LangFuse Integration Skill

SkillDatabases & data

LangFuse LLM observability integration for tracing, analytics, and cost tracking

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the LangFuse Integration Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/ai-agents-conversational/skills/langfuse-integration/SKILL.md and read by ahel’s review.

Capabilities

Set up LangFuse tracing for LLM calls
Configure cost tracking and analytics
Implement prompt management
Set up evaluation datasets
Design custom trace metadata
Create dashboards and alerts

Target Processes

llm-observability-monitoring
cost-optimization-llm

Implementation Details

Core Features

Tracing: Track LLM calls, chains, and agents
Prompts: Version and manage prompts
Analytics: Usage, latency, cost metrics
Datasets: Evaluation and testing data
Scores: Track output quality

Integration Methods

LangChain callback handler
Direct SDK integration
OpenAI drop-in replacement
Decorator-based tracing

Configuration Options

Public/secret keys
Host URL (cloud or self-hosted)
Sampling rate
Metadata configuration
User tracking

Best Practices

Consistent trace naming
Meaningful metadata
Regular prompt versioning
Set up alerting

Dependencies

langfuse
langchain (for callback integration)

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: langfuse-integration
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter