Third-party OpenAI-compatible endpoints (self-hosted gateways, OpenRouter,
Azure proxies) fronting gpt-4o / gpt-4.1 / gpt-5+ / o1-o4 models silently
received max_tokens and 400'd with unsupported_parameter, because the three
kwarg-selection sites only checked base_url_hostname(...) == "api.openai.com"
and fell through to max_tokens on every other host. The constraint is
enforced server-side by the model family, not by the URL, so name-based
detection is required as a fallback.
Changes:
- utils.py: new shared helper model_forces_max_completion_tokens(model) that
prefix-matches gpt-4o, gpt-4.1, gpt-5, o1, o3, o4 families on normalized
(lowercased, vendor-prefix-stripped) names.
- run_agent.py: _max_tokens_param ORs the helper into the URL check.
- agent/auxiliary_client.py:
- auxiliary_max_tokens_param gains an optional keyword-only model arg.
- _build_call_kwargs inline branch applies the same check for both
provider == "custom" and non-custom paths.
Tests:
- tests/test_model_forces_max_completion_tokens.py: 31 new cases covering
positive families, negatives (classic gpt-4, claude, llama, mistral, qwen,
deepseek), vendor prefixes, case-insensitivity, whitespace, None/empty,
and substring-not-prefix guards.
- tests/run_agent/test_run_agent.py::TestMaxTokensParam: 5 new model-based
cases (custom + gpt-5.4, openrouter + gpt-4o-mini, custom + o1-preview,
classic gpt-4-turbo keeps max_tokens, llama3 keeps max_tokens).
- tests/agent/test_auxiliary_client.py::TestAuxiliaryMaxTokensParam: new
class, 7 tests covering the URL x model matrix.
A one-off transient transport failure (streaming-close / incomplete
chunked read / 5xx / 408) on an auxiliary LLM call escalated straight to
provider/model fallback (or, for context compression, dropped the summary
and entered cooldown), even when an immediate retry on the same provider
would have succeeded.
Add a single same-target retry at the top of call_llm() and
async_call_llm() — before the existing except-chain — gated on a new
_is_transient_transport_error() that reuses the canonical
_is_connection_error() detector plus a 5xx/408 status check. A second
failure (or any non-transient error: auth, other 4xx, malformed payload)
falls through to first_err and the existing fallback handling unchanged.
This lives in call_llm so every auxiliary task (compression, memory flush,
title generation, session search, vision) shares one transient-retry
surface, rather than each caller re-implementing it. The context
compressor needs no change — it calls call_llm and inherits the retry; its
existing fallback-to-main path (#18458) now composes naturally (retry the
aux model once, then fall back to main only if the retry also fails).
Co-authored-by: ARegalado1 <alberto.regalado@ymail.com>
The auxiliary Codex adapter maintained its own chat->Responses conversion
loop that forwarded every non-system message's role verbatim into
Responses input[]. When flush_memories()/compression replayed session
history containing assistant tool_calls + role=tool results, those tool
messages leaked into the request and the Responses API rejected them with
HTTP 400: Invalid value: 'tool'.
Route _CodexCompletionsAdapter.create() through the same shared converter
the main agent transport uses (_chat_messages_to_responses_input), so tool
calls become function_call items and tool results become function_call_output
items with a valid call_id. Single conversion path means no future drift.
Also remove the now-dead _convert_content_for_responses() helper — its only
caller was the private conversion loop this change deletes.
Co-authored-by: ProgramCaiCai <techxacm@gmail.com>
The salvaged main-agent fix (sanidhyasin) applies model.default_headers
to the primary OpenAI client, but the auxiliary client (title generation,
context compression, vision routing) builds its own clients and did not
read the override. For a `provider: custom` endpoint behind a gateway/WAF
that rejects the OpenAI SDK's identifying headers, the main turn would
succeed while auxiliary calls to the same endpoint still failed with the
opaque 502/4xx from #40033.
Add agent.auxiliary_client._apply_user_default_headers() (user values win
over provider/SDK defaults; no-op when unconfigured) and apply it at every
OpenAI-wire client construction site:
- _try_custom_endpoint() — config-level `model.provider: custom`
- the named custom-provider branch (custom_providers/providers entries),
including the anthropic-SDK-missing OpenAI-wire fallback
- the api-key-provider, async-conversion, and main resolve_provider_client
fallback branches
To prevent the two clients ever drifting on precedence/value handling,
AIAgent._apply_user_default_headers (run_agent.py) now delegates the config
read + merge to this shared helper (run_agent already imports from
auxiliary_client). Native Anthropic/Bedrock branches are untouched (they
don't use the OpenAI wire).
8 new tests (helper semantics + config-level custom + named custom);
full aux + attribution header suites green (295).
The ChatGPT Codex OAuth backend hard-caps gpt-5.5 at a 272K context window
(verified live: a ~330K-token request to chatgpt.com/backend-api/codex/responses
is rejected with context_length_exceeded while ~250K succeeds; the same slug
exposes 1.05M on the direct OpenAI API / OpenRouter and 400K on Copilot). At the
default 50% trigger, auto-compaction fires at ~136K — half the usable window.
Raise the trigger to 85% (~231K) on this exact route only, gated by a new
compression.codex_gpt55_autoraise config flag (default true). When it fires,
emit a one-time notice (CLI inline print + gateway status_callback replay) with
the exact opt-back-out command. gpt-5.5 on any other provider keeps the user's
global threshold.
- _is_codex_gpt55() matches the 5.5 family only on provider=openai-codex
- _compression_threshold_for_model() now provider-aware + opt-out param
- config key + _config_version bump (27->28) for backfill
- docs + tests (40 cases in test_arcee_trinity_overrides.py)
The minimax / minimax-cn / minimax-oauth profiles still advertised
M2.7 (and M2.7-highspeed for OAuth) as their default_aux_model,
predating the M3 release (2026-06-01). The user-facing
_PROVIDER_MODELS['minimax'] catalog top entry is M3, and the
recommended config for a Token-Plan install now sets
model.default: MiniMax-M3, so the aux default was the only
remaining drift.
Updates:
* minimax default_aux_model: M2.7 -> M3
* minimax-cn default_aux_model: M2.7 -> M3
* minimax-oauth default_aux_model: M2.7-highspeed -> M2.7
(M3 is not on the OAuth / Coding Plan tier per
platform docs as of this PR; the highspeed
variant was the 2x-cost regression from #4082
that PR #6082 collapsed to plain M2.7 for
minimax / minimax-cn but missed OAuth)
* agent/auxiliary_client.py: drop the three legacy
_API_KEY_PROVIDER_AUX_MODELS_FALLBACK entries for the minimax
family. _get_aux_model_for_provider() reads from
ProviderProfile.default_aux_model first (line 250) and only
falls back to the dict when the profile has no aux model or
the profile import fails. With the profile now set, the dict
entries are dead code and a drift hazard. Mirrors the deepseek
cleanup in 773a0faca.
* tests/agent/test_minimax_provider.py: update the existing
TestMinimaxAuxModel assertions from MiniMax-M2.7 to MiniMax-M3
(the intent — 'standard, not highspeed' — is unchanged; the
pin value is).
* tests/plugins/model_providers/test_minimax_profile.py: new
file mirroring tests/plugins/model_providers/test_deepseek_profile.py.
Pins each of the three profiles' default_aux_model and
asserts _get_aux_model_for_provider() returns it. A second
class guards against the highspeed regression coming back.
Refs:
- Closes#36196 in spirit (M3 support — the catalog half of
that issue is #36212; this PR covers the profile half)
- Related: #4082 (M2.7-highspeed 2x-cost), #6082 (previous
M2.7-highspeed -> M2.7 fix that missed OAuth + the
auxiliary_client.py fallback dict)
- Pattern: 773a0faca (same profile-layer fix for deepseek)
The salvaged conversion emitted type:"input_video", which MiniMax M3 rejects
just like the original video_url block. Per MiniMax's Anthropic-compat docs,
the video content block is type:"video" with an image-style source (base64 or
url). Fixes the block type, converts URL-based videos too, and adds 4 video
conversion tests (none shipped with the original PR).
The video_analyze tool sends OpenAI-style 'video_url' content blocks, which
breaks Anthropic-protocol providers (minimax, minimax-cn). These providers
expect 'input_video' blocks with base64 data instead of data: URLs.
Extends _convert_openai_images_to_anthropic() to also handle video_url
blocks, converting them to Anthropic's input_video format when targeting
Anthropic-compatible endpoints.
Fixes#37219
A long-lived process (gateway, watcher) caches the Nous Portal's
recommended-models payload and can pin a model for its whole lifetime.
When that model is later dropped from the Nous -> OpenRouter catalog,
every auxiliary call 404s with 'model does not exist in our
configuration or OpenRouter catalog' until the process restarts.
Now such a 404 force-refreshes the Portal recommendation and retries
once with the current pick (or the gemini-3-flash-preview default).
Scoped to Nous-routed calls only.
- _is_model_not_found_error(): 404/400 'not found / does not exist /
not a valid model' predicate, excludes billing keywords so it never
overlaps _is_payment_error.
- _refresh_nous_recommended_model(): force-refresh fetch, returns a
model distinct from the one that failed, else the known-good default.
- Wired into both call_llm and async_call_llm error chains.
When a user configures a custom: provider (e.g. custom:openclaw-router),
set_runtime_main() only stored provider and model in process-local globals.
_resolve_auto() then had no base_url or api_key for the custom endpoint,
causing Step 1 to fail and auxiliary tasks (approval, compression, title
generation) to fall through to the aggregator chain and route to wrong
providers.
Fix: extend set_runtime_main() to accept base_url, api_key, and api_mode
keyword arguments; store them in new globals alongside the existing provider
and model; fall back to these globals in _resolve_auto() when the main_runtime
dict is empty. The call site in conversation_loop.py now passes all five
fields from the agent object.
Fixes#34777
* fix(auxiliary): stop capping output with max_tokens by default
Auxiliary LLM calls (compression, titles, vision, etc.) no longer send
max_tokens on the OpenAI-compatible chat-completions path. Most providers
treat an omitted max_tokens as "use the model max", which is what we want;
an explicit cap only risks truncation or a wire-format 400.
This was surfaced by GitHub Copilot / GPT-5 (#34530): those models reject
max_tokens and require max_completion_tokens, so compression 400'd and fell
back to a static context marker. Omitting the param sidesteps that quirk
(and ZAI vision's error 1210) entirely.
The Anthropic Messages wire (MiniMax + /anthropic endpoints) keeps
max_tokens because it is a mandatory field there.
* test(auxiliary): update temperature-retry assertions for omitted max_tokens
The temperature-retry tests asserted retry_kwargs["max_tokens"] == 500 on an
api.openai.com endpoint. Now that auxiliary calls omit max_tokens on
OpenAI-compatible endpoints (#34530), that key is absent. Assert it's absent
in both first and retry kwargs and use model as the survives-the-retry witness.
After the legacy session-key path was removed, two parameters became dead
surface on the Nous runtime-resolution chain:
- min_key_ttl_seconds: del'd inside refresh_nous_oauth_pure and pass-through /
telemetry-only in refresh_nous_oauth_from_state, _try_import_shared_nous_state,
_nous_device_code_login, and resolve_nous_runtime_credentials. It controlled the
now-deleted agent-key mint TTL and drives no behavior.
- inference_auth_mode: with the legacy mode gone, AUTO and FRESH are behaviorally
identical; the value only fed _normalize_nous_inference_auth_mode validation and
oauth trace output, never a branch.
Removing inference_auth_mode orphaned its whole supporting cluster
(NOUS_INFERENCE_AUTH_MODE_AUTO/FRESH, NOUS_INFERENCE_AUTH_MODES,
_normalize_nous_inference_auth_mode), and dropping min_key_ttl_seconds orphaned
DEFAULT_AGENT_KEY_MIN_TTL_SECONDS — all deleted here.
Updated every caller (run_agent, auxiliary_client, credential_pool, proxy adapter,
runtime_provider, web_server, main, auth_commands, setup) and pruned the matching
test kwargs. Deleted two tests that exercised the removed surface
(test_legacy_auth_mode_is_rejected, test_try_refresh_..._accepts_explicit_auth_mode).
No behavior change: net -134 LOC of dead code.
The import-failure fallback returned any 3-segment token without scope/
expiry validation, a divergent reimplementation of the canonical
_nous_invoke_jwt_is_usable check. The import is from the same module that
provides resolve_nous_runtime_credentials, so a failure means the whole
auxiliary Nous path is unavailable anyway; return "" instead so the caller
falls through to the clear 'run: hermes auth add nous' guidance rather than
handing back an unvalidated token.
xAI returns HTTP 403 (not 401) with unauthenticated:bad-credentials
when an OAuth2 access token has expired or is invalid. The existing
_is_auth_error() only checked for 401 status codes, so these tokens
were never refreshed and the 403 propagated as a generic permission
denied error.
Three fixes:
1. _is_auth_error: Recognize xAI's 403+bad-credentials pattern as
an auth failure, triggering token refresh instead of silent failure.
2. _refresh_provider_credentials: Add xai-oauth branch with
pool-level refresh (try_refresh_current with select to ensure
current entry) then fallback to singleton resolver with
force_refresh=True.
3. _recoverable_pool_provider: Map api.x.ai host to xai-oauth
pool for auto-resolved providers, matching existing pattern for
openai-codex/openrouter/nous/anthropic.
Includes 14 tests covering the new detection logic, host mapping,
and graceful fallback behavior.
Signed-off-by: moikapy <moikapy@devmoi.com>
The xAI tool-schema sanitizers (strip_slash_enum, strip_pattern_and_format)
mutate their input in place — that's their documented contract. The two
call sites (chat_completion_helpers.build_api_kwargs and the auxiliary
client) were passing agent.tools straight through, so the first xAI
request would permanently strip slash-containing enum constraints and
pattern/format keywords from the per-agent tool registry.
Effect: any subsequent non-xAI call from the same agent (auxiliary task
routed to Anthropic, OpenRouter fallback, mid-session model switch) saw
the already-stripped schema with no way for the user to notice from
their config.
Fix: deepcopy tools_for_api before sanitizing at both call sites.
The slash-enum bug itself (xAI 400ing on enums with '/') was fixed
earlier by #32443 (Nami4D) — that PR landed the strip but used the
sanitizers directly without copying. This salvages #27907's correctness
contribution (the deepcopy) while skipping its redundant parallel
sanitizer (strip_xai_incompatible_enum_values is functionally
equivalent to the existing strip_slash_enum) and its preflight-
neutrality argument (we chose model-gated preflight in #32443).
3 new tests in tests/run_agent/test_run_agent_codex_responses.py:
- strips_slash_enum_from_outgoing_request — outgoing kwargs has no
slash-containing enum values (functional contract preserved).
- does_not_mutate_agent_tools — headline #27907 regression. Snapshot
agent.tools before build_api_kwargs, assert it survives intact
after. Pre-fix this assertion would have caught the mutation.
- is_idempotent_across_repeated_calls — three xAI requests in a row
each strip cleanly AND don't progressively erode the source schema.
344/344 across tests/agent/test_auxiliary_client.py,
tests/agent/transports/test_codex_transport.py,
tests/run_agent/test_run_agent_codex_responses.py, and
tests/tools/test_schema_sanitizer.py.
Co-authored-by: Gabor Barany <barany.gabor@gmail.com>
Closes#33368.
`_CodexCompletionsAdapter.create()` iterates `final.output` from the
Codex Responses stream. The event-driven consumer (introduced in #33042)
always sets `final.output` to a list, so this shape can't come from our
own code path. But:
- Mocked clients in tests can return a typed Response with `output=None`
- Third-party shims / compatibility layers that bypass the consumer can
do the same
- A future code path that wraps a different consumer could regress
The old code `getattr(final, "output", [])` returns `None` (not the
default `[]`) when the attribute EXISTS but is `None`. Iterating
`None` then raises `TypeError: 'NoneType' object is not iterable` —
the exact error logged by title-generation when this fires.
Fix: `getattr(final, "output", None) or []` — single-line defensive
coerce. Cheap; zero risk.
Regression test asserts the auxiliary path handles a final whose
`.output` is `None` (via monkey-patched consumer) without raising and
returns the expected chat.completions-shaped response.
Reporter: @pavegrid-1 (issue #33368).
* remove Vercel AI Gateway provider and Vercel Sandbox terminal backend
Both Vercel-hosted integrations are removed end-to-end. Users on the AI
Gateway should switch to OpenRouter or one of the other aggregators
(Nous Portal, Kilo Code). Users on the Vercel Sandbox backend should
switch to Docker, Modal, Daytona, or SSH.
What's removed:
- `plugins/model-providers/ai-gateway/` provider plugin
- `hermes_cli/vercel_auth.py` Vercel-Sandbox auth helper
- `tools/environments/vercel_sandbox.py` terminal backend
- `ai-gateway` provider wiring across auth, doctor, setup, models,
config, status, providers, main, web_server, model_normalize, dump
- `vercel_sandbox` backend wiring across terminal_tool, file_tools,
code_execution_tool, file_operations, approval, skills_tool,
environments/local, credential_files, lazy_deps, prompt_builder,
cli, gateway/run
- `AI_GATEWAY_BASE_URL` constant, `_AI_GATEWAY_HEADERS` auxiliary-client
header set, run_agent base-URL header/reasoning special-cases
- `[vercel]` pyproject extra and `vercel`/`vercel-workers` from uv.lock
- env vars: `AI_GATEWAY_API_KEY`, `AI_GATEWAY_BASE_URL`, `VERCEL_TOKEN`,
`VERCEL_PROJECT_ID`, `VERCEL_TEAM_ID`, `VERCEL_OIDC_TOKEN`,
`TERMINAL_VERCEL_RUNTIME`
- Tests: deletes test_ai_gateway_models.py and
test_vercel_sandbox_environment.py; scrubs references across 23
surviving test files (no entire tests deleted unless they were
dedicated to AI Gateway / Sandbox)
- Docs: provider tables, env-var reference, setup guides, security
notes, tool config, terminal-backend tables — English plus zh-Hans
i18n parity
- `hermes-agent` skill: provider table entry and remote-backend list
What stays (intentional):
- `popular-web-designs/templates/vercel.md` — CSS design reference,
unrelated to Vercel-the-AI-product
- `x-vercel-id` in `stream_diag.py` headers — generic Vercel CDN
response header, useful diag signal on any Vercel-hosted endpoint
- `vercel-labs/agent-browser` URL in browser config — lightpanda
browser project, different OSS effort
- `userStories.json` historical contributor entry mentioning Vercel
Sandbox — archive, not active docs
Validation:
- 1153 tests in the 22 targeted files pass (`scripts/run_tests.sh`)
- Full repo `py_compile` clean
- Live import of every touched module + invariant check (no
`ai-gateway` in `PROVIDER_REGISTRY`, no `_AI_GATEWAY_HEADERS`, no
`vercel_sandbox` in `_REMOTE_TERMINAL_BACKENDS`)
* test: convert profile-count check from change-detector to invariant
The hardcoded "== 34" assertion broke when ai-gateway was removed.
Per AGENTS.md change-detector-test guidance, assert the relationship
(registry count >= number of plugin dirs) instead of a literal count.
Counts shift when providers are added/removed; that's expected.
* refactor(codex): drop SDK responses.stream() helper; consume events directly
The OpenAI Python SDK's high-level `client.responses.stream(...)` helper
does post-hoc typed reconstruction from the terminal
`response.completed.response.output` field. The chatgpt.com Codex
backend has been observed (today, gpt-5.5) to ship `response.output =
null` on terminal frames, which crashes the SDK with `TypeError:
'NoneType' object is not iterable` mid-iteration.
Carlton's #32963 patched the symptom by wrapping the helper in
try/except and recovering from the same per-event accumulator the SDK
was supposed to populate. This PR removes the helper from the call
path entirely: we now use `client.responses.create(stream=True)` (raw
AsyncIterable of SSE events) and assemble the final response object
ourselves from `response.output_item.done` events as they arrive. The
terminal event's `output` field is never read for content. Same
strategy OpenClaw uses for the same backend.
This makes Hermes structurally immune to the bug class, not patched.
The next time OpenAI ships a shape change to chatgpt.com's terminal
frame, our consumer keeps working because it doesn't read that frame
for content — only for usage/status/id.
Changes
- `agent/codex_runtime.py`: new `_consume_codex_event_stream()` shared
consumer; `run_codex_stream()` uses `responses.create(stream=True)`;
`run_codex_create_stream_fallback()` collapses into a thin alias
since the primary path now does what the fallback used to do.
- `agent/auxiliary_client.py`: `_CodexCompletionsAdapter` uses the
same consumer; old null-output recovery helpers deleted as
unreferenced.
- Tests migrated: fixtures that mocked `responses.stream` now mock
`responses.create` returning a raw iterable. New regression test
asserts the auxiliary path returns streamed items even when the
terminal event's `output` is literally `null`.
Validation
- Live: tested against fresh OAuth on `chatgpt.com/backend-api/codex`
with `gpt-5.5` — response built correctly with `response.output=null`
on the terminal frame, all events consumed, usage/reasoning tokens
propagated.
- `tests/run_agent/test_run_agent_codex_responses.py` +
`tests/agent/test_auxiliary_client.py`: 242 passed.
* test+fix(codex): migrate streaming tests, raise on truncated streams
CI surfaced 10 test failures across tests/run_agent/test_streaming.py
and tests/run_agent/test_codex_xai_oauth_recovery.py — both files had
their own `responses.stream(...)` mocks I missed in the first sweep.
agent/codex_runtime.py: _consume_codex_event_stream() now raises
"Codex Responses stream did not emit a terminal response" when the
stream ends without any terminal frame AND no usable content. This
preserves the signal callers used to get from the SDK's high-level
helper, which they distinguished from "completed with empty body"
in error handling.
Tests migrated:
- test_streaming.py: text-delta callback, activity-touch, and
remote-protocol-error tests all switch from mocking responses.stream
to responses.create returning an iterable of events.
- test_codex_xai_oauth_recovery.py: prelude-error tests are recast as
wire-error-event tests (the new path raises _StreamErrorEvent
directly when the wire emits type=error, which is strictly better
than the old two-phase "SDK RuntimeError → retry → fallback"). The
retry-on-transport-error test moves from responses.stream side-effect
to responses.create side-effect.
Verified live against chatgpt.com Codex with gpt-5.5 — AIAgent.chat()
through the full codex_responses path returns correctly, 319/319
targeted tests passing.
After key #1 is marked exhausted the retry still called the API with key #1
due to env-var bias in _get_cached_client / resolve_api_key_provider_credentials.
Fix: peek the pool and pass the active entry's key as explicit_api_key.
Secondary: api_key_hint in mark_exhausted_and_rotate pins the correct entry
under concurrent CLI+gateway calls; _is_payment_error matches GoUsageLimitError;
extract_api_error_context parses "Resets in Xhr Ymin".
Aux callers (title generation, vision, session search, etc.) can reach
resolve_provider_client() without an explicit model when the user
picked their main provider via 'hermes model' and didn't bother
configuring a per-task auxiliary.<task>.model override. The
expectation in that case is universal: 'use my main model for side
tasks too.'
Before, the OAuth providers (xai-oauth, openai-codex) silently
returned (None, None) on an empty model — both lack a catalog default
because their accepted-model lists drift on the backend. That caused
_resolve_auto to drop to its Step-2 fallback chain (OpenRouter /
Nous / etc.), so aux tasks billed against the wrong subscription
without warning.
The fix is at the top of resolve_provider_client() — a single
3-step universal fallback that runs before any provider branch, so
no provider-specific empty-model guards are needed (now or for any
future provider we add):
1. caller-passed model (caller knew what they wanted)
2. provider's catalog default (cheap aux model, if registered)
3. user's main model from config.yaml
Behaviour by provider class:
- OAuth providers (xai-oauth, openai-codex) — no catalog default, so
step 3 applies. Title gen runs on grok-4.3 / gpt-5.4 against the
user's actual subscription instead of leaking to OpenRouter.
- API-key providers (anthropic, gemini, kimi-coding, etc.) — catalog
default wins at step 2, preserving the original 'cheap aux model'
behaviour. Anthropic users still get claude-haiku-4-5 for titles,
not opus.
- Explicit-model callers (auxiliary.<task>.model config, programmatic
callers) — caller wins at step 1, no surprise switching.
Salvaged from @wysie's PR #31845 which fixed the xai-oauth branch
specifically. The universal shape supersedes the per-branch fix
and covers openai-codex (same bug class) plus any future OAuth
providers.
4 new tests in TestResolveProviderClientUniversalModelFallback:
- empty_model_for_oauth_provider_falls_back_to_main_model
- empty_model_for_codex_also_uses_main_model
- empty_model_for_catalog_provider_uses_catalog_default
- explicit_model_takes_precedence_over_fallbacks
365/365 across tests/agent/test_auxiliary_*, tests/run_agent/test_codex_xai_oauth_recovery.py, tests/hermes_cli/test_auth_xai_oauth_provider.py, and tests/hermes_cli/test_plugin_auxiliary_tasks.py.
Co-authored-by: wysie <wysie@users.noreply.github.com>
X Premium+ also grants Grok OAuth access — the 'SuperGrok Subscription'
wording suggested SuperGrok was the only entitlement path. Updated to
'SuperGrok / Premium+' across the picker label, setup wizard, auth flows,
and docs so Premium+ subscribers know the row applies to them too.
* fix(vision): route auxiliary.vision.provider=openai to api.openai.com, skip text-only main for vision
Fixes#31179. Three coupled fixes so a configured aux vision backend
actually serves vision tasks instead of silently routing images to the
user's main provider:
1. agent/auxiliary_client.py: `auxiliary.<task>.provider: openai` resolves
to `custom` + `https://api.openai.com/v1`. "openai" was not in
PROVIDER_REGISTRY (we have `openai-codex` for OAuth and `custom` for
manual base_url), so the obvious config name silently failed to build a
client. User-supplied base_url is still preserved; only the provider
name normalises to `custom` so resolution doesn't hit the
PROVIDER_REGISTRY-only path.
2. agent/auxiliary_client.py: the vision auto-detect chain now skips the
user's main provider when models.dev reports `supports_vision=False`.
Without this guard, a misconfigured aux provider would fall back to
`auto`, which happily returned the main-provider client. The caller
would then send image content to e.g. api.deepseek.com with model
`gpt-4o-mini` and get a cryptic `unknown variant 'image_url',
expected 'text'` from the provider's parser.
3. tools/vision_tools.py + tools/browser_tool.py: `check_vision_requirements`
now mirrors the runtime fallback chain (explicit provider, then auto),
so `vision_analyze` shows up whenever vision is actually serviceable.
`browser_vision` gets a new `check_browser_vision_requirements` check_fn
that AND-gates browser + vision availability, so it doesn't get
advertised to the model when the call would fail at runtime.
Reproduction (config from the bug report):
model.provider: deepseek
model.default: deepseek-v4-pro
auxiliary.vision.provider: openai
auxiliary.vision.model: gpt-4o-mini
Before: resolve_vision_provider_client() returns None for the explicit
provider, fallback auto returns the deepseek client with model='gpt-4o-mini',
image hits api.deepseek.com → 'unknown variant image_url'. vision_analyze
hidden from tool list; browser_vision exposed but fails at call time.
After: resolves to custom + api.openai.com/v1 with model gpt-4o-mini.
vision_analyze and browser_vision both gate correctly on capability.
Tests: tests/agent/test_vision_routing_31179.py covers all three fixes
(12 cases including the user's exact scenario, base_url preservation,
text-only-main skip, capability-unknown permissive fallback, and tool
gating parity). Existing 382 tests across auxiliary/vision/image_routing
suites still pass.
* test(vision): use exact hostname check to silence CodeQL substring-sanitization alert
* fix(auxiliary): drop model name from vision-skip debug log to silence CodeQL
The new `logger.debug(...)` added in the previous commit interpolated
both `main_provider` and `vision_model` (a public model slug \u2014 not
sensitive). CodeQL's `py/clear-text-logging-sensitive-data` heuristic
re-flagged it twice because the rule mis-detects multi-value
interpolations near tainted-via-config provider strings.
Drop the model from the log args (provider alone is enough to diagnose
the skip; the same sibling branch a few lines up already logs provider
only). Behavior unchanged; CodeQL false positive cleared.
Auxiliary LLM tasks (vision, compression, web_extract, etc.) currently
require modifications to core files for any plugin that needs its own
task slot — specifically the _AUX_TASKS list in hermes_cli/main.py and
the hardcoded env-var bridging dict in gateway/run.py. This violates
the 'plugins must not modify core files' rule and forces every memory
or context plugin that wants its own auxiliary task to either fork
core or open a coupled core+plugin PR.
This change adds a generic plugin surface for auxiliary task
registration:
ctx.register_auxiliary_task(
key='memory_retain_filter',
display_name='Memory retain filter',
description='hindsight pre-retain dedup/extract',
defaults={'timeout': 30, 'extra_body': {'reasoning_effort': 'low'}},
)
After registration, the task automatically:
- Appears in 'hermes model → Configure auxiliary models' picker via
a new _all_aux_tasks() merge of built-in + plugin tasks
- Has its provider/model/base_url/api_key bridged from config.yaml
to AUXILIARY_<KEY_UPPER>_* env vars at gateway startup
(gateway/run.py now uses a dynamic bridged-keys set instead of
a hardcoded per-task dict)
- Gets plugin-declared defaults (timeout, extra_body, etc.) layered
underneath user config so unconfigured plugin tasks still work
(agent/auxiliary_client._get_auxiliary_task_config)
- Resets to auto via 'Reset all to auto' alongside built-ins
Validation:
- Rejects shadowing of built-in keys (vision, compression, etc.)
- Rejects invalid key shapes (must match [A-Za-z0-9_]+)
- Rejects cross-plugin collisions (clear error)
- Allows same-plugin re-registration (idempotent updates)
Plugin discovery failures (rare) fall back gracefully — the aux
config UI still shows built-in tasks if get_plugin_auxiliary_tasks()
raises, and gateway env-var bridging keeps working for built-ins.
Built-in tasks remain hardcoded in _AUX_TASKS for stability — they're
the baseline UX, and DEFAULT_CONFIG already ships their defaults.
Plugin tasks layer on top.
Tests: 15 new tests in test_plugin_auxiliary_tasks.py covering API
validation, manager state lifecycle, helper sort order, _all_aux_tasks
merge semantics, _reset_aux_to_auto inclusion of plugin tasks, and
default-layering in auxiliary_client.
Updates the gateway-bridge code-parity test (test_auxiliary_config_bridge)
to assert the new dynamic shape rather than the hardcoded literal env
var names which no longer appear post-refactor.
Motivation: this unblocks PR #20262 (hindsight smart retain pipeline)
and similar plugins that need a dedicated aux task slot. The change
is non-breaking — built-in env vars (AUXILIARY_VISION_PROVIDER, etc.)
keep working since they're produced by the same f-string template
that built the hardcoded names.
XAI_BASE_URL / HERMES_XAI_BASE_URL let users repoint the OAuth-authenticated
inference endpoint, but the env override was an unguarded credential-leak
vector: a tampered .env or hostile shell init setting
XAI_BASE_URL=https://attacker.example/v1 would silently ship the SuperGrok
OAuth bearer to a third party on every request.
Add _xai_validate_inference_base_url() that pins the host to x.ai or a
*.x.ai subdomain and rejects non-HTTPS. On rejection, fall back to the
default with a warning rather than raise — a bad env var should not
deadlock auth, but should never leak the bearer either.
Apply at all three sites that read the env override for xai-oauth:
- hermes_cli/auth.py resolve_xai_oauth_runtime_credentials (main path)
- hermes_cli/auth.py _xai_oauth_loopback_login (initial login)
- agent/auxiliary_client.py _resolve_xai_oauth_for_aux (aux client)
E2E validated against four scenarios: attacker.example, lookalike
api.x.ai.evil.com, http:// downgrade on api.x.ai, and legit custom.x.ai
subdomain (which still resolves correctly).
Discovered while comparing against the opencode-grok-auth plugin
(github.com/ysnock404/opencode-grok-auth), which highlighted the same
guard on the OpenCode side.
xAI's /v1/responses and /v1/chat/completions endpoints reject tool schemas
whose enum values contain a forward slash with a generic HTTP 400 'Invalid
arguments passed to the model.' before any token is emitted — the schema
compiler trips on the '/' character regardless of where it appears.
Most commonly hit by MCP-derived tools whose enum lists HuggingFace model
IDs ('Qwen/Qwen3.5-0.8B', 'openai/gpt-oss-20b') or owner/name environment
identifiers.
Mirrors the existing strip_pattern_and_format sanitizer (PR for #27197).
The new strip_slash_enum walks tool parameters and drops the entire enum
keyword when any value contains '/' — keeping it partial would still 400
since xAI's failure is all-or-nothing on the enum. The field description
still reaches the model so the prompting hint is preserved.
Wired in at both code paths for parity:
- agent/chat_completion_helpers.py (main agent xAI Responses path)
- agent/auxiliary_client.py (aux client xAI Responses path, matching
the same parity guarantee 2fae8fba9 established for pattern/format)
Salvaged from #28021 by @Slimydog21 — contributor's branch was severely
stale (would have reverted ~5000 LOC across azure/kanban/i18n); fix
re-applied surgically on current main with their sanitizer + 9 tests
preserved verbatim. Author noreply email used (original was a Mac
hostname leak).
xAI's /responses endpoint rejects tool schemas that contain pattern or
format JSON Schema keywords with HTTP 400. chat_completion_helpers.py
already strips these for the main-agent xAI/xai-oauth path (lines
294-302), but _CodexCompletionsAdapter.create() — used for every xAI
OAuth auxiliary call (kanban decomposer, profile describer, etc.) —
passed raw tool schemas without sanitization.
MCP tools that carry pattern/format keywords (common for string fields)
silently caused every auxiliary call over xAI OAuth to fail with an
HTTP 400, while the main agent worked fine. Parity fix: call
strip_pattern_and_format() on the tool list before converting to
Responses API format, matching the main-agent guarantee.
Layered fallback for auxiliary tasks (compression, vision, tts, web_extract,
session_search, etc.):
1. Primary aux provider (existing)
2. User-configured auxiliary.<task>.fallback_chain (new)
3. Main agent provider + model (new — last-resort safety net)
4. Warn user + re-raise original error (new)
For users on 'auto' (no explicit aux provider), the existing
_try_payment_fallback auto-detection chain runs instead — its Step 1
already IS the main agent model, so they get the same behaviour without
configuration.
The configured fallback_chain config schema comes from #26882 / @zccyman;
the main-agent safety net + exhaustion warning were added on top.
Closes#26882. Builds on the capacity-error gate fix in the previous
commit (#26803 / @Bartok9).
Closes#26803
Root causes:
1. _is_payment_error() checked for billing keywords (credits, insufficient
funds, billing, payment required) but missed daily token quota exhaustion
phrases used by Bedrock, Vertex AI, and LiteLLM proxies — e.g.
'Too many tokens per day', 'quota exceeded', 'resource exhausted',
'daily limit'. These are functionally identical to credit exhaustion
(provider cannot serve the request) but don't trigger fallback.
2. The call_llm() fallback chain was gated on resolved_provider == 'auto'.
When a task resolves to a specific provider (e.g. 'custom' for a LiteLLM
proxy, or 'openrouter'), capacity failures (payment/quota/connection)
silently raise instead of trying alternatives. This is overly conservative:
capacity errors mean the provider *cannot* serve the request regardless of
user intent, so alternatives should always be tried.
Fixes:
- Add quota-related keywords to _is_payment_error(): quota_exceeded,
too many tokens per day, daily limit, tokens per day, daily quota,
resource exhausted (Vertex AI gRPC code).
- Allow fallback for capacity errors (payment + connection) even when
resolved_provider is not 'auto'. Rate-limit fallback stays gated on
is_auto to honour explicit provider constraints for transient limits.
- Apply both fixes to sync call_llm() and async acall_llm() paths.
- Add 6 targeted tests for the new quota-error detection cases.
_OPENROUTER_MODEL hardcoded 'google/gemini-3-flash-preview' which
returns 404 on OpenRouter, breaking all vision tasks for users who
rely on the OpenRouter default. Additionally, _try_openrouter()
ignored the user-configured auxiliary.vision.model entirely.
Changes:
- Update _OPENROUTER_MODEL default to google/gemini-2.5-flash (valid)
- Add optional 'model' parameter to _try_openrouter()
- Pass configured model from _resolve_strict_vision_backend() through
to _try_openrouter()
This allows users who set auxiliary.vision.model (e.g. x-ai/grok-4.3)
to have it actually used, while maintaining backward compatibility.
In resolve_provider_client(), the named custom provider code path at
~line 2914 only checked the ``key_env`` field when looking for an
environment-variable-based API key. The documented ``api_key_env``
snake_case alias was silently ignored, causing custom providers
configured with ``api_key_env`` to fall through to the
``no-key-required`` placeholder — which produces a confusing 401
(``****ired`` mask) on auth-required remote endpoints.
This mirrors the same fix already applied to run_agent.py in commit
6ddc48b05 (fix(fallback): resolve api_key_env in fallback chain entries).
Also adds a logger.warning() when the placeholder is reached, so
future alias gaps are easier to debug.
Closes#25091
Adds a new authentication provider that lets SuperGrok subscribers sign
in to Hermes with their xAI account via the standard OAuth 2.0 PKCE
loopback flow, instead of pasting a raw API key from console.x.ai.
Highlights
----------
* OAuth 2.0 PKCE loopback login against accounts.x.ai with discovery,
state/nonce, and a strict CORS-origin allowlist on the callback.
* Authorize URL carries `plan=generic` (required for non-allowlisted
loopback clients) and `referrer=hermes-agent` for best-effort
attribution in xAI's OAuth server logs.
* Token storage in `auth.json` with file-locked atomic writes; JWT
`exp`-based expiry detection with skew; refresh-token rotation
synced both ways between the singleton store and the credential
pool so multi-process / multi-profile setups don't tear each other's
refresh tokens.
* Reactive 401 retry: on a 401 from the xAI Responses API, the agent
refreshes the token, swaps it back into `self.api_key`, and retries
the call once. Guarded against silent account swaps when the active
key was sourced from a different (manual) pool entry.
* Auxiliary tasks (curator, vision, embeddings, etc.) route through a
dedicated xAI Responses-mode auxiliary client instead of falling back
to OpenRouter billing.
* Direct HTTP tools (`tools/xai_http.py`, transcription, TTS, image-gen
plugin) resolve credentials through a unified runtime → singleton →
env-var fallback chain so xai-oauth users get them for free.
* `hermes auth add xai-oauth` and `hermes auth remove xai-oauth N` are
wired through the standard auth-commands surface; remove cleans up
the singleton loopback_pkce entry so it doesn't silently reinstate.
* `hermes model` provider picker shows
"xAI Grok OAuth (SuperGrok Subscription)" and the model-flow falls
back to pool credentials when the singleton is missing.
Hardening
---------
* Discovery and refresh responses validate the returned
`token_endpoint` host against the same `*.x.ai` allowlist as the
authorization endpoint, blocking MITM persistence of a hostile
endpoint.
* Discovery / refresh / token-exchange `response.json()` calls are
wrapped to raise typed `AuthError` on malformed bodies (captive
portals, proxy error pages) instead of leaking JSONDecodeError
tracebacks.
* `prompt_cache_key` is routed through `extra_body` on the codex
transport (sending it as a top-level kwarg trips xAI's SDK with a
TypeError).
* Credential-pool sync-back preserves `active_provider` so refreshing
an OAuth entry doesn't silently flip the active provider out from
under the running agent.
Testing
-------
* New `tests/hermes_cli/test_auth_xai_oauth_provider.py` (~63 tests)
covers JWT expiry, OAuth URL params (plan + referrer), CORS origins,
redirect URI validation, singleton↔pool sync, concurrency races,
refresh error paths, runtime resolution, and malformed-JSON guards.
* Extended `test_credential_pool.py`, `test_codex_transport.py`, and
`test_run_agent_codex_responses.py` cover the pool sync-back,
`extra_body` routing, and 401 reactive refresh paths.
* 165 tests passing on this branch via `scripts/run_tests.sh`.
When the auxiliary client falls through Nous (e.g. no stored auth, or
runtime credential mint failed), users currently see only `debug`-level
lines, so the next provider in the fallback chain takes over silently.
Promote the no-auth path to a warning that tells operators to run
`hermes auth`, and add a debug breadcrumb on the rarer
mint-failed-but-stored-auth-still-present fallback path so the existing
behavior (use the raw stored token) is preserved while staying
investigable.
Salvaged from #23881 by @0xharryriddle. The contributor's original
patch also short-circuited the second branch with a return, which broke
the pool-entry fallback path covered by
`test_try_nous_uses_pool_entry` — kept the warning intent, dropped the
return so the fallback still works. Dropped the contributor's changes
to `hermes_cli/goals.py` because the goal-pause path is unreachable
when the auxiliary client is None (`judge_goal` returns
`parse_failed=False`, which resets `consecutive_parse_failures`),
so the reason string they added never surfaces in the pause message.
Refs #23876
When the auxiliary client fallback chain reaches a provider that has no
credentials configured (no API key, no pool entry), the current code
just returns (None, None) which counts toward the per-call timeout
budget on the next attempt. Mark the provider unhealthy with a short
TTL so the chain advances quickly to the next viable option.
Closes#25384.
Salvage of #25395 by @AllynSheep.
* feat(nous): unified client=hermes-client-v<version> tag on every Portal request
Every Hermes request to Nous Portal now carries the same
client=hermes-client-v<__version__> tag (e.g. client=hermes-client-v0.13.0
on this release), sourced live from hermes_cli.__version__. The release
script's regex bump auto-aligns it on every release.
Centralized in agent/portal_tags.py and wired into all four call sites:
- NousProfile.build_extra_body (main agent loop, every chat completion)
- auxiliary_client.NOUS_EXTRA_BODY + _build_call_kwargs (aux client)
- run_agent.py compression-summary fallback path
- tools/web_tools.py web_extract fallback
Replaces the client=aux marker added in #24194 with the unified version
tag. Tests assert against the helper output (invariant) rather than the
literal string, so they don't need updating on every release.
* feat(nous): cover /goal judge and kanban specify aux paths
Two aux-using surfaces bypassed call_llm by invoking
client.chat.completions.create() directly without extra_body, so they
were missing the unified Portal client tag:
- hermes_cli/goals.py — /goal standing-goal judge
- hermes_cli/kanban_specify.py — kanban triage specifier
Both now pass extra_body=get_auxiliary_extra_body() or None so they
inherit the version tag when the aux client points at Nous Portal, and
emit nothing otherwise (no tag leak to OpenRouter/Anthropic auxes).
_resolve_task_provider_model drops cfg_base_url and cfg_api_key when
returning a named provider, causing configured API keys and base URLs
to be lost. Pass them through so named providers can use custom
endpoints while still resolving credentials from provider-specific
env vars.
Closes#20139
Replace with for all literal-tuple
membership tests. Set lookup is O(1) vs O(n) for tuple — consistent
micro-optimization across the codebase.
608 instances fixed via `ruff --fix --unsafe-fixes`, 0 remaining.
133 files, +626/-626 (net zero).
#23482 fixed cache poisoning in the sync path: when a Codex auxiliary
timeout closes the underlying OpenAI client, _evict_cached_client_instance
walks CodexAuxiliaryClient wrappers via their _real_client attribute and
drops the cache entry so the next aux call rebuilds.
The cache key includes async_mode (see _client_cache_key), so the sync and
async clients for the same provider live in two distinct entries pointing
at the same underlying transport. The fix walked the sync wrapper's
_real_client correctly but the async wrappers
(AsyncCodexAuxiliaryClient, AsyncAnthropicAuxiliaryClient,
AsyncGeminiNativeClient) never exposed _real_client at all, so the async
entry survived eviction and kept handing out the poisoned client.
Effect on async aux callers: one timeout now poisons every subsequent
async aux call (compression, vision, session_search, title_generation)
with 'Connection error' until gateway restart -- even while the sync
route recovered as designed in #23482.
Mirror the sync wrapper's _real_client onto each async wrapper so the
existing eviction helper finds them. Three changes, one per wrapper:
- AsyncCodexAuxiliaryClient: self._real_client = sync_wrapper._real_client
(the underlying OpenAI client)
- AsyncAnthropicAuxiliaryClient: same shape
- AsyncGeminiNativeClient: self._real_client = sync_client (Gemini's
native facade is itself the leaf; no OpenAI client beneath it)
Update _evict_cached_client_instance docstring to reflect that it now
covers both sync and async wrappers via the same attribute walk.
Test: TestAuxiliaryClientPoisonedCacheEviction.test_evict_cached_client_instance_walks_async_wrapper
seeds both sync and async cache entries pointing at the same leaf and
asserts both are dropped on a single eviction call. Verified the test
fails without the wrapper changes ("async cache entry survived
eviction -- wrapper is missing _real_client") and passes with them.
Refs #23482, #23432
When an auxiliary provider returns HTTP 402 (credit / payment), every
subsequent compression / title-gen / session-search / vision call still
re-tried it as the FIRST entry in the chain — burning ~1 RTT to hit 402
again, then falling back. On a long Discord/LCM session that meant dozens
of doomed 402s per minute (issue #23570).
Add a per-process unhealthy-provider cache with a 10 min TTL. When any
caller observes a payment error against a provider, the label is marked
unhealthy and skipped by:
* _resolve_auto Step-1 (main provider use-as-aux path)
* _resolve_auto Step-2 (aggregator/fallback chain)
* _try_payment_fallback (used by call_llm/acall_llm on first 402)
Skip-logs are throttled to once per minute per label so a bursty session
doesn't spam agent.log. Entries auto-expire so a topped-up account
recovers without manual intervention. The cache is in-process only by
design — multi-profile users with different keys per profile must each
hit the 402 once.
Refs #23570
A Codex auxiliary timeout closes the underlying OpenAI client (so the
streaming hang doesn't sit until the user kills the session), but the
cached wrapper kept pointing at the now-dead transport. Subsequent
auxiliary calls (compression retry, memory flush, background review,
title generation routed via provider: main) reused that closed client
and failed fast with 'Connection error' until the gateway restarted —
even though the main agent route was healthy the whole time.
Sync `_get_cached_client` had no liveness check (async did, via loop
identity), and the connection-error fallback in `call_llm` only fired
on the auto provider path, so an explicit provider — including the
common `auxiliary.compression.provider: main` shape — never evicted.
Three fixes:
* New `_evict_cached_client_instance(target)` helper that drops the
cache entry whose stored client is target (or wraps it via
`_real_client`, for `CodexAuxiliaryClient`).
* `_CodexCompletionsAdapter._close_client_on_timeout` evicts the
wrapper after closing the inner OpenAI client.
* `call_llm` and `async_call_llm` evict on `_is_connection_error`
before re-raising, regardless of whether the provider is auto.
Net effect: one timeout costs one summary attempt + the existing 30s
compressor cooldown; the next compaction rebuilds the client and
works. Non-connection errors (4xx/5xx) do not evict, so cache hits
stay stable.
Closes#23432
When the active main model has native vision and the provider supports
multimodal tool results (Anthropic, OpenAI Chat, Codex Responses, Gemini
3, OpenRouter, Nous), vision_analyze loads the image bytes and returns
them to the model as a multimodal tool-result envelope. The model then
sees the pixels directly on its next turn instead of receiving a lossy
text description from an auxiliary LLM.
Falls back to the legacy aux-LLM text path for non-vision models and
unverified providers.
Mirrors the architecture used in OpenCode, Claude Code, Codex CLI, and
Cline. All four converge on the same pattern: tool results carry image
content blocks for vision-capable provider/model combinations.
Changes
- tools/vision_tools.py: _vision_analyze_native fast path + provider
capability table (_supports_media_in_tool_results). Schema description
updated to reflect new behaviour.
- agent/codex_responses_adapter.py: function_call_output.output now
accepts the array form for multimodal tool results (was string-only).
Preflight validates input_text/input_image parts.
- agent/auxiliary_client.py: _RUNTIME_MAIN_PROVIDER/_MODEL globals so
tools see the live CLI/gateway override, not the stale config.yaml
default. set_runtime_main()/clear_runtime_main() helpers.
- run_agent.py: AIAgent.run_conversation calls set_runtime_main at turn
start so vision_analyze's fast-path check sees the actual runtime.
- tests/conftest.py: clear runtime-main override between tests.
Tests
- tests/tools/test_vision_native_fast_path.py: provider capability
table, envelope shape, fast-path gating (vision-capable model uses
fast path; non-vision model falls through to aux).
- tests/run_agent/test_codex_multimodal_tool_result.py: list tool
content becomes function_call_output.output array; preflight
preserves arrays and drops unknown part types.
Live verified
- Opus 4.6 + Sonnet 4.6 on OpenRouter: model calls vision_analyze on a
typed filepath, gets pixels back, reads exact text from images that
no aux description could capture (font color irony, multi-line
fruit-count list, etc.).
PR replaces the closed prior efforts (#16506 shipped the inbound user-
attached path; this PR closes the gap for tool-discovered images).
Problem:
When a provider or proxy drops a streaming response mid-flight (httpcore
raises RemoteProtocolError: "incomplete chunked read", "peer closed
connection", "response ended prematurely", etc.), _generate_summary
would not classify it as a transient error. Instead of retrying on the
main model, it entered the generic 60-second cooldown, leaving context
growing unbounded until the cooldown expired. Issue #18458.
Root cause:
_is_connection_error in auxiliary_client.py did not match httpcore's
streaming premature-close error substrings. context_compressor.py's
_generate_summary except block never called _is_connection_error, so
those errors fell through to the 60-second generic cooldown rather than
triggering the retry-on-main fallback path used for timeouts.
Fix:
1. auxiliary_client.py — extend _is_connection_error keyword list with:
"incomplete chunked read", "peer closed connection",
"response ended prematurely", "unexpected eof",
"remoteprotocolerror", "localprotocolerror".
Also guard the `from openai import ...` with try/except ImportError
so the function works in environments without the openai package.
2. context_compressor.py — import _is_connection_error and call it in
_generate_summary's except block as _is_streaming_closed. Include
_is_streaming_closed in the fallback-to-main condition (alongside
_is_model_not_found, _is_timeout, _is_json_decode) and use the
shorter 30s transient cooldown for streaming-closed errors.
Tests:
4 new regression tests in TestStreamingClosedFallback:
- test_incomplete_chunked_read_falls_back_to_main
- test_peer_closed_connection_falls_back_to_main
- test_streaming_closed_on_main_uses_short_cooldown (stash-verified)
- test_non_streaming_unknown_error_still_uses_long_cooldown
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The previous revision of this PR added six GMI-specific branches
(`elif base_url_host_matches(..., 'api.gmi-serving.com')`) across
run_agent.py and agent/auxiliary_client.py, plus a _HERMES_UA_HEADERS
constant in auxiliary_client.py.
ProviderProfile already has a `default_headers: dict[str, str]` field
commented as 'Client-level quirks (set once at client construction)'.
Other plugins (ai-gateway, kimi-coding) already use it. Two of the four
auxiliary_client sites we previously patched already had a generic
`else: profile.default_headers` fallback that picked it up (so did
both run_agent sites).
This revision:
* Sets `default_headers={'User-Agent': 'HermesAgent/<ver>'}` on the
GMI profile in plugins/model-providers/gmi/__init__.py.
* Reverts all six GMI-specific branches in run_agent.py and
auxiliary_client.py.
* Adds the generic profile-fallback `else` block to the two
auxiliary_client sites (`_to_async_client`, `resolve_provider_client`)
that didn't have it yet. This benefits every provider whose profile
declares default_headers, not just GMI — e.g. Vercel AI Gateway's
HTTP-Referer/X-Title now flow through the async client path too.
* Replaces the GMI-specific URL-branch tests with a profile-level
assertion and keeps the run_agent integration test (with
`provider='gmi'` so the fallback picks up the profile).
Net diff vs main: +82/-0 across 5 files, touching only the GMI plugin,
two generic fallback blocks in auxiliary_client.py, AUTHOR_MAP, and
tests. No core files change.
Based on #20907 by @isaachuangGMICLOUD.
## Summary
- Forwards chat-completions `timeout` into the Codex Responses stream call.
- Adds total elapsed-time enforcement while the Responses stream is still yielding events.
- Closes the underlying client on timeout to unblock stalled streams, then raises `TimeoutError`.
- Adds focused tests for timeout forwarding and total timeout enforcement.
## Why
The Codex auxiliary adapter can be used by non-interactive auxiliary work such as context compression. If the stream keeps yielding progress-like events but never completes, SDK socket/read timeouts do not necessarily protect the full operation. This makes the CLI look stuck until the user force-interrupts the whole session.
This is a refreshed upstream-ready version of the earlier fork fix around `d3f08e9a0` / PR #3.
## Verification
- `python -m py_compile agent/auxiliary_client.py tests/agent/test_auxiliary_client.py`
- `python -m pytest -o addopts='' tests/agent/test_auxiliary_client.py::TestCodexAuxiliaryAdapterTimeout -q`
- `git diff --check`
Z.AI (智谱 GLM) vision models (glm-4v-flash, glm-4v-plus, etc.) have two
compatibility issues when used through the Anthropic-compatible endpoint:
1. **Error 1210 — max_tokens rejected on multimodal calls**: Z.AI rejects
the max_tokens parameter for vision model requests with error code 1210
("API 调用参数有误"). The error string does not contain "max_tokens",
so the existing unsupported-parameter retry logic never fires.
2. **Wrong endpoint inheritance**: When the main runtime provider uses Z.AI's
Anthropic-compatible endpoint (open.bigmodel.cn/api/anthropic), the vision
client inherits this endpoint. But Z.AI's Anthropic wire cannot properly
handle image content — models silently fail ("I can't see the image") or
reject max_tokens.
Changes:
- resolve_vision_provider_client(): force Z.AI vision to use OpenAI-compatible
endpoint (open.bigmodel.cn/api/paas/v4) instead of inheriting Anthropic wire
- _build_call_kwargs(): skip max_tokens for Z.AI vision models (4v/5v/-v suffix)
- _AnthropicCompletionsAdapter: support _skip_zai_max_tokens flag
- _to_openai_base_url(): rewrite Z.AI Anthropic URLs to OpenAI-compatible path
- call_llm() retry: detect Z.AI error 1210 and strip max_tokens before retry
Introduces providers/ package — single source of truth for every
inference provider. Adding a simple api-key provider now requires one
providers/<name>.py file with zero edits anywhere else.
What this PR ships:
- providers/ package (ProviderProfile ABC + 33 profiles across 4 api_modes)
- ProviderProfile declarative fields: name, api_mode, aliases, display_name,
env_vars, base_url, models_url, auth_type, fallback_models, hostname,
default_headers, fixed_temperature, default_max_tokens, default_aux_model
- 4 overridable hooks: prepare_messages, build_extra_body,
build_api_kwargs_extras, fetch_models
- chat_completions.build_kwargs: profile path via _build_kwargs_from_profile,
legacy flag path retained for lmstudio/tencent-tokenhub (which have
session-aware reasoning probing that doesn't map cleanly to hooks yet)
- run_agent.py: profile path for all registered providers; legacy path
variable scoping fixed (all flags defined before branching)
- Auto-wires: auth.PROVIDER_REGISTRY, models.CANONICAL_PROVIDERS,
doctor health checks, config.OPTIONAL_ENV_VARS, model_metadata._URL_TO_PROVIDER
- GeminiProfile: thinking_config translation (native + openai-compat nested)
- New tests/providers/ (79 tests covering profile declarations, transport
parity, hook overrides, e2e kwargs assembly)
Deltas vs original PR (salvaged onto current main):
- Added profiles: alibaba-coding-plan, azure-foundry, minimax-oauth
(were added to main since original PR)
- Skipped profiles: lmstudio, tencent-tokenhub stay on legacy path (their
reasoning_effort probing has no clean hook equivalent yet)
- Removed lmstudio alias from custom profile (it's a separate provider now)
- Skipped openrouter/custom from PROVIDER_REGISTRY auto-extension
(resolve_provider special-cases them; adding breaks runtime resolution)
- runtime_provider: profile.api_mode only as fallback when URL detection
finds nothing (was breaking minimax /v1 override)
- Preserved main's legacy-path improvements: deepseek reasoning_content
preserve, gemini Gemma skip, OpenRouter response caching, Anthropic 1M
beta recovery, etc.
- Kept agent/copilot_acp_client.py in place (rejected PR's relocation —
main has 7 fixes landed since; relocation would revert them)
- _API_KEY_PROVIDER_AUX_MODELS alias kept for backward compat with existing
test imports
Co-authored-by: kshitijk4poor <82637225+kshitijk4poor@users.noreply.github.com>
Closes#14418
When a provider returns a 429 rate-limit error (not billing-related),
the auxiliary client's call_llm/async_call_llm previously did NOT trigger
the fallback chain. This caused auxiliary tasks like session_search to
exhaust all 3 retries against the same rate-limited endpoint, losing
session metadata that depended on the summarization completing.
Root cause: `_is_payment_error()` only matched 429s containing billing
keywords ("credits", "insufficient funds", etc.). Provider-specific
rate-limit messages like Nous's "Hold up for a bit, you've exceeded the
rate limit on your API key" didn't match, so `_is_payment_error` returned
False, `_is_connection_error` returned False, and `should_fallback` was
False — all retries hit the same rate-limited provider.
Fix:
- New `_is_rate_limit_error()` function that detects 429 + rate-limit
keywords, generic 429 without billing keywords, and OpenAI SDK
`RateLimitError` class instances (which may omit .status_code).
- Updated `should_fallback` in both `call_llm` and `async_call_llm` to
include `_is_rate_limit_error`.
- Updated the max_tokens retry path to also check for rate-limit errors.
- Updated the reason string to include "rate limit".
This complements the Nous rate guard (PR #10568) which prevents new calls
to Nous when already rate-limited — this fix handles the case where a
request is already in flight when the 429 arrives.
Related: #8023, #12554, #11034
Co-authored-by: Zeejay <zjtan1@gmail.com>
OpenRouter's dashboard attributes usage via the `X-Title` header.
Hermes was sending `X-OpenRouter-Title`, which OpenRouter does not
recognize, so Hermes usage showed up unlabeled. Rename to `X-Title`
to match the canonical header (already used elsewhere in the same
file via _AI_GATEWAY_HEADERS).
Salvages the core fix from @JTroyerOvermatch's PR #13649. Dropped the
PR's `HERMES_OPENROUTER_TITLE` / `HERMES_OPENROUTER_REFERER` env-var
override plumbing per the '.env is for secrets only' policy — if
per-deployment attribution is needed later it should go under
`openrouter.title` / `openrouter.referer` in config.yaml instead.
When auxiliary.<task> config has base_url set but api_key is empty
(common when user expects env var fallback), _resolve_task_provider_model()
returned provider="custom" with api_key=None. This caused downstream
client construction to make API calls without an Authorization header,
resulting in HTTP 401 errors.
Fix: only return "custom" when BOTH cfg_base_url AND cfg_api_key are
non-empty. When base_url is set without api_key but with a known
provider (e.g. "openrouter"), pass through to that provider so it can
resolve credentials from environment variables.
Fixes#16829
auxiliary.<task>.extra_body.reasoning, but the new translation path in
_CodexCompletionsAdapter.create() reads the effort with
``reasoning_cfg.get("effort", "medium")``. That returns the configured
value verbatim when the key is present, so ``effort: null`` /
``effort: ""`` (both common YAML shapes) flow through as
``{"effort": null, "summary": "auto"}`` and Codex rejects the request
with "Invalid value for parameter ``reasoning.effort``".
agent/transports/codex.py::build_kwargs() — which the new adapter is
documented to mirror — uses a truthy check (``elif
reasoning_config.get("effort"):``) so the same falsy values keep the
"medium" default. Switch the auxiliary adapter to the same
``or "medium"`` truthy form so identical config produces identical
requests on both paths.
- [x] Two new regression tests cover ``effort: None`` and
``effort: ""`` and assert the request goes out as
``{"effort": "medium", "summary": "auto"}``.
- [x] Old behaviour fails the new tests (``{'effort': None} !=
{'effort': 'medium'}``); fixed behaviour passes all 11 tests in the
``TestCodexAdapterReasoningTranslation`` class.
- [x] Adjacent suites green: ``tests/agent/test_auxiliary_client.py``
(108 passed) and ``tests/agent/transports/test_codex_transport.py +
test_chat_completions.py`` (73 passed).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Keep the configured vision provider when base_url is overridden so credential-pool lookup still resolves provider-specific API keys (e.g. ZAI_API_KEY), and add a regression test for this path.
_try_anthropic() lacked the explicit_api_key parameter added to
_try_openrouter() in #18768. When resolve_provider_client() is called
with provider="anthropic" and an explicit key (e.g. from a fallback_model
entry with api_key set), the key was silently ignored — _try_anthropic()
always fell back to resolve_anthropic_token(), so the fallback returned
None,None for users without a default Anthropic credential configured.
Fix: add explicit_api_key: str = None to _try_anthropic() and use
explicit_api_key or <pool/env fallback> in both the pool-present and
no-pool paths. Pass explicit_api_key=explicit_api_key at the call site
in resolve_provider_client(). Symmetric with the _try_openrouter() fix.
No behavior change when explicit_api_key is None.
Enable OpenRouter's response caching feature (beta) via X-OpenRouter-Cache
headers. When enabled, identical API requests return cached responses for
free (zero billing), reducing both latency and cost.
Configuration via config.yaml:
openrouter:
response_cache: true # default: on
response_cache_ttl: 300 # 1-86400 seconds
Changes:
- Add openrouter config section to DEFAULT_CONFIG (response_cache + TTL)
- Add build_or_headers() in auxiliary_client.py that builds attribution
headers plus optional cache headers based on config
- Replace inline _OR_HEADERS dicts with build_or_headers() at all 5 sites:
run_agent.py __init__, _apply_client_headers_for_base_url(), and
auxiliary_client.py _try_openrouter() + _to_async_client()
- Add _check_openrouter_cache_status() method to AIAgent that reads
X-OpenRouter-Cache-Status from streaming response headers and logs
HIT/MISS status
- Document in cli-config.yaml.example
- Add 28 tests (22 unit + 6 integration)
Ref: https://openrouter.ai/docs/guides/features/response-caching
When resolve_provider_client() passes explicit_api_key for OpenRouter auxiliary
tasks, _try_openrouter() now accepts and honors this parameter instead of
silently ignoring it and falling back to OPENROUTER_API_KEY env var.
Root cause: _try_openrouter() had no explicit_api_key parameter, so even
when callers wanted to pass a runtime credential pool key, it could not be used.
Fix:
- Add explicit_api_key: str = None parameter to _try_openrouter()
- Prioritize explicit_api_key over pool key and env var
- Update resolve_provider_client() call site to pass explicit_api_key
Regression coverage:
- Test that explicit_api_key is passed to OpenAI client when provided
- Test that fallback to OPENROUTER_API_KEY still works when explicit_api_key is None
Closes#18338
Providers like Google Vertex, Azure, and Amazon Bedrock reject API
requests with duplicate tool names (HTTP 400: 'Tool names must be
unique'). The upstream injection paths in run_agent.py already dedup
after PR #17335, but two API-boundary functions pass tools through
without checking:
- agent/auxiliary_client.py: _build_call_kwargs() (all non-Anthropic
providers in chat_completions mode)
- agent/anthropic_adapter.py: convert_tools_to_anthropic() (Anthropic
Messages API path)
Add defensive dedup guards at both sites. Duplicates are dropped with
a warning log, converting a hard 400 failure into a recoverable
condition. This is intentionally conservative — the root-cause dedup
in run_agent.py is the primary defense; these guards add resilience
against future injection-path regressions.
Includes 8 new tests covering unique passthrough, duplicate removal,
empty/None edge cases.
Closes#18478
When a user defines `custom_providers: [{name: kimi, ...}]` and references
`provider: kimi` from fallback_model or the main config, the built-in alias
rewriting (`kimi` → `kimi-coding`) was hijacking the request before the
named-custom lookup ran. `_get_named_custom_provider` also refused to
return a match when the raw name resolved to any built-in (including aliases),
so the custom endpoint was unreachable.
Fix at both layers of the resolution chain so every caller benefits, not
just `_try_activate_fallback`:
- hermes_cli/runtime_provider.py: narrow `_get_named_custom_provider`'s
built-in-wins guard to canonical provider names only. An alias like
`kimi` that resolves to a different canonical (`kimi-coding`) no longer
blocks the custom lookup; a canonical name like `nous` still does.
- agent/auxiliary_client.py: in `resolve_provider_client`, try the named-
custom lookup with the original (pre-alias-normalization) name before the
alias-normalized one, so aliased requests reach the user's custom entry.
Also honour `explicit_base_url` and `explicit_api_key` in the API-key
provider branch so callers that pass explicit hints (e.g. fallback
activation) can override the registered defaults.
Tests added for:
- custom `kimi` shadowing built-in alias (regression for #15743)
- custom `nous` NOT shadowing canonical built-in (behaviour preserved)
- bare `kimi` without any custom entry still routing to built-in
- explicit base_url/api_key override on the API-key provider branch
Original PR #17827 by @Feranmi10 identified the same bug class and
implemented a narrower fix in `_try_activate_fallback`; this reshapes the
fix to live in the shared resolution layer so all callers benefit.
Fixes#15743
Co-authored-by: Feranmi10 <89228157+Feranmi10@users.noreply.github.com>
Fixes HTTP 404 errors when using Anthropic-compatible providers (Kimi Coding, MiniMax, MiniMax-CN) for auxiliary tasks.
Root cause: `_to_openai_base_url()` rewrites `/anthropic` → `/v1` so the OpenAI SDK hits the right endpoint. But the rewritten URL was then passed to `_maybe_wrap_anthropic`, whose `_endpoint_speaks_anthropic_messages` detector only fires on `/anthropic` or `api.kimi.com/coding`. Detector saw `/v1` → returned False → no Anthropic wrap → 404 on every aux call.
Fix: preserve the raw base_url before rewriting and pass it to `_maybe_wrap_anthropic` for transport detection, while still giving the rewritten URL to the OpenAI client constructor.
Closes#17705, #17413, #17086, #10469.
Co-authored-by: oak <chengoak@users.noreply.github.com>
The _CODEX_AUX_MODEL constant had already rotated twice in 6 weeks
(gpt-5.3-codex -> gpt-5.2-codex -> now broken again at gpt-5.2-codex)
because ChatGPT-account Codex gates which models it accepts via an
undocumented, shifting allow-list that OpenAI publishes no changelog
for. Any pinned default will keep going stale. Issue #17533 reports
the current breakage: every ChatGPT-account auxiliary fallback fails
with HTTP 400 "model is not supported" and the 60s pause loop degrades
long sessions.
Rather than reset the clock with another stale pin (PR #17544 proposes
gpt-5.2-codex -> gpt-5.4), remove the hardcoded second-order Codex
fallback entirely:
- Delete `_CODEX_AUX_MODEL`.
- Drop `_try_codex` from `_get_provider_chain()` (the auto chain now
ends at api-key providers; 4 rungs instead of 5).
- Rename `_try_codex() -> _build_codex_client(model)` and require an
explicit model from the caller. No more guessing.
- `resolve_provider_client("openai-codex", model=None)` now warns and
returns (None, None) instead of silently guessing a stale model ID.
- Remove `_try_codex` from the `provider="custom"` fallback ladder
(same stale-constant trap).
- `_resolve_strict_vision_backend("openai-codex")` routes through
`resolve_provider_client` so the caller's explicit model is honored.
Codex-main users are unaffected: Step 1 of `_resolve_auto` already
uses `main_provider` + `main_model` directly and passes the user's
configured Codex model through `resolve_provider_client`, which never
touched `_CODEX_AUX_MODEL`. Per-task overrides (`auxiliary.<task>.provider/model`)
continue to work and are the supported way to route specific aux tasks
through Codex.
Users whose main provider fails with a payment/connection error and
who have ONLY ChatGPT-account Codex auth will now see the 60s pause
without a stale-model-rejection noise line in between -- same outcome,
cleaner failure.
Closes#17533. Supersedes #17544 (which resets the clock on the
same stale-constant problem).
Wire MiniMax-M2.7 and MiniMax-M2.7-highspeed into the model catalog,
CLI model picker, and agent auxiliary/metadata subsystems.
Changes:
- hermes_cli/models.py:
- Add 'minimax-oauth' to _PROVIDER_MODELS with MiniMax-M2.7 and
MiniMax-M2.7-highspeed
- Add ProviderEntry('minimax-oauth', 'MiniMax (OAuth)', ...) to
CANONICAL_PROVIDERS near existing minimax entries
- Add aliases: minimax-portal, minimax-global, minimax_oauth in
_PROVIDER_ALIASES
- hermes_cli/main.py:
- Add 'minimax-oauth' to provider_labels dict
- Insert 'minimax-oauth' into providers list in
select_provider_and_model() near the other minimax entries
- Add 'minimax-oauth' to --provider argparse choices
- Add _model_flow_minimax_oauth() function: ensures login via
_login_minimax_oauth(), resolves runtime credentials, prompts for
model selection, saves model choice and config
- Add dispatch elif branch for selected_provider == 'minimax-oauth'
- agent/auxiliary_client.py:
- Add 'minimax-oauth': 'MiniMax-M2.7-highspeed' to
_API_KEY_PROVIDER_AUX_MODELS
- Add 'minimax-oauth' to _ANTHROPIC_COMPAT_PROVIDERS set
- agent/model_metadata.py:
- Add 'minimax-oauth' to _PROVIDER_PREFIXES frozenset
- MiniMax-M2.7 context length (200_000) already covered by the
existing 'minimax' substring match in DEFAULT_CONTEXT_LENGTHS
* docs(anthropic): correct OAuth scope to Max plan + extra usage credits only
The previous docs pass (#17399) overstated what Anthropic OAuth works
with. In practice Hermes can only route against a Claude Max plan that
has purchased extra usage credits — the base Max allowance is not
consumed, and Claude Pro is not supported at all. Without Max + extra
credits, users must fall back to an ANTHROPIC_API_KEY (pay-per-token).
Updates the four pages touched in #17399:
- integrations/providers.md
- user-guide/features/credential-pools.md
- reference/environment-variables.md
- getting-started/quickstart.md
* fix(aux): skip kimi-coding in vision auto-detect (closes#17076)
Kimi Coding Plan's /coding endpoint (Anthropic Messages wire) has no
image_in capability — Kimi's own docs confirm and suggest switching to
a vision-capable model. Vision lives on the separate Kimi Platform
(api.moonshot.ai, OpenAI-wire, pay-as-you-go). When the user has
kimi-coding as main provider and auxiliary.vision.provider=auto,
resolve_vision_provider_client was handing back an AnthropicAuxiliaryClient
wrapped around /coding which 404'd on every vision request.
Add a _PROVIDERS_WITHOUT_VISION frozenset ({kimi-coding, kimi-coding-cn})
and gate the main-provider vision branch on membership. On a skip the
auto-detect falls through to OpenRouter → Nous like any other
main-provider-unavailable case.
Explicit per-task overrides (auxiliary.vision.provider=kimi-coding) are
unaffected — the skip only applies when the caller is in auto mode.
Tests: 4 new targeted tests in TestVisionAutoSkipsKimiCoding covering
the skip path, CN variant, explicit-override passthrough, and a guard
against accidental skip-list widening.
* perf(startup): lazy-import OpenAI, Anthropic, Firecrawl, account_usage
Four heavy SDK/module imports are now deferred off the hot startup path.
Net savings on cold module imports:
cli 1200 → 958 ms (-242)
run_agent 1220 → 901 ms (-319)
tools.web_tools 711 → 423 ms (-288)
agent.anthropic_adapter 230 → 15 ms (-215)
agent.auxiliary_client 253 → 68 ms (-185)
Four independent changes in one PR since they all use the same pattern
and share the same risk profile (heavy SDK import → lazy proxy or
function-local import):
1. tools/web_tools.py:
'from firecrawl import Firecrawl' moved into _get_firecrawl_client(),
which is only called when backend='firecrawl'. Users on Exa/Tavily/
Parallel pay zero firecrawl cost.
2. cli.py + gateway/run.py:
'from agent.account_usage import ...' moved into the /limits handlers.
account_usage transitively pulls the OpenAI SDK chain; only needed
when the user runs /limits.
3. agent/anthropic_adapter.py:
'try: import anthropic as _anthropic_sdk' replaced with a cached
'_get_anthropic_sdk()' accessor. The three usage sites
(build_anthropic_client, build_anthropic_bedrock_client,
read_claude_code_credentials_from_keychain) now resolve via the
accessor. All pre-existing test patches of
'agent.anthropic_adapter._anthropic_sdk' keep working because the
accessor respects any value already in module globals.
4. agent/auxiliary_client.py AND run_agent.py:
'from openai import OpenAI' replaced with an '_OpenAIProxy()' module-
level object that looks like the OpenAI class but imports the SDK on
first call/isinstance check. This preserves:
- 15+ in-module OpenAI(...) construction sites in auxiliary_client
and the single site in run_agent's _create_openai_client (Python's
function-scope name lookup finds the proxy, forwards the call);
- 'patch("agent.auxiliary_client.OpenAI", ...)' and
'patch("run_agent.OpenAI", ...)' test patterns used by 28+ test
files (patch replaces the module attribute as usual).
Tried two alternatives first:
- 'from openai._client import OpenAI' — doesn't skip openai/__init__.py
(the audit's hypothesis here was wrong).
- Module-level __getattr__ — works for external access but Python
function-scope name resolution skips __getattr__, so in-module
OpenAI(...) calls NameError.
Note: 'openai' still loads on 'import cli' because
cli.py -> neuter_async_httpx_del() -> openai._base_client, and
run_agent.py -> code_execution_tool.py (module-level
build_execute_code_schema) -> _load_config() -> 'from cli import
CLI_CONFIG'. Deferring those is a separate, larger change — out of scope
for this PR. The savings above all come from avoiding the openai/*,
anthropic/*, and firecrawl/* top-level type-tree imports on paths that
don't need them.
Verified:
- 302/302 tests in tests/agent/{test_anthropic_adapter,
test_bedrock_1m_context, test_minimax_provider, test_anthropic_keychain}
pass. Two pre-existing failures on main unchanged.
- 106/106 tests/agent/test_auxiliary_client.py pass (1 pre-existing fail).
- 97/97 tests/run_agent/test_create_openai_client_kwargs_isolation.py,
test_plugin_context_engine_init.py, test_invalid_context_length_warning.py,
test_api_max_retries_config.py,
tests/hermes_cli/test_gemini_provider.py, test_ollama_cloud_provider.py
pass (1 pre-existing fail).
- Live hermes chat smoke: 2 turns + /model switch + tool calls, zero
errors in the 57-line agent.log window.
- Module-level import of run_agent + auxiliary_client + anthropic_adapter
no longer pulls 'anthropic' or 'firecrawl' at all.
* fix(gateway): restore top-level account_usage import for test-patch surface
CI caught two failures in tests/gateway/test_usage_command.py that I
missed locally:
AttributeError: 'module' object at gateway.run has no attribute 'fetch_account_usage'
The test uses monkeypatch.setattr('gateway.run.fetch_account_usage', ...)
to inject a fake account-fetch call. Moving the import inside the
handler deleted that module-level attribute, breaking the patch surface.
Restoring the top-level import in gateway/run.py gives up the ~230 ms
gateway-boot savings from that one lazy, but:
1. the gateway is a long-running daemon — boot cost is paid once per
install, not per turn;
2. the other four lazy-imports (firecrawl, openai, anthropic, cli's
account_usage) remain in place and still account for the bulk of
the savings reported in the PR body;
3. preserving the patch surface keeps the established
'gateway.run.fetch_account_usage' monkeypatch pattern working
without touching tests.
Verified: tests/gateway/test_usage_command.py — 8 passed, 0 failed.
Full targeted sweep (2336 tests across agent/gateway/hermes_cli/run_agent):
2332 passed, 4 failed — all 4 pre-existing on main.
---------
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
Auxiliary tasks (title_generation, vision, compression, web_extract,
session_search) now pick the correct wire protocol based on the
endpoint, not just on which resolve_provider_client branch built the
client. Fixes 404s on Kimi Coding Plan and any other named provider
whose endpoint speaks Anthropic Messages.
Root cause: the 'api_key' branch of resolve_provider_client (and the
Step 2 fallback chain inside _resolve_auto) always built a plain
OpenAI client regardless of what the endpoint actually spoke. For
provider=kimi-coding + model=kimi-for-coding, that meant:
POST https://api.kimi.com/coding/v1/chat/completions
{ "model": "kimi-for-coding", ... }
→ 404 resource_not_found_error
The /coding route only accepts the Anthropic Messages shape (the main
agent already uses api_mode=anthropic_messages for it). Earlier fixes
(#16819, #22ddac4b1) patched the anonymous-custom, named-custom, and
external-process branches — but the named api_key branch (kimi-coding,
minimax, zai, future /anthropic providers) was the fourth sibling and
never got the same treatment.
Fix: one module-level helper _maybe_wrap_anthropic() that rewraps a
plain OpenAI client in AnthropicAuxiliaryClient when:
- api_mode is explicitly 'anthropic_messages', OR
- the URL ends in '/anthropic', OR
- the host is api.kimi.com + path contains '/coding', OR
- the host is api.anthropic.com.
Wired into _wrap_if_needed (covers all resolve_provider_client
branches that already go through it) and into the Step 2 api_key
fallback chain inside _resolve_auto. Explicit api_mode still wins:
passing api_mode='chat_completions' forces OpenAI wire, and already-
wrapped specialized adapters (Codex, Gemini native, CopilotACP) pass
through unchanged.
E2E verified:
- resolve_provider_client('kimi-coding', 'kimi-for-coding')
→ AnthropicAuxiliaryClient (was plain OpenAI, which 404'd)
- _resolve_auto Step 1 for kimi-coding runtime → AnthropicAuxiliaryClient
- resolve_provider_client('openrouter', ...) → plain OpenAI (no regression)
- api_mode='chat_completions' override → plain OpenAI (explicit wins)
Tests:
- tests/agent/test_auxiliary_transport_autodetect.py (new): 21 tests
covering URL detection, wrap decisions, and integration.
- 204/205 existing auxiliary tests pass (1 pre-existing failure on
main, unrelated to this change).
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
Auxiliary callers that configure reasoning via
auxiliary.<task>.extra_body.reasoning were having that config silently
dropped by the Codex Responses adapter — it only forwarded
messages/model/tools through to responses.stream(), never translating
chat.completions-shaped reasoning hints into the Responses API's
top-level reasoning + include fields.
Mirror the main-agent translation from agent/transports/codex.py:
- extra_body.reasoning.effort → resp_kwargs.reasoning.{effort, summary:"auto"}
- 'minimal' → 'low' clamp (Codex backend rejects 'minimal')
- Always include ['reasoning.encrypted_content'] when reasoning is enabled
- {'enabled': False} → omit reasoning and include entirely
- Non-dict reasoning values are ignored defensively
Reported by @OP (Apr 26 feedback bundle).
## Changes
- agent/auxiliary_client.py: _CodexCompletionsAdapter.create() now reads
and translates extra_body.reasoning before calling responses.stream()
- tests/agent/test_auxiliary_client.py: 9 new tests covering all effort
levels, the minimal→low clamp, the disabled path, the no-op paths,
and defensive handling of wrong-shape inputs
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
* fix(anthropic): remove Claude Code fingerprinting from OAuth Messages API path
OAuth requests now identify as Hermes on the wire. Removed:
- "You are Claude Code, Anthropic's official CLI for Claude." system
prompt prepend
- Hermes Agent → Claude Code / Nous Research → Anthropic
system-prompt substitutions
- mcp_ tool-name prefix on outgoing tool schemas + message history
- Matching mcp_ strip on inbound tool_use blocks (strip_tool_prefix path
removed from AnthropicTransport.normalize_response, + all 5 call
sites in run_agent.py and auxiliary_client.py)
- user-agent: claude-cli/<v> (external, cli) and x-app: cli headers on
the Messages API client
Added:
- OAuth path strips context-1m-2025-08-07 — Anthropic rejects OAuth
requests carrying it with HTTP 400 'This authentication style is
incompatible with the long context beta header.'
Kept (auth plumbing, not identity spoofing):
- _is_oauth_token classifier and is_oauth flag threading
- Bearer vs x-api-key auth routing
- _OAUTH_ONLY_BETAS (claude-code-20250219, oauth-2025-04-20) — backend
requires these on the OAuth-gated Messages endpoint
- _OAUTH_CLIENT_ID (Claude Code's) — Anthropic doesn't issue OAuth
creds to third parties; this is the only way the login flow works
- claude-cli/<v> User-Agent on the OAuth token exchange + refresh
endpoints at platform.claude.com/v1/oauth/token — bare requests get
Cloudflare 1010 blocked
Verified live against api.anthropic.com with a fresh sk-ant-oat01-*
token:
- claude-haiku-4-5 simple message: HTTP 200, 'OK' response
- claude-haiku-4-5 tool call: HTTP 200, stop_reason=tool_use, tool
named 'terminal' (no mcp_ prefix) round-tripped correctly
- Outgoing wire: no user-agent, no x-app, real Hermes identity in
system prompt, real tool name in schema
Closes/supersedes #16820 (mcp_ PascalCase normalization patch — no longer
needed since the mcp_ round-trip is gone).
* fix(anthropic): resolve_anthropic_token() reads credential pool first
Close the gap where ~/.hermes/auth.json → credential_pool.anthropic
(where hermes login + dashboard PKCE flow write OAuth tokens) was not
in resolve_anthropic_token()'s source list.
Before: users who authed via hermes login got the token written into
the pool, but legacy fallback code paths (auxiliary_client, models
catalog fetch, explicit-runtime path) that call resolve_anthropic_token()
saw None and raised 'No Anthropic credentials found' — even though the
token was sitting in auth.json.
New priority 1: pool.select() with env-sourced entries skipped. Skipping
env:* entries preserves the existing env-var priority logic further
down the chain (static env OAuth → refreshable Claude Code upgrade via
_prefer_refreshable_claude_code_token).
Surfaced while writing the hermes-agent-dev skill playbook for
'finding a live OAuth token for an E2E test'.
---------
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
Registers tencent-tokenhub (https://tokenhub.tencentmaas.com/v1) as a
new API-key provider with model tencent/hy3-preview (256K context).
- PROVIDER_REGISTRY entry + TOKENHUB_API_KEY / TOKENHUB_BASE_URL env vars
- Aliases: tencent, tokenhub, tencent-cloud, tencentmaas
- openai_chat transport with is_tokenhub branch for top-level
reasoning_effort (Hy3 is a reasoning model)
- tencent/hy3-preview:free added to OpenRouter curated list
- 60+ tests (provider registry, aliases, runtime resolution,
credentials, model catalog, URL mapping, context length)
- Docs: integrations/providers.md, environment-variables.md,
model-catalog.json
Author: simonweng <simonweng@tencent.com>
Salvaged from PR #16860 onto current main (resolved conflicts with
#16935 Azure Anthropic env-var hint tests and the --provider choices=
list removal in chat_parser).
Follow-up to PR #16819 applying the same treatment to the two sibling
fallback sites in resolve_provider_client() that carry the identical bug
class as the anonymous-custom branch:
- Named custom provider (providers: / custom_providers: config entries):
apply _to_openai_base_url() on the OpenAI-wire path (chat_completions /
codex_responses), leave custom_base untouched on the anthropic_messages
path where the /anthropic surface is intentional. Prefer
main_runtime.get('model') over _read_main_model() so the entry model
still wins first. The ImportError fallback for anthropic_messages now
redoes query-param extraction against the rewritten URL so the final
OpenAI client hits /v1.
- external_process branch (copilot-acp): same main_runtime.get('model')
fallback before _read_main_model() so auxiliary tasks on this provider
track live /model switches instead of stale config.yaml.
Keeps the fix consistent across all three custom-endpoint fallback sites
in resolve_provider_client().
- config.py: remove dead ENV_VARS_BY_VERSION[17] entry (current _config_version
is 22, so all users are past version 17 and would never be prompted for
GMI_API_KEY on upgrade — consistent with how arcee was added)
- auxiliary_client.py: use google/gemini-3.1-flash-lite-preview as GMI aux
model instead of anthropic/claude-opus-4.6 (matches cheap fast-model pattern
used by all other providers: zai→glm-4.5-flash, kimi→kimi-k2-turbo-preview,
stepfun→step-3.5-flash, kilocode→google/gemini-3-flash-preview)
- test_gmi_provider.py: fix malformed write_text() call in doctor test
(was: write_text("GMI_API_KEY=*** encoding="utf-8") → missing closing quote,
wrote literal string 'GMI_API_KEY=*** encoding=' to .env file)
- test_gmi_provider.py + test_auxiliary_client.py: update aux model assertions
to match new cheaper default
- docs/integrations/providers.md: add 'gmi' to inline 'Supported providers'
fallback list (was only in the table, not the inline list at line ~1181)
- docs/reference/cli-commands.md: add 'gmi' to --provider choices list
Thread a vision-request flag through auxiliary provider resolution so Copilot clients can include Copilot-Vision-Request only for vision tasks. This preserves normal text requests while ensuring Copilot vision payloads reach the vision-capable route.
Add regression coverage for Copilot vision routing and keep cached text and vision clients separate so a text client without the header is not reused for vision.
Co-authored-by: dhabibi <9087935+dhabibi@users.noreply.github.com>
Azure OpenAI requires an `api-version` query parameter on every request.
When users include it in the base_url (e.g. `?api-version=2025-04-01-preview`),
the OpenAI SDK silently drops it during URL construction, causing 404 errors.
Extract query params from base_url and pass them via `default_query` so the
SDK appends them to every request. This is a generic solution that works for
any custom endpoint requiring query parameters, not just Azure.
No-op for URLs without query params — fully backward compatible.
The AIAgent.flush_memories pre-compression save, the gateway
_flush_memories_for_session, and everything feeding them are
obsolete now that the background memory/skill review handles
persistent memory extraction.
Problems with flush_memories:
- Pre-dates the background review loop. It was the only memory-save
path when introduced; the background review now fires every 10 user
turns on CLI and gateway alike, which is far more frequent than
compression or session reset ever triggered flush.
- Blocking and synchronous. Pre-compression flush ran on the live agent
before compression, blocking the user-visible response.
- Cache-breaking. Flush built a temporary conversation prefix
(system prompt + memory-only tool list) that diverged from the live
conversation's cached prefix, invalidating prompt caching. The
gateway variant spawned a fresh AIAgent with its own clean prompt
for each finalized session — still cache-breaking, just in a
different process.
- Redundant. Background review runs in the live conversation's
session context, gets the same content, writes to the same memory
store, and doesn't break the cache. Everything flush_memories
claimed to preserve is already covered.
What this removes:
- AIAgent.flush_memories() method (~248 LOC in run_agent.py)
- Pre-compression flush call in _compress_context
- flush_memories call sites in cli.py (/new + exit)
- GatewayRunner._flush_memories_for_session + _async_flush_memories
(and the 3 call sites: session expiry watcher, /new, /resume)
- 'flush_memories' entry from DEFAULT_CONFIG auxiliary tasks,
hermes tools UI task list, auxiliary_client docstrings
- _memory_flush_min_turns config + init
- #15631's headroom-deduction math in
_check_compression_model_feasibility (headroom was only needed
because flush dragged the full main-agent system prompt along;
the compression summariser sends a single user-role prompt so
new_threshold = aux_context is safe again)
- The dedicated test files and assertions that exercised
flush-specific paths
What this renames (with read-time backcompat on sessions.json):
- SessionEntry.memory_flushed -> SessionEntry.expiry_finalized.
The session-expiry watcher still uses the flag to avoid re-running
finalize/eviction on the same expired session; the new name
reflects what it now actually gates. from_dict() reads
'expiry_finalized' first, falls back to the legacy 'memory_flushed'
key so existing sessions.json files upgrade seamlessly.
Supersedes #15631 and #15638.
Tested: 383 targeted tests pass across run_agent/, agent/, cli/,
and gateway/ session-boundary suites. No behavior regressions —
background memory review continues to handle persistent memory
extraction on both CLI and gateway.
Generalize the temperature-specific 400 retry that shipped in PR #15621 so
the same reactive strategy covers any provider that rejects an arbitrary
request parameter — — not just temperature.
- agent/auxiliary_client.py:
* New _is_unsupported_parameter_error(exc, param): matches the same six
phrasings the old temperature detector did plus 'unrecognized parameter'
and 'invalid parameter', against any named param.
* _is_unsupported_temperature_error is now a thin back-compat wrapper so
existing imports and tests keep working.
* The max_tokens → max_completion_tokens retry branch in call_llm and
async_call_llm now (a) gates on 'max_tokens is not None' so we do not
pop a key that was never set and silently substitute a None value on
the retry, and (b) also matches the generic helper in addition to the
legacy 'max_tokens' / 'unsupported_parameter' substring checks — picking
up phrasings like 'Unknown parameter: max_tokens' that previously slipped
through.
- tests/agent/test_unsupported_parameter_retry.py: 18 new tests covering
the generic detector across params, the back-compat wrapper, and the two
hardenings to the max_tokens retry branch (None gate + generic phrasing).
Credit: retry-generalization pattern from @nicholasrae's PR #15416. That PR
also proposed the reactive temperature retry which landed independently via
PR #15621 + #15623 (co-authored with @BlueBirdBack). This commit salvages
the remaining hardening ideas onto current main.
Universal reactive fix for 'HTTP 400: Unsupported parameter: temperature'
across all providers/models — not just Codex Responses.
The same backend can accept temperature for some models and reject it for
others (e.g. gpt-5.4 accepts but gpt-5.5 rejects on the same OpenAI
endpoint; similar patterns on Copilot, OpenRouter reasoning routes, and
Anthropic Opus 4.7+ via OAI-compat). An allow/deny-list by model name does
not scale.
call_llm / async_call_llm now detect the concrete 'unsupported parameter:
temperature' 400 and transparently retry once without temperature. Kimi's
server-managed omission and Opus 4.7+'s proactive strip stay in place —
this is the safety net for everything else.
Changes:
- agent/auxiliary_client.py: add _is_unsupported_temperature_error helper;
wire into both sync and async call_llm paths before the existing
max_tokens/payment/auth retry ladder
- tests/agent/test_unsupported_temperature_retry.py: 19 tests covering
detector phrasings, sync + async retry, no-retry-without-temperature,
and non-temperature 400s not triggering the retry
Builds on PR #15620 (codex_responses fallback) which stripped temperature
up front for that one api_mode. This PR closes the gap for every other
provider/model combo via reactive retry.
Credit: retry approach and detector originate from @BlueBirdBack's PR #15578.
Co-authored-by: BlueBirdBack <BlueBirdBack@users.noreply.github.com>
Bedrock's aws_sdk auth_type had no matching branch in
resolve_provider_client(), causing it to fall through to the
"unhandled auth_type" warning and return (None, None). This broke
all auxiliary tasks (compression, memory, summarization) for Bedrock
users — the main conversation loop worked fine, but background
context management silently failed.
Add an aws_sdk branch that creates an AnthropicAuxiliaryClient via
build_anthropic_bedrock_client(), using boto3's default credential
chain (IAM roles, SSO, env vars, instance metadata). Default
auxiliary model is Haiku for cost efficiency.
Closes#13919
Keep auxiliary provider resolution aligned with the switch and persisted main-provider paths when models.dev returns github-copilot slugs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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