
Phoenix Tracing
Instrument LLM and agent apps with OpenInference spans so traces, tools, and chains show up correctly in Phoenix for debugging and eval.
Install
npx skills add https://github.com/arize-ai/phoenix --skill phoenix-tracingWhat is this skill?
- Flat rules/ index for span kinds (LLM, CHAIN, TOOL) and OpenInference attribute conventions
- Setup and instrumentation guides aligned with Phoenix Python, OTEL, and TypeScript client docs
- Annotations overview for span, document, and session feedback for eval and quality loops
- Links to OpenInference spec, Phoenix docs, and export/advanced tracing topics
Adoption & trust: 668 installs on skills.sh; 10k GitHub stars; 2/3 security scanners passed (skills.sh audits).
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Journey fit
Primary fit
Canonical shelf is Build because solo builders first apply this skill while wiring OTEL/OpenInference instrumentation into code and agents. Integrations is the right subphase: the skill is reference and rules for hooking tracing into apps, not generic frontend or PM work.
Common Questions / FAQ
Is Phoenix Tracing safe to install?
skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.
SKILL.md
READMESKILL.md - Phoenix Tracing
# Phoenix Tracing Skill OpenInference semantic conventions and instrumentation guides for Phoenix. ## Usage Start with `SKILL.md` for the index and quick reference. ## File Organization All files in flat `rules/` directory with semantic prefixes: - `span-*` - Span kinds (LLM, CHAIN, TOOL, etc.) - `setup-*`, `instrumentation-*` - Getting started guides - `fundamentals-*`, `attributes-*` - Reference docs - `annotations-*`, `export-*` - Advanced features ## Reference - [OpenInference Spec](https://github.com/Arize-ai/openinference/tree/main/spec) - [Phoenix Documentation](https://docs.arize.com/phoenix) - [Python OTEL API](https://arize-phoenix.readthedocs.io/projects/otel/en/latest/) - [Python Client API](https://arize-phoenix.readthedocs.io/projects/client/en/latest/) - [TypeScript API](https://arize-ai.github.io/phoenix/) # Annotations Overview Annotations allow you to add human or automated feedback to traces, spans, documents, and sessions. Annotations are essential for evaluation, quality assessment, and building training datasets. ## Annotation Types Phoenix supports four types of annotations: | Type | Target | Purpose | Example Use Case | | ----------------------- | -------------------------------- | ---------------------------------------- | -------------------------------- | | **Span Annotation** | Individual span | Feedback on a specific operation | "This LLM response was accurate" | | **Document Annotation** | Document within a RETRIEVER span | Feedback on retrieved document relevance | "This document was not helpful" | | **Trace Annotation** | Entire trace | Feedback on end-to-end interaction | "User was satisfied with result" | | **Session Annotation** | User session | Feedback on multi-turn conversation | "Session ended successfully" | ## Annotation Fields Every annotation has these fields: ### Required Fields | Field | Type | Description | | --------- | ------ | ----------------------------------------------------------------------------- | | Entity ID | String | ID of the target entity (span_id, trace_id, session_id, or document_position) | | `name` | String | Annotation name/label (e.g., "quality", "relevance", "helpfulness") | ### Result Fields (At Least One Required) | Field | Type | Description | | ------------- | ----------------- | ----------------------------------------------------------------- | | `label` | String (optional) | Categorical value (e.g., "good", "bad", "relevant", "irrelevant") | | `score` | Float (optional) | Numeric value (typically 0-1, but can be any range) | | `explanation` | String (optional) | Free-text explanation of the annotation | **At least one** of `label`, `score`, or `explanation` must be provided. ### Optional Fields | Field | Type | Description | | ---------------- | ------ | --------------------------------------------------------------------------------------- | | `annotator_kind` | String | Who created this annotation: "HUMAN", "LLM", or "CODE" (default: "HUMAN") | | `identifier` | String | Unique identifier for upsert behavior (updates existing if same name+entity+identifier) | | `metadata` | Object | Custom metadata as key-value pairs | ## Annotator Kinds | Kind | Description | Example | | ------- | ------------------------------ | --------------------------------- | | `HUMAN` | Manual feedback from a person | User ratings, expert labels |