langfuse-integration
LangFuse LLM observability integration for tracing, analytics, and cost tracking
Best use case
langfuse-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
LangFuse LLM observability integration for tracing, analytics, and cost tracking
Teams using langfuse-integration should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/langfuse-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How langfuse-integration Compares
| Feature / Agent | langfuse-integration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
LangFuse LLM observability integration for tracing, analytics, and cost tracking
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 1. **Tracing**: Track LLM calls, chains, and agents 2. **Prompts**: Version and manage prompts 3. **Analytics**: Usage, latency, cost metrics 4. **Datasets**: Evaluation and testing data 5. **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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