| name | phoenix-graphql |
| description | Write efficient GraphQL queries against the Phoenix API. Load this skill in two cases: (1) before composing any non-trivial GraphQL query yourself for data analysis (via the `phoenix-gql` bash command) — it contains schema entrypoints and patterns that eliminate the need for introspection; (2) when the user asks for help writing GraphQL queries for their own scripts, tools, or integrations against Phoenix — it covers the endpoint, authentication, and client examples.
|
| summary | Answer data questions with efficient GraphQL queries, or get working GraphQL for your own scripts and integrations against the Phoenix API. |
Two modes
- Internal data analysis — you are querying Phoenix yourself to answer a question. Apply the schema facts, efficiency rules, and patterns below directly.
- Helping the user integrate — the user wants GraphQL queries for their own code or tools. Use the same schema facts and patterns, plus the "External API usage" section for endpoint, auth, and client examples. Queries you hand to the user should use variables and include pagination handling.
Entrypoints
Top-level Query entrypoints get you to a starting entity; per-entity schema details live in the resources listed under "Schema map" below.
node(id: ID!) — global lookup for any entity by its Relay global id; resolve with an inline fragment, e.g. node(id: $id) { ... on Dataset { name } }. This is the primary way to fetch datasets, prompts, experiments, sessions, and annotations, which have no by-name/by-id helpers.projects(...), datasets(...), prompts(...), evaluators(...) → Relay connections, each with filter/sort inputs to find an entity when you only have a name.- By-X helpers (the only ones that exist):
getProjectByName(name: String!), getProjectSessionById(sessionId: String!), getDatasetExampleByExternalId(datasetId: GlobalID!, externalId: String!), getSpanByOtelId(spanId: String!), getTraceByOtelId(traceId: String!). There is no getDatasetByName, getPromptByName, or getExperimentById — use node(id:) or a connection filter instead.
viewer → the authenticated User; projectCount, datasetCount, promptCount — cheap counts.compareExperiments(baseExperimentId: GlobalID!, compareExperimentIds: [GlobalID!]!, first, after, filterCondition) → experiment comparison.
Schema map
Per-entity field references and examples are split into resources. Read only the one(s) you need with read_skill_resource, after loading this skill:
project-spans-traces — Project aggregates and spans; Span and Trace fields. The starting point for most trace analysis.sessions — ProjectSession: multi-turn session metrics, token/cost, session traces.datasets — Dataset and DatasetExample: examples, versions, splits, labels.experiments — Experiment and ExperimentRun: runs, aggregate metrics, comparison.prompts — Prompt and PromptVersion: versions, templates, tags.annotations — Span/Trace/ExperimentRun annotation fields and how to read them.
Conventions
These apply to every entity:
- Pagination is Relay-style:
first/after args; responses have edges { node { ... } } and pageInfo { hasNextPage endCursor }. Cursors are opaque strings. Some connections (e.g. Project.spans, Experiment.runs, ProjectSession.traces) are forward-only.
- IDs: the
id field on any node is a Relay global ID (base64 of TypeName:rowId) — use it with node(id:). OpenTelemetry hex IDs come from Span.spanId and Trace.traceId — use those for OTel lookups. Note a Span has no traceId field; read it via the nested trace { traceId }. Never mix global IDs with OTel IDs.
TimeRange input: { start: DateTime, end: DateTime } — ISO 8601 strings; end is exclusive; both optional.SpanSort input: { col: SpanColumn, dir: SortDir }, e.g. { col: startTime, dir: desc }. Useful SpanColumn values: startTime, latencyMs, tokenCountTotal, cumulativeTokenCountTotal, tokenCostTotal.filterCondition is a Python-like DSL string over span fields, e.g. span_kind == 'LLM', status_code == 'ERROR', latency_ms > 1000, 'timeout' in output.value, annotations['Hallucination'].label == 'hallucinated', or trace_annotations['quality'].score < 0.5. annotations[...] references annotations on an individual span; trace_annotations[...] matches every span belonging to an annotated trace. Combine clauses with and/or.
Efficiency rules
- Do not run full schema introspection. Read the relevant
Schema map resource instead; it covers the fields and arguments for that entity. Only when a resource does not cover a field you need, introspect a single type: { __type(name: "Project") { fields { name args { name type { name kind } } } } }.
- Batch independent lookups with aliases in one query instead of multiple round trips, e.g.
p50: latencyMsQuantile(probability: 0.5) p99: latencyMsQuantile(probability: 0.99).
- Select only the fields you need; keep page sizes small (10–50) and paginate only when necessary.
- Pass values via query variables, never string interpolation.
- Span
input/output payloads can be huge — request input { truncatedValue } (first 100 chars) when surveying; fetch input { value } (full payload) only for spans you intend to read closely.
Patterns
Two canonical shapes to orient you; entity-specific examples live in each resource.
Reach an entity and read fields via node(id:) + an inline fragment:
query GetEntity($id: ID!) {
node(id: $id) {
... on Dataset { name exampleCount }
}
}
Batch independent project aggregates with aliases in one round trip:
query Overview($name: String!, $timeRange: TimeRange) {
getProjectByName(name: $name) {
traceCount(timeRange: $timeRange)
p50: latencyMsQuantile(probability: 0.5, timeRange: $timeRange)
p99: latencyMsQuantile(probability: 0.99, timeRange: $timeRange)
errorCount: recordCount(timeRange: $timeRange, filterCondition: "status_code == 'ERROR'")
}
}
Execution surfaces (internal mode)
phoenix-gql (bash): run phoenix-gql --help for flags and current permissions. Use --data-only when piping to jq, --output <file> for large results, --vars '<json>' for variables. Mutations are allowed only when runtime permissions say so; the tool reports its permissions on every invocation.
External API usage (user-facing mode)
Facts users need to call the API themselves:
- Endpoint:
POST <phoenix-endpoint>/graphql with a JSON body { "query": "...", "variables": { ... } }, where <phoenix-endpoint> is the Phoenix base URL from PHOENIX_ENDPOINT. A GraphiQL IDE is served on GET at the same path.
- Auth: send a Phoenix API key as a bearer token:
Authorization: Bearer <API_KEY>. API keys are created in Phoenix settings.
- The GraphQL schema is primarily designed for the Phoenix UI and may change between versions; for stable programmatic access, recommend the REST API (
/v1/...) and the arize-phoenix-client Python / @arizeai/phoenix-client TypeScript packages where they cover the need, and GraphQL for everything else.
curl:
curl -s "$PHOENIX_ENDPOINT/graphql" \
-H "Authorization: Bearer $PHOENIX_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query": "query($n: String!) { getProjectByName(name: $n) { traceCount } }", "variables": {"n": "default"}}'
Python:
import httpx
resp = httpx.post(
f"{endpoint}/graphql",
headers={"Authorization": f"Bearer {api_key}"},
json={"query": query, "variables": variables},
)
resp.raise_for_status()
data = resp.json()["data"]
When handing users a query, include: the full operation with variable definitions, an example variables payload, and a note on paginating via pageInfo { hasNextPage endCursor } → pass endCursor as after.
