Two lookalike gaps found auditing the titler.
_MACHINE_PREFIXES missed the compressor's legacy summary opener and the
"[System note:" injections, so a compacted or resumed session could be
named after the note that carried it. Take the summary prefix from the
compressor that emits it rather than keeping a fourth local copy.
The fast-model exclude list covered embedders but not the other non-chat
siblings a provider names after its chat model — "gpt-4o-mini-tts"
satisfies the "-mini" rung and cannot answer a prompt.
The fast-model picker reads /v1/models to find the small model a provider
currently serves, and it asked anonymously. Most of those endpoints need a
key, so the fetch 401'd and the empty result read as "this provider has no
small model" — the picker fell back to its curated list and never noticed.
Worse, a failed fetch cached its empty result forever, so one bad moment
during startup disabled live model discovery for the life of the process,
and the processes that read this run for weeks. Give the failure an expiry
and pass the provider's credentials.
The bare family rungs (-mini, -flash, haiku) also picked whichever id
sorted first, which is the oldest generation a provider still serves:
gpt-3.5-mini over gpt-5.4-mini, claude-3-haiku over claude-haiku-4.5.
Compare the digit runs as numbers so the rung meant to keep us current
does.
Titling ran on the user's main chat model, so a five-word title was billed
to a frontier reasoning model and inherited its latency. Pinning a cheap
model id instead just moves the problem: the hardcoded default was already
dead upstream and every call paid a 404 before the retry net caught it.
Match model FAMILIES against the provider's live /v1/models catalog,
preferring rolling '-latest' aliases where a provider publishes them, and
order the families by measured latency. Nothing to bump when a provider
ships a new mini/flash/haiku. Opt-in per task, so compression, vision, and
search keep 'auto means my chat model'.
Cherry-picked from PR #78959 by @JoaoMarcos44 with authorship preserved.
Follow-up: hoist _cache_scope_from_session_id(session_id) to a local in
build_kwargs so it's computed once instead of 4 times per call.
Closes#78941. Closes#79012. Closes#79013. Closes#79014. Closes#79015.
Co-authored-by: JoaoMarcos44 <joaomarcosdias444@gmail.com>
- Merge _aux_free_only() + _aux_openrouter_model() into single
_aux_openrouter_settings() that reads config once via
load_config_readonly (avoids double deepcopy).
- Remove 15-line block comment and 5-line inline comment that
restated what the code already says.
- Trim module docstring from 10 lines to 3.
- Update test patches to target load_config_readonly.
_call_fallback_candidate_sync replans messages/tools for each resolved
destination, but its async mirror still shipped the caller's decorated
sections verbatim — the primary destination's markers (including a
direct-native tool marker) leaked to fallback candidates with different
cache contracts, and the relay saw the display label instead of the
resolved provider/api_mode. Mirror the sync path: resolve the
destination, replan both sections, thread provider/api_mode through
_relay_async_completion, and replan again for the auth-refresh retry
client. Mutation-checked: the new parity test fails on the verbatim
pass-through shape.
Follow-up to #76032 (#20880).
Python keeps only the last definition of a name in a scope, so a duplicate
silently deletes the first. Four had accumulated, and two cost real coverage.
tests/agent/test_auxiliary_client.py grew a second _clean_env autouse fixture
alongside an NVIDIA feature. Being module-level with the same name, it
replaced the original for all 158 tests in the file: the ANTHROPIC_API_KEY,
ANTHROPIC_TOKEN, CLAUDE_CODE_OAUTH_TOKEN, OPENAI_MODEL, LLM_MODEL and
NOUS_INFERENCE_BASE_URL stripping went away, and so did the _aux_unhealthy_*
cache reset between tests. Two tests had already started clearing that cache
by hand to work around the leak, one of them with a comment describing the
pollution. The NVIDIA keys are folded into the original fixture and the
duplicate removed.
tests/gateway/test_mattermost.py had two copies of
test_progress_send_with_invalid_thread_root_never_falls_back_flat. The
surviving copy omitted the recorded 400 "invalid root_id" state, so the case
the name describes never ran. Both now run under distinct names.
The other two were harmless but hid the pattern: an exact duplicate in
test_tts_media_routing.py, and a dead _codex_auth_store in
test_credential_pool.py that nothing calls.
tests/test_no_shadowed_test_definitions.py walks every test module and fails
on any repeated definition in one scope, exempting the property/setter/
register family and throwaway _ callbacks. It fails on main listing exactly
these four.
Narrow the moa_aggregator Relay bypass to CodexAuxiliaryClient and add
coverage that call_llm(stream=True) returns the provider's direct
create() result for Responses-shim clients.
Salvaged from PR #74903 by @liusencomic-cyber.
Main's 243c9182b1/a16fd675df/7142dc4580 added load_config_readonly
sibling stubs across 38 files; our pruned versions of 11 of those files
kept only the load_config stubs. Re-applied the pairing at every
surviving site (26 patch()/setattr sites) — same return_value/
side_effect as the adjacent load_config stub. 494 tests green across
the 11 files.
The readonly swaps mean agent/ modules now read config through
load_config_readonly(); tests that stubbed only load_config stopped
intercepting those reads. Added sibling readonly stubs (same
return_value/side_effect/lambda) at every affected site across 11 test
files, found via a tree-wide sweep of load_config stubs cross-referenced
against the swapped modules. 3,915 tests green across the 38-file
sibling sweep.
Second, deeper pass over tools/gateway/hermes_cli plus first pass over
the trees wave 1 missed (acp, acp_adapter, skills, computer_use, docker,
dashboard, conformance, monitoring, secret_sources, hermes_state,
providers). Same rubric as wave 1 (AGENTS.md test policy); security,
alternation/caching invariants, issue-number regressions, and E2E kept.
Real test-quality fixes found and rooted out along the way:
- tests/tools/test_command_guards.py made real auxiliary-LLM HTTPS calls
(DEFAULT_CONFIG smart-approval leaked in) — pinned approval
mode=manual via autouse fixture: 17.4s → 0.4s.
- test_model_switch_custom_providers.py / test_user_providers_model_switch.py
silently probed live provider catalogs (~2s/test) — stubbed
cached_provider_model_ids/provider_model_ids/fetch_api_models.
- test_telegram_noise_filter.py: 15-platform copy-paste matrix over
shared gateway.run logic → 3 representative platforms (55s → 3.9s).
- test_gateway_shutdown.py: stop()'s 5s interrupt-deadline loop spun on
MagicMock agents — interrupt.side_effect now clears _running_agents
(22s → 1.0s).
- test_gateway_inactivity_timeout.py poll-harness timings shrunk 3-5x
(24s → 1.1s); test_mcp_stability.py backoff/SIGTERM-grace sleeps
patched (15.4s → 2.5s); test_async_delegation.py negative-drain wait
5s → 0.5s.
- test_telegram_init_deadline.py: loop-block margin restored to 1.0s
with rationale comment — the watchdog-dump assertion needs the loop
blocked well past deadline+grace under parallel load (flaked once in
the 40-worker verification run at a 0.2s margin).
Verification: full hermetic suite via scripts/run_tests.sh —
2,438 files, 21,718 tests passed, 0 failed, 293.9s wall.
Suite totals vs original baseline: 46,820 → 19,757 test functions
(−57.8%), wall 583.5s → 293.9s (−50%), subprocess CPU 13,564s → 11,623s.
gemini-2.5-flash shuts down Oct 16 2026 (Google deprecation schedule)
and gemini-3-flash-preview is superseded. Update every hardcoded
default to the current GA flash model:
- gemini_native_adapter: probe_gemini_tier + _create_chat_completion
default params; free-tier guidance de-pinned from a specific model's
RPD number so it doesn't stale again
- auxiliary_client: gemini/kilocode fallback aux models,
_OPENROUTER_MODEL, _NOUS_MODEL -> google/gemini-3.6-flash
(verified live on both OpenRouter and Nous portal /models)
- provider plugins: kilocode + vertex default_aux_model
- hindsight memory plugin: gemini provider default
- setup wizard gemini list: 3-flash-preview -> 3.6-flash (matches the
curated picker catalog)
- tests: aux-client assertions that pinned the old default literal now
reference the _NOUS_MODEL constant, so the next default bump can't
break them (change-detector cleanup)
Fixes#32360.
Widen okalentiev's failed_model narrowing (#59561) to
_try_main_agent_model_fallback. The safety-net layer still skipped on a
provider-label match alone, so single-provider users whose aux compression
model and main model share one custom endpoint had ZERO fallbacks: the aux
model timing out exhausted the chain in one hop and compression aborted.
Real incident (0.19.0 debug dump): aux zai-org/glm-5.2 hung 324s and timed
out while main mindai/macaron-v1-venti on the SAME endpoint was serving
448K-token turns — the label-only skip discarded the one viable summarizer,
the session wedged over threshold, and the anti-thrash breaker tripped.
Same convention as the chain fix: model-specific failures (timeout,
connection, rate limit) pass failed_model so only the exact failed model is
skipped; provider-wide failures (auth 401 / payment 402) pass None and keep
the whole-provider skip. Both sync and async call_llm sites pass it.
Sabotage-verified: the new regression test fails on the provider-only skip.
Follow-up to the same-provider fallback fix: narrowing the configured-chain
skip to the exact failed model is only correct for model-specific failures.
Auth (401) and payment (402) errors are provider-wide — every model on the
provider shares the same broken credentials/account — so trying a sibling
model can't recover and merely burns another doomed request before the
aux task fails. Worse, returning that sibling client bypasses the
main-agent-model safety net that a provider-wide skip would have reached.
Only forward failed_model to _try_configured_fallback_chain for
model-specific failures (timeout, connection, rate limit, model-incompatible,
invalid response). Auth/payment keep failed_model=None (whole-provider skip),
preserving the pre-existing safety-net behaviour for credential/billing
failures.
Adds an integration test that a timeout forwards the failed model, and
updates the payment-error test to assert failed_model=None.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
_try_configured_fallback_chain skipped every fallback_chain entry whose
provider matched the one that just failed. A chain that intentionally lists
several models under the same provider (e.g. two more NVIDIA NIM models
after the primary NIM model times out) was therefore skipped wholesale,
falling straight through to the main-agent-model safety net instead of
trying the other configured models on that provider.
Add failed_model so the skip narrows to the exact (provider, model) pair
that failed. Callers that only know the provider (client-build failures,
where the whole provider is unreachable regardless of model) keep the old
provider-wide skip; the two runtime request-error call sites (call_llm,
async_call_llm) now pass the model that just failed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The gateway's pre-agent session-hygiene compression killed the summary
call at a fixed 30s wall-clock deadline (compression.hygiene_timeout_seconds),
regardless of whether the summary model was hung or merely slow. A reasoning
model happily streaming a large summary was cut off mid-generation, the user
got '⚠️ Context compression timed out after 30.0s', and a 300s failure
cooldown left the session oversized — a doom loop for slow-but-healthy
auxiliary models.
Timeouts are now liveness-based instead of wall-clock-based:
- agent/auxiliary_client.py: new thread-local aux_progress_hook. When
installed (only by context compression today), the primary call_llm
attempt streams (stream=True) and aggregates chunks back into a complete
response, ticking the hook per chunk. The configured timeout then acts
per stream read (idle) instead of as a total budget. Providers that
reject streaming fall back to the plain non-streaming call; auth/payment/
rate-limit/transport errors propagate unchanged into the existing
recovery chains. Codex Responses (per SSE event) and Anthropic Messages
(per stream event, via the new create_anthropic_message on_stream_event
callback) tick the same hook from inside their wire adapters.
- agent/conversation_compression.py: CompressionCommitFence gains
touch_progress()/seconds_since_progress(); compress_context() installs
fence.touch_progress as the progress hook around the compress call.
- gateway/run.py: the hygiene wait loop treats hygiene_timeout_seconds as
an INACTIVITY budget — while the fence reports fresh progress the wait
extends, bounded by the new compression.hygiene_total_ceiling_seconds
(default 600s, clamped >= the idle budget) so a degenerate trickle
stream still dies. The timeout warning now says the summary model
produced no output, which is the only case that still triggers it.
- config/docs: hygiene_total_ceiling_seconds added to DEFAULT_CONFIG and
configuration.md; hygiene_timeout_seconds documented as inactivity-based.
Tests: tests/agent/test_aux_progress_streaming.py (hook plumbing, stream
aggregation incl. tool-call deltas and reasoning deltas, rejection
fallback, ceiling kill, fence progress surface); two new gateway tests
prove a slow-but-streaming worker survives past the fixed timeout
(sabotage-verified: fails with the old fixed deadline) and a
forever-trickling worker is still cut off at the ceiling.
OpenAI documents GPT-5.5 / GPT-5.5 Pro as extended-cache-only: in-memory
prompt cache retention is not available for them, and only
prompt_cache_retention: "24h" is supported. Responses requests that omit
the field see near-zero cached_tokens even with a stable prompt_cache_key
and identical prefixes (observed on an OpenAI-compatible Responses relay:
0 cached across repeated identical calls before; 97% cache reads after).
Send the field for the gpt-5.5 model family (bare and namespaced ids like
openai.gpt-5.5) on OpenAI-compatible Responses routes, mirrored in the
auxiliary Codex adapter, and pass it through preflight normalization.
Skipped for xAI, GitHub/Copilot, and the chatgpt.com Codex backend, which
reject or ignore body-level cache fields.
The Vertex AI provider (added same-day, commit c73e74386) was never added to
either of the two provider registries that agent/auxiliary_client.py and the
MoA slot-resolution chain depend on, breaking Vertex outside the main
conversation loop:
1. hermes_cli/auth.py::PROVIDER_REGISTRY had no "vertex" entry. The
plugin-auto-extend loop that normally fills gaps explicitly skips
non-api_key auth types (`if _pp.auth_type != "api_key": continue`), and
Vertex was never hand-declared like "bedrock" is. Because
resolve_provider_client() in agent/auxiliary_client.py gates everything
on `pconfig = PROVIDER_REGISTRY.get(provider)` and returns (None, None)
immediately when pconfig is None, its `elif pconfig.auth_type == "vertex"`
branch was permanently dead code — every auxiliary Vertex call (vision,
title generation, reflection, context compression, MoA reference/
aggregator slots) failed outright, not just a MoA-specific edge case.
2. hermes_cli/providers.py::HERMES_OVERLAYS also had no "vertex" entry, so
hermes_cli.providers.get_provider("vertex") returned None. This backs
_preserve_provider_with_base_url() in agent/auxiliary_client.py, which a
MoA slot's resolved (base_url, api_key) pair needs to keep its "vertex"
identity instead of silently collapsing to "custom" — losing the
identity _refresh_provider_credentials() needs to re-mint an expired
OAuth2 token (~1h lifetime) on a 401, and permanently breaking every
subsequent call in that MoA preset for the rest of the session.
Fix mirrors the existing "bedrock"/aws_sdk entries in both registries
exactly, plus adds a "vertex" branch to _refresh_provider_credentials() (it
had branches for openai-codex/nous/anthropic/xai-oauth but not vertex,
so a 401 fell through to `return False` without evicting the stale cached
client).
- hermes_cli/auth.py: hand-declared vertex ProviderConfig(auth_type="vertex")
in PROVIDER_REGISTRY, matching bedrock's shape.
- hermes_cli/providers.py: vertex HermesOverlay(auth_type="vertex") in
HERMES_OVERLAYS + "Google Vertex AI" label override.
- agent/auxiliary_client.py: vertex branch in _refresh_provider_credentials
that re-mints the token via get_vertex_config() and evicts the stale
cached client.
- 8 new regression tests across tests/hermes_cli/test_vertex_provider.py and
tests/agent/test_auxiliary_client.py: registry membership, end-to-end
resolve_provider_client("vertex", ...) building a working client (proving
the previously-dead branch is now reachable), and the 401-refresh/cache-
eviction path.
Requested in review: builder-level assertions that the gemini-native
branch forwards max_tokens (provider names and the native
generativelanguage.googleapis.com base_url, max_tokens=600), plus a
control showing gemini models on OpenAI-compatible endpoints — including
Gemini's own /openai compatibility endpoint — keep the existing omission
behavior (#34530).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Per review feedback from teknium1: reference_max_tokens is an advisors-only
contract. The aggregator is the acting model and must not be capped by the
reference budget. Changed _is_moa from startswith('moa_') to exact match on
'moa_reference'. Added regression test proving aggregator does NOT receive
max_tokens.
Copilot review pointed out that hardcoding kwargs['max_tokens'] would
400 on models requiring max_completion_tokens (GPT-5 family, Copilot).
The existing auxiliary_max_tokens_param() helper already selects the
correct parameter name per model — use it instead of hardcoding.
Test updated to parametrize expected_key so the Copilot gpt-5.5 case
correctly asserts max_completion_tokens instead of max_tokens.
Addresses Copilot review comments on both files.
PR #56756 added reference_max_tokens to cap MoA advisor output and cut
turn latency. The value is correctly threaded through five layers of MoA
code (moa_config → conversation_loop → aggregate_moa_context →
_run_references_parallel → _run_reference → call_llm(task='moa_reference',
max_tokens=800, ...)).
However, _build_call_kwargs() in auxiliary_client.py silently drops
max_tokens for all OpenAI-compatible providers (PR #34845, which fixed
endpoints and NVIDIA NIM keep it. This means reference_max_tokens never
reached the API for the vast majority of providers.
The bug affects every OpenAI-compatible MoA reference/aggregator slot:
Z.AI (coding plan), OpenRouter, OpenAI, GitHub Copilot, and local
providers. Only Anthropic-compat endpoints (MiniMax, /anthropic URLs)
worked — by coincidence, not MoA-aware design.
Fix: thread the 'task' parameter through all six _build_call_kwargs()
call sites. When task starts with 'moa_', max_tokens is always included
in the request kwargs regardless of provider. Non-MoA auxiliary tasks
(compression, titles, vision, etc.) keep PR #34845 behavior unchanged.
Verified end-to-end:
- Z.AI GLM-5.2 with max_tokens=50 → returned exactly 50 tokens
- Z.AI GLM-5.2 with max_tokens=20 → returned exactly 20 tokens
- Z.AI GLM-5.2 uncapped → returned 315 tokens
- 7 new regression tests covering 4 providers, Anthropic wire, non-MoA
tasks, and prefix-matching boundary
- 288 auxiliary_client tests pass (was 281, +7 new), 84 MoA tests pass
- Zero regressions
'auto' is a sentinel meaning "inherit from main runtime / auto-detect",
not a literal model id -- already handled for cfg_model (config-derived)
in _resolve_task_provider_model, but not for the explicit `model` kwarg.
MoA reference/aggregator slots (agent/moa_loop.py's _slot_runtime) forward
a preset's `model:` field as this explicit argument rather than through
auxiliary.<task> config, so a MoA preset configured with `model: auto`
(a natural thing to try given the existing auxiliary.*.model: auto
convention) reached this function as the explicit `model` arg and took
the `model or cfg_model` branch, bypassing the cfg_model-only sentinel
check entirely -- sending the literal string "auto" to the wire as a
model id.
Normalize both the explicit `model` and `cfg_model` the same way, fixing
this at the single chokepoint every caller (MoA included) already goes
through, rather than patching moa_loop.py separately.
Follow-up to srojk34's explicit-provider unwrap (PR #56691):
- Extract _resolve_moa_aggregator() as the single preset->aggregator
resolver shared by _resolve_auto(), _resolve_task_provider_model(),
and resolve_provider_client() so preset lookup/validation can't drift.
- When the main provider is moa, the aggregator model is now the default
for every UNSET auxiliary model: _read_main_model_for_aux() substitutes
the preset's acting (aggregator) model wherever fallback chains
pre-filled from _read_main_model() (router prefill, custom-endpoint
fallback, named-custom default, external-process default,
_try_main_agent_model_fallback).
- Unwrap moa at the resolve_provider_client() chokepoint so direct
callers (vision auto-detect, plugin code) can't dead-end in the
unknown-provider branch, and unwrap the vision auto-detect main
provider before capability probes run against the preset name.
- Real-config tests: temp HERMES_HOME + actual config.yaml exercising
the genuine load_config()/resolve_moa_preset() boundary.
_resolve_task_provider_model() returned an explicit provider="moa" override
(from a caller-passed arg, or auxiliary.<task>.provider: moa in config.yaml)
verbatim, with no MoA-preset unwrap. Only the *implicit* "main provider is
moa" path inside _resolve_auto() unwraps to the aggregator slot (#53827) —
this function never goes through _resolve_auto() at all, so the explicit
case was never covered.
MoA is a virtual provider with no real HTTP endpoint: resolve_provider_client()
looks "moa" up in PROVIDER_REGISTRY (no such entry), falls to the
unknown-provider dead end, and call_llm surfaces a nonsensical "Provider
'moa' is set in config.yaml but no API key was found. Set the MOA_API_KEY
environment variable..." error for a provider that was never meant to be
reached over the wire.
Fix mirrors #53827's aggregator-resolution approach exactly: when either the
explicit `provider` arg or the config-derived `cfg_provider` is "moa",
resolve the named (or default) MoA preset via resolve_moa_preset() and
continue with its aggregator's real provider+model, dropping any explicit
base_url/api_key (the moa:// virtual endpoint and placeholder key belong to
the facade, not the aggregator's real provider). If the preset can't be
resolved (renamed/deleted), degrades gracefully to the pre-fix behavior
instead of raising harder.
- agent/auxiliary_client.py: _unwrap_moa_provider() helper + call sites for
both the explicit-arg and config-derived provider="moa" cases in
_resolve_task_provider_model(). Also tightened base_url/api_key parameter
types to Optional[str] (matching their actual None-accepting behavior),
which incidentally resolved 5 pre-existing ty diagnostics at call sites.
- 5 new regression tests in tests/agent/test_auxiliary_client.py: explicit
arg unwrap, config-derived unwrap, default-preset fallback when no model
is configured, graceful degradation on preset-resolution failure, and a
non-moa regression guard.
* 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
Five tests for the salvaged #37217 Bug B fix: vendor-field passthrough,
reasoning-key + private-key exclusion, merge-over-existing (fast-mode
speed), no-extra_body regression guard, reasoning-only adds nothing.
Live probes against api.anthropic.com informed the exclusion design:
Anthropic strictly validates the request body (unknown keys 400 with
'Extra inputs are not permitted'), so the passthrough forwards only
caller-configured fields and never the OpenAI-shaped reasoning dict
(translated natively) or _-private plumbing keys.
The just-merged auxiliary.<task>.reasoning_effort shorthand applied
ensemble-wide to MoA (one value for every advisor) — wrong granularity.
Per-slot preset config supersedes it:
moa:
presets:
deep_review:
reference_models:
- {provider: ..., model: ..., reasoning_effort: low}
- {provider: ..., model: ..., reasoning_effort: xhigh}
aggregator:
{provider: ..., model: ..., reasoning_effort: high}
- Remove reasoning_effort from the moa_reference/moa_aggregator
DEFAULT_CONFIG blocks; _get_task_extra_body now warns-and-ignores the
key on MoA tasks, pointing at the preset config
- Guard tests: MoA aux blocks must not regrow the key; task-level value
is rejected with the pointer warning
- Docs: configuration.md notes the MoA exception and links the MoA page
Every auxiliary task block (vision, web_extract, compression,
title_generation, curator, background_review, moa_reference, ...) now
accepts a reasoning_effort shorthand:
auxiliary:
compression:
reasoning_effort: low
vision:
reasoning_effort: none
_get_task_extra_body() folds it into extra_body.reasoning, which every
auxiliary wire already translates: chat.completions passes it through,
the Codex Responses adapter maps it to top-level reasoning/include, and
the Anthropic auxiliary adapter now forwards it into
build_anthropic_kwargs(reasoning_config=...) (previously hardcoded None).
An explicit extra_body.reasoning on the same task wins over the
shorthand. Invalid levels are ignored with a warning. Empty string
(the shipped default) is a no-op — zero behavior change.
Config: reasoning_effort added to all 16 auxiliary task blocks in
DEFAULT_CONFIG (no version bump — deep-merge handles new keys).
_CodexCompletionsAdapter (agent/auxiliary_client.py) is a second,
independent producer of Codex Responses input — used by auxiliary
calls (context compression, flush_memories, MoA aggregation,
session_search) that route through CodexAuxiliaryClient instead of
the main agent's ResponsesApiTransport.build_kwargs. It calls
_chat_messages_to_responses_input() directly without is_github_responses,
so the previous commit's fix didn't cover it: an auxiliary call made
against a Copilot-backed session could still replay a connection-scoped
codex_message_items id and hit the same HTTP 401.
Detect the Copilot host from the adapter's own client.base_url (same
check the adapter already does further down for prompt_cache_key
opt-out) and pass is_github_responses through, closing the gap.
Still #32716.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
A fallback candidate can itself carry a stale credential (e.g. an
expired ANTHROPIC_TOKEN picked up by _try_anthropic). Its 401 previously
propagated out of the fallback call site and aborted the auxiliary task
— for compression: a 60s cooldown + context marker while the session
kept growing past the context cap. Live case: mattalachia debug dump
(Jul 2026), Codex timeout → Anthropic 401 x5 → 296K 'Cannot compress
further'.
Now each fallback candidate call is wrapped: on auth error, refresh the
candidate's provider credentials and retry once; if unrefreshable, mark
the provider unhealthy and walk the discovery chain again so the next
viable candidate serves. Sync + async paths. Non-auth errors still
raise unchanged.
Infer the concrete auxiliary auth provider from the selected client base
URL so provider:auto routes can refresh Copilot/Codex/Anthropic/Nous
credentials after auth errors, instead of skipping refresh because
resolved_provider stayed 'auto'. Adds the copilot branch to
_refresh_provider_credentials and evicts the stale auto-route cache
before retrying.
Fixes#20832. Salvaged from PR #20837, reapplied surgically onto current
main (branch predated the _retry_same_provider_sync/async extraction).
Follow-up to the #55911 salvage: inherit model.api_key only when the aux
base_url resolves to the same hostname as the main model's base_url
(runtime override or config). A misconfigured aux endpoint on a different
host keeps the fail-safe no-key-required placeholder instead of leaking
the main credential cross-host.
When an auxiliary task is configured with provider=custom and an explicit
base_url but an empty api_key, the custom_key fallback chain in
resolve_provider_client() jumped straight to the no-key-required
placeholder without consulting model.api_key from config.yaml. Users
on self-hosted gateways who share the same endpoint and credentials for
both the main model and auxiliary tasks got 401 auth errors.
Add _read_main_api_key() following the same pattern as _read_main_model()
and _read_main_provider(): checks _RUNTIME_MAIN_API_KEY (runtime override)
first, then config.yaml model.api_key. Insert it into the fallback chain
before no-key-required so real credentials are used when available, while
local servers without auth still get the placeholder.
_resolve_task_provider_model returns early on an explicit provider arg,
which skips the config block that consults auxiliary.<task>.base_url /
api_key. Any caller passing provider explicitly (e.g.
resolve_vision_provider_client(provider="custom", ...)) bypasses the
configured custom endpoint and falls through to main-runtime resolution,
silently routing the task to the wrong backend.
Adopt the task's configured base_url/api_key before the early returns,
but only when no explicit base_url was given and the config targets the
same provider (or names none) — a caller forcing a *different* provider
keeps full explicit-arg priority, and an explicit base_url still wins
over config.
Fixes#58515
Two related hardening fixes for auxiliary calls (which include MoA reference
advisors — a pinned-model path where provider fallback is not a meaningful
recovery):
1. Transient-transport retries: the same-provider retry on a connection reset /
timeout / 5xx / 408 was a single attempt, then fallback. For a pinned aux
call a second blip silently loses the call (root of the run2 double-advisor
'Connection error' collapse — a genuine upstream blip). Now retries N times
with exponential backoff, N = auxiliary.transient_retries (default 2 -> 3
total attempts, clamped [0,6]). Compression-on-timeout fast-fail carve-out
preserved.
2. Per-model client-cache isolation: _client_cache_key excluded the model, so
two concurrent auxiliary calls to the same provider/base_url/key but
different models (e.g. an opus + gpt-5.5 MoA fan-out) shared one cache entry
and could race each other's client lifecycle. Model now participates in the
key -> distinct clients, no cross-call races. Same-model reuse unchanged.
- agent/auxiliary_client.py: _transient_retry_count() + backoff loop; model in
_client_cache_key and both call sites.
- hermes_cli/config.py: auxiliary.transient_retries default (2).
- tests: new retry/isolation tests; updated 2 stale-expectation tests to the
corrected behavior (per-model resolve; N-retry escalation).
Backoff base is overridable (_TRANSIENT_RETRY_BACKOFF_BASE) so tests don't sleep.
Follow-up to the salvaged fix: the regression test asserted a frozen
max_tokens == 128_000 literal, coupling it to the Opus-4-8 model table.
Assert against _get_anthropic_max_output("claude-opus-4-8") plus > 2000
instead, so the test survives model-table churn while still catching a
regression to the old `or 2000` fallback.
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