* Revert "fix(credits): remove the 'Grant spent · $X top-up left' notice"
This reverts commit 5dc6a14c14.
* fix(credits): reintroduce grant_spent behind an in-session crossing gate
The removed notice nagged because its condition is a steady state for
accounts living on top-up, the latch is per-session, and the cold-start seed
runs the policy at every session open — every session re-announced
'Grant spent · $X top-up left'. Reintroduce it gated so only a session that
WATCHES the grant run out announces:
- seen_grant_unspent crossing gate, mirroring seen_below_90: opens when the
session observes the grant meaningfully unspent (>= GRANT_UNSPENT_MIN_MICROS,
1 cent — portal-seeded states derive micros from float dollars and can carry
sub-cent residue where headers report exactly spent). Seeds never prime it.
- The gate guards only the show branch and is consumed by the announcement —
one announcement per crossing. Header flicker (used_fraction None and back)
clears the sticky line but cannot re-announce; a renewal re-opens the gate.
- new_credits_latch() centralizes the latch shape (agent build, lazy re-init,
and the policy test helper all build through it).
- Tests: steady-state-open stays silent (seed + policy seams), live crossing
announces once, flicker/renewal/residual cases locked; the rendering test
primes the gate and asserts its leg count so a gate regression cannot
silently shrink coverage.
Salvage of PR #52188. The original PR raised RuntimeError when LM Studio
load was rejected or unverifiable, which would abort agent startup on
transient network failures. Replace with logger.warning + fallback to
configured context length, preserving the old graceful-degradation behavior.
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.
A /steer redirect during a thinking phase serialized the streamed
reasoning into the persisted assistant checkpoint ('Reasoning shown
before the interruption: ...'). An assistant turn exposing its own
chain-of-thought reads to Anthropic's output classifier as
reasoning-injection/prefill jailbreak, so every subsequent call on the
session deterministically returned 'Provider returned an empty
response' — and because the checkpoint is persisted and replayed, no
retry, nudge, or empty-recovery branch could ever escape it. Four
sessions were permanently bricked this way in the week of Jul 21-27
(42+ blocked calls; every reasoning-free checkpoint that week was
untouched — same mechanism as the prefill.json incident).
Class fix: streamed reasoning is now display-only state. The
_current_streamed_reasoning_text accumulator is removed entirely
(producer in _fire_reasoning_delta, resets, and init), so no future
path can serialize CoT into replayable content. The checkpoint keeps
only the visible response text; the model regenerates its reasoning on
the retried turn. Invariant documented in _apply_active_turn_redirect.
Regression tests: CoT never appears in either checkpoint shape,
reasoning-only interrupts produce a bare checkpoint, reasoning deltas
stay display-only.
Three mechanisms to detect and notify when gateway sessions stall silently:
1. Mid-turn activity heartbeats stamped to SessionDB so hermes sessions list
and hermes status show progress during long turns without new message rows.
2. Stall watchdog: when a busy session has pending inbound and the shared
activity clock is idle past agent.session_stall_timeout (default 300),
log a WARNING and notify the user once to try /new. Notify-only; does
not kill the turn.
3. Compaction timeout: fenceless compress_context callers get a progress-aware
host budget (compression.context_timeout_seconds default 120 idle,
compression.context_total_ceiling_seconds default 600 ceiling). On timeout,
cancel via commit fence, skip compaction without dropping messages, and
continue the turn.
Closes#72016 (slices 1-3; slice 4 cumulative SSE stream-retry deadline
remains a follow-up).
Cherry-picked from PR #72424 by @fangliquanflq.
is_truthy_value(..., default=False) and getattr(..., False) disagreed with
compression.in_place: true from #38763, so partial/failed config loads fell
back into rotation mode and re-armed the pre-lease drift path. Also report
compression.in_place in hermes dump overrides so stale false values are visible.
The default max_iterations/agent.max_turns budget was set when long
agentic runs were rare; complex tasks now routinely exceed 90 tool
calls. Raise the default to 500 across every surface that hardcodes
the fallback: AIAgent constructor, DEFAULT_CONFIG, CLI resolution
chain, gateway env bridge, cron scheduler, and TUI gateway. Explicit
user config values are unaffected (deep-merge preserves them; no
_config_version bump needed).
Docs (en + zh-Hans), CLI help text, tips, and pinned tests updated
to match.
Replace 3 duplicated entry_id resolution blocks (try/except +
entry_id_for_api_key + fallback to None) in agent_init.py,
chat_completion_helpers.py, and switch_model with a single
sync_credential_pool_entry_id(agent) function in agent_runtime_helpers.
Follow-up to #70323.
Track the selected credential by stable pool entry ID so token refreshes and shared cursor movement cannot detach failures from the entry that issued them. Stop unmatched single-entry pools from reporting a no-op rotation as successful recovery.
Co-authored-by: Maxim Esipov <maksesipov@gmail.com>
Keep MoA reference display events off the machine-readable -Q stdout
surface (platform=cli with tool_progress_mode=off) while preserving them
everywhere else. Extracts the relay into module-level helpers so the
policy is testable.
Salvaged from #67334.
Adds per-reference progress events and a phase-transition marker to the
MoA display pipeline so TUI / CLI / desktop surfaces can render a status
bar like `MOA: 2/3 refs done` and surface which phase (reference vs
aggregator) is currently active.
- `moa.progress` — fired once per reference completion with
`refs_done`, `refs_total`, and the source label
- `moa.phase` — fired on phase transitions (currently the single
`phase="aggregator"` transition once the fan-out
finishes)
Plumbed through the existing `reference_callback` →
`tool_progress_callback` → gateway path; no new UI surface. The legacy
`moa.reference` / `moa.aggregating` events are unchanged for backwards
compatibility.
AI-assisted fix by https://github.com/SquabbyZ/peaks-loop
Follow-up to the salvaged core of #53802: a naive MoAClient(preset) rebuild
restores a working facade but silently drops the reference_callback relay
wired in agent_init, so moa.reference / moa.aggregating display events stop
reaching every frontend for the rest of the session.
Introduce agent.moa_loop.build_moa_facade(agent, preset) as the single
construction point for the MoA facade and use it at:
- initial client construction (agent_init.py)
- turn-start fallback restore (restore_primary_runtime)
- transient transport recovery (try_recover_primary_transport — previously
fell through to _create_openai_client with MoA's empty client_kwargs and
died with 'api_key client option must be set')
- mid-session model switches (switch_model)
The relay reads agent.tool_progress_callback at emit time, so callbacks
attached after construction are picked up automatically.
Adds test_moa_restored_facade_still_emits_reference_events covering event
delivery through a restored facade.
Follow-up fixes on top of the salvaged #22566 mechanism:
- N-collector now counts only REAL actionable user turns via
_is_actionable_user_turn + _is_synthetic_compression_user_turn —
the same filter pair _find_last_user_message_idx uses post-#69291.
The contributor's bare role=='user' + _is_context_summary_content
check let blank platform echoes and continuation/todo rows consume
N slots, silently degrading the guarantee.
- Default flipped 3 -> 1 (behavior-preserving): a default of 3 was
measured to change the tail cut on transcripts whose budget covers
only the last turn. min_tail_user_messages=1 delegates to the
existing single-user anchor; N>1 is opt-in, and the call site is
gated so the default path is byte-identical to main.
- Hardened config parse in agent_init (bool rejected, fractional
floats rejected, floor 1) matching the max_attempts parser shape.
- Wired the recurring external-PR config gaps: hermes_cli/config.py
DEFAULT_CONFIG + cli-config.yaml.example (PR only had cli.py).
- Regression tests: blank echoes / synthetic rows don't count toward
N; tool-call/result pairs never split by the N-boundary (no-orphan
both directions); N-guarantee wins over tail_token_budget and the
_MAX_TAIL_MESSAGE_FLOOR (floor is a minimum, not a cap); default
parity pin; DEFAULT_CONFIG pin.
Add _ensure_last_n_user_messages_in_tail to guarantee the last N user
messages survive compression in the uncompressed tail, with surrounding
assistant/tool context preserved.
- Add min_tail_user_messages parameter (default 3) to ContextCompressor
- New _ensure_last_n_user_messages_in_tail method generalizes single-user protection
- Skip context-summary handoff banners when counting user messages
- User messages are clean boundaries — skip _align_boundary_backward
- Wire through cli.py, agent_init.py, and gateway cache busting keys
Config:
compression:
min_tail_user_messages: 3
Co-Authored-By: Claude <noreply@anthropic.com>
The phase-1 tool-result prune only runs inside compress(), which fires
near 50% of the context window, so it never triggers on large-window
models; old tool outputs then ride in history and are re-sent every turn.
Add prune_tool_results_only(): the same no-LLM prune on a separate, low
proactive_prune_tokens trigger, run as an elif to the compression branch.
Opt-in (default 0), protects the recent tail by message count.
Add the method to the ContextEngine base as a no-op default so pluggable
engines inherit it safely (the post-tool-call path never AttributeErrors on
a non-built-in engine); the built-in compressor supplies the real prune.
Register both keys under the top-level compression config with defaults and
document them.
A follow-up sent while the model is still generating previously ended the
turn: Hermes kept only the visible partial text (reasoning was display-only),
cleared the loop, and replayed the message as a fresh next turn. If the
correction referred to something that only appeared in the thinking stream,
the model no longer had that context.
Add `AIAgent.redirect(text)`: a corrective interrupt distinct from a hard
stop. It cancels only the in-flight model request (not tool workers or child
agents), stashes the correction under a lock shared with `interrupt()` so a
concurrent `/stop` always wins, and lets the loop rebuild the same logical
iteration. `_apply_active_turn_redirect()` checkpoints the reasoning that was
actually shown to the user plus any visible partial text as an ordinary
assistant message, then appends the correction as a real user turn — never
replaying incomplete signed/encrypted provider reasoning, and keeping strict
role alternation and prompt-cache stability intact. During tool execution it
degrades to `steer()` so a running tool finishes at a safe boundary.
`_fire_reasoning_delta` now only records reasoning that a display callback
actually consumed, so `show_reasoning: false` never leaks hidden provider
thinking into the persisted transcript.
Long-lived sessions (e.g. a Telegram thread resumed over hours/days)
accumulate a large context that the existing size-based threshold only
trims once it crosses `threshold × context_window`. Until then every
turn re-reads the full history, which on large-context models can mean
hundreds of K of cache-read tokens per call even across long idle gaps.
Add a time-based trigger that complements (does not replace) the size
threshold: when a session resumes after `compression.idle_compact_after_seconds`
of inactivity, compact the accumulated history up front, before the first
reply. Disabled by default (0), so existing behaviour is unchanged.
The trigger reuses `_last_activity_ts` (the last time the turn loop did
work) to measure the idle gap at turn start, gates the token estimate
behind a cheap gap pre-check, and skips compaction when the context is
already at/below the post-compression target (threshold × target_ratio)
so a short idle thread never pays for a summarization that saves nothing.
It also defers to an active compression-failure cooldown.
The decision is factored into a pure predicate, `_should_idle_compact`,
which is unit-tested without a live agent.
Add compression.threshold_tokens config option that sets an absolute
token cap for auto-compaction. When configured alongside the existing
ratio-based threshold, the effective trigger point is the lower of the
two, so compression never fires later than the user's preferred token
count regardless of which model is active.
This solves the problem where switching between models with different
context windows (e.g. 1M → 400K) shifts the absolute trigger point,
causing premature or delayed compression.
Rework from PR #24279 addressing sweeper feedback:
- The cap is now a first-class compressor configuration value
(threshold_tokens_cap parameter on ContextCompressor.__init__),
not a post-construction patch on the live instance.
- Applied in both __init__ and update_model() so it survives model
switches and fallback activations (the old approach was undone by
update_model() restoring _configured_threshold_percent).
- Clamped to the model's context length so a cap above the window is
a no-op (ratio-based threshold wins).
- Works with max_tokens output-token reservations.
- Added 9 tests covering cap-vs-ratio selection, model switch survival,
context-length clamping, max_tokens interaction, and invalid values.
- Updated user-facing configuration docs.
- Removed unrelated background-review/curator/Honcho changes (main
already contains background-review memory isolation in 973f27e95).
Config example:
compression:
threshold: 0.50
threshold_tokens: 200000 # never compress later than 200K tokens
Follow-up to the salvaged contributor commit, closing the three gaps
flagged in the sweeper review:
1. Init ordering: assign compression.model_thresholds to a selected
plugin context engine BEFORE the initial update_model() call in
agent_init.py, so the initial model's override applies from init
(previously it only took effect after the first /model switch).
Base-class ContextEngine.update_model() now snapshots the
pre-override percent once so repeated switches fall back to the
engine's configured threshold, not a previous model's override.
2. DEFAULT_CONFIG: add compression.model_thresholds (empty map) to
hermes_cli/config.py — additive key, no _config_version bump.
3. Docs: document the key in
website/docs/developer-guide/context-compression-and-caching.md
(yaml example, parameter table, dedicated section) and update the
plugin-boundary note in context-engine-plugin.md to state the
explicit context-engine contract for model_thresholds.
Adds tests/run_agent/test_per_model_threshold_init_ordering.py:
plugin-engine AIAgent init regression (override applies at init,
empty map unchanged), DEFAULT_CONFIG key presence, floor interaction
on the model-switch path (override below the small-context floor is
raised to the floor; above the floor wins), and base-class config
snapshot across repeated switches. Also maps @bennybuoy in
contributors/emails/.
Addresses teknium1 review feedback on PR #60781:
1. Gateway cache invalidation: added ('compression', 'model_thresholds')
to _CACHE_BUSTING_CONFIG_KEYS so a live config edit to the map
invalidates the cached compressor (previously kept stale thresholds).
2. Integrated resolver with small-context floor: per-model overrides are
resolved FIRST, then the existing 75% floor for <512K models is applied
on top. The floor is no longer replaced — it stacks. An override below
75% on a small-context model still gets floored to 75% (raise-only);
an override above 75% wins.
3. Clean rebase on upstream main — no unrelated deletions or anti-thrashing
changes. Only the per-model threshold feature is added.
Changes:
- resolve_model_threshold() module-level helper (longest substring match)
- ContextCompressor.__init__ accepts model_thresholds dict
- _base_threshold_percent stores the per-model resolved value
- _config_threshold_percent stores the raw config value (fallback base)
- update_model() re-resolves on /model switch, falls back to config value
- ContextEngine base class update_model() applies overrides for plugin engines
- agent_init.py reads compression.model_thresholds from config, passes to ctor
- gateway/run.py cache busting key added
- cli-config.yaml.example documents the feature
- 17 tests covering resolve helper, compressor init (large/small context,
override above/below floor), update_model (re-resolve, fallback), base class
Co-authored-by: Copilot <copilot@github.com>
skip_memory=True was meant to skip the external memory *provider* for flush/
background agents, but it also suppressed creation of the built-in file-backed
MemoryStore. When a caller still enables the "memory" toolset, the memory tool
dispatched with store=None and every call failed with "Memory is not available",
silently losing the main automatic memory-capture path.
Now the built-in store is created whenever memory is enabled in config OR the
memory toolset is explicitly enabled, while the external-provider block stays
gated on skip_memory (preserving flush-agent intent).
Follow-up to the salvaged #64010 (Kenmege) and #63870 (dombejar) commits,
making one resolved compression.max_attempts cap govern ALL per-turn
compression attempt sites:
- conversation_loop: resolve max_compression_attempts ONCE at turn start
(it was previously re-resolved inside the API-call loop) and route the
pre-API pressure gate through it — that gate still hardcoded
'compression_attempts < 3' and logged 'attempt=%s/3'.
- conversation_loop: the salvaged post-tool compaction gate now uses the
resolved cap instead of a hardcoded 3.
- turn_context: the preflight compaction loop was 'for _pass in range(3)';
it now sizes itself from the same resolved cap.
- agent_init: harden the max_attempts parser — reject booleans (bool
subclasses int; 'true' would coerce to 1), reject fractional floats
instead of truncating them, keep accepting integral floats and numeric
strings; anything else falls back to 3 (floor 1, ceiling 10 unchanged).
- tests: replace #63870's inspect.getsource source-shape test with
behavioral loop tests (post-tool compaction fires <= cap times per turn,
shares its budget with the pre-API gate, resets between turns); add an
e2e test proving a 4th preflight pass runs at config cap=6 while the
unset default still stops at 3; extend the #64010 config tests with the
bool/float parser semantics.
Salvages #64010 by @Kenmege and #63870 by @dombejar.
The conversation loop hardcodes max_compression_attempts = 3. Sessions
that legitimately need more rounds are stranded: on a restart history
reload, incompressible tool schemas can keep the per-request estimate
above the compressor threshold even though the message floor compresses
correctly, so three rounds cannot clear it and the turn dies with
"Context length exceeded: max compression attempts (3) reached" — the
same failure class as #62605, where the rough estimate similarly leaves
3 retries short.
Make the cap a config key, compression.max_attempts:
- default 3 = identical to today, so an unset key is behavior-neutral;
- parsed and validated in agent_init alongside the other compression.*
keys (>= 1, hard-capped at 10, non-integer values fall back to 3),
attached as agent.max_compression_attempts;
- the loop reads it via getattr(agent, "max_compression_attempts", 3),
so objects without the attribute keep the prior behavior;
- documented in the DEFAULT_CONFIG compression block.
Tests pin the parse/validate/attach seam: default preserved, custom
value honored, floor and ceiling enforced, garbage tolerated, and the
loop-side getattr degradation.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The autoraise banner hardcoded '272K' for the gpt-5.4/5.5/5.6 family, but
the Codex /models catalog is authoritative and shifts server-side (gpt-5.6
served 372K during July 9-18, 2026 before OpenAI rolled it back). Pass the
compressor's live-resolved context_length through so the notice reports the
window the session actually got; the static 272K/128K text remains as the
fallback when no resolved value is available.
When the stale-stream detector reconnects past a stream whose socket abort
raced (the close never actually stopped the old worker), the superseded stream
and the retry's stream both write deltas into the same turn. The persisted
transcript is then two coherent responses interleaved token-by-token —
de-interleaving the stored text by alternation yields two complete, independent
answers to the same prompt, which is a dual-writer race in the harness, not a
model/context failure (#65991).
The interrupt path already positively cancels before force-closing (#6600), but
the stale-kill path relies only on the socket abort, and nothing fenced late
chunks from a superseded stream out of the shared delta sink.
Enforce a single-writer invariant on the sink itself, guarded by attempt id
rather than only socket state: every streaming attempt (chat_completions,
anthropic_messages, and bedrock paths) claims a monotonic writer token before
it begins consuming its stream. A newer claim supersedes any older one, so the
consume loop bails the instant it is superseded and _fire_stream_delta /
_fire_reasoning_delta / _record_streamed_assistant_text drop chunks from a
stale writer. The token is stored per-thread, so a thread that never claimed
(a non-streaming delta caller) is never fenced — the guard can only ever drop a
superseded stream, never the single legitimate writer. Discards are counted and
logged sparsely so a real provider problem stays visible instead of being
silently swallowed.
Commentary delivery is on by default; users who find the extra mid-turn
narration noisy can set display.show_commentary: false to restore the
previous behavior (commentary routed to the reasoning channel, visible
only with show_reasoning).
- hermes_cli/config.py: display.show_commentary default true
- agent/agent_init.py: wire config -> agent.show_commentary
- run_agent.py: gate structured commentary extraction on the flag
- agent/codex_runtime.py: gate live-stream commentary callback (falls
back to legacy reasoning-channel routing when off)
- docs + 2 tests (interim path off, live stream fallback)
Also adds AUTHOR_MAP entries for davidrobertson and 100yenadmin.
Provider auto-detection (URL-based inference for Anthropic, OpenAI Codex,
and xAI endpoints) runs before credential-pool validation in AIAgent init,
but #63048 placed the pool validation before auto-detection. When the agent
is constructed with provider=None and a recognized endpoint URL, the pool
is validated against an empty provider identity and discarded, even though
auto-detection correctly resolves the provider moments later.
Fix: move the credential-pool validation block to after the URL-based
auto-detection chain. The pool is stored on the agent before
auto-detection; validation now checks the resolved provider and only
nullifies agent._credential_pool when the pool's scoped provider genuinely
doesn't match.
Regression test covers all three auto-detection paths:
- Anthropic (api.anthropic.com)
- OpenAI Codex (chatgpt.com/backend-api/codex)
- xAI (api.x.ai)
Fixes#63425.
Preserve one durable staged input across terminal close and the worker's early turn flush, without duplicating resumed transcripts or creating a session with a null prompt. Fixes#63766.
_custom_provider_model_matches() only compared the session model
against the entry's single 'model' field. A custom provider declaring
a multi-model catalog (providers.<name>.models mapping / models list)
whose default model differed from the session model silently failed to
match — dropping the entry's extra_body entirely. Real impact: an
OpenAI custom provider pinning service_tier=flex via extra_body ran
every request at STANDARD tier (~2.3x billing) with zero signal.
- Model matching now accepts the session model when it appears in the
entry's models catalog (dict keys or list), case-insensitive;
single-model 'model' field behavior unchanged; entries with neither
still match everything.
- Usage report ('hermes -z --usage-file') now carries service_tier
(the tier requested via request_overrides.extra_body) so batch
pipelines can audit the billed tier per run.
Validation: 8 new tests; live E2E via real 'hermes -p sweeper -z'
with httpx-level wire capture — service_tier=flex present in the
outgoing /v1/responses body and in the usage report.
Retain the provider-boundary core of #52799 while reusing the pool reload and handoff paths already landed in #53591 and #62417.
Co-authored-by: Flownium <157689911+itsflownium@users.noreply.github.com>
Add a token-free, curated affection matcher (agent/reactions.py) — the single
source of truth for detecting user "vibes" (ily / <3 / good bot / heart emoji).
No model call, no tokens. Generalized to return a reaction *kind* so future
reactions can ride the same signal.
Wire an opt-in AIAgent.reaction_callback that fires from build_turn_context on
the incoming user message. It never touches the conversation (cache-safe) and
never fatal — a purely cosmetic side-beat each host can consume.
Close the remaining end-to-end gaps so the full gpt-5.6 family (sol/
terra/luna + their -pro high-effort modes, 6 slugs) works on every
surface a user can reach them through:
- agent/auxiliary_client.py: the Codex OAuth backend hard-caps context
at 272K for gpt-5.6 exactly as it does for 5.4/5.5, but the default
50% compaction trigger would summarize at ~136K and waste half the
usable window. Extend the existing _is_codex_gpt54_or_gpt55 chokepoint
(single enforced predicate feeding _compression_threshold_for_model)
to match gpt-5.6* on the openai-codex route so those sessions get the
same 0.85 auto-raise. Direct-API/OpenRouter routes (full 1.05M window)
are unaffected; the historical codex_gpt55_autoraise opt-out still
applies. The one-time notice banner is model-dynamic and already
renders the correct slug/cap.
- hermes_cli/config.py, agent/agent_init.py: refresh the autoraise
comments/notice to mention the 5.6 family.
- hermes_cli/codex_models.py: add the -pro variants to DEFAULT_CODEX_MODELS
+ forward-compat so ChatGPT-OAuth (openai-codex) Pro users see the full
family in /model, not just the base tiers.
Supersedes the earlier commit's note that 5.6 was intentionally kept out
of the codex catalog: the slugs are confirmed routable (OpenRouter live
+ codex backend), so they belong there like every other codex-capable
gpt-5.x slug.
E2E verified across all 6 slugs: direct-API ctx 1.05M, codex ctx 272K,
pricing reachable from openai + openai-api routes, codex compaction
override 0.85 (and None on direct-API + when opted out), present in
openai-api picker + codex catalog, /model gpt resolves to sol on both
native routes. Guard tests added for the compaction route matrix.
When context.engine selects a plugin engine (e.g. LCM), the host
compression threshold — including the Codex gpt-5.5 50% -> 85%
autoraise — only configures the built-in ContextCompressor and never
reaches the plugin. The autoraise notice still fired, telling the user
auto-compaction was raised when nothing actually changed, and the
startup context-limit line printed the host percent next to the
engine's own threshold_tokens, contradicting itself.
- Clear _compression_threshold_autoraised when a plugin engine is
selected, suppressing both the CLI startup notice and the gateway
turn-1 replay via _compression_warning.
- Print the active engine's own threshold_percent in the startup
context-limit line so percent and token count agree.
- Built-in behavior is preserved, including the fallback path where a
configured engine fails to load and the built-in compressor takes
over.
Fixes#44439
The Codex gpt-5.5 compaction-threshold autoraise notice re-fired on every
agent init. Because the gateway rebuilds the agent per inbound message, the
notice spammed long-running Discord/Telegram/etc. sessions, and the only
documented remedy (`compression.codex_gpt55_autoraise false`) disables the
useful autoraise behavior itself.
Gate both emission surfaces — the CLI startup print and the gateway
`_compression_warning` replay — on a persisted per-profile marker under
`$HERMES_HOME` (`.codex_gpt55_autoraise_notice`), keyed on the from→to
percentages the notice displays. The notice now shows at most once per
profile; the autoraise still fires and `codex_gpt55_autoraise: false` still
disables it; and a later change to the raised threshold re-notifies once.
Docs updated to match.
The Codex gpt-5.5 compaction autoraise (#40957) overrode the effective
threshold unconditionally. If a user had set compression.threshold above
0.85, agent_init dropped them down to 0.85. That wastes usable window and
contradicts the feature's whole point: use more of the context, not less.
It happened silently too, since the one-time notice is suppressed when the
override doesn't raise.
The override is an autoraise. It must only raise. Pulled the apply logic
into a small pure helper that clamps the Codex case to never lower a
higher-or-equal user threshold, and emits the notice only when it actually
fires. Other overrides (Arcee Trinity) keep their existing unconditional
behavior.
Fixes the Codex gpt-5.5 compaction autoraise lowering a user's higher
configured threshold. A user on the Codex OAuth route with
compression.threshold > 0.85 was silently clamped to 0.85, compacting
earlier than they asked and using less of the 272K window the feature was
meant to unlock. The autoraise now only ever raises.
N/A
- [x] 🐛 Bug fix (non-breaking change that fixes an issue)
- [ ] ✨ New feature (non-breaking change that adds functionality)
- [ ] 🔒 Security fix
- [ ] 📝 Documentation update
- [ ] ✅ Tests (adding or improving test coverage)
- [ ] ♻️ Refactor (no behavior change)
- [ ] 🎯 New skill (bundled or hub)
- `agent/agent_init.py`: added `_resolve_compression_threshold()`, a pure
helper that combines the global threshold with a per-model override. The
Codex gpt-5.5 autoraise never lowers a higher-or-equal user threshold;
the notice is returned only when it actually raises. Rewired `init_agent`
to call it, replacing the unconditional `compression_threshold = _model_cthresh`.
- `tests/agent/test_arcee_trinity_overrides.py`: added 5 cases for the
helper — raise from default, never-lower regression, equal-is-noop,
no-override passthrough, and non-codex (Trinity) unconditional apply.
1. Set `compression.threshold: 0.90` and run gpt-5.5 on provider `openai-codex`.
2. Before: effective threshold drops to 0.85, no notice. After: stays 0.90.
3. Run `scripts/run_tests.sh tests/agent/test_arcee_trinity_overrides.py`.
Stash `agent/agent_init.py` and the new cases fail; restore and they pass.
- [x] I've read the [Contributing Guide](https://github.com/NousResearch/hermes-agent/blob/main/CONTRIBUTING.md)
- [x] My commit messages follow [Conventional Commits](https://www.conventionalcommits.org/) (`fix(scope):`, `feat(scope):`, etc.)
- [x] I searched for [existing PRs](https://github.com/NousResearch/hermes-agent/pulls) to make sure this isn't a duplicate
- [x] My PR contains **only** changes related to this fix/feature (no unrelated commits)
- [x] I've run `pytest tests/ -q` and all tests pass
- [x] I've added tests for my changes (required for bug fixes, strongly encouraged for features)
- [x] I've tested on my platform: macOS 15 (Darwin 25.5)
- [x] I've updated relevant documentation (README, `docs/`, docstrings) — or N/A
- [x] I've updated `cli-config.yaml.example` if I added/changed config keys — or N/A
- [x] I've updated `CONTRIBUTING.md` or `AGENTS.md` if I changed architecture or workflows — or N/A
- [x] I've considered cross-platform impact (Windows, macOS) per the [compatibility guide](https://github.com/NousResearch/hermes-agent/blob/main/CONTRIBUTING.md#cross-platform-compatibility) — or N/A
- [x] I've updated tool descriptions/schemas if I changed tool behavior — or N/A
The ChatGPT Codex OAuth backend caps both gpt-5.4 and gpt-5.5 at a 272K
context window, but the autoraise that lifts the compaction trigger to 85%
only matched gpt-5.5. On gpt-5.4 the global 50% threshold fired at ~136K —
half the usable window — compacting far earlier than necessary.
Rename _is_codex_gpt55 -> _is_codex_gpt54_or_gpt55 and match both families.
The one-time user notice is now model-aware (shows the actual slug). The
config key codex_gpt55_autoraise is kept as-is for backward compatibility.
Adds gpt-5.4 coverage to the autoraise tests.
Four independent pre-request stalls sat on the critical path between
prompt submission and the first streamed token, measured with cProfile
against a live process:
1. Discord capability detection (~2.0s, worst 5s): get_tool_definitions
-> _get_dynamic_schema made a BLOCKING https call to discord.com
inside AIAgent.__init__ for any user with DISCORD_BOT_TOKEN set, on
every platform, every cold process. Now non-blocking: memory cache ->
24h disk cache -> permissive default + one background detection that
seeds the disk cache for the next process. The permissive default is
pinned per-process so tool schemas never flip mid-conversation
(prompt-cache safety); it mirrors the existing detection-failure
fallback (all actions exposed, 403s enriched at call time).
2. Ollama /api/show probe (~0.3s): get_model_context_length step 5e
POSTed to <base_url>/api/show for KNOWN providers (openrouter etc.),
got a 404, and never cached the miss - so every fresh process paid a
full HTTP round-trip. Known non-Ollama providers now skip the probe;
local/custom/unknown endpoints keep the exact previous behavior.
3. env_probe subprocess sweep (~0.5s): the Python-toolchain probe ran
4-8 subprocess calls inside the FIRST system prompt build. Now warmed
off-thread during agent init; the prompt build hits the cache (same
lock, so a mid-flight warm just joins instead of recomputing).
4. tools.mcp_tool import (~0.4s): the between-turns MCP refresh in
build_turn_context imported the whole mcp package even with zero MCP
servers configured. MCP tools can only exist if tools.mcp_tool was
already imported (discovery/reload paths), so gate the import on
sys.modules membership - no behavior change for MCP users.
CLI additionally pre-imports run_agent + openai off-thread during the
idle banner window (same pattern as the /model picker prewarm), hiding
the remaining ~1.5s of module imports while the user types. Fixes 1-4
apply to every interaction layer (CLI, gateway, TUI, desktop, cron).
Measured cold first turn (submit -> request dispatched, openrouter,
discord token set): 4.3s before -> 0.9s after CLI prewarm (~80%); the
agent-side non-import cost drops 2.9s -> 0.36s (init) + 0.27s (turn
prologue).
