phoenix-cli

arize-ai/phoenix · updated Apr 8, 2026

$npx skills add https://github.com/arize-ai/phoenix --skill phoenix-cli
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summary

The CLI uses singular resource commands with subcommands like list and get:

skill.md

Phoenix CLI

Invocation

px <resource> <action>                          # if installed globally
npx @arizeai/phoenix-cli <resource> <action>    # no install required

The CLI uses singular resource commands with subcommands like list and get:

px trace list
px trace get <trace-id>
px span list
px dataset list
px dataset get <name>
px project list
px annotation-config list
px auth status

Setup

export PHOENIX_HOST=http://localhost:6006
export PHOENIX_PROJECT=my-project
export PHOENIX_API_KEY=your-api-key  # if auth is enabled

Always use --format raw --no-progress when piping to jq.

Auth

px auth status                                # check connection and authentication
px auth status --endpoint http://other:6006   # check a specific endpoint

Projects

px project list                                            # list all projects (table view)
px project list --format raw --no-progress | jq '.[].name' # project names as JSON

Traces

px trace list --limit 20 --format raw --no-progress | jq .
px trace list --last-n-minutes 60 --limit 20 --format raw --no-progress | jq '.[] | select(.status == "ERROR")'
px trace list --since 2025-01-15T00:00:00Z --limit 50 --format raw --no-progress | jq .
px trace list --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]'
px trace get <trace-id> --format raw | jq .
px trace get <trace-id> --format raw | jq '.spans[] | select(.status_code != "OK")'

Trace JSON shape

Trace
  traceId, status ("OK"|"ERROR"), duration (ms), startTime, endTime
  rootSpan  — top-level span (parent_id: null)
  spans[]
    name, span_kind ("LLM"|"CHAIN"|"TOOL"|"RETRIEVER"|"EMBEDDING"|"AGENT"|"RERANKER"|"GUARDRAIL"|"EVALUATOR"|"UNKNOWN")
    status_code ("OK"|"ERROR"|"UNSET"), parent_id, context.span_id
    attributes
      input.value, output.value          — raw input/output
      llm.model_name, llm.provider
      llm.token_count.prompt/completion/total
      llm.token_count.prompt_details.cache_read
      llm.token_count.completion_details.reasoning
      llm.input_messages.{N}.message.role/content
      llm.output_messages.{N}.message.role/content
      llm.invocation_parameters          — JSON string (temperature, etc.)
      exception.message                  — set if span errored

Spans

px span list --limit 20                                    # recent spans (table view)
px span list --last-n-minutes 60 --limit 50                # spans from last hour
px span list --since 2025-01-15T00:00:00Z --limit 50       # spans since a timestamp
px span list --span-kind LLM --limit 10                    # only LLM spans
px span list --status-code ERROR --limit 20                # only errored spans
px span list --name chat_completion --limit 10             # filter by span name
px span list --trace-id <id> --format raw --no-progress | jq .   # all spans for a trace
px span list --parent-id null --limit 10                   # only root spans
px span list --parent-id <span-id> --limit 10              # only children of a span
px span list --include-annotations --limit 10              # include annotation scores
px span list output.json --limit 100                       # save to JSON file
px span list --format raw --no-progress | jq '.[] | select(.status_code == "ERROR")'

Span JSON shape

Span
  name, span_kind ("LLM"|"CHAIN"|"TOOL"|"RETRIEVER"|"EMBEDDING"|"AGENT"|"RERANKER"|"GUARDRAIL"|"EVALUATOR"|"UNKNOWN")
  status_code ("OK"|"ERROR"|"UNSET"), status_message
  context.span_id, context.trace_id, parent_id
  start_time, end_time
  attributes
    input.value, output.value          — raw input/output
    llm.model_name, llm.provider
    llm.token_count.prompt/completion/total
    llm.input_messages.{N}.message.role/content
    llm.output_messages.{N}.message.role/content
    llm.invocation_parameters          — JSON string (temperature, etc.)
    exception.message                  — set if span errored
  annotations[] (with --include-annotations)
    name, result { score, label, explanation }

Sessions

px session list --limit 10 --format raw --no-progress | jq .
px session list --order asc --format raw --no-progress | jq '.[].session_id'
px session get <session-id> --format raw | jq .
px session get <session-id> --include-annotations --format raw | jq '.annotations'

Session JSON shape

SessionData
  id, session_id, project_id
  start_time, end_time
  traces[]
    id, trace_id, start_time, end_time

SessionAnnotation (with --include-annotations)
  id, name, annotator_kind ("LLM"|"CODE"|"HUMAN"), session_id
  result { label, score, explanation }
  metadata, identifier, source, created_at, updated_at

Datasets / Experiments / Prompts

px dataset list --format raw --no-progress | jq '.[].name'
px dataset get <name> --format raw | jq '.examples[] | {input, output: .expected_output}'
px dataset get <name> --split train --format raw | jq .    # filter by split
px dataset get <name> --version <version-id> --format raw | jq .
px experiment list --dataset <name> --format raw --no-progress | jq '.[] | {id, name, failed_run_count}'
px experiment get <id> --format raw --no-progress | jq '.[] | select(.error != null) | {input, error}'
px prompt list --format raw --no-progress | jq '.[].name'
px prompt get <name> --format text --no-progress   # plain text, ideal for piping to AI

Annotation Configs

px annotation-config list                                           # list all configs (table view)
px annotation-config list --format raw --no-progress | jq '.[].name' # config names as JSON

GraphQL

For ad-hoc queries not covered by the commands above. Output is {"data": {...}}.

px api graphql '{ projectCount datasetCount promptCount evaluatorCount }'
px api graphql '{ projects { edges { node { name traceCount tokenCountTotal } } } }' | jq '.data.projects.edges[].node'
px api graphql '{ datasets { edges { node { name exampleCount experimentCount } } } }' | jq '.data.datasets.edges[].node'
px api graphql '{ evaluators { edges { node { name kind } } } }' | jq '.data.evaluators.edges[].node'

# Introspect any type
px api graphql '{ __type(name: "Project") { fields { name type { name } } } }' | jq '.data.__type.fields[]'

Key root fields: projects, datasets, prompts, evaluators, projectCount, datasetCount, promptCount, evaluatorCount, viewer.

Docs

Download Phoenix documentation markdown for local use by coding agents.

px docs fetch                                # fetch default workflow docs to .px/docs
px docs fetch --workflow tracing             # fetch only tracing docs
px docs fetch --workflow tracing --workflow evaluation
px docs fetch --dry-run                      # preview what would be downloaded
px docs fetch --refresh                      # clear .px/docs and re-download
px docs fetch --output-dir ./my-docs         # custom output directory

Key options: --workflow (repeatable, values: tracing, evaluation, datasets, prompts, integrations, sdk, self-hosting, all), --dry-run, --refresh, --output-dir (default .px/docs), --workers (default 10).

Discussion

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general reviews

Ratings

4.728 reviews
  • Kwame Huang· Dec 20, 2024

    Useful defaults in phoenix-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Shikha Mishra· Dec 12, 2024

    Registry listing for phoenix-cli matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Henry Perez· Nov 11, 2024

    I recommend phoenix-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • William Wang· Oct 2, 2024

    phoenix-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Aarav Jackson· Sep 25, 2024

    Solid pick for teams standardizing on skills: phoenix-cli is focused, and the summary matches what you get after install.

  • Soo Choi· Sep 9, 2024

    We added phoenix-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Rahul Santra· Sep 5, 2024

    phoenix-cli reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Charlotte Garcia· Aug 28, 2024

    Solid pick for teams standardizing on skills: phoenix-cli is focused, and the summary matches what you get after install.

  • Pratham Ware· Aug 24, 2024

    I recommend phoenix-cli for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Mia Chen· Aug 16, 2024

    We added phoenix-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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