Phoenix Tracing
This is your complete reference for instrumenting LLM apps with Phoenix's OpenInference tracing. It covers both Python and TypeScript, walking you through auto-instrumentation for popular frameworks like OpenAI and LangChain, plus manual span creation when you need fine-grained control. The real value is in the nine span type specs (LLM, chain, retriever, tool, agent, etc.) with full attribute schemas, so you know exactly what metadata to attach. Production patterns for batching and PII masking are here too. If you're shipping an LLM app and need observability beyond print statements, this gives you the complete OpenInference implementation without hunting through docs.
- skills.sh installs
- 1K
- GitHub stars
- 41K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add github/awesome-copilot --skill phoenix-tracing --agent claude-codeInstalls into .claude/skills of the current project.
Files
Phoenix Tracing
Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference files covering setup, instrumentation, span types, and production deployment.
When to Apply
Reference these guidelines when:
- Setting up Phoenix tracing (Python or TypeScript)
- Creating custom spans for LLM operations
- Adding attributes following OpenInference conventions
- Deploying tracing to production
- Querying and analyzing trace data
Reference Categories
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- Repository
- github.com/github/awesome-copilot
- Categories
- DevOps & CI/CD, Git & Pull Requests, AI & Agent Building
- First seen
- Apr 16, 2026
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