Trace Intelligence Reference

How to query Trace Intelligence (private beta) from the Mastra platform. Trace Intelligence analyzes completed agent traces and groups them into recurring themes across four trace signals: goal, outcome, behavior, and sentiment.

Use this reference when the user asks to investigate agent health, find recurring failures or behavior issues, identify ways to improve an agent, understand what users ask for, inspect recurring goal/outcome/behavior/sentiment themes, or query Trace Intelligence data programmatically.

Concepts

• Trace signal: one-sentence description generated per completed trace, per dimension (goal, outcome, behavior, sentiment).
• Theme: durable cluster of similar trace signals for one dimension, with a label and description. Theme IDs are stable across snapshots for one signal.
• Snapshot: a moving analysis window over recent traces. Identified by an opaque snapshotId.
• Noise: traces in a snapshot that did not cluster into any theme. Window-local, no durable identity.

Prerequisites

• The project uses Mastra platform Observability and has completed traces.
• The project is enrolled in the Trace Intelligence private beta. Non-enrolled projects get 403 from direct project reads.

Analysis is asynchronous: a project generally needs 100+ completed traces before themes exist. Empty responses usually mean not enough analyzed data yet, not an error.

Access paths

All Trace Intelligence routes are read-only GET requests under /api/learning/.

1. mastra api learning CLI (preferred): use the same credential model as hosted observability commands. No --url or --header is required if MASTRA_PLATFORM_ACCESS_TOKEN and MASTRA_PROJECT_ID are set, or if .mastra-project.json is present. The CLI also resolves X-Mastra-Organization-Id from MASTRA_ORGANIZATION_ID or .mastra-project.json.

mastra api learning entities '{"entityType":"agent"}'

Pass --url and --header only when overriding the hosted Trace Intelligence target or credentials.

2. Local dev server proxy: mastra dev proxies GET http://localhost:4111/api/learning/* to the platform using its normal platform credentials. Loopback only.

curl -fsS "http://localhost:4111/api/learning/entities?entityType=agent" | jq

3. Direct platform endpoint (no CLI or dev server needed): call https://output.signals.mastra.ai with explicit auth, project, and organization headers.

BASE="https://output.signals.mastra.ai"
AUTH=(
  -H "Authorization: Bearer $MASTRA_PLATFORM_ACCESS_TOKEN"
  -H "X-Mastra-Project-Id: $MASTRA_PROJECT_ID"
  -H "X-Mastra-Organization-Id: $MASTRA_ORGANIZATION_ID"
)

curl -fsS "${AUTH[@]}" "$BASE/api/learning/entities?entityType=agent" | jq

The curl examples below use $BASE and "${AUTH[@]}"; for the local proxy, replace $BASE with http://localhost:4111 and drop the headers.

CLI commands

Every route has a CLI command. Positional args carry entityId/themeId; the JSON input carries the query params from the route summary. Pass --schema to any command to print its input schema, and --pretty for readable output.

Same workflow as the curl steps below:

# 1. Discover entities and their available signals
mastra api learning entities '{"entityType":"agent"}'

# 2. List snapshots (signalNames is ordered, comma-separated)
mastra api learning snapshots my-agent \
  '{"entityType":"agent","signalNames":"goal,outcome,behavior,sentiment","limit":10}'

# 3. Themes for one signal in one snapshot (snapshotId from step 2)
mastra api learning theme list my-agent \
  '{"entityType":"agent","signalName":"goal","snapshotId":"<snapshotId>"}'

# 4. Drill into one theme (numeric themeId from step 3)
mastra api learning theme examples my-agent 42 \
  '{"entityType":"agent","signalName":"goal","snapshotId":"<snapshotId>","limit":10}'
mastra api learning theme history my-agent 42 \
  '{"entityType":"agent","signalName":"goal"}'

Investigation workflow

For broad agent-health or improvement questions, start with Trace Intelligence to find recurring patterns, then use trace/log/metric/score APIs for concrete evidence from specific runs. For a specific failed run or error, start with mastra api trace or mastra api log, then use Trace Intelligence to check whether the issue is recurring.

Follow this order for aggregate Trace Intelligence analysis. Later calls need values returned by earlier calls.

1. Discover entities

Lists entities (agents) that have theme output, with which signals are available:

curl -fsS "${AUTH[@]}" "$BASE/api/learning/entities?entityType=agent" \
  | jq '.entities[] | {entityId, availableSignals, latestWindow}'

Only request signalNames that appear in availableSignals in later calls.

2. List snapshots

signalNames is an ordered, comma-separated list (1-4 unique values). A snapshot is returned only when every requested signal has usable output for the window:

ENTITY="my-agent" # TODO: entityId from step 1
curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/theme-snapshots?entityType=agent&signalNames=goal,outcome,behavior,sentiment&limit=10" \
  | jq '.snapshots[] | {snapshotId, ordinal, total, startedAt, endedAt, traceCount}'

Optional from/to (ISO timestamps with offset) bound the snapshot cutoffs; cursor paginates newest-first via nextCursor.

3. Read themes or the cross-signal flow

Use each trace signal for a different diagnostic angle:

• goal: what users are trying to do.
• outcome: what completes, fails, gets blocked, or remains unresolved.
• behavior: how the agent behaves, including tool use, loops, refusals, recovery, or drift.
• sentiment: how user emotion changes across interactions.

Themes for one signal in one snapshot:

SNAPSHOT="..." # TODO: snapshotId from step 2
curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/themes?entityType=agent&signalName=goal&snapshotId=$SNAPSHOT" \
  | jq '{themes: [.themes[] | {themeId, label, state, traceCount, coverage, trend}], noise}'

Cross-signal flow (Sankey-style stages and links; counts are distinct traces):

curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/theme-flow?entityType=agent&signalNames=goal,outcome&snapshotId=$SNAPSHOT" \
  | jq '{stages: [.stages[] | {signalName, nodes: [.nodes[] | {label, kind, traceCount, stageShare}]}], links}'

4. Drill into one theme

Use examples to move from aggregate themes to concrete traces. After identifying a suspicious theme, inspect its examples, then use the returned traceId with mastra api trace, logs, metrics, or scores when you need execution-level evidence.

Detail, examples (raw trace signal texts), and history:

THEME="42" # TODO: numeric themeId from step 3
curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/themes/$THEME?entityType=agent&signalName=goal&snapshotId=$SNAPSHOT" | jq '.theme'

curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/themes/$THEME/examples?entityType=agent&signalName=goal&snapshotId=$SNAPSHOT&limit=10" \
  | jq '.examples[] | {traceId, signalText}'

curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/themes/$THEME/history?entityType=agent&signalName=goal" \
  | jq '{points: [.points[] | {state, traceCount, coverage}], relationships}'

History does not take snapshotId; it returns the theme's lifecycle (birth, continue, split, merge, death, resurrection) across snapshots, plus split/merge relationships.

5. Noise and per-trace paths

Noise bucket and its examples (same query shape as themes, using /noise and /noise/examples):

curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/noise?entityType=agent&signalName=goal&snapshotId=$SNAPSHOT" | jq '.noise'

Per-trace assignments across the ordered signals (trace-level companion to theme-flow; paginate with limit/offset until nextOffset is absent):

curl -fsS "${AUTH[@]}" \
  "$BASE/api/learning/entities/$ENTITY/theme-paths?entityType=agent&signalNames=goal,outcome&snapshotId=$SNAPSHOT&limit=100" \
  | jq '{themes, paths: .paths[:5]}'

paths[].assignments maps each signal to a theme key (resolved in the themes dictionary) or "noise". Use this to join themes back to concrete traceId values, then inspect those traces with mastra api trace (see mastra-api.md).

Route summary

:themeId is numeric. Flow/paths/snapshots take plural ordered signalNames; theme/noise routes take singular signalName.

Rules and caveats

• snapshotId is opaque. Send it back unchanged, with the same entity and signal selection it came from. It is rejected for a different project, entity, or signal set. Never construct or reuse snapshot IDs across scopes.
• Counts are distinct traces, not assignment rows. coverage, stageShare, sourceShare, targetShare are fractions of the deduplicated counts.
• other nodes in theme-flow are lower-volume themes collapsed per stage. They have no themeId and cannot be drilled into; raise themeLimitPerStage to expand them.
• Noise is window-local. It has no durable ID, label, or trend, and differs between snapshots.
• Results are AI-generated summaries. Verify conclusions against theme examples and the underlying traces before acting on them.

Errors

• 401: bad or missing bearer token. Check MASTRA_PLATFORM_ACCESS_TOKEN.
• 403 mentioning X-Mastra-Organization-Id: the organization header is missing. Set MASTRA_ORGANIZATION_ID (direct curl) or run from a directory containing .mastra-project.json (CLI).
• 403: project not enrolled in the private beta, or the local proxy was called from a non-loopback host.
• 503 from the local proxy: MASTRA_PLATFORM_ACCESS_TOKEN / MASTRA_PROJECT_ID missing from the dev server environment.
• Empty entities or snapshots: not enough analyzed traces yet, or the requested signalNames are not all available. Re-check availableSignals from the entities call.