Cherry-picked from PR #78959 by @JoaoMarcos44 with authorship preserved.
Follow-up: hoist _cache_scope_from_session_id(session_id) to a local in
build_kwargs so it's computed once instead of 4 times per call.
Closes#78941. Closes#79012. Closes#79013. Closes#79014. Closes#79015.
Co-authored-by: JoaoMarcos44 <joaomarcosdias444@gmail.com>
The PR's concurrency wrapper splits call_llm into a semaphore-guarded
entry + _call_llm_impl; main added extra_headers to call_llm's
signature after the PR's base, so the split has to forward it too
(dropped silently otherwise — Azure Foundry and custom-endpoint
callers set it).
Makes interrupt-protected context compression cancellable by an explicit
user or lifecycle stop, without weakening protection against ordinary
incoming messages, voice interjections, or active-turn redirects.
Separates explicit hard cancellation from ordinary interrupt/redirect
state with a dedicated threading.Event; introduces
AuxiliaryExplicitCancellation as an attempt-local frozen-cause signal;
isolates the synchronous provider callback in a bounded daemon worker
during protected compression; atomically linearizes Codex timeout
cleanup against explicit cancellation; propagates hard cancellation
through child agents and explicit stop surfaces; serializes hard-cancel
admission against compression commit admission with
CompressionCommitFence; aborts before session rotation or late DB commit,
restores in-place transcript mutations and compressor state, and releases
the heartbeat and compression lease.
Based on #74449 by @suparious. Resolved merge conflicts in
agent/context_compressor.py (feasibility check + try/except) and
tui_gateway/methods_session.py.
- Merge _aux_free_only() + _aux_openrouter_model() into single
_aux_openrouter_settings() that reads config once via
load_config_readonly (avoids double deepcopy).
- Remove 15-line block comment and 5-line inline comment that
restated what the code already says.
- Trim module docstring from 10 lines to 3.
- Update test patches to target load_config_readonly.
_call_fallback_candidate_sync replans messages/tools for each resolved
destination, but its async mirror still shipped the caller's decorated
sections verbatim — the primary destination's markers (including a
direct-native tool marker) leaked to fallback candidates with different
cache contracts, and the relay saw the display label instead of the
resolved provider/api_mode. Mirror the sync path: resolve the
destination, replan both sections, thread provider/api_mode through
_relay_async_completion, and replan again for the auth-refresh retry
client. Mutation-checked: the new parity test fails on the verbatim
pass-through shape.
Follow-up to #76032 (#20880).
Three copies of the same logic landed with #76032:
- MoA's _call_prepared_aggregator and auxiliary_client's
_replan_synchronous_cache_sections both implemented stub → policy →
strip → plan for a resolved destination. Extract
plan_cache_sections_for_destination() into agent_runtime_helpers (which
already owns the policy functions) and route both through it. Also
removes a redundant full-transcript deepcopy+strip per request (the
caller pre-stripped what build_prompt_cache_plan strips again).
- The fallback_chain[N] label regex + chain-entry lookup lived in
_fallback_entry_timeout AND _fallback_destination. Extract
_fallback_chain_entry() and reuse.
MoA's cache-plan failure log is promoted debug → warning: the call-block
site skips MoA, so this block is the aggregator's only decoration path —
a silent failure ships an undecorated request (the 0%-cache MoA bug class).
Behavior-preserving; 195 targeted tests green.
Follow-up to #76032 (#20880).
resolve_entry_api_key() and the duplicated _fallback_entry_api_key()
read key_env via a raw os.getenv(), bypassing per-profile secret
scoping in the multiplexed gateway. Under multiplexing this can hand
a fallback request another profile's credential. Both now resolve
through agent.secret_scope.get_secret(), which reads the active
profile scope when multiplexing is on and falls back to os.environ
unchanged when it's off, so single-profile behavior is preserved.
Closes#74311
Narrow the moa_aggregator Relay bypass to CodexAuxiliaryClient and add
coverage that call_llm(stream=True) returns the provider's direct
create() result for Responses-shim clients.
Salvaged from PR #74903 by @liusencomic-cyber.
Convert a completed MoA aggregator response into one valid Chat
Completions delta chunk at the MoA facade boundary, normalize completed
message.tool_calls into indexed stream deltas, and classify these local
MoA adapter-shape errors as non-fallback format errors so a local
compatibility bug cannot silently drift the user's MoA route to a
single model (#55933 follow-up).
Salvaged from PR #74903 by @liusencomic-cyber.
Salvaged from #56085 (@Stoltemberg), rebased onto current main: sites
main had already converted (credential_pool, auxiliary_client MoA
paths, model_metadata, moa_loop, agent_runtime_helpers) resolve to
main's versions; the remaining ~29 read-only sites across 16 agent/
files swap to the no-deepcopy readonly loader (~135us saved per call).
Full per-site mutation audit performed (every enclosing function read,
escapes traced): 23 SAFE, 5 ESCAPES with read-only consumers, 1 UNSAFE
path (init_agent -> get_compatible_custom_providers -> normalizer
in-place alias writes) fixed by the preceding no-mutate commits, which
make the normalizer copy-safe for ALL callers.
gemini-2.5-flash shuts down Oct 16 2026 (Google deprecation schedule)
and gemini-3-flash-preview is superseded. Update every hardcoded
default to the current GA flash model:
- gemini_native_adapter: probe_gemini_tier + _create_chat_completion
default params; free-tier guidance de-pinned from a specific model's
RPD number so it doesn't stale again
- auxiliary_client: gemini/kilocode fallback aux models,
_OPENROUTER_MODEL, _NOUS_MODEL -> google/gemini-3.6-flash
(verified live on both OpenRouter and Nous portal /models)
- provider plugins: kilocode + vertex default_aux_model
- hindsight memory plugin: gemini provider default
- setup wizard gemini list: 3-flash-preview -> 3.6-flash (matches the
curated picker catalog)
- tests: aux-client assertions that pinned the old default literal now
reference the _NOUS_MODEL constant, so the next default bump can't
break them (change-detector cleanup)
Fixes#32360.
Direct tool HTTP calls already identified as Hermes-Agent, but the main
OpenAI-SDK chat path still sent OpenAI/Python. Set Hermes-Agent/<ver> for
api.x.ai clients (xai + xai-oauth) so normal text traffic is attributed correctly.
Every fallback/dedup/skip decision asks one question — 'is this candidate
the same backend as the one that failed, along the axis that failure
invalidated?' — but it was re-implemented inline at six sites across four
subsystems, each comparing whatever string was locally convenient. Each
incident fixed one site while the others kept the bug: #22548, #70893,
#59561, #72468, #62984/#54250/#57584.
agent/backend_identity.py now owns the concept: BackendIdentity (provider /
model / base_url axes), FailureScope (MODEL / CREDENTIAL / ENDPOINT — each
failure class invalidates a different axis), and should_skip_candidate().
Unknown axes never manufacture a skip (over-skipping strands failover; a
wrong try costs one RTT).
Migrated sites:
- chat_completion_helpers.try_activate_fallback: replaces the provider+model
early-exit (the #62984 bug: ignored base_url, stranding multi-endpoint
pools) AND _fallback_entry_is_same_backend_by_base_url (deleted)
- auxiliary_client._try_configured_fallback_chain +
_try_main_agent_model_fallback: replace label/model comparisons; auth and
payment map to CREDENTIAL scope, keeping the #59561 carve-out
- hermes_cli/fallback_cmd add: primary-match + duplicate checks now identity-
aware (#54250/#57584): same provider+model on a different explicit
base_url is a pool entry, not a duplicate
_mark_provider_unhealthy stays label-keyed deliberately: its only triggers
are confirmed 402s, which ARE credential-scoped.
Owner-level tests pin each incident's semantics by number; sabotage-verified
(removing the base_url axis fails the #62984 test).
Widen okalentiev's failed_model narrowing (#59561) to
_try_main_agent_model_fallback. The safety-net layer still skipped on a
provider-label match alone, so single-provider users whose aux compression
model and main model share one custom endpoint had ZERO fallbacks: the aux
model timing out exhausted the chain in one hop and compression aborted.
Real incident (0.19.0 debug dump): aux zai-org/glm-5.2 hung 324s and timed
out while main mindai/macaron-v1-venti on the SAME endpoint was serving
448K-token turns — the label-only skip discarded the one viable summarizer,
the session wedged over threshold, and the anti-thrash breaker tripped.
Same convention as the chain fix: model-specific failures (timeout,
connection, rate limit) pass failed_model so only the exact failed model is
skipped; provider-wide failures (auth 401 / payment 402) pass None and keep
the whole-provider skip. Both sync and async call_llm sites pass it.
Sabotage-verified: the new regression test fails on the provider-only skip.
Follow-up to the same-provider fallback fix: narrowing the configured-chain
skip to the exact failed model is only correct for model-specific failures.
Auth (401) and payment (402) errors are provider-wide — every model on the
provider shares the same broken credentials/account — so trying a sibling
model can't recover and merely burns another doomed request before the
aux task fails. Worse, returning that sibling client bypasses the
main-agent-model safety net that a provider-wide skip would have reached.
Only forward failed_model to _try_configured_fallback_chain for
model-specific failures (timeout, connection, rate limit, model-incompatible,
invalid response). Auth/payment keep failed_model=None (whole-provider skip),
preserving the pre-existing safety-net behaviour for credential/billing
failures.
Adds an integration test that a timeout forwards the failed model, and
updates the payment-error test to assert failed_model=None.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
_try_configured_fallback_chain skipped every fallback_chain entry whose
provider matched the one that just failed. A chain that intentionally lists
several models under the same provider (e.g. two more NVIDIA NIM models
after the primary NIM model times out) was therefore skipped wholesale,
falling straight through to the main-agent-model safety net instead of
trying the other configured models on that provider.
Add failed_model so the skip narrows to the exact (provider, model) pair
that failed. Callers that only know the provider (client-build failures,
where the whole provider is unreachable regardless of model) keep the old
provider-wide skip; the two runtime request-error call sites (call_llm,
async_call_llm) now pass the model that just failed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Some OpenAI-compatible endpoints — notably Tencent Copilot
(copilot.tencent.com) — only accept streaming chat requests; any
non-streaming call returns HTTP 400 (code 11101, 'Non-stream chat
request is currently not supported'). The main conversation loop already
streams, so interactive chat works, but every auxiliary task (title
generation, compression, web extraction) used the non-streaming path and
failed on each call.
_provider_requires_stream() detects stream-only endpoints
(copilot.tencent.com built in, plus user-configurable
auxiliary.stream_only_base_urls substring markers in config.yaml).
Matching sync auxiliary calls route through _create_with_progress
(force_stream=True) and async calls through the new
_acreate_with_stream, aggregating the chunk stream — including tool-call
deltas and reasoning deltas — into a complete response via the shared
_ChatStreamAccumulator.
Salvaged from PR #60686 by @kudi88 onto the progress-aware streaming
machinery from #71508, addressing both sweeper-review gaps: the async
path now consumes the stream with 'async for' (awaiting create() and
iterating synchronously raised on AsyncOpenAI streams), and tool-call
deltas are reassembled instead of dropped (MCP passes tools= through
call_llm). Under force_stream there is no silent non-streaming retry —
a stream-only provider rejects those by definition, so the original
error surfaces to the normal recovery chains.
The gateway's pre-agent session-hygiene compression killed the summary
call at a fixed 30s wall-clock deadline (compression.hygiene_timeout_seconds),
regardless of whether the summary model was hung or merely slow. A reasoning
model happily streaming a large summary was cut off mid-generation, the user
got '⚠️ Context compression timed out after 30.0s', and a 300s failure
cooldown left the session oversized — a doom loop for slow-but-healthy
auxiliary models.
Timeouts are now liveness-based instead of wall-clock-based:
- agent/auxiliary_client.py: new thread-local aux_progress_hook. When
installed (only by context compression today), the primary call_llm
attempt streams (stream=True) and aggregates chunks back into a complete
response, ticking the hook per chunk. The configured timeout then acts
per stream read (idle) instead of as a total budget. Providers that
reject streaming fall back to the plain non-streaming call; auth/payment/
rate-limit/transport errors propagate unchanged into the existing
recovery chains. Codex Responses (per SSE event) and Anthropic Messages
(per stream event, via the new create_anthropic_message on_stream_event
callback) tick the same hook from inside their wire adapters.
- agent/conversation_compression.py: CompressionCommitFence gains
touch_progress()/seconds_since_progress(); compress_context() installs
fence.touch_progress as the progress hook around the compress call.
- gateway/run.py: the hygiene wait loop treats hygiene_timeout_seconds as
an INACTIVITY budget — while the fence reports fresh progress the wait
extends, bounded by the new compression.hygiene_total_ceiling_seconds
(default 600s, clamped >= the idle budget) so a degenerate trickle
stream still dies. The timeout warning now says the summary model
produced no output, which is the only case that still triggers it.
- config/docs: hygiene_total_ceiling_seconds added to DEFAULT_CONFIG and
configuration.md; hygiene_timeout_seconds documented as inactivity-based.
Tests: tests/agent/test_aux_progress_streaming.py (hook plumbing, stream
aggregation incl. tool-call deltas and reasoning deltas, rejection
fallback, ceiling kill, fence progress surface); two new gateway tests
prove a slow-but-streaming worker survives past the fixed timeout
(sabotage-verified: fails with the old fixed deadline) and a
forever-trickling worker is still cut off at the ceiling.
Path.read_text() without an explicit encoding uses the platform's
default encoding. On Windows this is typically cp1252 or mbcs, which
causes UnicodeDecodeError or silent data corruption when reading
UTF-8 content (JSON files, user text, config with non-ASCII chars).
This is the read-side companion to the write_text() encoding fix.
Fixed the most critical locations that read JSON data, user content,
and config files across 14 files with 31 call sites.
Pattern: .read_text() → .read_text(encoding='utf-8')
json.loads(path.read_text()) → json.loads(path.read_text(encoding='utf-8'))
OpenAI documents GPT-5.5 / GPT-5.5 Pro as extended-cache-only: in-memory
prompt cache retention is not available for them, and only
prompt_cache_retention: "24h" is supported. Responses requests that omit
the field see near-zero cached_tokens even with a stable prompt_cache_key
and identical prefixes (observed on an OpenAI-compatible Responses relay:
0 cached across repeated identical calls before; 97% cache reads after).
Send the field for the gpt-5.5 model family (bare and namespaced ids like
openai.gpt-5.5) on OpenAI-compatible Responses routes, mirrored in the
auxiliary Codex adapter, and pass it through preflight normalization.
Skipped for xAI, GitHub/Copilot, and the chatgpt.com Codex backend, which
reject or ignore body-level cache fields.
The Vertex AI provider (added same-day, commit c73e74386) was never added to
either of the two provider registries that agent/auxiliary_client.py and the
MoA slot-resolution chain depend on, breaking Vertex outside the main
conversation loop:
1. hermes_cli/auth.py::PROVIDER_REGISTRY had no "vertex" entry. The
plugin-auto-extend loop that normally fills gaps explicitly skips
non-api_key auth types (`if _pp.auth_type != "api_key": continue`), and
Vertex was never hand-declared like "bedrock" is. Because
resolve_provider_client() in agent/auxiliary_client.py gates everything
on `pconfig = PROVIDER_REGISTRY.get(provider)` and returns (None, None)
immediately when pconfig is None, its `elif pconfig.auth_type == "vertex"`
branch was permanently dead code — every auxiliary Vertex call (vision,
title generation, reflection, context compression, MoA reference/
aggregator slots) failed outright, not just a MoA-specific edge case.
2. hermes_cli/providers.py::HERMES_OVERLAYS also had no "vertex" entry, so
hermes_cli.providers.get_provider("vertex") returned None. This backs
_preserve_provider_with_base_url() in agent/auxiliary_client.py, which a
MoA slot's resolved (base_url, api_key) pair needs to keep its "vertex"
identity instead of silently collapsing to "custom" — losing the
identity _refresh_provider_credentials() needs to re-mint an expired
OAuth2 token (~1h lifetime) on a 401, and permanently breaking every
subsequent call in that MoA preset for the rest of the session.
Fix mirrors the existing "bedrock"/aws_sdk entries in both registries
exactly, plus adds a "vertex" branch to _refresh_provider_credentials() (it
had branches for openai-codex/nous/anthropic/xai-oauth but not vertex,
so a 401 fell through to `return False` without evicting the stale cached
client).
- hermes_cli/auth.py: hand-declared vertex ProviderConfig(auth_type="vertex")
in PROVIDER_REGISTRY, matching bedrock's shape.
- hermes_cli/providers.py: vertex HermesOverlay(auth_type="vertex") in
HERMES_OVERLAYS + "Google Vertex AI" label override.
- agent/auxiliary_client.py: vertex branch in _refresh_provider_credentials
that re-mints the token via get_vertex_config() and evicts the stale
cached client.
- 8 new regression tests across tests/hermes_cli/test_vertex_provider.py and
tests/agent/test_auxiliary_client.py: registry membership, end-to-end
resolve_provider_client("vertex", ...) building a working client (proving
the previously-dead branch is now reachable), and the 401-refresh/cache-
eviction path.
_build_call_kwargs omitted max_tokens for every provider except
anthropic-compat endpoints and NVIDIA NIM. Gemini's native
generateContent maps max_tokens -> maxOutputTokens and, when it is
omitted, applies a fixed 65,535-token ceiling (not "the model's full
budget"), so dropping the value made MoA's reference_max_tokens a
silent no-op for gemini advisors — they ran effectively uncapped
(observed ~2900 output tokens against a configured cap of 600),
inflating per-turn MoA latency.
Forward max_tokens for the gemini-native path (provider name or native
base_url). Gemini supports maxOutputTokens, so the cap is safe here;
providers that reject max_tokens (Copilot, GPT-5 max_completion_tokens,
ZAI vision) are unaffected — they still omit it as before.
Per review feedback from teknium1: reference_max_tokens is an advisors-only
contract. The aggregator is the acting model and must not be capped by the
reference budget. Changed _is_moa from startswith('moa_') to exact match on
'moa_reference'. Added regression test proving aggregator does NOT receive
max_tokens.
Copilot review pointed out that hardcoding kwargs['max_tokens'] would
400 on models requiring max_completion_tokens (GPT-5 family, Copilot).
The existing auxiliary_max_tokens_param() helper already selects the
correct parameter name per model — use it instead of hardcoding.
Test updated to parametrize expected_key so the Copilot gpt-5.5 case
correctly asserts max_completion_tokens instead of max_tokens.
Addresses Copilot review comments on both files.
PR #56756 added reference_max_tokens to cap MoA advisor output and cut
turn latency. The value is correctly threaded through five layers of MoA
code (moa_config → conversation_loop → aggregate_moa_context →
_run_references_parallel → _run_reference → call_llm(task='moa_reference',
max_tokens=800, ...)).
However, _build_call_kwargs() in auxiliary_client.py silently drops
max_tokens for all OpenAI-compatible providers (PR #34845, which fixed
endpoints and NVIDIA NIM keep it. This means reference_max_tokens never
reached the API for the vast majority of providers.
The bug affects every OpenAI-compatible MoA reference/aggregator slot:
Z.AI (coding plan), OpenRouter, OpenAI, GitHub Copilot, and local
providers. Only Anthropic-compat endpoints (MiniMax, /anthropic URLs)
worked — by coincidence, not MoA-aware design.
Fix: thread the 'task' parameter through all six _build_call_kwargs()
call sites. When task starts with 'moa_', max_tokens is always included
in the request kwargs regardless of provider. Non-MoA auxiliary tasks
(compression, titles, vision, etc.) keep PR #34845 behavior unchanged.
Verified end-to-end:
- Z.AI GLM-5.2 with max_tokens=50 → returned exactly 50 tokens
- Z.AI GLM-5.2 with max_tokens=20 → returned exactly 20 tokens
- Z.AI GLM-5.2 uncapped → returned 315 tokens
- 7 new regression tests covering 4 providers, Anthropic wire, non-MoA
tasks, and prefix-matching boundary
- 288 auxiliary_client tests pass (was 281, +7 new), 84 MoA tests pass
- Zero regressions
Justin Bennington
joaomarcos
Jeffrey Quesnelle
kshitijk4poor
konsisumer
Alex Fournier
Teknium
Shaun Prince
webtecnica
Rod Boev
Kyle
knocksen
AKAZIK-py
rob-maron
Gabriel Stoltemberg
Jeffrey Quesnelle
Jaaneek
Oleksii Kalentiev
kudi88
AlexFucuson9
Israel Lot
dyreckt
srojk34
aui
Janig88