The AI session threads API returned only a network call *summary*
(total/blocked counts + top domains). This adds the per-call list so the
session detail can render individual Agent Firewall network calls.
`ListAIBridgeSessionNetworkCalls` reuses the same sequence-number
windowing as the existing summary and includes all protocols. The list
is exposed as `network_call_logs` on the threads response and is capped
server-side at 100 rows. The summary (`network_calls.total`/`blocked`)
remains authoritative for whole-session totals: the list length and its
blocked count equal the summary only when a session has at most 100
calls, and are truncated beyond that.
### PR map (merge strictly bottom-up)
This change is a 4-PR stack. Each PR depends on all the ones below it,
so merge in this exact order:
1. #27417 — backend network summary
2. #27418 — frontend summary rows
3. #27425 — backend per-call list `network_call_logs`
4. #27426 — frontend network-calls panel
Refs AIGOV-464
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Backend for the AI session network summary. Exposes total/blocked
network calls and top destination domains on the session threads
endpoint (`GET /api/v2/ai-gateway/sessions/{id}`).
Total and blocked reuse the existing Agent Firewall aggregation from the
sessions list query, so the numbers match the sessions table. Top
domains are a new server-side aggregation
(`GetAIBridgeSessionTopDomains`) over boundary logs, using the same
interception-window correlation. There is no network-error state,
matching the current data model.
Frontend consuming these fields is in a separate stacked PR.
### PR map (merge strictly bottom-up)
This change is a 4-PR stack. Each PR depends on all the ones below it,
so merge in this exact order:
1. #27417 — backend network summary (base `main`)
2. #27418 — frontend summary rows (base #27417)
3. #27425 — backend per-call list `network_call_logs` (base #27418)
4. #27426 — frontend network-calls panel (base #27425)
Refs AIGOV-463
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Cian Johnston <cian@coder.com>
## Stack Context
This stack removes native chat cost tracking and native chat usage
limits, making the AI Gateway the single source of AI spend data and
budget enforcement.
1. **This PR:** re-back the per-chat cost endpoint with AI Gateway data.
2. Remove native chat usage limits end to end, rewiring the sidebar
indicator to gateway spend.
3. Remove native chat cost tracking end to end, deleting the
Analytics/Spend cost UI.
## What?
`GET /api/experimental/chats/{chat}/cost` summed
`chat_messages.total_cost_micros`, which native chat cost tracking
maintained. It now aggregates AI Gateway interception data instead, and
has no native fallback.
- New `GetAIBridgeChatCost` query, authorized through the root chat so
members can read their own chat's cost without gaining access to raw
interception rows.
- Response fields renamed: `priced_message_count` -> `request_count`,
`unpriced_messages_having_usage_count` -> `unpriced_request_count`.
- The chat summary sidebar keys its cost cache by root chat, and hides
the cost row where the AI Gateway is off or unlicensed. The root cost is
invalidated when a chat leaves an active status and when a generated
title lands, since title generation bills its own gateway request.
`GetChatModelUsageCostByChatID` and the rest of native cost tracking are
untouched here; PR 3 removes them.
## Why?
Native cost tracking duplicates what the AI Gateway already records, and
the two disagree. Repointing the endpoint first means the cost UI keeps
working while the native implementation is deleted later in the stack.
Two behaviour changes follow from gateway semantics and are intentional:
- **Requests, not messages.** The gateway records interceptions, so
counts are requests. Title-generation traffic now counts.
- **Whole-tree totals.** The gateway records the *spawning* chat's ID as
the interception session ID, so a subagent's requests are attributed to
its immediate parent, not always the root. Only a whole chat tree can be
summed, so the query resolves the root and aggregates the tree, and
every chat in a tree reports the same total. Native returned per-subtree
totals.
## Attribution and counting semantics
The aggregate groups token usage per interception before counting, so
the reported numbers are per request even though a request records one
usage row per provider response:
- `RequestCount` counts finished `Coder Agents` interceptions in the
tree, including unpriced ones.
- `UnpricedRequestCount` counts requests with at least one usage row the
gateway could not price. It is a subset of `RequestCount`.
- `TotalCostMicros` omits only unpriced usage, so a partially priced
request still contributes its priced portion. The sidebar therefore says
`Excludes unpriced usage from N request(s)` rather than claiming whole
requests were dropped.
A recorded cost of zero is a free request, not an unpriced one. Usage
without an effective group is excluded, matching what never reached
`ai_user_daily_spend`.
## Authorization
Reads go through `ExtractChatParam` plus `ResourceChat`, with no
cost-specific RBAC widening. `TestGetChatCost/MemberCanReadOwnChat`
covers a scoped `agents-access` member reading their own chat's cost,
and `MemberCannotReadOtherUsersChat` still asserts 404 for a non-owner.
Plain members without `agents-access` cannot create or read chats at
all, so they never reach this endpoint.
## Known limitation
AI Gateway data has its own retention period, 60 days by default and
configured independently of chat retention, so spend for requests older
than that is no longer reported. A chat whose gateway records have all
been purged reports zero cost, which is indistinguishable from genuinely
free usage under this contract. The endpoint documents the caveat;
#27330 documents it on the Spend Management page.
In-flight interceptions are excluded, since cost is only known once the
response is recorded. A chat's cost therefore lags the active turn by
one request.
## Rebase note
Rebased onto `main` after #27579 removed the `ai-gateway-cost-control`
experiment. The per-chat cost row is now gated on the `aibridge` feature
alone, matching how #27579 degated the other cost-control surfaces.
> Mux prepared this PR on Mike's behalf.
Add a "Total/blocked network calls" column to the AIBridge sessions
table.
Update `ListAIBridgeSessions` query to calculate network called made and
blocked per session. See query plan
[here](https://explain.dalibo.com/plan/54355c90b165ggb4).
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Categorises the terminal error of a failed interception and persists it
on the interception record, then surfaces it on the AI Gateway API.
- Categorise into an enum (`bad_request`, `unauthorized`,
`rate_limited`, `overloaded`, `server_error`, `unknown`), unwrapping
the ResponseError envelope, the upstream Anthropic/OpenAI SDK errors,
and key-pool exhaustion so blocking and streaming paths agree.
- Thread the type and raw message through the recorder dRPC into the
`aibridge_interceptions` row (optional proto fields; NULL on success).
- Expose the error on the AI Gateway thread API from the root
interception.
*This PR was produced by opencode (agent) using the `anthropic/claude-opus-4-8` model, under human direction and review.*
## Summary
Plumbs the Responses output item id (added as `ToolUsageRecord.ItemID` in #26855) through to the database, captured independently of the `provider_tool_call_id` correlation key. Hosted tools (`web_search_call`, etc.) only have an item id; agentic tools have both.
`provider_item_id` is specific to the OpenAI Responses API; it stays empty for chat completions and Anthropic messages, which have no separate item id.
## Changes
- Migration `000534`: nullable `provider_item_id` column on `aibridge_tool_usages`.
- Proto: `item_id` field 11 on `RecordToolUsageRequest`.
- Server handler: persists `provider_item_id` and adds it to structured logging.
- Translator: maps `ToolUsageRecord.ItemID` to the proto field.
## Tests
- `TestRecordToolUsageProviderItemID`: real-database round-trip asserting `provider_item_id` persists for both hosted and agentic tools, independently of `provider_tool_call_id`.
Stacked on #26855. Linear: AIGOV-96
---
_This PR was produced by opencode (agent) using the_ _`anthropic/claude-opus-4-8`_ _model, under human direction and review._
Implements
https://linear.app/codercom/issue/AIGOV-286/add-interception-cost-calculation-to-aibridge-token-usages
Adds spend attribution to AI Gateway. After the upstream response, each
token-usage record now captures the user's effective group, the
per-token prices in effect at that moment, and a computed cost — so
spend is recorded as an immutable, point-in-time snapshot.
Concretely, `aibridge_token_usages` gains `effective_group_id`,
`input_price_micros`, `output_price_micros`, `cache_read_price_micros`,
`cache_write_price_micros`, and `cost_micros`. When a usage record is
written, the effective group is resolved (per-user override, else the
deployment budget policy), the `(provider, model)` price is looked up
and snapshotted onto the row, and cost is computed from the
provider-reported token counts. A model that isn't in the price table
records its tokens with a `NULL` cost; any *other* resolution failure
fails the write, so a `NULL` cost unambiguously means "model not priced"
rather than "lookup errored."
All values are stored in micro-units (1 unit = 1,000,000 micro-units;
Phase 1 assumes USD, so 1 micro-unit = $0.000001). Prices are quoted per
million tokens.
This also grants the AI Bridge RBAC subject `read` on `ai_model_prices`
(the per-interception price lookup needs it; it previously only had
`update` for the startup seeder).
## Cost precision
Cost is computed per token category as `tokens × price / 1_000_000` with
integer division, then the four categories are summed. The division is
done **per category** (not once over the summed numerator) on purpose:
it keeps the per-category line items summing exactly to the stored total
— no "the parts don't add up to the whole" in reporting).
Integer division truncates sub-micro-unit fractions. For example, a
cheap model at $0.10 per million tokens is a price of `100_000`; 9
tokens cost `9 × 100_000 / 1_000_000 = 900_000 / 1_000_000 = 0` (the
true 0.9 micro-units floors to 0). At real list prices this rarely bites
— $3/M input is a price of `3_000_000`, so even a single token is 3
micro-units. The per-record under-count is bounded below 1 micro-unit
per category, so under $0.000004 total across the four categories, which
is acceptable for list-price-based cost approximation.
## Overflow safety
`cost_micros` is a `BIGINT` (int64), and the largest intermediate value
is a single category's `tokens × price` before division. int64's ceiling
is ≈ `9.223e18`.
- At a steep $75/M model (price `75_000_000`), overflow would require
~123 billion tokens in one response: `123e9 × 75e6 = 9.225e18`, just
over the limit. `122e9` stays under at `9.15e18`.
- A realistically maxed-out Opus 4.8 response (≈1M input + 128K output
at list prices) costs about $15, with a numerator around `1.5e13` —
roughly six orders of magnitude below the ceiling.
So overflow is unreachable from real token counts.
### Multi-currency support
In the future, we may encounter issues with multi-currency support,
especially when dealing with currencies that have very large exchange
rates relative to USD, for example:
IRR: ~1,300,000 IRR ≈ 1 USD
VND: ~26,000 VND ≈ 1 USD
For currencies with such large denominations, numeric overflow is
technically possible, considering that we have only about six orders of
magnitude of headroom before reaching the limit (see above).
## `effective_group_id` has no foreign key
`effective_group_id` records the group a spend was attributed to, as an
immutable historical fact. It is intentionally **not** a foreign key, so
the record survives deletion of the group.
Alternatives were considered and rejected:
- **`ON DELETE SET NULL`** would mutate an "immutable" record — deleting
a group silently erases that interception's attribution and under-counts
the group's historical spend.
- **`RESTRICT` / `NO ACTION`** would block group deletion entirely
(groups are hard-deleted).
- **`CASCADE`** would delete spend history when a group is deleted — the
worst outcome for an audit record.
There is also no insert-time check that the group still exists: the id
comes from a budget that was just resolved, meaning it was valid at some
point.
## Open question: group name snapshotting
Should we also snapshot the group *name* onto each record? Two options:
- **Denormalize it now** — readable in historical reports even after a
group is deleted, but the snapshot can drift from the current name on
rename, raising a "show point-in-time vs. current name" question.
- **Postpone until needed** — it's a purely additive column later, and
the name is display-only (not correctness-bearing like the price). The
cost: names of groups deleted before the column is added can't be
backfilled.
Leaning toward postponing until a concrete reporting need settles the
drift question.
Add `agent_firewall_session_id` (UUID NULL) and
`agent_firewall_sequence_number` (INT NULL) to `aibridge_interceptions`
with a partial index on `agent_firewall_session_id`. No FK to
`boundary_sessions` (soft reference, resolved at query time).
`RecordInterception` reads the new fields from the proto request (merged
in #25884) via `parseOptionalUUID` / `parseOptionalInt32` helpers.
> This PR was authored by Coder Agents.
## Summary
Removes the deprecated `/api/v2/aibridge/interceptions` endpoint and the
Request Logs frontend page, both replaced by the session-based view.
Closes https://linear.app/codercom/issue/AIGOV-266
Closes https://linear.app/codercom/issue/AIGOV-324
## Changes
### Backend
- Remove `GET /api/v2/aibridge/interceptions` HTTP handler and route
- Remove SDK types and client method (`AIBridgeInterception`,
`AIBridgeTokenUsage`, `AIBridgeUserPrompt`, `AIBridgeToolUsage`,
`AIBridgeListInterceptionsResponse`, `AIBridgeListInterceptionsFilter`)
- Remove SQL queries `CountAIBridgeInterceptions` and
`ListAIBridgeInterceptions`
- Remove `searchquery.AIBridgeInterceptions` parser
- Remove dbauthz wrappers, in-memory implementations, metrics, and mocks
for the interceptions list queries
- Remove the `coder aibridge interceptions list` CLI command and golden
files
- Regenerate API docs, swagger, mocks, and metrics
The `/models`, `/clients`, and `/sessions` endpoints stay; the sessions
list page still consumes all three.
### Frontend
- Delete the entire `RequestLogsPage/` directory (page, view, row,
filter, stories, tests)
- Remove the `/aibridge/request-logs` route and its lazy import
- Remove the `getAIBridgeInterceptions` API method,
`paginatedInterceptions` query, and mock interception entities
- `git mv` the shared filter and icon components used by the sessions
pages:
- `RequestLogsPage/RequestLogsFilter/{Client,Model,Provider}Filter.tsx`
→ `AIBridgePage/filters/`
- `RequestLogsPage/icons/AIBridge{Client,Model,Provider}Icon.tsx` →
`AIBridgePage/icons/`
- Drop the `getProviderIconName` hack and the duplicate `anthropic-neue`
icon case now that the FIXME no longer applies
## Commits
1. `refactor: remove interceptions API and request logs view` — the bulk
removal, with explicit renames for the shared filter/icon files.
2. `refactor(site/src/pages/AIBridgePage): drop getProviderIconName
hack` — cleanup of the FIXME that depended on RequestLogsPage existing.
> [!NOTE]
> Generated by Coder Agents on behalf of @dannykopping
## Problem
Centralized requests recorded *the first available key from the pool at
`CreateInterceptor` time* as `credential_hint`, so the interception
could be persisted in the database with a hint that didn't match the key
that actually served the request. The fix consists in storing, at
end-of-interception, the hint of the key that succeeded, or the last
attempted key if all keys are unavailable.
## Changes
- Add `Key.Hint()` and update `credential_hint` on every failover
attempt so it reflects the actually-used key.
- Stop pre-populating `credential_hint` at `CreateInterceptor`.
Centralized starts empty and is updated by the key failover loop.
- Persist the final hint via `RecordInterceptionEnded`; SQL updates
`credential_hint` only when `credential_kind = 'centralized'` so BYOK
keeps its start-time value.
- Log the actually-used hint on interception end/failure; start log uses
a `<keypool-pending>` placeholder for centralized.
> [!NOTE]
> Initially generated by Claude Opus 4.7, modified and reviewed by
@ssncferreira
Previously, the sessions list sorted by `MIN(started_at)` across
interceptions, so sessions with old start times but recent activity
would sink to the bottom of the list regardless of how recently they
were used.
`ListAIBridgeSessions` now sorts by `COALESCE(MAX(prompt.created_at),
MIN(started_at)) DESC`, exposed as the non-nullable `last_active_at`
field. Sessions with prompts surface by last activity; sessions with no
prompts fall back to their start time.
The original implementation used two separate columns (`last_active_at`
as a nullable prompt timestamp and `sort_at` as the non-nullable cursor
key). This revision collapses them into a single `last_active_at` that
is always set — simplifying the SQL, the Go conversion, the API type,
and the frontend.
🤖 Generated with [Claude Code](https://claude.ai/claude-code)
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
## Summary
Adds `credential_kind` and `credential_hint` columns to
`aibridge_interceptions` to record how each LLM request was
authenticated and provide a masked credential identifier for audit
purposes.
This enables admins to distinguish between centralized API keys,
personal API keys, and subscription-based credentials in the
interceptions audit log.
## Changes
- New migration adding `credential_kind`and `credential_hint` to
`aibridge_interceptions`
- Updated `InsertAIBridgeInterception` query and proto definition to
carry the new fields
- Wired proto fields through `translator.go` and `aibridgedserver.go` to
the database
Depends on https://github.com/coder/aibridge/pull/239
Two new columns added to aibridge_token_usages:
- cache_read_input_tokens (BIGINT, default 0)
- cache_write_input_tokens (BIGINT, default 0)
Migration backfills existing rows by extracting values from the metadata
JSONB column (cache_read_input, input_cached, prompt_cached for reads
(max value selected since only 1 should be set), cache_creation_input
for writes).
All references to data from metadata were updated to reference new
columns. No other changes then changing where data is extracted from.
Requires aibridge library version bump to include:
https://github.com/coder/aibridge/pull/229
Fixes: https://github.com/coder/aibridge/issues/150
## Description
Adds `provider_name` to aibridge interceptions to store the provider
instance name alongside the provider type. This allows distinguishing
between multiple instances of the same provider type (e.g. `copilot` vs
`copilot-business`).
## Changes
* Add `provider_name` column to `aibridge_interceptions` table with
backfill from `provider`.
* Add `provider_name` field to the proto `RecordInterceptionRequest`
message.
* Add `ProviderName` to the `codersdk.AIBridgeInterception` API
response.
_Disclaimer: initially produced by Claude Opus 4.6, modified and
reviewed by @ssncferreira ._
_Disclaimer: produced using Claude Opus 4.6, reviewed by me, and
validated against Dogfood dataset._
The `ListAIBridgeSessions` query materialized and aggregated all
matching interceptions before paginating, then ran expensive
token/prompt lookups across the full dataset. For a page of 25 sessions
against ~200k interceptions (our dogfood dataset), this meant:
- Three CTEs scanning all rows (filtered_interceptions, session_tokens,
session_root)
- ARRAY_AGG(fi.id) collecting every interception ID per session
- Lateral prompt lookup via ANY(array_of_all_ids) running for every
session, not just the page
- ~90MB of disk sorts and JIT compilation kicking in
The improvement is to restructure to paginate first and enrich after: a
single CTE groups interceptions into sessions with only cheap aggregates
(MIN, MAX, COUNT), applies cursor pagination and LIMIT, then lateral
joins fetch metadata, tokens, and prompts for just the ~25-row page.
Measured against 220k interceptions / 160k sessions:
| Metric | Before | After |
|--------------------|--------|-------|
| Execution time | 1800ms | 185ms |
| Shared buffer hits | 737k | 2.6k |
| Disk sort spill | 86MB | 16MB |
| Lateral loops | 160k | 25 |
https://grafana.dev.coder.com/goto/fbODPGtvR?orgId=1 the results are
identical, just _much_ faster.
---
Also includes some additional tests which I added prior to refactoring
the query to ensure no regressions on edge-cases.
---------
Signed-off-by: Danny Kopping <danny@coder.com>
Closes#22136
This pull-request implements a `<ClientFilter />` to our `Request Logs`
page for AI Bridge. This will allow the user to select a client which
they wish to filter against. Technically the backend is able to actually
filter against multiple clients at once however the frontend doesn't
currently have a nice way of supporting this (future improvement).
<img width="1447" height="831" alt="image"
src="https://github.com/user-attachments/assets/0be234e2-25f2-4a89-b971-d74817395da1"
/>
---------
Co-authored-by: Jeremy Ruppel <jeremy.ruppel@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
<!--
If you have used AI to produce some or all of this PR, please ensure you have read our [AI Contribution guidelines](https://coder.com/docs/about/contributing/AI_CONTRIBUTING) before submitting.
-->
_Disclaimer:_ _initially_ _produced_ _by_ _Claude_ _Opus_ _4\.6,_ _heavily_ _modified_ _and_ _reviewed_ _by_ _me._
Closes https://github.com/coder/internal/issues/1360
Adds a new `/api/v2/aibridge/sessions` API which returns "sessions".
Sessions, as defined in the [RFC](https://www.notion.so/coderhq/AI-Bridge-Sessions-Threads-2ccd579be59280f28021d3baf7472fbe?source=copy_link), are a set of interceptions logically grouped by a session key issued by the client.
The API design for this endpoint was done in [this doc](https://github.com/coder/internal/issues/1360).
If the client has not provided a session ID, we will revert to the thread root ID, and if that's not present we use the interception's own ID (i.e. a session of a single interception - which is effectively what we show currently in our `/api/v2/aibridge/interceptions` API).
The SQL query looks gnarly but it's relatively simple, and seems to perform well (~200ms) even when I import dogfood's `aibridge_*` tables into my workspace. If we need to improve performance on this later we can investigate materialized views, perhaps, but for now I don't think it's warranted.
---
_The PR looks large but it's got a lot of generated code; the actual changes aren't huge._
Adds database columns and server-side logic to track interception lineage via tool call IDs. When an interception ends, the server resolves the correlating tool call ID to find the parent interception and links them via `parent_id`.
New `provider_tool_call_id` column on `aibridge_tool_usages` and `parent_id` column on `aibridge_interceptions`, with indexes for lookup. `findParentInterceptionID` queries by tool call ID and filters out the current interception to find the parent.
Adapted from the [coder/coder `dk/prompt_provenance_poc`](https://github.com/coder/coder/compare/main...dk/prompt_provenance_poc) branch.
Depends on [coder/aibridge#188](https://github.com/coder/aibridge/pull/188).
Closes https://github.com/coder/internal/issues/1334
This pull-request implements a simple filtering logic so that we're able
to pick which model the user actually used when logs were sent to AI
Bridge.
- Add `GET /aibridge/models` API endpoint that returns distinct model
names from AI Bridge interceptions, with pagination and search support
- New `ListAIBridgeModels` SQL query using case-sensitive prefix
matching (`LIKE model || '%'`) to allow B-tree index usage
- Hand-written `ListAuthorizedAIBridgeModels` in `modelqueries.go` for
RBAC authorization filter injection
- `AIBridgeModels` search query parser in searchquery/search.go
(defaults bare terms to the `model` field)
- dbauthz wrappers, dbmetrics, and dbmock implementations for the new
query
<img width="292" height="185" alt="image"
src="https://github.com/user-attachments/assets/134771df-2d26-4c54-acc4-27f58128b351"
/>
Previously setting AI Bridge retention to 0 would cause records to be
deleted immediately since we didn't check for the zero value before
calculating the deletion threshold.
This adds a check for aibridgeRetention > 0 to skip deletion when
retention is disabled, matching the pattern used for other retention
settings (connection logs, audit logs, etc.).
Also fixes the return type of DeleteOldAIBridgeRecords from int32 to
int64 since COUNT(*) returns bigint in PostgreSQL.
Refs #21055
Addresses [`aibridge#54`](https://github.com/coder/aibridge/issues/54)
When querying against the values in the database for
`/api/experimental/aibridge/interceptions` we found strange behaviour
wherein there was interceptions that lacked prompting and other various
fields we want. Generally this was as a result of the data not actually
existing for these values (as they were inflight).
The simple solution to this was to hide them if they didn't exist. This
PR addresses that.
---------
Co-authored-by: Danny Kopping <danny@coder.com>
- Adds a new table to keep track of which payloads have already been
reported since we only report for the last clock hour
- Adds a query to gather and aggregate all the data by
provider/model/client
Relates to https://github.com/coder/coder-telemetry-server/issues/27
- Adds FK from `aibridge_interceptions.initiator_id` to `users.id`
- This is enforced by deleting any rows that don't have any users. Since
this is an experimental feature AND coder never deletes user rows I
think this is acceptable.
- Adds `name` as a property on `codersdk.MinimalUser`
- This matches the `visible_users` view in the database. I'm unsure why
`name` wasn't already included given that `username` is.
- Adds a new `initiator` field to `codersdk.AIBridgeInterception` which
contains `codersdk.MinimalUser` (ID, username, name, avatar URL)
- Removes `initiator_id` from `codersdk.AIBridgeInterception`
- Should be fine since we're still in early access
Necessary for the frontend to be able to paginate easily. Cursor
pagination is good for fetching all events, but doesn't play very well
when a pagination component gets involved.
Adds support for `?offset=x` to the existing endpoint. The cursor-based
pagination (`?after_id=x`) is still supported. The two pagination modes
are mutually exclusive, and are documented as such. If both are
supplied, the request will be rejected.
Also adds a `total` property to the response that contains the full
count of items matching the filter. We already have indices in place so
I don't think this will impact performance (or we can revisit it before
GA).
