Provide implementations of advanced data structures
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a5c-ai/babysitter1.9KUpdated Sep 17, 2026
FreePublished on GitHub
langfuse-integration
LangFuse LLM observability integration for tracing, analytics, and cost tracking
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See how to installFiles included (2)
- SKILL.md1.5 KB
- README.md458 B
SKILL.md (original)
Review the instructions the agent will receive before you install.
LangFuse Integration Skill
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)
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