning allows teams to monitor how modifications affect model behavior, enabling systematic debugging and performance improvement across different application versions.
Yes, you can evaluate LLM outputs and manage datasets in TypeScript or JavaScript environments. The skill supports client SDKs for both Python and TypeScript, allowing you to score model performance and manage curated evaluation data.
Yes, you need a Langfuse account and API keys to track LLM costs and latency. The SDK requires these keys to authenticate your client environment and send tracing data for observability and evaluation metrics.
To set up evaluation and scoring, initialize the Langfuse client SDK in your application to assess model outputs. You can then log generations and apply scores to evaluate quality using curated datasets within your Python or TypeScript environment.
The best way to debug LLM behavior is by using end-to-end observability with prompt versioning and tracing. This approach allows you to track prompt changes, monitor latency and costs, and evaluate outputs against curated datasets to identify performance issues.
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