Analytics — AVCodex Docs

The analytics endpoints provide aggregate data on how your agent is being used: session counts, message volume, consumer growth, and token consumption broken down by model. Useful for tracking adoption across a dealer program or measuring how often field techs are using the agent.

Usage analytics#

code

GET /api/v1/apps/{appId}/analytics/usage

Returns a usage summary with a timeseries breakdown.

Query parameters#

Parameter	Type	Default	Description
`started_after`	ISO 8601	none (all time)	Start of the date range.
`started_before`	ISO 8601	none (all time)	End of the date range.
`group_by`	string	`day`	Timeseries granularity: `day`, `week`, `month`.

Example#

bash

curl "https://app.avcodex.com/api/v1/apps/YOUR_APP_ID/analytics/usage?started_after=2025-06-01T00:00:00Z&group_by=day" \
  -H "Authorization: Bearer avcodex_YOUR_API_KEY"

Response#

json

{
  "data": {
    "summary": {
      "total_sessions": 1247,
      "unique_consumers": 342,
      "total_messages": 8934
    },
    "timeseries": [
      {
        "period": "2025-06-01T00:00:00.000Z",
        "session_count": 45,
        "message_count": 312
      },
      {
        "period": "2025-06-02T00:00:00.000Z",
        "session_count": 52,
        "message_count": 387
      }
    ]
  }
}

Response fields#

Summary

Field	Type	Description
`total_sessions`	integer	Total sessions in the date range.
`unique_consumers`	integer	Distinct consumers who started a session.
`total_messages`	integer	Total messages sent and received.

Timeseries entries

Field	Type	Description
`period`	ISO 8601	Start of the time bucket.
`session_count`	integer	Sessions in this period.
`message_count`	integer	Messages in this period.

Weekly grouping#

bash

curl "https://app.avcodex.com/api/v1/apps/YOUR_APP_ID/analytics/usage?group_by=week&started_after=2025-01-01T00:00:00Z" \
  -H "Authorization: Bearer avcodex_YOUR_API_KEY"

Token usage#

code

GET /api/v1/apps/{appId}/analytics/token-usage

Returns token consumption totals, broken down by model. Useful for tracking spend and understanding which models the agent relies on.

Query parameters#

Parameter	Type	Default	Description
`started_after`	ISO 8601	none (all time)	Start of the date range.
`started_before`	ISO 8601	none (all time)	End of the date range.

Example#

bash

curl "https://app.avcodex.com/api/v1/apps/YOUR_APP_ID/analytics/token-usage?started_after=2025-06-01T00:00:00Z" \
  -H "Authorization: Bearer avcodex_YOUR_API_KEY"

Response#

json

{
  "data": {
    "total": {
      "input_tokens": 2450000,
      "output_tokens": 1230000,
      "total_tokens": 3680000,
      "request_count": 4521
    },
    "by_model": [
      {
        "model": "gpt-4.1",
        "input_tokens": 1200000,
        "output_tokens": 600000,
        "total_tokens": 1800000,
        "request_count": 2100
      },
      {
        "model": "claude-sonnet-4-20250514",
        "input_tokens": 1250000,
        "output_tokens": 630000,
        "total_tokens": 1880000,
        "request_count": 2421
      }
    ]
  }
}

Response fields#

`total` object

Field	Type	Description
`input_tokens`	integer	Total prompt/input tokens consumed.
`output_tokens`	integer	Total completion/output tokens consumed.
`total_tokens`	integer	Sum of input and output tokens.
`request_count`	integer	Total number of LLM requests.

Per-model breakdown (`by_model` entries)

Field	Type	Description
`model`	string	Model identifier.
`input_tokens`	integer	Input tokens for this model.
`output_tokens`	integer	Output tokens for this model.
`total_tokens`	integer	Total tokens for this model.
`request_count`	integer	Number of LLM requests for this model.

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