The ChatGPT Codex backend answers a rejected encrypted-reasoning replay with the
same bare 400 {code: invalid_prompt, message: "Request blocked."} it uses for
genuine blocks. classify_api_error bucketed it as format_error, so the one-shot
repair that already exists for invalid_encrypted_content (disable replay, strip
cached codex_reasoning_items, retry once) never ran and the turn aborted.
Classify exactly that envelope from provider openai-codex — SDK 400 body, SSE
``error`` frame, or the ``response.failed`` text — as invalid_encrypted_content
while keeping format_error's abort-and-fallback hints, so the only behavioural
delta is turn_recovery's replay strip, which still requires cached reasoning
items. A block with nothing to strip aborts exactly as before; other providers,
other messages/codes and the #18028 safety refusals are untouched.
evals/codex_masked_replay_review.py drives a real AIAgent (codex_responses)
against a synthetic local Responses server for the before/after check.
Refs #92353, #92357, #94077.
Reasoning-mandatory routes answer reasoning: {enabled: false} with HTTP 400
"Reasoning is mandatory for this endpoint and cannot be disabled". Hermes
sends that disable for /reasoning none, agent.reasoning_effort: none, and the
one-shot thinking-exhaustion continuation override (which GLM-5.3-flash
triggers on its own). The Nous profile's catalog guard swallows the disable
only when its per-process capability cache already says mandatory; a gateway
that warmed the cache before the route flipped kept sending it, and the 400
was classified as a non-retryable format_error that aborted the turn.
- error_classifier: new reasoning_mandatory reason (retryable, no fallback,
no compression), matched before the request-validation branch.
- conversation_loop: one-shot recovery — set agent._reasoning_disable_rejected,
queue a catalog refresh for the provider, retry.
- chat_completion_helpers: _reasoning_config_for_wire drops every disable
(configured or ephemeral) once the route has rejected one.
- hermes_cli/models: refresh_reasoning_caps_async(provider) forces a
background re-fetch of the Nous/OpenRouter catalog.
- openrouter profile: omit a disable when the catalog marks the route
mandatory (parity with the Nous profile).
Live: z-ai/glm-5.3-flash on the Portal with a poisoned mandatory:false cache.
Before: turn aborted with the 400. After: one retry, thinking stays on, turn
completes.
The permanent-brick class in #69078: xAI returns 'Invalid PNG image'
when a re-serialized image part in replayed history becomes
undecodable. The existing image-error patterns cover only Anthropic
'exceeds max dimension' wordings and 'model does not support images'
strings, so the classifier lands on a generic non-retryable 400 and
neither the shrink path nor the strip path fires. Every subsequent
turn (even bare text) fails identically because the poison stays in
history — the session is permanently wedged until deleted.
Two recovery layers, deliberately separate:
- Semantic split: new FailoverReason.image_corrupt with
_IMAGE_CORRUPT_PATTERNS ('invalid png image' / 'invalid jpeg image'),
checked BEFORE _IMAGE_TOO_LARGE_PATTERNS in both _classify_400 and
_classify_by_message. Corrupt bytes route to strip-and-retry, never
to the shrink path (shrinking corrupt bytes cannot help).
- Generic fallback: any non-retryable 400 whose outgoing messages
still contain image parts gets one strip-and-retry via the existing
_strip_images_from_messages helper, guarded by a new
stripped_images_this_turn one-shot flag on TurnRetryState. This
un-bricks the session for any current or future provider wording
without adding another pattern list to maintain.
Item 3 from the report (multimodal-part integrity across FTS
persistence + compaction handoff) is a separate investigation and
remains follow-up work.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: paultaki <paultaki@users.noreply.github.com>
Consolidates the 429-quota-classifier cluster on top of the merged #93419
Anthropic core. Three independent contributor findings salvaged into one
coherent change to the single 429 branch:
- Broaden the 429 usage-limit check from the narrow 'usage limit' string to
the full _USAGE_LIMIT_PATTERNS ('quota', 'limit exceeded', 'key limit
exceeded') and add _BILLING_PATTERNS detection on 429 ('insufficient
credits' wrapped in a 429 instead of 402), guarded by a _RATE_LIMIT_PATTERNS
exclusion so an explicit 'Rate limit exceeded' never promotes to
non-retryable billing. (credit @Pluviobyte, #39441 — earliest submitter)
- Add 'resets in' to the transient signals: Codex's 'Weekly usage limit
reached. Resets in 6hr 29min.' wrongly read as terminal billing because
main only had 'reset in' (no substring match). (credit @LeonSGP43, #63021)
- Add 'reset after' / 'available in' / 'per minute' / 'per second' transient
signals. (credit @jtstothard, #74785)
Supersedes #65633 (defective branch placement, no tests). The aux-client
path already covers these shapes (_is_payment_error catches weekly/quota
walls; _is_rate_limit_error treats 'resets in' as transient), so no change
there.
Tests: 6 new cases (generic quota wall, insufficient-credits 429, rate-limit
guard, Codex resets-in, extra transient phrases). Guard sabotage-verified.
Co-authored-by: Pluviobyte <Pluviobyte@users.noreply.github.com>
Co-authored-by: LeonSGP43 <LeonSGP43@users.noreply.github.com>
Co-authored-by: jtstothard <jtstothard@users.noreply.github.com>
The Codex OAuth backend (chatgpt.com/backend-api/codex) intermittently
injects prompt_cache_retention into its own upstream call and then rejects
it, returning HTTP 400 invalid_parameter. Hermes never sends that field on
this route (see agent/transports/codex.py::_default_prompt_cache_retention_
for_request, which only sets it for api.meta.ai and bedrock-mantle hosts).
Reproduced live: a minimal 1-message request carrying no cache parameters
at all failed 4/20 (20%) with this error, so the rejection is not
deterministic and retrying the identical request is the correct recovery.
Previously the catch-all in _classify_400 returned format_error/
retryable=False, which tripped the is_client_error abort gate in
conversation_loop and killed the turn on the first attempt - burning an
entire large-context request (~550k tokens) per failure.
Classify these as retryable server_error (should_compress=False - the
request shape was never the problem). The same guard is applied to the
sibling 5xx request-validation branch, where a fronting proxy can surface
the identical rejection.
Deliberately narrow: keyed on parameters we only send on specific routes,
and skipped when the current provider is one that legitimately sends them,
so a genuine client-side bad parameter (max_tokens on GPT-5) still fails
fast as a format_error.
Port from anomalyco/opencode#40707: connection-establishment and DNS
failure messages wrapped in generic exceptions (RuntimeError from local
shims, MCP bridges, SDKs re-raising without chaining) fell through to
FailoverReason.unknown, which misses the retry loop's eager transport
fallback — the full retry budget burned against a dead endpoint before
provider fallback.
New _CONNECTION_MESSAGE_PATTERNS (connect refused, no route, network
unreachable, DNS phrasings across Python/glibc/macOS/Node, fetch failed,
Envoy upstream connect error) classify as retryable timeout via
_classify_by_message, mirroring _TIMEOUT_MESSAGE_PATTERNS. Mid-stream
disconnect strings are deliberately excluded — they keep their
_SERVER_DISCONNECT_PATTERNS routing (large-session compression).
Review follow-up (egilewski): the previous commit only hedged the guidance
text; the exact Anthropic 400 was still classified, persisted, and surfaced
as confirmed billing exhaustion. Carry the ambiguity all the way through:
- agent/error_classifier.py: 'out of extra usage' matches on the 400 and
status-less paths now attach error_context {billing_unverified,
possible_content_filter}. Reason stays FailoverReason.billing (rotation +
fallback remain the right recovery either way); ClassifiedError grows a
billing_unverified property.
- agent/credential_pool.py: new FAILURE_REASON_BILLING_UNVERIFIED. An
unverified billing exhaustion gets the short transient cooldown instead of
the one-hour bench, regardless of pool size: a content-filter rejection
leaves the credential healthy and fails identically on every key, and the
hour-long sole-credential latch is what replayed the stored error and made
real fixes look ineffective. A true 402 keeps the full bench. The marker
persists with the entry so a restart cannot upgrade it back to a bench.
- agent/agent_runtime_helpers.py + run_agent.py: recover_with_credential_pool
threads billing_unverified and hands the pool 'billing_unverified' as the
persisted failure_reason.
- agent/conversation_loop.py: the fallback-switch status, max-retries status,
terminal label, and both structured terminal results hedge when the verdict
is unverified. New _billing_terminal_label + _billing_failure_result build
the returned terminal response in one place; the result dict now carries
billing_unverified and the billing_block gains 'unverified': true. The
confirmed-billing path (a real 402 or an API-key credit depletion) keeps
the original assertive wording, so the caveat no longer dilutes it.
Regression tests: classifier marking (400 + status-less + unambiguous-body
negative), pool cooldown TTLs + persistence round-trip, pool failure_reason
plumbing, and the returned terminal response for both unverified and
confirmed verdicts.
Note: tests/agent/test_credential_pool_routing.py::TestFailureAttribution::
test_unmatched_key_does_not_retry_only_pool_entry fails identically on
current main without this change (pre-existing, unrelated).
Run the expensive-model warning for explicit startup `-m` / `--provider`
overrides before the chat loop starts, and fail closed for non-interactive
invocations that select an expensive or known-confusing model.
Also classify Nous paid-model 404s that say credits are required as billing
exhaustion so they fail fast with billing guidance.
Tested:
- scripts/run_tests.sh tests/hermes_cli/test_cli_startup_model_cost_guard.py tests/hermes_cli/test_model_cost_guard.py tests/agent/test_error_classifier.py -- --tb=short -q
Add regressions for the Discord-shaped applyPromptTemplate 400 that embeds
"No user query found", bare no-user-query on a large session, and the genuine
llama.cpp unable-to-generate-parser grammar path that must stay recoverable.
Selecting an NVIDIA NIM model whose id reached config without the nvidia/
prefix produced a bare "HTTP 404: 404 page not found" — retried three times,
never naming the model. It reads exactly like an outage or an auth failure,
which is where the Discord thread spent its time before the id was spotted.
normalize_model_for_provider() had no branch for nvidia, so a bare id passed
straight through to the API. Repair it from the provider's curated catalogue:
a bare name that matches exactly one entry modulo the prefix gets it back.
That's a lookup, not a guess — build.nvidia.com also fronts local NIM
containers and third-party models, and anything absent from the catalogue is
left alone. Because the repair runs on every runtime setup, an already-broken
config self-heals on the next turn and prints what it changed.
If a bare id still reaches the wire, the 404 now explains itself. The
classifier consults the same catalogue: a prefix-less id the provider only
serves as vendor/model is a deterministic failure, so it classifies as
model_not_found instead of burning three retries on a retryable "unknown",
and the error trace names the id to use.
Fixes#78796
Add distinct, greppable log lines at both fix sites so the condition is
observable in the field instead of only inferable from the absence of the old
'Cannot compress further' spiral:
- chat_completion_helpers: warn when an empty partial-stream stub is replaced
with the placeholder (0 chars recovered, no tool call).
- error_classifier: warn when a malformed-body 400 is classified as
format_error rather than context overflow, with num_messages/approx_tokens.
Extend the litellm-shape classifier test to assert the warning is emitted.
A stream that dies after delivering deltas but with 0 recovered chars (and
no captured tool call) produced a content-less assistant stub. That empty
non-final message is invalid for the Anthropic schema, so every later request
400s with 'all messages must have non-empty content' (INVALID_REQUEST_BODY).
On a large session the 400 was misclassified as context overflow, dropping the
loop into a compression spiral that ends in 'Cannot compress further' — a
misleading context-size error on a session nowhere near its limit.
Root cause: substitute a minimal '[response interrupted]' placeholder so the
stub is never empty.
Misclassification: add _INVALID_MESSAGE_BODY_PATTERNS (checked before the
context-overflow heuristic) to classify these as non-retryable format_error,
and teach the body extractors the litellm/Bedrock errorMessage/errorCode/
errorArgs shape so descriptive proxy errors are not mistaken for generic ones.
Adds RED-verified regression tests for the stub path and three classifier tests
(including a guard that real overflows still compress).
Port from anomalyco/opencode#37848 (+ dev-branch twin #37840): expand
context-overflow patterns and guard against rate-limit messages that
mention tokens.
- 'Throttling error: Too many tokens, please wait before trying again.'
(AWS Bedrock / proxy shape) classified as context_overflow and routed a
healthy session into compression on every throttle. Added 'throttling'
to _RATE_LIMIT_PATTERNS, which the message-only path checks BEFORE the
overflow list.
- 'Input length N exceeds the maximum allowed input length of M tokens.'
(Together/Fireworks shape) fell through to unknown — no compression
recovery. Added 'maximum allowed input length' to overflow patterns.
- 'request_too_large' / 'Request exceeds the maximum size' (Anthropic 413
type re-wrapped without a status code by aggregators/proxies) fell
through to unknown. Added to _PAYLOAD_TOO_LARGE_PATTERNS.
All three shapes proven live on main before the fix; 265 classifier +
bedrock tests and 238 sibling rate-guard/compression tests pass.
Port from anomalyco/opencode#35671: Z.AI / Zhipu GLM returns
'tokens in request more than max tokens allowed' (error code 1210) on
context overflow. This matched no pattern in _CONTEXT_OVERFLOW_PATTERNS,
so the error classified as unknown/retryable — the agent would retry the
oversized request instead of triggering context compression.
Proven live on main before the fix: classify_api_error() returned
FailoverReason.unknown for the exact Z.AI error shape; now returns
context_overflow.
Keep each xAI OAuth auth-add login as an independent manual device-code pool entry and recognize xAI personal-team spending-limit 403 responses as billing exhaustion. Preserve the structured top-level error message so the failed credential is quarantined and the next healthy account is selected without attempting a pointless token refresh.
Route direct xAI HTTP consumers through the credential pool as well. Proactive and 401-reactive refreshes update the exact issuing manual entry, preserve validated xAI base URL overrides, and serialize single-use refresh-token rotation across concurrent pool instances.
The cross-turn stale-call circuit breaker (_check_stale_giveup in
agent/chat_completion_helpers.py) raises a RuntimeError when the
provider has been unresponsive for N consecutive stale attempts.
This error was classified as FailoverReason.unknown (retryable=True,
should_fallback=False), causing the retry loop to burn all max_retries
against the same dead provider — each retry hitting the circuit breaker
instantly with zero network overhead — before fallback was attempted.
Add a classification rule (section 7b in classify_api_error) that
recognizes the stale-breaker RuntimeError by its signature phrases and
classifies it as FailoverReason.timeout with retryable=False,
should_fallback=True. This makes the retry loop skip retries and go
straight to fallback provider activation on the first hit.
Test: test_stale_breaker_runtime_error_triggers_fallback_not_retry
_classify_by_status() routes every other transient HTTP status to a retryable
reason (500/502 -> server_error, 503/529 -> overloaded, 429 -> rate_limit,
413 -> payload_too_large), but 408 Request Timeout fell through to the generic
`400 <= status < 500` branch and was classified as a non-retryable
format_error -- the same bucket as a 400 Bad Request.
A 408 is a transient timing failure the server itself flags as safe to retry
(RFC 9110 15.5.9), not a malformed request, so the retry loop aborted the turn
when a simple retry would recover. Common trigger: a reverse proxy in front of
a self-hosted backend (llama.cpp / Ollama / vLLM) returns 408 when a long
generation outruns the proxy's request-read window.
Route 408 to the existing FailoverReason.timeout (rebuild client + retry).
Add a regression test plus a boundary test asserting 400 stays non-retryable.
Inspired by Claude Code v2.1.199 (July 2, 2026): SSL certificate errors
(TLS-inspecting proxies, missing CA bundles, expired certs) no longer
burn retries before showing actionable guidance — they fail immediately
with the fix hint.
- agent/error_classifier.py: new FailoverReason.ssl_cert_verification +
_SSL_CERT_VERIFY_PATTERNS, checked BEFORE the transient-SSL patterns
(cert-verify messages also contain '[SSL:' and previously retried
forever as timeout). Non-retryable, no compression, no fallback churn.
- agent/conversation_loop.py: dedicated status line + per-cause fix
hints (corporate proxy CA bundle, certifi refresh, self-signed local
endpoints) on the non-retryable abort path.
- 7 new tests incl. regression guards (transient alerts still retry,
large-session cert failure doesn't trigger compression).
Review nit (helix4u): the fix covers 500/502/503/529 but the positive tests
only asserted 500 and 503. Parametrize over all four so 502/529 are covered
too; keep the plain-5xx negatives.
Local inference servers (llama.cpp/llama-server, vLLM/Ollama behind a
Cloudflare/Tailscale hop) report context overflow with HTTP 500/502/503/529
instead of 400/413. _classify_by_status returned server_error/overloaded and
retried blindly, then dropped the turn with no compaction. Route explicit
_CONTEXT_OVERFLOW_PATTERNS matches on those 5xx codes to context_overflow
(should_compress=True); plain 500 stays server_error, plain 503 overloaded.
Anthropic returns HTTP 400 with "You're out of extra usage. Add more at
claude.ai/settings/usage and keep going." when the account's extra-usage
allowance is depleted. The existing _BILLING_PATTERNS list did not
include this wording, so classify_api_error fell through to generic
format_error — non-retryable and should_fallback=False — causing the
agent to abort instead of engaging the configured fallback chain.
Add the pattern and a regression test covering the exact Anthropic body.
OpenRouter returns 429 in two shapes: an account-level throttle on the
user's key, and an upstream-provider throttle (DeepSeek/Anthropic/etc.
rate-limiting OpenRouter's aggregate traffic). The classifier treated
both identically and rotated/exhausted OPENROUTER_API_KEY on every 429 —
burning the key for ~24min and silently disabling auxiliary features
(compression, summarization, vision) on an upstream throttle where the
key was healthy.
Add a FailoverReason.upstream_rate_limit classified from OpenRouter's
unambiguous wrapper message "Provider returned error" (the same signal
the metadata-raw parser already trusts). Recovery skips credential
rotation and defers to the fallback chain to switch models instead.
Co-authored-by: Hermes Agent <127238744+teknium1@users.noreply.github.com>
Z.AI / Zhipu reuse HTTP 429 for server-wide overload. The 429 status
path classified these unconditionally as rate_limit with
should_rotate_credential=True, so an overloaded provider exhausted the
credential pool after two errors — fatal for a single-key user, who has
nothing to rotate to.
The credential is valid; the server is just busy. Disambiguate the 429
body against a shared _OVERLOADED_PATTERNS list and route overload
language to FailoverReason.overloaded (retryable, no rotation), matching
the existing 503/529 path and the message-only path (#52890). Genuine
rate limits (no overload language) still rotate.
Extracted the inline overloaded tuple #52890 added into the shared
_OVERLOADED_PATTERNS constant so the status-code and message paths use
one list.
Closes#14038.
Anthropic returns a 400 when the thinking/redacted_thinking blocks in the
latest assistant message are mutated upstream: 'thinking or redacted_thinking
blocks in the latest assistant message cannot be modified. These blocks must
remain as they were in the original response.'
The classifier's thinking_signature branch only matched on the substring
'signature', so this variant fell through to a non-retryable client error
and hard-aborted the turn -- even though the existing strip-reasoning_details
-and-retry recovery would have healed it.
Broaden the 400 match to also catch 'cannot be modified' / 'must remain as
they were' (still gated on 'thinking'), routing it to the same recovery.
Adds a negative-case test so unrelated 'cannot be modified' 400s are not
swept in.
Defense-in-depth, orthogonal to the root-cause work in #35975 / #17861
(which prevent the block mutation in the first place). Only changes a
terminal-failure into a one-shot recovery.
Signed-off-by: Ian Culling <ian@culling.ca>
A GPT-5 model rejecting max_tokens returns a 400 whose message contains the
literal substring 'max_tokens' — one of the _CONTEXT_OVERFLOW_PATTERNS. The 400
path in _classify_400 checked overflow patterns before any request-validation
check (which only existed on the 5xx path), so the parameter error was routed
into the compression loop, re-sent with the same bad param, and ended in
'Cannot compress further' on a tiny context.
Hoist a request-validation guard (unsupported/unknown parameter) above the
context-overflow check in _classify_400. Deliberately excludes the generic
invalid_request_error code, which OpenAI also stamps on real overflow 400s, so
genuine overflows still compress. Pairs with the max_completion_tokens param
fix that stops the bad request at the source.
Also adds AUTHOR_MAP entry for the salvaged PR #13902 commit.
* fix(agent): fallback immediately on provider content-policy blocks
Provider safety-filter refusals (e.g. OpenAI Codex 'flagged for possible
cybersecurity risk', OpenAI moderation 'violates our usage policies',
Anthropic safety-system rejections, Azure content_filter) are
deterministic decisions about a specific prompt. Retrying the same
prompt up to api_max_retries times just reproduces the same refusal and
burns paid attempts before surfacing the generic 'API failed after 3
retries — <provider message>' to Telegram / cron with no indication that
the failure came from the model provider rather than Hermes itself.
Classify these as a new FailoverReason.content_policy_blocked
(non-retryable, should_fallback=True) and route them through the
existing is_client_error path so the loop:
- skips the 3x retry backoff
- activates a configured fallback model immediately
- emits a clear provider-safety message to the user (not the generic
'Non-retryable error (HTTP None)') and surfaces actionable guidance
when no fallback is configured (rephrase, narrow context, or set
fallback_model in hermes config)
- returns a final_response that explicitly tells the user this came
from the model provider, so gateway delivery is unambiguous and
cron last_status reflects the safety block rather than a vague
'agent reported failure'
Patterns are intentionally narrow — verbatim refusal phrasings keyed to
specific provider safety pipelines, not generic words like 'policy' or
'violation' that would collide with billing / format / auth errors.
Regression guards in test_18028_content_policy_blocked.py verify
billing 402s, generic 400s, and OpenRouter account-level
provider_policy_blocked remain distinct classifications.
Salvaged from #18164 onto current main (file restructure: loop logic
moved from run_agent.py to agent/conversation_loop.py, _emit_status →
_buffer_status), broadened patterns beyond the original OpenAI Codex
cybersecurity case to cover OpenAI moderation, Anthropic safety system,
and Azure content_filter; added user-actionable guidance and a clear
final_response so cron/gateway surfaces the policy block instead of a
generic non-retryable error, and added a regression-guard test module
mirroring the is_client_error predicate.
Addresses #18028.
Co-authored-by: Kuan-Chieh Huang <kchuang1015@users.noreply.github.com>
* chore: add kchuang1015 to AUTHOR_MAP
---------
Co-authored-by: Kuan-Chieh Huang <kchuang1015@users.noreply.github.com>
* fix(codex-responses): gracefully recover from invalid_encrypted_content (salvage #10144)
When an OpenAI-compatible Responses API surface accepts an initial
request but later rejects the replayed `codex_reasoning_items`
encrypted blob with HTTP 400 `invalid_encrypted_content`, the
session previously got stuck retrying the same poisoned payload.
Recovery: classify the error as a dedicated FailoverReason, and on the
first hit disable encrypted reasoning replay for the rest of the
session, strip cached items from message history, and retry once.
Changes:
* error_classifier: add FailoverReason.invalid_encrypted_content
branch in _classify_400 (before context_overflow so the messages
that mention 'encrypted content … could not be verified' don't trip
context heuristics), in _classify_by_error_code, and extend
_extract_error_code to peek inside wrapped JSON in error.message and
ignore the bare '400' as a code.
* agent_init: initialize `_codex_reasoning_replay_enabled = True` on
every agent.
* run_agent: add AIAgent._disable_codex_reasoning_replay() helper
that flips the flag and pops cached items.
* codex_responses_adapter: thread a `replay_encrypted_reasoning`
kwarg through _chat_messages_to_responses_input so that when the
flag is False we don't replay codex_reasoning_items.
* transports/codex.py: read `replay_encrypted_reasoning` from params,
thread it into the adapter, and gate the
`include=['reasoning.encrypted_content']` request hint on it.
* chat_completion_helpers: pass the agent's replay flag through to
the transport.
* conversation_loop: in the retry loop, add an
invalid_encrypted_content recovery branch that fires once per
session, only when api_mode == codex_responses, only when replay is
still enabled, and only when at least one assistant message in
history actually carries cached reasoning items (otherwise the 400
has nothing to do with our cache and the normal retry path handles
it).
Tests:
* test_error_classifier: new wrapped-JSON _extract_error_code case;
new TestClassifyApiError cases proving the 400 is retryable with
no fallback, that the broad message match doesn't catch a generic
'parsed' message, and that the error code match is
case-insensitive.
* test_run_agent_codex_responses: end-to-end test of the recovery
branch firing once and disabling replay, plus a sibling test that
proves the branch does *not* fire (and the flag stays True) when
history has no cached reasoning items.
Salvages PR #10144 onto the post-refactor module layout
(error_classifier / codex_responses_adapter / transports/codex /
conversation_loop / agent_init) since the original diff was written
against the pre-refactor monolithic run_agent.py.
* chore(release): map victorGPT in AUTHOR_MAP for #10144 salvage
---------
Co-authored-by: victorGPT <wuxuebin1993@gmail.com>
Standard OpenAI returns request-validation failures (unknown/
unsupported parameter, malformed request) as 4xx. Some
OpenAI-compatible gateways return them as 5xx instead — codex.nekos.me
returns 502 for an unknown parameter.
The generic '5xx -> retryable server_error' rule then misfires: the
error is deterministic (every retry gets the identical rejection), so
the retry loop burns all 3 attempts, the transport-recovery path
resets the counter and burns 3 more, and the result is a request
flood against a request that can never succeed.
Fix: when a 500/502 body carries an unambiguous request-validation
signal — 'unknown parameter' / 'unsupported parameter' /
'invalid_request_error' in the message text, or invalid_request_error
/ unknown_parameter / unsupported_parameter as the structured error
code — classify as a non-retryable format_error so the loop fails
fast and falls back. Genuine 502 Bad Gateway with no such signal
stays retryable as before.
Origin: local-author
Upstream-PR: none
Patch-State: local-only
Some providers (Xiaomi MiMo, some Alibaba endpoints, a long tail of
OpenAI-compatible servers) follow the OpenAI spec strictly and require
tool message `content` to be a string — they reject our list-type
content (text + image_url parts) with HTTP 400 'text is not set' /
'tool message content must be a string'.
Instead of an allowlist of known-good providers (maintenance burden,
guaranteed to miss aggregators like OpenRouter where the underlying
model determines support, not the aggregator name), this lands a
reactive recovery:
1. New `FailoverReason.multimodal_tool_content_unsupported` with a
small pattern list covering the common 400 wordings.
2. `AIAgent._try_strip_image_parts_from_tool_messages` walks the API
message list, downgrades any `role:tool` message whose content is
list-with-image to a plain text summary (preserves text parts) in
place, AND records the active (provider, model) in a session-scoped
`_no_list_tool_content_models` set.
3. `_tool_result_content_for_active_model` short-circuits to a text
summary when (provider, model) is in the cache — so after the first
400 + retry, subsequent screenshots in the same session skip the
round trip entirely.
4. Retry hook in `agent.conversation_loop` mirrors the existing
`image_too_large` recovery: detect the reason, run the helper,
retry once, fall through to the normal error path if no list-type
tool content was actually present.
Cache is transient (per-session) by design — next session retries in
case the provider added support, no persistent state to maintain.
Fixes#27344. Closes#27351 (allowlist approach superseded by reactive
recovery).
RuntimeError('claude CLI turn timed out') from a local OpenAI-compatible
shim was falling through to FailoverReason.unknown, surfacing as 'Empty
response from model' and burning 3 retry slots on the same failing
endpoint. _classify_by_message had no timeout-message branch — only
billing/rate_limit/auth/context_overflow/model_not_found patterns. The
type-based check at line 565 also requires isinstance(error, (TimeoutError,
ConnectionError, OSError)) — a plain RuntimeError doesn't match.
Add _TIMEOUT_MESSAGE_PATTERNS for 'timed out', 'deadline exceeded',
'request timed out', 'operation timed out', 'upstream timed out', 'turn
timed out'. _classify_by_message returns FailoverReason.timeout (retryable=True)
when any pattern matches.
Salvage of #22664's classifier portion. The original PR also bundled a
fallback self-selection guard which is now redundant (already on main
via #22780) plus DeepSeek thinking and session_search fixes that are
their own separate concerns.
Follow-up to #22780 — fixes the still-broken classification of
generic-typed provider-shim timeouts that #22780's dedup didn't cover.
MCP servers commonly emit JSON Schema `pattern` (e.g. `\\d{4}-\\d{2}-\\d{2}`
for date-time params) and `format` keywords. llama.cpp's
`json-schema-to-grammar` converter rejects regex escape classes
(\\d/\\w/\\s) and most format values, returning HTTP 400
"parse: error parsing grammar: unknown escape at \\d" — the whole request
fails.
Cloud providers (OpenAI, Anthropic, OpenRouter, Gemini) accept these
keywords fine and use them as prompting hints. Stripping unconditionally
loses useful hints for every cloud user to fix a llama.cpp-only bug.
Approach: classify the llama.cpp grammar-parse 400 in the error
classifier, and on match do a one-shot in-place strip of pattern/format
from `self.tools`, then retry. Follows the existing
`thinking_signature` recovery pattern. Cloud users hit zero overhead;
llama.cpp users pay one failed request per session.
Changes
- agent/error_classifier.py: new `FailoverReason.llama_cpp_grammar_pattern`
+ narrow HTTP-400 branch matching "error parsing grammar",
"json-schema-to-grammar", or "unable to generate parser ... template".
- tools/schema_sanitizer.py: new `strip_pattern_and_format()` helper —
reactive, walks schema nodes, skips property names (search_files.pattern
survives). Returns strip count for logging.
- run_agent.py: new one-shot recovery block in the retry loop. Strips,
logs, continues. Falls through to normal retry if nothing to strip.
- tests: 4 classifier tests (3 variants + 1 non-400 negative), 7 strip
tests including the property-name preservation and idempotency checks.
Co-authored-by: Chris Danis <cdanis@gmail.com>
Generic 400 and server-disconnect heuristics used absolute token/message-count fallbacks that are too aggressive for 1M context sessions. Gate those absolute fallbacks to smaller context windows while preserving relative pressure checks.
Fixes#16351
Keep context-1m-2025-08-07 in OAuth requests by default so 1M-capable
subscriptions retain full context. When Anthropic rejects a request with
400 'long context beta is not yet available for this subscription',
disable the beta for the rest of the session, rebuild the client, and
retry once.
Addresses #17680 (thanks @JayGwod for the clean reproduction) without
forcing every OAuth user off the 1M context window.
Changes:
- agent/error_classifier.py: new FailoverReason.oauth_long_context_beta_forbidden;
pattern matches 400 + 'long context beta' + 'not yet available'. Narrow
enough that the existing 429 tier-gate pattern keeps its own reason.
- agent/anthropic_adapter.py: _common_betas_for_base_url,
build_anthropic_client, build_anthropic_kwargs gain drop_context_1m_beta
kwarg. Default=False (1M stays). OAuth OAUTH_ONLY_BETAS unchanged.
- agent/transports/anthropic.py: build_kwargs forwards the flag.
- run_agent.py: self._oauth_1m_beta_disabled flag, retry-once guard,
recovery branch next to the image-shrink path. _rebuild_anthropic_client
honors the flag. The main build_kwargs call site threads it through for
fast-mode extra_headers.
- hermes_cli/doctor.py, hermes_cli/models.py: sibling OAuth /v1/models
probes get the same reactive retry — previously they'd falsely report
the Anthropic API as unreachable for affected subscriptions.
Tests: 2190 tests/agent/ + 94 adjacent integration tests pass. New unit
tests cover the classifier pattern (including the collision guard against
the 429 tier-gate) and the drop_context_1m_beta adapter behavior (default
keeps 1M, flag strips only 1M while preserving every other beta).
* feat(image-input): native multimodal routing based on model vision capability
Attach user-sent images as OpenAI-style content parts on the user turn when
the active model supports native vision, so vision-capable models see real
pixels instead of a lossy text description from vision_analyze.
Routing decision (agent/image_routing.py::decide_image_input_mode):
agent.image_input_mode = auto | native | text (default: auto)
In auto mode:
- If auxiliary.vision.provider/model is explicitly configured, keep the
text pipeline (user paid for a dedicated vision backend).
- Else if models.dev reports supports_vision=True for the active
provider/model, attach natively.
- Else fall back to text (current behaviour).
Call sites updated: gateway/run.py (all messaging platforms), tui_gateway
(dashboard/Ink), cli.py (interactive /attach + drag-drop).
run_agent.py changes:
- _prepare_anthropic_messages_for_api now passes image parts through
unchanged when the model supports vision — the Anthropic adapter
translates them to native image blocks. Previous behaviour
(vision_analyze → text) only runs for non-vision Anthropic models.
- New _prepare_messages_for_non_vision_model mirrors the same contract
for chat.completions and codex_responses paths, so non-vision models
on any provider get text-fallback instead of failing at the provider.
- New _model_supports_vision() helper reads models.dev caps.
vision_analyze description rewritten: positions it as a tool for images
NOT already visible in the conversation (URLs, tool output, deeper
inspection). Prevents the model from redundantly calling it on images
already attached natively.
Config default: agent.image_input_mode = auto.
Tests: 35 new (test_image_routing.py + test_vision_aware_preprocessing.py),
all existing tests that reference _prepare_anthropic_messages_for_api
still pass (198 targeted + new tests green).
* feat(image-input): size-cap + resize oversized images, charge image tokens in compressor
Two follow-ups that make the native image routing safer for long / heavy
sessions:
1) Oversize handling in build_native_content_parts:
- 20 MB ceiling per image (matches vision_tools._MAX_BASE64_BYTES,
the most restrictive provider — Gemini inline data).
- Delegates to vision_tools._resize_image_for_vision (Pillow-based,
already battle-tested) to downscale to 5 MB first-try.
- If Pillow is missing or resize still overshoots, the image is
dropped and reported back in skipped[]; caller falls back to text
enrichment for that image.
2) Image-token accounting in context_compressor:
- New _IMAGE_TOKEN_ESTIMATE = 1600 (matches Claude Code's constant;
within the realistic range for Anthropic/GPT-4o/Gemini billing).
- _content_length_for_budget() helper: sums text-part lengths and
charges _IMAGE_CHAR_EQUIVALENT (1600 * 4 chars) per image/image_url/
input_image part. Base64 payload inside image_url is NOT counted
as chars — dimensions don't matter, only image-presence.
- Both tail-cut sites (_prune_old_tool_results L527 and
_find_tail_cut_by_tokens L1126) now call the helper so multi-image
conversations don't slip past compression budget.
Tests: 9 new in test_image_routing.py (oversize triggers resize,
resize-fails-returns-None, oversize-skipped-reported), 11 new in
test_compressor_image_tokens.py (flat charge per image, multiple images,
Responses-API / Anthropic-native / OpenAI-chat shapes, no-inflation on
raw base64, bounds-check on the constant, integration test that an
image-heavy tail actually gets trimmed).
* fix(image-input): replace blanket 20MB ceiling with empirically-verified per-provider limits
The previous commit imposed a hardcoded 20 MB base64 ceiling on all
providers, triggering auto-resize on anything larger. This was wrong in
both directions:
* Too loose for Anthropic — actual limit is 5 MB (returns HTTP 400
'image exceeds 5 MB maximum' above that).
* Too strict for OpenAI / Codex / OpenRouter — accept 49 MB+ without
complaint (empirically verified April 2026 with progressive PNG
sizes).
New behaviour:
* _PROVIDER_BASE64_CEILING table: only anthropic and bedrock have a
ceiling (5 MB, since bedrock-on-Claude shares Anthropic's decoder).
* Providers NOT in the table get no ceiling — images attach at native
size and we trust the provider to return its own error if it
disagrees. A provider-specific 400 message is clearer than us
guessing wrong and silently degrading image quality.
* build_native_content_parts() gains a keyword-only provider arg;
gateway/CLI/TUI pass the active provider so Anthropic users get
auto-resize protection while OpenAI users don't pay it.
* Resize target dropped from 5 MB to 4 MB to slide safely under
Anthropic's boundary with header overhead.
Empirical measurements (direct API, no Hermes in the loop):
image b64 anthropic openrouter/gpt5.5 codex-oauth/gpt5.5
0.19 MB ✓ ✓ ✓
12.37 MB ✗ 400 5MB ✓ ✓
23.85 MB ✗ 400 5MB ✓ ✓
49.46 MB ✗ 413 ✓ ✓
Tests: rewrote TestOversizeHandling (5 tests): no-ceiling pass-through,
Anthropic resize fires, Anthropic skip on resize-fail, build_native_parts
routes ceiling by provider, unknown provider gets no ceiling. All 52
targeted tests pass.
* refactor(image-input): attempt native, shrink-and-retry on provider reject
Replace proactive per-provider size ceilings with a reactive shrink path
on the provider's actual rejection. All providers now attempt native
full-size attachment first; if the provider returns an image-too-large
error, the agent silently shrinks and retries once.
Why the previous design was wrong: hardcoding provider ceilings
(anthropic=5MB, others=unlimited) meant OpenAI users on a 10MB image
paid no tax, but Anthropic users lost quality on anything >5MB even
though the empirical behaviour at provider-reject time is the same
(shrink + retry). Baking the table into the routing layer also
requires updating Hermes every time a provider's limit changes.
Reactive design:
- image_routing.py: _file_to_data_url encodes native size, no ceiling.
build_native_content_parts drops its provider kwarg.
- error_classifier.py: new FailoverReason.image_too_large + pattern
match ("image exceeds", "image too large", etc.) checked BEFORE
context_overflow so Anthropic's 5MB rejection lands in the right
bucket.
- run_agent.py: new _try_shrink_image_parts_in_messages walks api
messages in-place, re-encodes oversized data: URL image parts
through vision_tools._resize_image_for_vision to fit under 4MB,
handles both chat.completions (dict image_url) and Responses
(string image_url) shapes, ignores http URLs (provider-fetched).
New image_shrink_retry_attempted flag in the retry loop fires the
shrink exactly once per turn after credential-pool recovery but
before auth retries.
E2E verified live against Anthropic claude-sonnet-4-6:
- 17.9MB PNG (23.9MB b64) attached at native size
- Anthropic returns 400 "image exceeds 5 MB maximum"
- Agent logs '📐 Image(s) exceeded provider size limit — shrank and
retrying...'
- Retry succeeds, correct response delivered in 6.8s total.
Tests: 12 new (8 shrink-helper shapes + 4 classifier signals),
replaces 5 proactive-ceiling tests with 3 simpler 'native attach works'
tests. 181 targeted tests pass. test_enum_members_exist in
test_error_classifier.py updated for the new enum value.
OpenRouter returns a 404 with the specific message
'No endpoints available matching your guardrail restrictions and data
policy. Configure: https://openrouter.ai/settings/privacy'
when a user's account-level privacy setting excludes the only endpoint
serving a model (e.g. DeepSeek V4 Pro, which today is hosted only by
DeepSeek's own endpoint that may log inputs).
Before this change we classified it as model_not_found, which was
misleading (the model exists) and triggered provider fallback (useless —
the same account setting applies to every OpenRouter call).
Now it classifies as a new FailoverReason.provider_policy_blocked with
retryable=False, should_fallback=False. The error body already contains
the fix URL, so the user still gets actionable guidance.
Mid-stream SSL alerts (bad_record_mac, tls_alert_internal_error, handshake
failures) previously fell through the classifier pipeline to the 'unknown'
bucket because:
- ssl.SSLError type names weren't in _TRANSPORT_ERROR_TYPES (the
isinstance(OSError) catch picks up some but not all SDK-wrapped forms)
- the message-pattern list had no SSL alert substrings
The 'unknown' bucket is still retryable, but: (a) logs tell the user
'unknown' instead of identifying the cause, (b) it bypasses the
transport-specific backoff/fallback logic, and (c) if the SSL error
happens on a large session with a generic 'connection closed' wrapper,
the existing disconnect-on-large-session heuristic would incorrectly
trigger context compression — expensive, and never fixes a transport
hiccup.
Changes:
- Add ssl.SSLError and its subclass type names to _TRANSPORT_ERROR_TYPES
- New _SSL_TRANSIENT_PATTERNS list (separate from _SERVER_DISCONNECT_PATTERNS
so SSL alerts route to timeout, not context_overflow+compress)
- New step 5 in the classifier pipeline: SSL pattern check runs BEFORE
the disconnect check to pre-empt the large-session-compress path
Patterns cover both space-separated ('ssl alert', 'bad record mac')
and underscore-separated ('ERR_SSL_SSL/TLS_ALERT_BAD_RECORD_MAC')
forms. This is load-bearing because OpenSSL 3.x changed the error-code
separator from underscore to slash (e.g. SSLV3_ALERT_BAD_RECORD_MAC →
SSL/TLS_ALERT_BAD_RECORD_MAC) and will likely churn again — matching on
stable alert reason substrings survives future format changes.
Tests (8 new):
- BAD_RECORD_MAC in Python ssl.c format
- OpenSSL 3.x underscore format
- TLSV1_ALERT_INTERNAL_ERROR
- ssl handshake failure
- [SSL: ...] prefix fallback
- Real ssl.SSLError instance
- REGRESSION GUARD: SSL on large session does NOT compress
- REGRESSION GUARD: plain disconnect on large session STILL compresses
The 404 branch in _classify_by_status had dead code: the generic
fallback below the _MODEL_NOT_FOUND_PATTERNS check returned the
exact same classification (model_not_found + should_fallback=True),
so every 404 — regardless of message — was treated as a missing model.
This bites local-endpoint users (llama.cpp, Ollama, vLLM) whose 404s
usually mean a wrong endpoint path, proxy routing glitch, or transient
backend issue — not a missing model. Claiming 'model not found' misleads
the next turn and silently falls back to another provider when the real
problem was a URL typo the user should see.
Fix: only classify 404 as model_not_found when the message actually
matches _MODEL_NOT_FOUND_PATTERNS ("invalid model", "model not found",
etc.). Otherwise fall through as unknown (retryable) so the real error
surfaces in the retry loop.
Test updated to match the new behavior. 103 error_classifier tests pass.
Adds regression tests for list-typed, int-typed, and None-typed message
fields on top of the dict-typed coverage from #11496. Guards against
other provider quirks beyond the original Pydantic validation case.
Credit to @elmatadorgh (#11264) for the broader type coverage idea.
Xipong