Chandler · usage
Questions
47
LLM cost
$594.46
all calls priced
Cost / question
$12.6481
Success rate
97.8%
1 failed
Latency p50
143.9 s
p95 221.3 s
Users
5
Sessions
18
2.6 questions each
Tokens
91,312,231 / 646,352
in / out

Questions per day

010202026-09-01 · 2 questions2026-09-02 · 0 questions2026-09-03 · 0 questions2026-09-04 · 13 questions2026-09-05 · 1 questions2026-09-06 · 0 questions2026-09-07 · 0 questions2026-09-08 · 1 questions2026-09-09 · 0 questions2026-09-10 · 3 questions2026-09-11 · 1 questions2026-09-12 · 0 questions2026-09-13 · 0 questions2026-09-14 · 1 questions2026-09-15 · 2 questions2026-09-16 · 0 questions2026-09-17 · 1 questions2026-09-18 · 1 questions2026-09-19 · 0 questions2026-09-20 · 4 questions2026-09-21 · 0 questions2026-09-22 · 4 questions2026-09-23 · 1 questions2026-09-24 · 9 questions2026-09-25 · 0 questions2026-09-26 · 0 questions2026-09-27 · 0 questions2026-09-28 · 3 questions2026-09-29 · 0 questions2026-09-30 · 0 questions2026-10-01 · 0 questions01 Sep16 Sep01 Oct

LLM cost per day

$0.00$100.00$200.002026-09-01 · $1.9762 recorded2026-09-02 · $0.0000 recorded2026-09-03 · $0.0000 recorded2026-09-04 · $17.7602 recorded2026-09-05 · $1.6469 recorded2026-09-06 · $0.0000 recorded2026-09-07 · $0.0000 recorded2026-09-08 · $0.6828 recorded2026-09-09 · $0.0000 recorded2026-09-10 · $1.7284 recorded2026-09-11 · $1.6014 recorded2026-09-12 · $0.0000 recorded2026-09-13 · $0.0000 recorded2026-09-14 · $1.8646 recorded2026-09-15 · $68.7470 recorded2026-09-16 · $0.0000 recorded2026-09-17 · $34.4365 recorded2026-09-18 · $8.3274 recorded2026-09-19 · $0.0000 recorded2026-09-20 · $82.8115 recorded2026-09-21 · $0.0000 recorded2026-09-22 · $103.8230 recorded2026-09-23 · $19.0560 recorded2026-09-24 · $189.8039 recorded2026-09-25 · $0.0000 recorded2026-09-26 · $0.0000 recorded2026-09-27 · $0.0000 recorded2026-09-28 · $60.1953 recorded2026-09-29 · $0.0000 recorded2026-09-30 · $0.0000 recorded2026-10-01 · $0.0000 recorded01 Sep16 Sep01 Oct

By tool

ask
47
$594.4611 recorded

By outcome

45

Feedback

Positive
0
Negative
1
Neutral
0
Unlinked
0

Feedback only attaches to a question when the calling model hands back a valid request_id. Unlinked rows are real feedback that reached no question page — listed below.

What Chandler said beyond the number

Answers carrying a caveat
6 of 45
Answers offered a sharper question
1
Asked what they expected
0
Asked which field was meant
1
Advice on a plain lookup
0

Advice on a plain lookup must read 0 — the firing policy refuses it, so any other value is a bug rather than a dial to turn. A high offered a sharper question rate is the thing to fix first: a check that fires on everything gets ignored on everything, and once the closing lines read as boilerplate the source citation is skipped with them. Asked and never resumed counts users left holding a question nobody relayed an answer to — a client that dropped the conversation. If that climbs, switch off the probe before the ambiguity check: one is a product experiment, the other is Chandler refusing to guess between two readings.

Question shapes

(not classified)
24
$96.0075 recorded
decision_shaped
13
$261.8028 recorded
lookup
10
$236.6509 recorded

(not classified) is a real state, not a gap: an exact cache hit answers before the router runs. If decision_shaped runs much above a third of classified questions the router is too generous, and the offer will feel constant however tight the gates below it are.

Top topics

47
$594.4611 recorded

Top users by cost

$276.1269
15 questions
$165.7467
10 questions
$131.9301
20 questions
$19.0560
1 questions
$1.6014
1 questions

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.