---
name: phoenix-cli
description: Debug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, review experiments, inspect datasets, and query the GraphQL API. Use when debugging AI/LLM applications, analyzing trace data, working with Phoenix observability, or investigating LLM performance issues.
license: Apache-2.0
metadata:
author: arize-ai
version: "1.0"
---
# Phoenix CLI
## Invocation
```bash
px <command> # if installed globally
npx @arizeai/phoenix-cli <command> # no install required
```
## Setup
```bash
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`.
## Traces
```bash
px traces --limit 20 --format raw --no-progress | jq .
px traces --last-n-minutes 60 --limit 20 --format raw --no-progress | jq '.[] | select(.status == "ERROR")'
px traces --format raw --no-progress | jq 'sort_by(-.duration) | .[0:5]'
px trace <trace-id> --format raw | jq .
px trace <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")
status_code ("OK"|"ERROR"), 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
```
## Datasets / Experiments / Prompts
```bash
px datasets --format raw --no-progress | jq '.[].name'
px dataset <name> --format raw | jq '.examples[] | {input, output: .expected_output}'
px experiments --dataset <name> --format raw --no-progress | jq '.[] | {id, name, failed_run_count}'
px experiment <id> --format raw --no-progress | jq '.[] | select(.error != null) | {input, error}'
px prompts --format raw --no-progress | jq '.[].name'
px prompt <name> --format text --no-progress # plain text, ideal for piping to AI
```
## GraphQL
For ad-hoc queries not covered by the commands above. Output is `{"data": {...}}`.
```bash
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`.
