Follow-ups on top of @xxxigm's salvaged bridge (#33294):
- Remove the now-dead narrow item/started-only mapper from #38835
(_codex_note_to_tool_progress) — the full bridge supersedes it and
keeps the same tool-name contract; its tests are repointed at the
bridge helpers.
- Preserve main's request_routing/approval-bypass wiring on the
CodexAppServerSession constructor (landed after the PR was filed).
- Gate agentMessage interim delivery on display.show_commentary so the
app-server runtime honors the same toggle as the codex_responses
commentary channel (tool progress is unaffected).
- Add json import (bridge helpers use json.dumps) and modernize the
wiring test's stub agent for main's usage-accounting attributes.
Commentary delivery is on by default; users who find the extra mid-turn
narration noisy can set display.show_commentary: false to restore the
previous behavior (commentary routed to the reasoning channel, visible
only with show_reasoning).
- hermes_cli/config.py: display.show_commentary default true
- agent/agent_init.py: wire config -> agent.show_commentary
- run_agent.py: gate structured commentary extraction on the flag
- agent/codex_runtime.py: gate live-stream commentary callback (falls
back to legacy reasoning-channel routing when off)
- docs + 2 tests (interim path off, live stream fallback)
Also adds AUTHOR_MAP entries for davidrobertson and 100yenadmin.
Keep each xAI OAuth auth-add login as an independent manual device-code pool entry and recognize xAI personal-team spending-limit 403 responses as billing exhaustion. Preserve the structured top-level error message so the failed credential is quarantined and the next healthy account is selected without attempting a pointless token refresh.
Route direct xAI HTTP consumers through the credential pool as well. Proactive and 401-reactive refreshes update the exact issuing manual entry, preserve validated xAI base URL overrides, and serialize single-use refresh-token rotation across concurrent pool instances.
Add current-main regression coverage for both the registered provider
profile and core GitHub Responses path while leaving live catalog loading
to the complementary catalog-resolution work in #51953.
_extract_parallel_scope_path used Path.cwd() (process cwd) instead of the
tool's actual execution cwd, and os.path.abspath() instead of os.path.realpath(),
so symlink aliases and relative/absolute path pairs that resolve to the same
physical file were treated as distinct targets and placed in the same parallel
segment. On case-insensitive platforms (Windows) os.path.normcase() was also
absent, allowing Foo.txt and foo.txt to race.
Changes:
- agent/tool_dispatch_helpers.py: introduce _canonical_path(raw_path,
execution_cwd) applying expanduser->abspath->realpath->normcase; thread
execution_cwd through _extract_parallel_scope_path and
_plan_tool_batch_segments
- agent/tool_executor.py: pass get_active_env(effective_task_id).cwd as
execution_cwd to _plan_tool_batch_segments; add pathlib.Path import
- run_agent.py: pass active env cwd to _plan_tool_batch_segments at the
second call site inside _execute_tool_calls
- tests/run_agent/test_tool_batch_segmentation.py: add 5 regression tests
covering relative/absolute same target, symlink alias, execution_cwd vs
process cwd, symlink parent + nonexistent write target, and Windows
case-insensitive alias (skipped on non-Windows)
Fixes a file-corruption / lost-update race introduced by the mixed
tool-batch segmentation feature (perf commit #64460).
* feat(analytics): record auxiliary model usage per task in session accounting
Auxiliary LLM calls (vision, compression, title_generation, web_extract,
session_search, ...) discarded their token usage, leaving dashboard
analytics blind to aux model spend (issue #23270).
- hermes_state.py: session_model_usage gains a task PK dimension
(''=main loop) via v22 table-rebuild migration (SQLite can't alter a
PK); record_auxiliary_usage() writes per-(model,provider,task) deltas
WITHOUT touching sessions counters (gateway overwrites those with
absolute main-loop totals — folding aux in would double-count or be
clobbered). Aux rows never inherit the session's main-loop route.
- agent/aux_accounting.py: ContextVar ambient accounting context
(mirrors the portal_tags conversation context); record_aux_usage()
normalizes usage via usage_pricing.normalize_usage, estimates cost,
and is strictly best-effort. moa_reference/moa_aggregator excluded —
conversation_loop already folds MoA usage+cost into the main delta.
- agent/auxiliary_client.py: _validate_llm_response is the recording
chokepoint — every successful non-streaming aux response passes
through it exactly once, sync and async, including fallback paths
(model read from the response itself stays accurate across
fallbacks).
- run_agent.py: run_conversation publishes/resets the accounting
context; agent/title_generator.py republishes on its bare thread.
- hermes_cli/web_server.py: /api/analytics/usage folds aux rows into
by_model (aux-only models finally appear) and adds a by_task
summary; /api/analytics/models surfaces aux rows on the Models page.
Design per review of PR #62850 by @eeksock (thread-local + separate
auxiliary_usage table): rebuilt on ContextVar (async-safe — thread-local
cross-attributes concurrent coroutines on one event loop) and the
existing session_model_usage table instead of a parallel accounting
path, extended beyond vision to every aux task, and wired the analytics
endpoints so the dashboard actually shows it. Credit to @eeksock for
the approach and @tboatman for the detailed root-cause analysis.
* test(moa): match _validate_llm_response mock to new accounting-hint signature
* test(aux): accept accounting-hint kwargs in remaining _validate_llm_response mocks
The end-to-end _build_api_kwargs parity test asserted the Nous Portal
tags exactly equal the base two-tag list. With the per-session
conversation tag, a real agent (which has a session_id) now emits a
third `conversation=<session_id>` tag. Assert against
nous_portal_tags(session_id=agent.session_id) so the check stays exact.
Two consecutive incomplete assistant interims with identical visible
content (content + reasoning) are collapsed even when opaque provider
state (encrypted reasoning item ids, message item phases) drifts per
continuation — previously that drift defeated dedup and caused message
storms (#52711). The latest opaque payload is written onto the existing
message in place, so continuation replay still uses fresh provider
state.
Salvage note: the original PR also made the 3-retry continuation cap
cumulative per turn (no reset on progress). That half is intentionally
dropped — a legitimate long turn alternating incomplete/progress would
hard-fail at 3 cumulative, changing semantics beyond the reported bug.
Map Codex Responses status=incomplete with incomplete_details.reason=content_filter to finish_reason=content_filter so the existing refusal/fallback path runs instead of burning incomplete continuation attempts.
Community reports of GPT 'infinitely thinking' are usually a slow or
overloaded provider plus silent retry machinery: the CLI/TUI/Desktop
spinner shows a generic 'cogitating...' verb for the whole wait and the
gateway heartbeat says only 'Working — N min'.
Add AIAgent._emit_wait_notice(): rewrites the live spinner/status line
(thinking_callback → CLI prompt_toolkit widget, thinking.delta → TUI +
Desktop) and updates the activity tracker (included in the gateway's
'⏳ Working — N min' heartbeat). Wired at the four wait points:
- non-streaming wait loop: after 30s with no response, the line becomes
'⏳ waiting on <model> — Ns with no response yet (provider may be slow
or overloaded; auto-reconnect at Ns)'
- streaming wait loop: same explanation after 30s with no chunks,
including the long-thinking case
- TTFB / stale-stream kills: '⚠ no response from provider in Ns —
reconnecting...'
- Codex continuation retries: '↻ model returned reasoning with no final
answer — asking it to continue (n/3)' instead of silence
Notices are fail-open (display errors never break the wait loop) and
gateway sessions without a display callback still get the improved
activity description.
grok-4.x on the xAI /v1/responses surface sometimes ends a turn with only
reasoning items — no message output item, no tool calls — and those
reasoning items carry no encrypted_content. Two compounding problems:
1. The model occasionally emits its final answer INSIDE the reasoning
channel, delimited by grok's internal "<response>" tag. The answer
exists but is classified reasoning-only → finish_reason=incomplete.
2. An interim assistant message holding only plain-text reasoning replays
as nothing in _chat_messages_to_responses_input, so every continuation
request is byte-identical to the one that just failed. The model
deterministically repeats the reasoning-only response until the retry
budget is exhausted and the turn dies with "Codex response remained
incomplete after 3 continuation attempts".
Fixes:
- _normalize_codex_response (xai_responses only): salvage the
<response>-delimited tail from the reasoning text and promote it to
assistant content; the untagged prefix stays as thinking text.
- Codex-incomplete continuation path: when the interim message has
nothing the input converter will replay (no content, no encrypted
reasoning items, no message items), append a user-role nudge so the
retry actually differs and explicitly asks for the final answer /
pending tool call. Mirrors the existing _get_continuation_prompt
pattern used for length truncation.
Observed live with grok-4.20 on xai-oauth (2026-07-13); sibling of the
grok-composer web_search incomplete-loop fix in transports/codex.py.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Follow-up to the salvaged #64449: the status-trusting branch flipped
github_responses to 'stop' alongside unknown relays. Copilot fronts the
same OpenAI model family as codex_backend and shows the same
reasoning-only 'still thinking' degeneration, so it stays on the
continuation path. Only unrecognized (other:*) backends trust
response.status='completed' as terminal.
The chat_completions path raises EmptyStreamError when a stream yields
no chunks and no finish_reason (#64420). _call_anthropic() had no
equivalent, and the eventless failure surfaces differently by client:
- Real Anthropic SDK: no message_start means no final-message snapshot,
so get_final_message() raises a bare AssertionError — not in the
retry loop's transient set, so it burned no retries and surfaced raw.
- OpenAI-compat shims: may fabricate a contentless Message with no
stop_reason (or return None), flowing out as a 'successful' empty turn.
Track whether any stream event arrived and normalize both shapes to
EmptyStreamError, giving the anthropic path the same transient retry
budget in the shared _call() retry loop. A real completed response
always carries a stop_reason, so the guard cannot fire on legitimate
turns (including eventless mocks with stop_reason='end_turn').
Follow-up to #64420.
Follow-up to @michaelHMK's cherry-picked fix for #50892:
- agent/tool_dispatch_helpers.py: guard the segment planner's except-path
debug log against non-string arguments (the planner replaced the old
_should_parallelize_tool_batch body after #64460 and inherited the same
latent arguments[:200] slice).
- tests: the PR's executor-coercion hunks were dropped as redundant —
both executors now route through _parse_tool_arguments, which already
rejects null/non-object args with a structured error result instead of
coercing to {} (the 'we do not repair bad model outputs' contract).
Reworked the salvaged tests to pin the current behavior: None args are
rejected without dispatch, valid siblings still run, the planner treats
them as a barrier without raising, and the mainline run_conversation
path (which normalizes None to '{}' before dispatch) stays crash-free
under verbose logging.
A model response containing several parallel-safe reads plus one unsafe
tool used to lose ALL concurrency: _should_parallelize_tool_batch was
all-or-nothing, so a single barrier call (terminal, clarify, unknown
tool, malformed args) forced the entire batch onto the sequential path.
_plan_tool_batch_segments now splits the batch into ordered segments:
maximal contiguous runs of parallel-safe calls execute on the existing
concurrent path, barrier calls on the sequential path, strictly in the
model's emission order. Invariants preserved:
- one tool result per call, appended in emission order (segments are
contiguous, so no result reordering across a barrier)
- side-effect boundaries: no call starts before an earlier barrier ends
- overlapping file targets split into separate ordered parallel runs
- turn-end budget enforcement + /steer injection run exactly once per
batch (segment executors run with finalize=False; the segmented
dispatcher owns the whole-turn finalize)
- interrupt during segment k drains segments k+1..n with cancelled
results, keeping one result per tool_call_id
Homogeneous batches keep their original single-path dispatch (zero
behavior delta); _should_parallelize_tool_batch remains as a thin view
over the planner for existing callers and tests.
Add agent.reasoning_overrides dict to config.yaml. Users can now set
a reasoning_effort per model, overriding the global agent.reasoning_effort.
Example:
agent:
reasoning_effort: "medium" # global default
reasoning_overrides:
"openrouter/anthropic/claude-opus-4.5": "xhigh"
"openai/gpt-5": "low"
"claude-sonnet-4.6": "high" # bare model name also works
The helper is spelling-tolerant: override keys match regardless of
provider prefix or dots-vs-dashes normalization, so users can write
keys in any sensible form and they'll match.
Resolution priority:
1. Session-scoped /reasoning --session override (gateway only; unchanged)
2. Per-model override from agent.reasoning_overrides (spelling-tolerant)
3. Global agent.reasoning_effort (existing)
4. Provider default (unchanged)
Wired into:
- CLI startup (cli.py)
- Messaging gateway agent construction (gateway/run.py)
- Desktop/TUI _load_reasoning_config (tui_gateway/server.py)
- Cron job scheduler (cron/scheduler.py)
- /model mid-session switch (agent/agent_runtime_helpers.py)
+ _primary_runtime now tracks reasoning_config for correct fallback recovery
- Fallback activation (agent/chat_completion_helpers.py::try_activate_fallback)
+ Re-resolves reasoning_config for the fallback model (best-effort)
Closes#21256 (per-model reasoning_effort defaults).
Note: no hermes config set agent.reasoning_overrides.<model> support;
users edit the YAML directly. _set_nested splits on "." and would
corrupt model keys containing version dots.
Follow-up to the cherry-picked empty-user-turn drop: the placeholder
introduced in 8582f35d9 fired for whitespace-only STRING turns too
(content=' ' flattens to non-stripping text but isn't in the
(None, '', []) exclusion set), fabricating an attachment note for a turn
that carried nothing. Gate the placeholder on isinstance(content, list)
so only genuinely structured (e.g. image-only) turns get it; empty and
whitespace-only string turns now fall through to the drop path.
Edge cases verified: trailing empty user turn still ends the view on the
synthetic advisory marker; an all-empty transcript degenerates to [].
MoA's _reference_messages() unconditionally appended every user-role
message to the advisory view sent to reference models, even when the
message content was an empty string or a non-string/multimodal payload
that the text-extraction step flattens to "".
Strict providers (Kimi/Moonshot, and others that enforce non-empty user
content) reject such a message with:
400 Invalid request: the message at position N with role 'user'
must not be empty
Lenient providers (DeepSeek) accept it, so an identical rendered view
passes on one reference and 400s on another within the same fan-out —
the user sees "kimi doesn't support MoA" when the real cause is an empty
user turn leaking into the advisory transcript.
Skip empty user turns, mirroring the existing behavior for empty
assistant turns (which are already dropped when they carry no parts).
The end-on-user invariant is preserved: the synthetic advisory-request
user turn is still appended when the view would otherwise end on an
assistant turn.
Adds a regression test asserting the advisory view contains no empty
user turn and still ends on a user turn.
Review follow-up to the reasoning_config cache-parity fix:
- Only inherit the parent's reasoning_config when the fork runs on the
parent's model (not routed). On the routed aux path
(auxiliary.background_review.{provider,model}) the cache is cold
regardless, so parity buys nothing, and the parent's effort vocabulary
can be invalid for the routed model/provider: OpenRouter
extra_body.reasoning.effort is forwarded unclamped
(chat_completions.py) and codex_responses only maps max/ultra for
gpt-5.6 — an exotic parent effort routed to a strict provider could
400 the review. Mirrors the existing 'not _routed' gate on
_cached_system_prompt / session_start three lines below.
- Add a routed-path regression test asserting reasoning_config is
omitted from the fork kwargs when _resolve_review_runtime returns
routed=True.
- Extract the four copy-pasted recorder stubs in
test_background_review_cache_parity.py into a single
_make_recorder_class() factory so a new fork attribute needs one stub
edit, not four.
PR #17276 painstakingly pinned `_cached_system_prompt`, `session_start`,
`session_id`, and the toolset config on the background-review fork so its
outbound request body would byte-match the parent's and hit Anthropic's
exact-prefix cache. The contributor measured a ~26% end-to-end cost
reduction on Sonnet 4.5.
That optimization is currently being silently undone by a missing
`reasoning_config` kwarg. The fork's `AIAgent(...)` call omits it, so the
fork's `reasoning_config` defaults to `None`. `anthropic_adapter.build_anthropic_kwargs`
(line ~2165) then short-circuits the `thinking` / `output_config` block,
and the fork's request body lands in a DIFFERENT Anthropic cache namespace
from the parent's.
Result on the wire: 0 `cache_read_input_tokens`, full `cache_creation_input_tokens`
of the entire parent prefix — every single background review.
7 days of midagent.db traffic from one host running stock Hermes against
Anthropic Sonnet:
```
Background-review FIRST calls (the moment a review fork is born):
count = 68
cache_write tokens = 7,004,297
cache_read tokens = 1,016,335
Cost on Sonnet ($3.75/M write vs $0.30/M read):
Spent on these writes: $26.27
Cost if they had hit parent cache instead: $2.10
WASTED: $24.16 / week / user
```
That is from one user. Multiply by Hermes's installed base for the full
impact.
Tested against api.anthropic.com directly (see refs/api-tests/ in the
attached investigation repo if needed):
| pair | cache_r | cache_w |
|---------------------------------------------|---------|---------|
| parent fresh | 0 | 24,047 |
| parent same again | 24,047 | 0 |
| fork: appends 2 new tail msgs, thinking ON | 24,047 | 22 |
| fork: appends 2 new tail msgs, thinking OFF | 0 | 24,047 |
Same fork-shape request, only difference is `thinking`. With the fix,
the fork hits the parent's full prefix and only writes the delta
(the `Review the conversation above…` prompt block, ~3-5K tokens).
One line in `agent/background_review.py`: pass
`reasoning_config=getattr(agent, "reasoning_config", None)` to the
`AIAgent(...)` constructor of the review fork. A short comment block
above it explains why so the next person who reads this code doesn't
re-introduce the regression.
`tests/run_agent/test_background_review_cache_parity.py` already covers
the system-prompt / session-id / toolset-config parity contracts that
PR #17276 introduced. I added:
* a `reasoning_config` attribute to `_make_agent_stub` so the stub has
a non-None parent value the test can verify is propagated.
* `test_review_fork_inherits_parent_reasoning_config()` — asserts the
fork's `AIAgent(...)` kwargs carry the parent's `reasoning_config`.
Pre-fix this test fails with `None vs expected {'enabled': True, 'effort': 'medium'}`;
post-fix all 4 tests in the file pass.
```
$ python -m pytest tests/run_agent/test_background_review_cache_parity.py -v
test_review_fork_inherits_parent_cached_system_prompt PASSED
test_review_fork_pins_session_start_and_session_id PASSED
test_review_fork_inherits_parent_toolset_config PASSED
test_review_fork_inherits_parent_reasoning_config PASSED ← new
```
Also runs against the broader background-review test suite:
`test_background_review.py` (4), `test_background_review_summary.py` (8),
`test_background_review_toolset_restriction.py` (3) — 19/19 pass.
`agent/curator.py:1691` has the same omission for the umbrella-curation
fork, but curator's prompt is "curate all skills" — it shares no prefix
with any user conversation, so cache-parity is a non-issue there. Worth
auditing if the curator ever takes a parent conversation as input, but
not part of this PR.
The `agent/auxiliary_client.py:1006` `reasoning_config=None` hardcode is
intentional (title/summary one-shots on short prompts — per-call cost
of namespace flip is negligible) and is also out of scope.
Provider auto-detection (URL-based inference for Anthropic, OpenAI Codex,
and xAI endpoints) runs before credential-pool validation in AIAgent init,
but #63048 placed the pool validation before auto-detection. When the agent
is constructed with provider=None and a recognized endpoint URL, the pool
is validated against an empty provider identity and discarded, even though
auto-detection correctly resolves the provider moments later.
Fix: move the credential-pool validation block to after the URL-based
auto-detection chain. The pool is stored on the agent before
auto-detection; validation now checks the resolved provider and only
nullifies agent._credential_pool when the pool's scoped provider genuinely
doesn't match.
Regression test covers all three auto-detection paths:
- Anthropic (api.anthropic.com)
- OpenAI Codex (chatgpt.com/backend-api/codex)
- xAI (api.x.ai)
Fixes#63425.
Salvage of #63888. The original fix clears stale _last_content_with_tools
on substantive tool-only turns but doesn't clear _mute_post_response, which
a prior housekeeping turn may have set. This suppresses tool progress
output via _vprint until the no-tool-call branch resets it at line ~4834
— after all tools have finished executing.
Fix: also reset _mute_post_response = False when clearing stale fallback.
Added test: verify pure housekeeping turns (content + only housekeeping
tools) still set the fallback correctly — the original use case the
fallback was designed for.
Co-authored-by: liuhao1024 <sunsky.lau@gmail.com>
A cached _last_content_with_tools response from a housekeeping-only turn
could survive a later substantive tool-only turn. When the model returned
an empty response, Hermes incorrectly finalized the older housekeeping
narration instead of invoking the post-tool empty-response nudge.
Production impact: scheduled cron jobs could return early without completing
their actual work (e.g., daily report job returning a housekeeping message
instead of producing the report artifact).
Root cause: The fallback state was only updated when a turn had both
content AND tool_calls. A turn with tool_calls but empty visible content
would skip state updates entirely, leaving stale fallback state intact.
Fix: Classify tools in every tool-call turn (regardless of visible content).
When any tool is substantive (non-housekeeping), clear the older fallback state
before processing later empty responses. This prevents two-turn-old housekeeping
narration from being treated as if it belonged to the immediately preceding
substantive tool turn.
Regression test added: tests/run_agent/test_conversation_fallback_state.py
Fixes#63860
Cache-decorated turns (apply_anthropic_cache_control converts string
content to [{type: text, ..., cache_control}] lists — applied BEFORE the
MoA facade since the #57675 cache-cold fix) and multimodal turns
(text + image_url parts) flattened to empty strings in
_reference_messages, which only read str content. On turn 1 of a
provider:moa session with a Claude aggregator the references received a
single EMPTY user message: Anthropic-side providers 400'd ('messages: at
least one message is required') while tolerant models answered 'no user
request is present' (live incident Jul 14 2026, preset 'closed').
Fixes, in totality:
- _reference_messages: extract visible text via
agent/message_content.flatten_message_text for user/assistant/tool
turns (skips image parts, so no base64 leaks into the advisory view);
decorated and undecorated transcripts now produce a byte-identical
advisory view (advisor cache prefix stays stable).
- image-only user turns get a placeholder instead of an empty message
(Anthropic rejects empty text blocks) or a silently dropped turn
(would break user/assistant alternation).
- degenerate-case fallback flattens structured content too.
- _attach_reference_guidance: a decorated/multimodal trailing user turn
now receives the guidance as a NEW text part appended AFTER the
cache_control-marked part (cached prefix byte-stable) instead of
falling through to a second consecutive user message (strict providers
reject user/user).
- conversation_loop MoA injection: multimodal user turns get the MoA
context appended as a trailing text part instead of being dropped;
user_prompt for the one-shot path flattens content lists instead of
str()-ing them (which leaked base64 payloads into the prompt).
Live-verified on the 'closed' preset (real OpenRouter wire, 2 user
turns, tool loop): all 4 reference calls carry the full document +
rendered tool state, end on user, zero tool-role/tool_calls; advisor
cache_write 7968 then cache_read 5909+; aggregator cache_read
14880-15237 on iterations 2+.
Co-authored-by: bo.fu <bo.fu@meituan.com>
Salvage of #63862. is_output_cap_error() returns False for vLLM/LM Studio
error messages that contain 'prompt contains ... input tokens' (treated as
input-overflow signal). But parse_available_output_tokens_from_error() CAN
extract a valid available_tokens from those same messages. The
compression-disabled guard only checked is_output_cap_error(), so vLLM/LM
Studio users with compression off still got a terminal failure instead of
the max-tokens retry.
Fix: also exempt when parse_available_output_tokens_from_error() returns a
value — that function determines whether the retry path can actually handle
the error, so it's the right predicate for the exemption.
Added test: verify vLLM-format error with compression_disabled=False still
triggers the max-tokens retry path.
Co-authored-by: dmabry <dmabry@users.noreply.github.com>
Bundle Fireworks AI as a first-class BYOK provider across the CLI, web/TUI,
and desktop onboarding.
- New model-provider plugin with attribution headers (HTTP-Referer / X-Title)
so Fireworks can attribute Hermes traffic; PAYG-safe default aux + fallback
models (accounts/fireworks/models/...), IDs tracking fw-ai/fireconnect.
- Registered in CANONICAL_PROVIDERS so it appears in the CLI/web/TUI pickers.
- Alias wiring (fireworks-ai, fw) into both CLI resolvers.
- First-class wiring: OPTIONAL_ENV_VARS, HERMES_OVERLAYS (FIREWORKS_BASE_URL
override), doctor env hints. Live catalog + model_metadata are auto-derived.
- doctor: treat Fireworks' native slash-form IDs (accounts/fireworks/...) as
valid, not aggregator vendor prefixes, so it no longer tells Fireworks users
to switch to openrouter or drop the prefix.
- picker: plugin providers with no static curated list now lead with their
profile fallback_models, so the default is an agentic chat model instead of
whatever the live catalog returns first (Fireworks listed an image model,
flux-*, ahead of its chat models).
- Desktop onboarding: Fireworks as a RECOMMENDED hero card with the official
Fireworks logomark and a brand-purple badge, routing to the BYOK key form;
i18n in en/ja/zh/zh-hant.
- Tests: profile contract, first-class wiring (both resolvers, overlay, config,
doctor incl. the slash-form regression, aux headers, credentials), discovery
spot-check, and a live smoke test driven through the Hermes runtime.
Fire Pass (fpk_) support is coming soon; the future wiring is kept as a
commented-out scaffold in the plugin.
