| Last active | User | First match | Questions | Cost | Feedback |
|---|---|---|---|---|---|
| 2026-08-06 10:20 | detelina.vassileva@dext.com | Which fields in our Omni model represent each of these account signals? For each, give me the exact field path (model.view.field) and what value(s) indicate "true": 1. Whether an account is on a plan that includes Core Agents / AI Assist by default (i.e. Core Agents license present) 2. Whether an account has ever started an AI Assist trial (trial start date, trial active/expired flag) 3. Whether an account is currently a paid AI Assist customer (has a paid AI Assist add-on) 4. Whether an account has ever used LIX free credits (LIX free credits consumed > 0 at any point) 5. Whether an account currently purchases LIX bundles (active LIX bundle purchase) Don't return data rows — I only need the field paths and the truthiness rule for each. | 17 | $9.1634 | — |
| 2026-07-23 07:02 | detelina.vassileva@dext.com |
List of accounts that have removed their AI Assist (Agents) license. For each such account, return CRN, account name, country, account type, and remove reason.
2 failed
|
7 | $12.9755 | — |
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.