* fix(agent): attribute Grok usage from the turn's own model id
A resumed Grok session with no configured model recorded its entire spend
under the model id "unknown", which matches no pricing row — so the task
reported $0 cost instead of its real spend.
grok.go only learned the model from the session handshake, and ACP's
`session/load` carries no model id (only `session/new` does). When neither
the agent nor MULTICA_GROK_MODEL pins a model, `daemon.go` legitimately
passes an empty model, leaving nothing to attribute the usage to.
Every Grok turn stamps `result._meta.modelId` with what it actually billed
against. Parse it in the shared ACP result parser and use it as the fallback
in grok.go. Other ACP backends are untouched — they keep whatever the
handshake gave them.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* fix(metrics): price the Grok catalog in server-side cost metrics
server/internal/metrics/pricing.go carried no Grok rows at all, so
RecordLLMUsage took the unpriced branch for every Grok turn: llm_cost_usd
reported zero Grok spend while the tokens accumulated in
llm_unpriced_tokens. Internal cost monitoring simply could not see Grok.
Add the six SKUs xAI publishes rates for, mirroring the frontend table in
packages/views/runtimes/utils.ts. Aliases are anchored exact matches like
the gpt-5.6 rows, so `grok-composer-*` (in the catalog, absent from the
price sheet) stays unmapped instead of inheriting a guessed rate.
Short-context tier on purpose: xAI bills a request at 2x once its prompt
reaches 200K tokens, but a usage record aggregates every model call in a
turn and cannot say which tier an individual request hit.
A regression test re-derives the cost of a real grok 0.2.106 turn from the
table and checks it against the costUsdTicks xAI returned for that turn.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* docs(changelog): scope the Grok cost claim to what was actually fixed
The v0.4.9 entry promised "accurate cost" in all four languages, but the
fix corrected catalog pricing and cached-input double-counting — it did not
implement xAI's 2x long-context tier, so a turn whose requests reach 200K
prompt tokens still under-reports by up to 50%. Say what was fixed instead.
Also correct two stale claims in the pricing comment: the daemon tags usage
rows with the runtime provider `grok`, not `xai` (the bare `grok-*` keys are
what make them resolve), and record why thresholding the long-context tier
on an aggregated row would be worse than not pricing it at all.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* feat(usage): carry the provider's own cost through to the usage record
Cost has always been derived client-side as tokens x a static rate, which
cannot express request-level pricing rules. xAI bills a Grok request at 2x
once its prompt reaches 200K tokens, and a task_usage row aggregates every
model call in a turn — so the stored token counts genuinely cannot say which
tier any individual request hit. Thresholding on the aggregate would be worse
than the status quo: it turns a bounded 50% under-estimate into an unbounded
over-estimate for turns made of many short requests.
Grok already reports what it charged, per turn, in `_meta.usage.costUsdTicks`.
Parse it, carry it through agent -> daemon -> API, and store it on task_usage
as a nullable BIGINT of 1e-10 USD ticks (integer, so sub-cent turns stay exact
end to end). NULL means the provider reported no cost — every pre-existing row
and every provider that doesn't return one. No backfill: there is no
authoritative figure to recover for those, and inventing one is the guess this
removes.
A single hourly bucket can mix rows that carry a cost with rows that don't, so
task_usage_hourly gains both halves: `cost_usd_ticks` sums the authoritative
side, and `uncosted_*_tokens` carry exactly the tokens that still need a
rate-table estimate. Consumers report authoritative + estimate(uncosted),
which degrades to today's behaviour when nothing in the bucket is
authoritative. The existing token columns keep covering every row, so token
displays are untouched. The new columns are additive with defaults, so the
unique key, the dirty-queue shape, and migration 102's triggers are unaffected.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* feat(usage): prefer the provider's own cost over the rate table
With the authoritative figure now stored, both cost consumers use it: the
usage dashboard (estimateCost / estimateCostBreakdown) and the server-side
llm_cost_usd metric. Each reports `authoritative + estimate(uncosted tokens)`,
so a row or bucket that mixes priced and unpriced sources stays whole.
The static rate tables remain, but for Grok they are now a fallback — they
still price usage recorded by a daemon too old to report cost, and every
provider that reports none. Custom pricing overrides likewise apply only to
the estimated half: they are a user's guess at a rate, and the authoritative
half is not a guess. A model with no rate-table row but a provider-reported
cost now also drops out of the "unmapped models" banner, since asking the user
to supply a rate for it would invite overriding a real bill.
llm_cost_usd is labelled by token_type and the provider reports one number per
turn, so the charge is distributed across the buckets in the rate table's own
proportions. Only the total is authoritative; the split stays an estimate,
which is why this scales the existing buckets rather than inventing a label.
estimateCostBreakdown does the same, keeping the stacked chart summing to the
headline figure instead of silently under-drawing every Grok row.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* docs(changelog): say Grok cost now follows xAI's actual charge
The earlier wording scoped the claim down to catalog pricing and cached input
because the long-context tier was still unhandled. It is handled now — the
cost comes from what xAI charged for the turn — so the entry can say so.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* fix(usage): keep the provider's cost when the model has no rate row
Both cost consumers bailed out before reading the authoritative figure when
the rate table had no row for the model. A `grok-composer-*` turn — in the
Grok Build catalog, absent from xAI's price sheet — was therefore reported as
$0 spend even though xAI told us exactly what it charged.
Worse on the client: estimateCost returned the real cost while
estimateCostBreakdown returned zeros, so the headline and the stacked chart
disagreed on precisely the rows whose cost is exact — and the unmapped-models
banner was (correctly) hidden, so nothing explained the discrepancy.
Handle the charge before the rate lookup in both places. Without rates there
is nothing to split a total by, so it lands whole in the `input` bucket, the
same fallback distributeAuthoritativeCost already uses when it has no shape to
scale. Tokens with no rate keep going to llm_unpriced_tokens: "unpriced"
describes the rate table, not the money.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* perf(usage): drop the historical rewrite from the cost-split migration
Migration 213 rewrote every existing task_usage_hourly row to seed the
uncosted counters. That is a full-table UPDATE inside a schema migration —
lock time, WAL and bloat all scaling with table size — for rows this issue
explicitly does not care about.
Deleting the UPDATE alone would have zeroed historical cost: with
`NOT NULL DEFAULT 0`, an untouched row asserts "nothing here needs
estimating", so every pre-split bucket would report $0 until the rollup
happened to touch it. Make the uncosted columns nullable with no default
instead. NULL means "never recomputed since the split existed", readers
COALESCE it to the row's own token total ("estimate all of it"), and the
pre-split behaviour is preserved exactly — with nothing to seed, so no
rewrite. A bare ADD COLUMN is metadata-only, so this is now fast DDL.
Rows heal into the split naturally as the rollup recomputes their buckets.
Verified on a fresh database: a legacy-shaped row reads back as its full
tokens to estimate, and a group mixing legacy and post-split buckets sums to
the authoritative cost plus both rows' estimable tokens.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
---------
Co-authored-by: Bohan-J <bohan@devv.ai>
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
Grok Build reports cachedReadTokens inside inputTokens (totalTokens ==
input + output on a real 0.2.106 turn, and that turn's costUsdTicks
matches xAI's rates only when the cached prefix is billed once). The
shared ACP parser persisted both counters raw, so the usage dashboard
charged the cached prefix at the full input rate *and* the cache-read
rate — ~4x the real spend on a cache-heavy turn.
Re-bucket cached reads out of input when totalTokens proves the overlap,
the same normalization codex.go already applies. Backends that report
mutually-exclusive buckets or omit totalTokens are untouched.
Co-authored-by: J <agent@multica.ai>
Co-authored-by: multica-agent <github@multica.ai>
* fix(agent): parse Grok ACP token usage from session/prompt _meta
Grok Build places per-turn metering under result._meta (and
_meta.usage), not the top-level ACP usage field. Multica's shared
parser only read result.usage, so Grok tasks recorded empty token
usage and cost dashboards stayed at zero.
Fall back to _meta.usage (then flat _meta counters) when top-level
usage is absent. Prefer standard top-level usage when both are
present. Update the Grok fake ACP fixture and add regression tests
against the live 0.2.x payload shape.
* test(agent): cover zero ACP usage meta fallback
Co-authored-by: multica-agent <github@multica.ai>
---------
Co-authored-by: Eve <eve@multica-ai.local>
Co-authored-by: multica-agent <github@multica.ai>
Add xAI Grok Build (`grok`) as a first-class Multica runtime over ACP
(`grok --no-auto-update agent --always-approve stdio`), reusing
hermesClient like traecli and kimi. Includes daemon discovery,
protocol_family migration 174, model discovery, MCP passthrough,
thinking effort, frontend branding, and product docs.
Follows xAI's documented headless ACP flow: after `initialize`, read the
advertised `authMethods` and send `authenticate` (preferring xai.api_key
when XAI_API_KEY is set, else the cached login token) before any session
operation — a real, logged-in CLI rejects session/new and session/load
without it. Model discovery performs the same handshake so it returns the
live catalog instead of the static fallback.
`--no-auto-update` is passed as a global flag (and kept daemon-owned in
the blocked custom-arg set) so a background update check can't stall an
unattended ACP task. Thinking effort uses the current `--effort` flag,
and the minimum grok version is 0.2.89 (ACP + authenticate + session/load
+ session/set_model + MCP + --effort).
Closes#2895
