Openrouter Analytics Query

OpenRouter Analytics Query Execution

Full reference for constructing and executing analytics queries against the OpenRouter API.

Prerequisites

• OPENROUTER_API_KEY must be set to a management key. Management keys are separate from regular API keys — get one at https://openrouter.ai/settings/management-keys

Query Endpoint

POST https://openrouter.ai/api/v1/analytics/query
Authorization: Bearer sk-or-v1-...
Content-Type: application/json

Or via the openrouter-analytics skill scripts:

cd <openrouter-analytics-skill-path>/scripts && npx tsx query-analytics.ts --metrics request_count

Source of truth (avoid drift): the metric/dimension names and the request schema below are illustrative. The live, authoritative list comes from the meta endpoint — call GET /api/v1/analytics/meta (or discover-schema.ts) and treat its response as canonical. For agent-readable docs, OpenRouter publishes llms.txt. Prefer those over the inline examples in this file if they ever disagree.

Request Schema

{
  "metrics": ["request_count", "total_usage"],
  "dimensions": ["model"],
  "granularity": "day",
  "time_range": {
    "start": "2026-05-01T00:00:00Z",
    "end": "2026-05-20T00:00:00Z"
  },
  "filters": [
    { "field": "model", "operator": "eq", "value": "anthropic/claude-sonnet-4" }
  ],
  "order_by": { "field": "total_usage", "direction": "desc" },
  "limit": 100,
  "group_limit": 20,
  "classifier_dimensions": {
    "classifier_id": "<uuid>",
    "dimension_names": ["category"],
    "include_nulls": false
  },
  "classifier_filters": {
    "classifier_id": "<uuid>",
    "filters": [
      { "field": "sentiment", "operator": "eq", "value": "positive" }
    ]
  }
}

Required Fields

Optional Fields

Filter Object Shape

{ "field": "<dimension_name>", "operator": "<op>", "value": "<value>" }

• Scalar operators (eq, neq, gt, gte, lt, lte): value is a string or number
• Array operators (in, not_in): value is an array of strings or numbers
• Several dimensions are label-resolved in query results (returned as human-readable names), but filters must use the underlying ID:
  • api_key_id — numeric ID (from generation metadata) or 64-char SHA-256 hash (from GET /api/v1/keys). Hashes are auto-resolved to numeric IDs before querying.
  • user — Clerk user ID (e.g. user_abc123), not the display name/email shown in results.
  • workspace — workspace UUID, not the workspace name shown in results.
  • app — numeric app ID, not the app title shown in results.
  • model — permaslug (e.g. openai/gpt-4o), not the display name.
• Other dimensions (provider, origin, country, finish_reason, external_user, etc.) are not enriched — filter values match what's returned in results.

Order By

{ "field": "<metric_or_dimension_or_date>", "direction": "asc" | "desc" }

When granularity is set and no order_by is specified, results are ordered by time descending.

Classifier Dimensions

Classifier dimensions allow grouping by dynamic, user-defined classification labels (e.g., topic, sentiment, category) produced by a classifier attached to your account.

{
  "classifier_dimensions": {
    "classifier_id": "550e8400-e29b-41d4-a716-446655440000",
    "dimension_names": ["category"],
    "include_nulls": false
  }
}

Constraints:

• Limits the query time range to 31 days
• Single dimension name → result column is aliased to that name (e.g., category)
• Multiple dimension names → result uses generic clf_dimension_name / clf_dimension_value columns

Classifier Filters

Classifier filters narrow results to generations matching specific classification values. They can be used independently or alongside classifier_dimensions.

{
  "classifier_filters": {
    "classifier_id": "550e8400-e29b-41d4-a716-446655440000",
    "filters": [
      { "field": "category", "operator": "eq", "value": "billing" },
      { "field": "sentiment", "operator": "in", "value": ["positive", "neutral"] }
    ]
  }
}

Constraints:

• Limits the query time range to 31 days
• Only equality/set operators supported (classification values are strings — ordered comparisons would be lexicographic)
• All filter field names must be configured dimensions on the classifier

Response Schema

{
  "data": {
    "data": [
      { "date__day": "2026-05-19", "model": "anthropic/claude-sonnet-4", "request_count": "1523", "total_usage": 4.27 },
      { "date__day": "2026-05-18", "model": "openai/gpt-4o", "request_count": "892", "total_usage": 2.15 }
    ],
    "metadata": {
      "query_time_ms": 142,
      "row_count": 2,
      "truncated": false
    },
    "cachedAt": 1747699200000,
    "warnings": ["Could not resolve api_key_id hash: abc123..."]
  }
}

Response Fields

Numeric types: Count metrics (request_count, tokens_*, etc.) are returned as strings ("1523"). Cost and rate metrics (total_usage, cache_hit_rate, latency, throughput) are returned as numbers (4.27). Parse count values with Number() or parseInt() before arithmetic.

Label resolution: Dimensions api_key_id, app, user, and workspace return human-readable labels in data rows (key names, app titles, user names, workspace names), not raw IDs.

CLI Reference

The query-analytics.ts script in the openrouter-analytics skill accepts these flags:

The CLI prints a single JSON object to stdout with two keys — data (the result rows) and metadata:

{
  "data": [ { "model": "anthropic/claude-sonnet-4", "total_usage": 4.27 } ],
  "metadata": { "query_time_ms": 142, "row_count": 2, "truncated": false }
}

A human-readable stats line (row count, query time, truncation/cache flags) is written to stderr for terminal use only.

When parsing output programmatically, always check metadata.truncated. If true, the result was capped at --limit and is a partial dataset — increase --limit or paginate before reporting totals/rankings. Dimensions api_key_id, user, app, and workspace are already resolved to human-readable names in the data rows.

Multi-filter queries: the CLI builds a multi-element filters array (ANDed together) from the unindexed base flag (--filter-field/--filter-op/--filter-value) plus the indexed --filter-field-N/--filter-op-N/--filter-value-N flags. Each filter must supply all three parts (field, op, value); a partial triplet is rejected. Up to 20 filters total (the base flag plus indices 1–19), matching the API cap. Indices may be sparse (e.g. base + -2 with -1 omitted is fine — gaps are skipped, not silently dropped). For a query like model = X AND provider = Y:

npx tsx query-analytics.ts --metrics request_count \
  --filter-field model --filter-op eq --filter-value anthropic/claude-sonnet-4 \
  --filter-field-1 provider --filter-op-1 eq --filter-value-1 anthropic

Flag-value caveat: the CLI's argument parser treats any token starting with -- as a new flag, so a filter value that begins with -- cannot be passed via the CLI flags. Dimension values (model IDs, provider names, etc.) do not start with --, so this is rarely an issue in practice — but if you need such a value, use the direct curl form below instead.

Direct API Usage (curl)

If you prefer calling the API directly instead of using the scripts:

curl -X POST https://openrouter.ai/api/v1/analytics/query \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "metrics": ["total_usage", "request_count"],
    "dimensions": ["model"],
    "granularity": "day",
    "time_range": {
      "start": "2026-05-13T00:00:00Z",
      "end": "2026-05-20T00:00:00Z"
    },
    "order_by": { "field": "total_usage", "direction": "desc" },
    "limit": 10,
    "group_limit": 7
  }'

Query Construction Guide

Aggregates (no time series)

Omit granularity to get a single aggregate row per dimension combination:

{
  "metrics": ["total_usage", "request_count"],
  "dimensions": ["model"],
  "order_by": { "field": "total_usage", "direction": "desc" },
  "limit": 10
}

Time Series (with granularity)

Add granularity to get one row per time bucket (and per dimension combination if dimensions are set):

{
  "metrics": ["request_count"],
  "granularity": "day",
  "time_range": {
    "start": "2026-05-01T00:00:00Z",
    "end": "2026-05-20T00:00:00Z"
  }
}

Filtered Queries

Narrow results with filters. Multiple filters are ANDed:

{
  "metrics": ["total_usage", "avg_latency"],
  "dimensions": ["provider"],
  "filters": [
    { "field": "model", "operator": "eq", "value": "anthropic/claude-sonnet-4" }
  ]
}

Multi-Dimension Queries

Combine up to 2 dimensions for cross-tabulation:

{
  "metrics": ["request_count"],
  "dimensions": ["model", "provider"],
  "order_by": { "field": "request_count", "direction": "desc" },
  "limit": 20
}

Error Handling

Time Range Behavior

Some metric/dimension combinations support time ranges up to 365 days (with daily granularity), while others are limited to 31 days. The server resolves this automatically based on the requested metrics and dimensions.

Usage breakdown metrics follow the same pattern: credits_usage, usage_upstream, usage_cache, usage_data, usage_web, and usage_upstream_web support up to 365 days, while openrouter_usage, byok_fees, usage_file, usage_upstream_file, usage_web_fetch, and usage_upstream_web_fetch are limited to 31 days.

Classifier dimensions and classifier filters always force the 31-day time range limit.

If a query times out, try:

• Narrowing the time range
• Removing latency/throughput metrics
• Removing per-generation dimensions (provider, origin, country, finish_reason, etc.)
• Removing classifier dimensions/filters (they are more expensive to compute)