Chandler · usage
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Prompt

The Omni dashboard shows 11,046 paying partners, but you returned 11,172 (a difference of 126). What could explain the difference? Which snapshot date did you use, and how does your paying partner definition and filters compare to the governed Paying Partners metric in Omni?

Outcome
needs_input
Cost
$0.1399
excludes embeddings
Latency
3.9 s
Query attempts
0
Rows returned
—
Tokens
13,842 / 29
in / out

Request

request_id
35382512-cb64-4951-87cf-66ccd0fd5528
When
2026-10-01 09:11
Tool
ask
User
mihail.iliev@dext.com · Mihail Iliev
Identity
google
Session
f625c317-5c7d-4e6e-8148-303004f8af81
Model
not recorded

Cost was recorded but no model name was. This happens when the answer came from an approved query — that path returns before the model name is stamped — so the cost figure is correct while the model behind it is not identified here. Cost was still priced per call at the right rate.

Route taken

Topic
—
Omni model
—
Match method
agent loop
Nudge shown
—
Question shape
decision_shaped
Caveat raised
—
Sharper question offered
—
Asked the user
probe

Per-step detail — each LLM call, each Omni request, each rejected draft — is traced to stdout only and is not stored, so it cannot be shown here. Query attempts counts run_query executions, not model turns.

Feedback

No feedback linked to this request.

Cost is the LLM completion spend LiteLLM priced for each call, summed per request. Chandler is running on the Codex CLI, which draws ChatGPT plan quota rather than per-token API billing, so this figure is not money paid — it is what the same traffic would have cost on the API, priced from the token counts Codex reports. Read it as the size of the bill avoided. Two known limits: embedding spend is not recorded, so retrieval and matching cost is missing, and because the figure is one sum per request it cannot be split by model within a request — a request's classifier and agent calls can use different models while llm_model holds only one name. Only authenticated calls are logged, USAGE_LOG_ENABLED can switch logging off, and log writes are fail-soft — this is not a complete record of traffic. Questions are grouped into sessions: a session is exact when the caller echoed its id back to us and otherwise inferred from a 30-minute gap in that user’s activity, so a grouping is only as good as the source shown on the session itself. A call with no attributable user gets no session at all; those questions are listed separately rather than dropped.