Command-type TTS providers validated output_format against a hardcoded
{mp3,wav,ogg,flac} set; any other value was silently coerced back to mp3,
which then mismatched the output path the post-run check expects. This
blocked common ffmpeg-producible containers/codecs — notably m4a (AAC),
the portable choice for WeChat/iOS/mobile voice files — with no
config-only path (only a local source patch, lost on every update).
Widen COMMAND_TTS_OUTPUT_FORMATS to add m4a, aac, amr, opus. This only
permits a command provider to declare these; the user's command still
produces the file (e.g. via ffmpeg). No built-in provider behavior
changes and no new required config.
Update the two tests that pinned the old set, and add a positive case
covering the new formats. Document the supported output_format values.
Follow-up on the salvaged #69915 commits:
- verified env vars against block/buzz crates/buzz-acp/src/config.rs
(BUZZ_RELAY_URL, BUZZ_PRIVATE_KEY, BUZZ_API_TOKEN, BUZZ_ACP_AGENT_COMMAND/
ARGS, BUZZ_ACP_RELAY_OBSERVER, BUZZ_ACP_AGENT_OWNER) and kind 24200
observer frames against docs/nips/NIP-AO.md
- dropped unverifiable claims: docs/hermes-agent-acp.md link (404 upstream),
relay-directory profile publication / External-agents card walkthrough,
'bot role' membership phrasing
- replaced key provisioning with the actual buzz-admin generate-key /
add-member flow from the buzz-acp README
- retitled the section 'Buzz channels (relay bridge)' and cross-linked it
with the Buzz Desktop managed-runtime section both ways; permission
paragraph now points at the owner-only warning instead of duplicating it
- zh-Hans translation of the new section + retitle to ACP 宿主集成
Address review feedback on #31693:
- Regression tests assert extra_body == {"lang_code": ...} is forwarded
when tts.openai.language is configured, and omitted when unset/empty
- Document tts.openai.language as intended for OpenAI-compatible
endpoints that support lang_code (e.g. Kokoro-FastAPI)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The `text_to_speech` tool schema accepted only `text` and `output_path`,
so style direction (tone, emotion, pacing, whispering) could never reach
the OpenAI backend — even though `gpt-4o-mini-tts` (Hermes's OpenAI
provider default) treats `instructions` as its primary voice-design
control.
This plumbs an optional `instructions` argument through the tool schema,
the handler lambda, and `text_to_speech_tool()` into
`_generate_openai_tts`, where it is forwarded to
`client.audio.speech.create()` only when truthy. Empty/None values still
omit the key entirely, preserving behavior on `tts-1`/`tts-1-hd` and
strict OpenAI-compatible servers.
The same passthrough unblocks self-hosted OpenAI-compatible voice-design
servers (Qwen3-TTS-VoiceDesign on oMLX, etc.) that are already wired in
via `tts.openai.base_url` — the established convention per #9004 and the
TTS config docs — without inventing a new provider backend.
Tests: `tests/tools/test_tts_instructions.py` covers backend passthrough,
tool-level threading, schema declaration, and the empty-string/absent
omission cases. `tests/tools/test_tts_max_text_length.py` fake_openai
signature widened to accept the new kwarg.
Refs NousResearch/hermes-agent#14196
The Buzz Desktop section covered discovery only. The combination that
actually needs stating: the hermes-acp toolset carries terminal and
execute_code, and buzz-acp answers session/request_permission itself with
allow_once instead of surfacing it. A Hermes agent in Buzz runs shell
commands on the host unattended.
Buzz defaults every agent to owner-only and that default holds through to
the spawned process env, so nobody reaches the open state by accident.
But Anyone is one dropdown change away with no warning shown, and it
hands channel-wide shell access to the host.
Also record that the two obvious mitigations do not work: approvals.mode
manual raises the request but Buzz auto-approves it anyway, and
platform_toolsets.acp does not narrow the ACP toolset. Both verified by
running rm -rf through the ACP path under each setting.
Amend the Approvals section too — it promised prompts route back to the
editor, which is only true for hosts that choose to surface them.
en + zh-Hans.
Signed-off-by: SHL0MS <SHL0MS@users.noreply.github.com>
Existing installs predate the install.sh hermes-acp launcher, and hermes
update never re-runs setup_path, so ACP hosts (Zed, JetBrains, Buzz)
still resolve Hermes as unavailable until a reinstall. _ensure_acp_launcher()
writes the launcher next to an existing hermes command in ~/.local/bin or
/usr/local/bin during hermes update — delegating to the sibling launcher so
it is correct for every install layout. Never follows symlinks (#21454),
skips unwritable dirs, no-op on Windows (venv Scripts is already on PATH).
Docs: add a Buzz Desktop section to the ACP page (en + zh-Hans).
Per-job cron inference pins are now user-owned: the agent-facing cronjob
tool schema no longer exposes model/provider/base_url, and the registered
handler ignores them even if a model hallucinates the old parameters.
Users set pins via the dashboard, hermes cron create/edit --model/--provider,
or jobs.json directly — and once set, a pin sticks until the user changes it.
Existing agent-era pins are grandfathered untouched.
New cron.model / cron.model_provider config keys give the cron fleet its
own default model, independent of the chat model. Fire-time resolution:
per-job pin > cron.model > HERMES_MODEL > model.default. An axis covered
by the explicit cron-fleet default is deliberate routing, not drift, so
the #44585 fail-closed guard skips it — switching your chat model with
/model or hermes model no longer breaks unpinned cron fleets.
- tools/cronjob_tools.py: drop model param from agent schema + handler;
remove now-dead _resolve_model_override
- cron/scheduler.py: cron.model/model_provider resolution + per-axis
drift-guard skip
- cron/jobs.py: snapshot resolution mirrors the new precedence
- hermes_cli/subcommands/cron.py + hermes_cli/cron.py: --model/--provider
on hermes cron create/edit
- hermes_cli/config.py: cron.model / cron.model_provider defaults
- docs: cron.md model-resolution tip rewritten
Saying 'stop' in a voice chat did nothing — the transcript was just
submitted to the agent as a normal turn, so the conversation never ended.
The only way out was the mouse/hotkey. That's not how a hands-free voice
assistant should work.
useVoiceConversation now checks each finished utterance against a spoken
stop-command matcher BEFORE submitting: 'stop', 'stop listening', 'never
mind', 'goodbye', 'cancel', 'that's all', etc., optionally addressed
('hey hermes, stop'). A match ends the conversation (flips enabled=false,
which drives the existing end() teardown — mic close, playback stop, wake
re-arm) instead of sending a turn.
Deliberately conservative: only a WHOLE-utterance stop phrase matches, so
substantive requests that merely contain 'stop' ('stop the docker
container', 'how do I stop a process') still go through.
- apps/desktop/src/lib/voice-stop-word.ts: isVoiceStopCommand() matcher
- use-voice-conversation.ts: onStopWord option + intercept before submit
- use-composer-voice.ts: wire onStopWord -> end the conversation
- 6 matcher tests (bare/multi-word/addressed stop; substantive requests
with 'stop' pass through; bare address words don't match)
- docs: note the spoken-stop behavior
'hey hor' triggered the wake word: the default sensitivity was 0.5, which
for openWakeWord IS the raw per-frame score threshold — openWakeWord's own
permissive baseline that near-misses clear. Raised the default to 0.6 so
phonetic near-misses fall short while real 'hey hermes' (typically 0.9+)
still fires easily.
Also fixed a real cross-engine inconsistency found while checking: the
sensitivity knob is documented 'higher = stricter' and behaves that way
for openWakeWord (threshold = sensitivity) and sherpa (0.05 + 0.4*s), but
Porcupine's own 'sensitivities' param runs the opposite way (higher = MORE
false alarms, per Picovoice). Turning sensitivity up made Porcupine looser
— backwards. Now inverted (1 - sensitivity) so 'higher = stricter' holds
for every engine.
- tools/wake_word.py: default 0.6; _sensitivity fallback uses _DEFAULTS;
Porcupine sensitivity inverted with rationale
- hermes_cli/config.py + docs: default + consistent-direction note
- tests: Porcupine inversion, default>=0.6 regression, fallback-to-default
openWakeWord scores one ~80ms frame at a time and the detector fired the
instant a SINGLE frame crossed threshold — so a stray phoneme in background
conversation could trigger the wake word unintentionally (reported in
testing). A real utterance of the phrase holds a high score across several
consecutive frames; an ambient blip spikes just one.
_OpenWakeWordEngine now requires N consecutive over-threshold frames
(wake_word.confirmation_frames, default 3) before firing. The streak resets
on any sub-threshold frame and on engine reset() (pause/resume), so a
pre-pause frame can't count toward a post-resume fire. confirmation_frames=1
restores the old single-frame behavior; clamped 1..10.
Only openWakeWord is affected — sherpa (streaming transducer) and porcupine
decode the whole phrase internally and already reject single-frame spikes.
- tools/wake_word.py: _confirmation_frames() accessor, streak logic in
process()/reset(), config default
- hermes_cli/config.py: wake_word.confirmation_frames documented default
- tests: 5 new (spike rejected, sustained fires once, =1 legacy behavior,
reset clears streak, config clamp) — 58 wake tests green
- docs: 'Reducing false triggers on ambient speech' section
openWakeWord's ONNX backend returns near-zero scores on Apple Silicon
(dscripka/openWakeWord#336), so "Hey Hermes" never crossed the 0.5
threshold: the listener armed, the microphone worked, and nothing fired.
Bisecting the pipeline puts the fault in exactly one stage — feeding the
same audio through both backends, the melspectrogram front-end is
bit-identical (maxdiff 0.00000) and the wake classifier agrees on
identical features, while the shared embedding model diverges by 45.44.
Cross-feeding confirms it: tflite features scored through the *onnx*
classifier give 0.9948 vs 0.000009 for onnx features. A telling
secondary symptom is that scores fall as input gets louder (0.5x ->
0.00031, 8x -> 0.000066), which is garbage inference rather than a weak
detection.
Selecting tflite in config alone does not fix it. openWakeWord hardcodes
`import tflite_runtime.interpreter` but declares tflite-runtime for
`platform_system == "Linux"` only; on macOS the equivalent wheel is
ai-edge-litert, so that import always fails and model.py silently
downgrades back to onnx. The result is a detector that reports itself
listening and can never fire.
- default the backend per platform (tflite on macOS ARM64, onnx
elsewhere) instead of hardcoding onnx, and pick the matching bundled
model artifact
- bridge tflite_runtime -> ai_edge_litert through sys.modules, in-process,
with no writes to site-packages
- refuse the silent onnx downgrade on macOS ARM64 and report the missing
runtime through check_wake_word_requirements() so the GUI surfaces an
actionable hint rather than arming a dead ear
- lazy-install ai-edge-litert via its own feature key, because lazy-dep
specs cannot carry PEP 508 markers (_spec_is_safe rejects ";")
An explicit `inference_framework` in config still wins, so anyone pinning
a backend keeps it.
Verified on macOS 26.5.2 / M-series: "hey hermes" scores 0.0005 on onnx
and 0.9423 on tflite from the same clip, with cross-phrase controls at
0.0003. Live over-the-air through the real microphone fires 4/4
utterances (peak 0.9532).
Two fixes from live testing (Teknium):
1. Desktop installs now ship the wake/voice stacks up front.
install.sh + install.ps1 desktop stages run 'uv pip install
-e .[wake,voice]' (best-effort, lazy-install remains the fallback)
before building the app, so the first ear-click arms instantly
instead of sitting through a multi-minute onnxruntime download.
CLI-only installs keep the lazy path — [all] curation unchanged.
2. The vanished ear: the STT/TTS gate made wake.status call
check_tts_requirements(), whose edge path runs _import_edge_tts →
lazy_deps.ensure — a synchronous PIP INSTALL inside a status poll.
On a venv without edge-tts that blew the desktop's 30s RPC timeout,
armWakeWord caught the error, the atom never learned enabled=true,
and the ear unmounted. _tts_ready is now a pure probe: deps missing
+ lazy installs allowed counts as ready (installs at first speak)
WITHOUT touching pip; check_tts_requirements only runs once deps
are present. Regression test asserts the probe never calls it while
deps are missing.
The wake loop is wake → record → STT → agent → TTS. Arming without
either end configured gives a mic that hears you and then does nothing
perceivable — a useless experience. check_wake_word_requirements now
probes both (same probes /voice uses: stt.enabled + provider != none;
check_tts_requirements) and refuses with a pointer to `hermes tools`
naming exactly which half is missing. The desktop ear hides (available:
false already hides the button), /wake on prints the hint on CLI/TUI.
wake.start also validates requirements BEFORE persisting
wake_word.enabled, so a refused gesture can't leave config claiming on
while nothing can ever arm.
Tests: per-half and both-missing hint assertions; existing requirements
tests pinned via _voice_loop_ready so they don't depend on the test
venv's installed voice stack. E2E: stt.enabled=false in a real config
-> unavailable with the speech-to-text hint.
Internal testing (macOS): clicking the ear froze the button for ~30s,
then it went blue but 'hey hermes' never fired even though STT worked.
Two distinct bugs:
1. Frozen-then-timeout button: first-use wake.start lazy-installs the
detection engine (onnxruntime is a large wheel), which blows the
desktop's default 30s WS request timeout — the RPC 'failed' client-
side while the backend kept installing and armed later on its own.
wake.start now gets a 180s budget and the pending state says
'arming — first use may take a minute while the engine installs'
instead of a silent disabled button.
2. Armed but deaf: macOS grants mic permission per PROCESS. The
renderer having mic access (STT working) does not grant the Python
backend anything — CoreAudio hands an unentitled process a 'working'
stream that delivers zeros forever, so the listener looks healthy
and can never hear the phrase. The detector now tracks frame peaks:
10s of consecutive near-zero frames sets audio_silent, logged with
the exact macOS Settings path, cleared automatically when audio
appears. Surfaced everywhere: wake.status (audio_silent + hint),
desktop ear tooltip (kept visible while listening), /wake status on
TUI (⚠ line) and classic CLI, plus a docs troubleshooting section.
Tests: detector silent-flag set/recover cycle (fake silent/loud
streams), desktop tooltip keeps the dead-mic hint while listening.
44 wake Python tests, 22 desktop + 14 TUI vitest green.
Clicking the desktop ear button or running /wake on|off now writes
wake_word.enabled to config.yaml (live, saved for future sessions), so the
feature no longer requires hand-editing config before the UI toggle works.
- wake.start accepts persist:true (explicit gesture): flips
wake_word.enabled on in config before arming; response reports
enabled_persisted. Passive auto-arm paths (desktop gateway-ready,
TUI reconnect) never pass it, so a mic can't become persistently
enabled without a deliberate user action.
- wake.stop accepts persist:true: writes wake_word.enabled: false so
auto-arm stays off next session; reports disabled_persisted.
- Split the refusal reason: 'disabled' (feature off in config — a
persisted gesture turns it on) vs 'disabled_for_surface' (explicit
wake_word.surface scoping, which persist does NOT override).
- Classic CLI /wake on|off and bare-toggle persist the flag too
(skips the write when config already matches).
- Desktop tooltip now maps refusal codes to friendly text (mirrors
the TUI's START_REASON_TEXT) instead of showing raw codes like
disabled_for_surface.
- Docs: quick-start notes the toggle persists; ear-button mention.
One sherpa listener now enrolls every wake-enabled profile's phrase
(defaulting to "hey <profile name>") and reports WHICH phrase fired.
wake.detected gains a profile field; the desktop live-switches to the
matching profile (same path as the profile rail), opens a fresh session
there, and starts hands-free voice. The single-profile CLI/TUI print the
hermes -p switch command for foreign-profile phrases instead of
answering as the wrong profile. Opt out per listener with
wake_word.profile_routing: false.
New "sherpa" wake_word provider: the configured phrase is BPE-tokenized
at runtime against a small streaming zipformer KWS model (~13 MB English,
one-time download cached under HERMES_HOME), so ANY typed phrase works
with zero training — including per-profile phrases like "hey coder".
wake.sherpa lazy-dep group + [wake] extra grow sherpa-onnx/sentencepiece;
requirements probe routes per provider; sensitivity maps onto sherpa
keywords_threshold. E2E-verified on real audio (target phrase fires,
foreign phrase stays silent, reset drops buffered state).
From #53378: ships hey_hermes.onnx/.tflite (openWakeWord pipeline,
Apache-2.0) under tools/wakewords/, resolves the default (and hey_hermes
aliases) to the bundled file, ensures openWakeWord base feature models
are fetched for custom paths too, and updates config defaults + docs
from hey_jarvis to hey hermes.
Regenerate uv.lock for the [wake] extra (openwakeword, pvporcupine,
onnxruntime) so uv lock --check passes. Replace angle-bracket URLs in
wake-word.md with markdown links — MDX treats <https://...> as JSX.
Makes the wake word a tri-surface feature with one configurable owner.
- wake_word.surface ("auto" | "cli" | "tui" | "gui") + shared
wake_surface_enabled() gate consulted by every surface, so exactly one
place owns the listener and the new session it opens.
- tui_gateway: wake.start/stop/pause/resume/status RPCs + a wake.detected
event, sharing one server-side detector for both TUI and desktop. The
detector yields the mic to voice.record (pause on capture start, resume
on terminal) and to the desktop's browser mic (wake.pause/resume).
- TUI (Ink): arm wake.start on gateway.ready; on wake.detected open a
fresh session and start voice capture.
- Desktop (Electron): arm wake.start on connect; on wake.detected open a
fresh session.
- CLI now gates on wake_surface_enabled("cli"); /wake status shows surface.
- Tests for the surface gate; docs cover the surface knob + cross-surface.
Adds an opt-in, on-device hotword listener for the CLI. With
wake_word.enabled (or /wake on), Hermes listens in the background for a
wake phrase; on detection it starts a fresh session, captures one
utterance through the existing voice pipeline, and answers — the
"Hey Siri" pattern.
- tools/wake_word.py: provider-pluggable detector (openWakeWord, free
local default; Porcupine, premium) over the shared 16 kHz sounddevice
capture path. Background daemon thread with pause/resume so it yields
the mic during a voice turn.
- CLI wiring: startup listener (off-thread), on-wake flow, an idle
watchdog that resumes the detector after each turn, cleanup hook, and
a /wake [on|off|status] command.
- config.yaml wake_word section; PORCUPINE_ACCESS_KEY as an optional
secret. Engines lazy-install via the [wake] extra.
- Hands a transcript to the input queue exactly like voice mode, so no
system-prompt/cache mutation. No new core model tool.
- Tests (mocked, no live audio/network) + feature docs.
When an operator changes the global model/provider config, warn that
unpinned cron jobs with stored snapshots will fail-closed on their next
run. Adds a cron.model_drift_guard config opt-out (default true) for
fleets that should deliberately track changing global defaults.
Addresses #59031. Original PR #59177 by @doncazper.
Figma's mcp.figma.com register endpoint is a client_name allowlist
(Claude Code / Codex succeed; Hermes Agent 403s) and returns a client
secret while advertising auth_method=none, then requires the secret on
token exchange. Auto-set client_name + client_secret_post for Figma
hosts, pass oauth cfg through login/add paths, force interactive OAuth
for hermes mcp login from non-TTY desktop shells, and ship a catalog
entry. Proven: hermes mcp login figma → 26 tools.
Saying EXACTLY a configured stop phrase (default: 'stop') and nothing
else now ends the voice conversation instead of being sent to the agent
as a prompt. Match is deliberately strict — whole utterance,
case-insensitive, surrounding punctuation stripped — so 'stop doing
that and try X' still reaches the agent.
- tools/voice_mode.py: is_voice_stop_phrase() + voice.stop_phrases
config loader (default ['stop'], [] disables, malformed config falls
back safely).
- hermes_cli/voice.py: shared continuous loop (TUI + desktop) halts on
a stop phrase exactly like the silent-cycle limit (fires
on_silent_limit so every UI turns voice off); stop_continuous
force-transcribe path swallows the phrase without counting a silent
cycle.
- cli.py: classic CLI push-to-talk/continuous path and barge-in
utterance path disable voice mode on a stop phrase.
- Config default voice.stop_phrases: ['stop']; docs updated.
Tests: tests/tools/test_voice_stop_phrase.py (27 tests,
sabotage-verified: loop test fails when detection is disabled);
voice suites green (110 + 44 passed).
- Normalize stt.groq.language: cast to str, strip, treat
empty/whitespace as unset (parity with xAI's str().strip()).
- Clarify "blank = auto-detect" inline comments in 4 docs/configs to
reflect the env-var fallback (HERMES_LOCAL_STT_LANGUAGE).
- Document that HERMES_LOCAL_STT_LANGUAGE also drives the local
faster-whisper provider, not just the CLI fallback.
- Add unit tests covering: omitted language when unset, config-supplied
language, env fallback, config-over-env precedence, whitespace
normalized to unset.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Read stt.groq.language from config.yaml (with HERMES_LOCAL_STT_LANGUAGE
env fallback) and forward it to the Groq Whisper API to skip
auto-detection on known-language audio. Omit when unset so Groq
auto-detects, preserving today's behavior. Bonus: swap xAI's hardcoded
env literal for the LOCAL_STT_LANGUAGE_ENV constant for consistency.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The include/exclude filter matched exact names only — glob-style entries
('*_radar_*') silently matched nothing, so a Cloudflare flat-mode config
meant to trim 3,320 tools to ~1,900 actually registered 3,319. Unmatched
patterns produced no warning.
- matches_name_filter(): exact membership first (O(1) for literal lists),
then fnmatch.fnmatchcase for entries containing * ? [ — same pattern
semantics as approvals.deny. Entries without metacharacters stay
strictly literal ('docs' never matches 'docs_search').
- _should_register() uses it for both include and exclude (symmetric)
- hermes mcp tools picker (mcp_config.py) pre-selection uses the same
matcher so the UI agrees with runtime registration
E2E against the live Cloudflare capture with the real exclude list:
3,320 -> 1,905 surviving (1,415 excluded); radar/DLP gone,
purge_cache/dns_records kept. 220/220 mcp_tool tests (4 new).
Teknium review changes on the tiered policy:
1. threshold_pct default 10 -> 5 (listing budget = min(5% of context,
listing_max_tokens)); unknown-context fallback 20K -> 10K.
2. Tier 2 no longer leaves the model blind: when even names-only doesn't
fit, the bridge description now carries a one-line-per-server summary
('cloudflare (3320 tools)') plus an instruction to search FIRST rather
than substitute a generic tool or claim the capability is missing —
the measured tier-2 failure mode (core-tool substitution) at zero
meaningful token cost (~50 tokens/server).
3. Listing degradation is now PER SERVER, largest first: one oversized
server (Cloudflare) collapses to its summary line while small
co-attached servers (Linear) keep their full per-tool listings
('mixed' form). Previously global: attaching Cloudflare next to
Linear silently cost Linear its listing. Greedy fit is deterministic
(size then label) so the rendered block stays byte-stable per catalog
— prompt-prefix cache safe.
E2E on real captures (defaults, 200K ctx): linear alone -> tier 1 full;
unreal alone -> tier 2 groups (5% budget) / tier 1 names at 1M;
cloudflare alone -> tier 2 groups; linear+cloudflare -> tier 1 MIXED
(linear fully listed, cloudflare summarized). 48/48 tests.
Tier 0: no MCP/plugin tools -> everything eager (pass-through).
Tier 1: deferred tools whose catalog listing fits min(threshold_pct%
of context, listing_max_tokens) -> bridge + skills-style
listing, degrading to names-only over budget.
Tier 2: listing over budget even names-only (Cloudflare's flat API
surface: 3,320 tools, names alone ~32K tokens) -> bare bridge,
discovery through tool_search only.
The old activation threshold (defer only when schemas > threshold_pct
of context) let mid-size catalogs ride eager and pay full schema cost;
with servers like Cloudflare (~597K tokens of schema, would not even
fit a 200K window) the binary gate is the wrong shape. Activation is
now driven purely by deferrable-tool presence; threshold_pct is
repurposed as the listing budget's context-relative leg.
- AssemblyResult gains tier + listing_form for observability
- listing_max_tokens default 4000 -> 20000 (cap 60000) so an 830-tool
catalog keeps a names-only listing while Cloudflare-scale drops to
bare bridge
- E2E verified against real captures: Linear 24 tools -> tier 1 full,
Epic UE 5.8 830 tools -> tier 1 names-only, Cloudflare 3,320 tools
-> tier 2 (both 200K and 1M context)
Deferred MCP/plugin tools become invisible once the tool_search bridge
activates — live benchmarking (48 runs, Claude Haiku 4.5) showed models
substituting visible core tools (terminal/web_search/browser) for deferred
capabilities or declaring them nonexistent instead of searching: 16/24
task success vs 24/24 with eager loading.
Skills never had this failure mode because every skill keeps a ~21-token
name+description listing line in the system prompt. This ports that exact
pattern to the tool bridge: when tool_search activates, a grouped manifest
of every deferred tool (name + first sentence of description, clipped to
60 chars, grouped per MCP server / toolset) is embedded in the tool_search
bridge description.
- tools/tool_search.py: build_catalog_listing() with deterministic
ordering (byte-stable across assemblies -> prompt prefix stays
cacheable); token-budget fallbacks full -> names-only -> legacy bare
count; bridge_tool_schemas(listing=...) embeds it and instructs the
model to skip tool_search when the exact name is visible (one fewer
round-trip per use)
- config: tools.tool_search.listing auto|on|off (default auto),
listing_max_tokens (default 4000, clamped 200..20000); legacy bool
shapes keep working
- tests: 8 new tests (config parsing/clamps, short-desc clipping,
deterministic rendering, budget fallbacks, bridge embedding, assembly
on/off paths); full file green (47 passed)
- docs: tool-search.md config table + rationale
- scripts/tool_search_livetest2.py: benchmark harness v2 with real
per-call token accounting (normalize_usage spy) and a third 'listing'
mode for A/B/C comparison
The background write guard decided ownership from `isinstance(usage_rec, dict)`,
so a local skill with NO usage record passed. That successful write called
bump_patch(), which created a `created_by: null` record — and the identical
write was refused from then on. "Allowed exactly once, then never" is a race
with our own bookkeeping, not a policy. Reproduced on main: patch #1 succeeds,
patch #2 with the same arguments is refused.
Option B from the issue. Option A (split `session_review` from
`scheduled_curator` and let the session fork patch user-owned skills it
consulted) would widen autonomous write permission onto skills the user owns
with no user present to consent — wrong direction for a no-user-present actor.
- skill_manager_tool: missing and explicit-null records now resolve
IDENTICALLY, both fail closed. The refusal names the reason and points at
`hermes curator adopt <name>`.
- background_review: both review prompts told the reviewer to patch any skill
consulted in the session and claimed pinned skills could be improved, while
enforcement refused both. Prompts now list pinned, external, and user-owned
skills as protected, and tell the reviewer to RECOMMEND adoption instead of
attempting a write that will be refused.
- skill_usage: document that `created_by` is a curator-management policy flag,
not a provenance claim, and add `is_curator_managed()` so call sites read as
the question they ask. Field name retained — it is on disk in every
`.usage.json` and renaming would strand those records.
- curator CLI: `hermes curator list-unmanaged` itemizes unmanaged skills with
the reason each is unmanaged (completes the #67139 spec).
Foreground writes are untouched: a user-directed edit to a user-owned skill
still works, including on pinned skills.
Sibling tests: 9 failures in test_skill_manager_tool.py were fixtures that
created record-less skills to exercise OTHER guards (consolidation-delete,
read-before-write) and relied on ownership falling through. Fixed at the
fixture, since the real curator only ever operates on managed sediment. One
test asserted the old "manually authored" wording; rewritten to assert the
behavior contract instead of the string.
Validation: 274 targeted tests + all 7 background-review files (60 tests) pass.
E2E on a temp HERMES_HOME (30 checks) covers the flip, foreground writes,
adoption unblocking, pin semantics, prompt/enforcement parity, and the new verb.
Each new test sabotage-verified: revert the fix, confirm it goes red.
Fixes#67140
`hermes curator status` reported only the skills it manages, staying silent
about curation-eligible skills it can never touch. On a 237-skill library that
meant 112 skills were invisible to every automatic transition with no signal
anywhere — the curator looked broken when it was working as designed.
A skill becomes curator-managed only when `created_by: agent` lands on its
usage record, and only the background review fork writes that marker. Two
populations therefore never qualify: records written before the marker existed
(no key at all, authorship unknowable) and every foreground
`skill_manage(create)` (unset by design — those skills belong to the user).
- skill_usage: `list_unmanaged_skill_names()` / `unmanaged_report()` enumerate
the blind spot, tagging each row with `has_provenance_key` so the two causes
are distinguishable. `adopt_skill()` writes the marker on user declaration
and refuses bundled, hub-installed, external, and protected built-ins.
Adoption never resets the inactivity clock.
- curator CLI: status prints an `unmanaged (no provenance marker)` block on
BOTH the managed and no-managed-skills paths; new `adopt` verb takes names or
`--all-unmanaged`, with `--dry-run` and a confirmation prompt on bulk.
- skills_sync: `_backfill_optional_provenance()` matched candidates by
repo-derived path only, so a skill installed at `mlops/chroma` that upstream
later moved to `mlops/vector-databases/chroma` was skipped forever and
`hermes skills repair-optional` could not fix it. Falls back to an
unambiguous name match, still gated on identical content, and records the
ACTUAL install path.
Provenance stays a declaration, never an inference: a high patch count proves
the agent MAINTAINS a skill, not that it authored one, since Hermes edits
user-written skills on the user's behalf routinely. An "looks agent-made"
heuristic would eventually archive hand-written work.
Validation: 347 targeted tests pass. Each new test verified via sabotage run
(revert the fix, confirm the test goes red) — one initially passed for the
wrong reason because the fixture pinned `prune_builtins` off, masking the guard
under test; fixed to force the shipped default on.
The Codex image backend rejected our own request shape for every account, and
we then translated that rejection into "Image generation is not enabled for the
current Codex account. Switch the image provider to OpenAI API key, FAL, or
xAI." — telling every affected user to abandon a provider that had never
actually been tried. That message is why this reads as a setup failure rather
than a bug: the wire error was replaced with a confident, wrong diagnosis.
Removes the classifier and its exception, so any HTTP failure surfaces
verbatim. The paired request-shape fix (previous commit) is what makes the
400 stop happening; this commit makes the next one diagnosable.
Also fixes error-body truncation: bodies were head-truncated at 500 chars, and
Codex error payloads can carry hundreds of bytes of leading metadata, so the
user got a wall of padding and no message. _summarize_error_body() prefers the
parsed error.message and falls back to a truncated raw body.
Docs: drop the unqualified image-to-image claim for the Codex backend and note
that the hosted tool call cannot be forced, so it is best-effort.
Verified E2E against a local fake Codex backend: success path writes a real PNG
with no tool_choice on the wire; the 400 path now returns api_error carrying
"Tool choice 'image_generation' not found in 'tools' parameter" (148 chars)
instead of the entitlement message. Sabotage run confirms all 4 regression
tests fail when the old behavior is restored.
Refs #19505, #49008, #31335.
Adds a 'Host integration' section to the ACP guide and a row in the
environment variable reference so the next ACP host implementer does not
have to read the adapter source.
Documents the exact contract the tests already pin: the value must be
exactly `1`; unset/empty/`0`/`false` keep the default behavior; only
globally configured config.yaml MCP discovery is skipped, and servers
supplied by the ACP session through session/new are still registered.
Framed as a host-set process marker rather than user configuration - the
same shape as the existing HERMES_KANBAN_TASK entry - so it does not read
as a behavioral setting that belongs in config.yaml.
Co-authored-by: amanning3390 <adam.manning@pro-serveinc.com>
Signed-off-by: amanning3390 <adam.manning@pro-serveinc.com>
Add authenticated GET /api/model/options to the gateway API server,
sharing the dashboard/TUI picker payload builder so external clients
can sync to the user's configured Hermes provider catalog instead of
scraping the single OpenAI-compatible /v1/models alias.
- new shared hermes_cli.inventory.build_model_options_payload() wraps
build_models_payload with the stable picker shape and safe
custom-provider probe policy (probe current only on normal open,
probe all + cache bust on explicit refresh)
- dashboard web_server and TUI gateway model.options refactored onto
the shared builder; dashboard build moved off the event loop via
run_in_threadpool
- capabilities endpoint advertises model_options
- docs for both API server and programmatic integration
Salvaged from PR #54689 by @abundantbeing.
Follow-ups on the salvaged #54426 routing contract:
- Bare `model` without `provider` on the OpenAI-compatible endpoints
(/v1/chat/completions, /v1/responses) is now opt-in via
gateway.platforms.api_server.direct_model_requests (default off) —
generic OpenAI clients hardcode model names ('gpt-4o', ...) and
existing deployments rely on those falling back to the gateway
default. Explicit `provider` requests and the Hermes-native
session-chat + /v1/runs surfaces are always honored.
Idea credit: PR #22825 by @mssteuer.
- A model_routes alias with no `model` key can no longer leak the
alias string as the executing model name (defensive; parse-time
validation already drops such routes).
- Fix mis-indented _run_agent call args in _handle_session_chat_stream.
- Docs: document the opt-in flag.
Carry model, provider, and model_options through the API server's
execution surfaces (session chat, Chat Completions, Responses, /v1/runs)
without mutating global configuration. Precedence: session /model
override -> model_routes alias -> direct request selection -> global
defaults. Conflicting route/provider mixes fail closed with 400.
model_options stays request-scoped regardless of which selection wins.
Salvaged from PR #54426 by @abundantbeing.
Adds a device authorization grant flow alongside the existing loopback
OAuth flow, so `hermes setup` can connect to Honcho cloud from SSH and
other no-browser environments.
- oauth.py: new HTTP seams — _http_post_form_status (non-raising, since
RFC 8628 polling reads the OAuth error off a 400) and _http_get_json
for the RFC 8414 metadata probe
- oauth_flow.py: DeviceCode, request_device_code, poll_for_token with
slow_down backoff (+5s, capped at 60s) bounded by expires_in, typed
errors (AccessDenied, DeviceCodeExpired, AuthorizationTimeout), and
supports_device_login (fail-closed metadata gate); device flow ends in
the same install_grant tail as loopback so refresh/status work
unchanged
- oauth_flow.py: loopback callback now serves a "sign-in was not
completed" page on consent cancel instead of the success page
- cli.py: cloud menu offers oauth / device / apikey; the device option
only appears when the host advertises the grant, and becomes the
default when no browser is detected
- 18 new tests covering the full flow against a local fake AS, backoff
schedule, error mapping, deadline bound, metadata gate, and wizard
branches
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Flips the default fan-out cadence from per_iteration (advisors re-run on
every tool iteration, multiplying advisor spend by tool-loop depth) to
user_turn (advisors run once on the first message of each user turn; the
acting aggregator works the rest of the tool loop with that turn's
advice). Until per-mode benchmarks justify a costlier default, MoA
defaults to the cheapest, lowest-impact cadence (#67199).
One default for everyone — no split legacy/new-preset semantics; presets
that want per-step advising set fanout: per_iteration explicitly. All
three modes (user_turn / per_iteration / every_n:N) remain selectable;
every_n:1 still collapses to per_iteration (semantic identity), while
unparseable values now fall to user_turn (the default).
Docs updated with a default-change note; the per-iteration rerun test
pins its mode explicitly.
Co-authored-by: skyer-flyyy <188930297+skyer-flyyy@users.noreply.github.com>
Make the x_search / xurl boundary explicit in the skill, feature docs,
toolset metadata, setup note, and reference pages, while keeping the
model-facing x_search schema generic (no static xurl name).
Regression tests assert behavioral routing invariants rather than frozen
prose snapshots. Drop the stale CI-only plugin/hangup hunks already on
main so this rebases cleanly.
skills/computer-use/SKILL.md sat at the root of the bundled skills
tree as an uncategorized single-skill directory. Move it to
skills/autonomous-ai-agents/computer-use/ alongside claude-code,
codex, opencode, and hermes-agent.
- git mv skills/computer-use -> skills/autonomous-ai-agents/computer-use
(history preserved)
- root-level-skill docstring examples (commands.py,
generate-skill-docs.py) now use a generic placeholder instead of
naming a specific skill, since categorizing root-level skills is
ongoing
- docs: move the bundled page to autonomous-ai-agents-computer-use,
update sidebars.ts, skills-catalog.md, and the two skill-path
references in features/computer-use.md
E2E validated: fresh sync_skills() copies to the new nested path;
existing installs with the old flat copy keep it untouched (manifest
name-keyed, hash unchanged -> skipped, no duplicate);
_get_category_from_path resolves 'autonomous-ai-agents'; docusaurus
build green; 396 targeted tests pass.
The hermes-agent skill body was a 51KB monolith loaded in full on every
trigger. It is now a lightweight hub (~12KB): identity, quick start, the
tmux orchestration guide (kept in-body — the autonomous-ai-agents category
contract), surface orientation, and hard invariants, with a routing table
into 18 reference files that carry the depth.
Absorbed four Hermes-specific skills as first-class references so their
content gains a discoverable home and room to grow without bloating any
primary body:
- skills/hermes-themes -> references/themes.md + templates/skin.yaml
- skills/hermes-desktop-plugins -> references/desktop-plugins.md + templates/plugin.js
- skills/productivity/tui-widgets -> references/tui-widgets.md + templates/clock.mjs
- skills/productivity/petdex -> references/petdex.md
New references extracted from the old body: cli-reference, slash-commands,
providers-and-models, configuration, project-context-files,
security-privacy, background-systems, windows-quirks, troubleshooting,
contributor-guide (native-mcp and webhooks already existed). Also commits
delegate-task-concurrency-diagnosis and portal-auth-for-third-party-apps,
which the old body referenced but the repo never shipped.
Description updated to cover the widened scope:
'Use, configure, theme, extend, and orchestrate Hermes Agent.' (60 chars)
Extends the fanout enum with 'every_n:<N>' (N >= 2): advisors run on the
first iteration of each user turn and every Nth tool iteration after it;
off-cadence iterations REUSE the cached guidance from the last on-cadence
run via the same cache mechanism the user_turn fanout uses, so the
aggregator still gets advice on every step. The cadence counter is scoped
per user turn (resets on a new user message) and only advances when the
advisory state actually changes, so streaming retries never consume a
cadence slot. Mapping form {mode: every_n, n: N} normalizes to the
canonical string. Unknown/degenerate values fall back to per_iteration.
Addresses issue #63393 (advisor fan-out multiplies turn latency/cost by
the tool-iteration count). Redesigned from PR #63448: the submitted shape
skipped references entirely on off-cadence iterations (aggregator ran
advice-less); this version keeps the last advice in play, credited for
the idea and cadence framing.
Config-gated, default-off (default fanout remains per_iteration).
Co-authored-by: webtecnica <75556242+webtecnica@users.noreply.github.com>
Build the full reaction pipeline on top of the #29916 base:
- Opt-in gate: slack.reaction_triggers (default OFF — reaction events
stay acked-and-dropped so busy channels don't wake the agent on every
emoji). 'true' routes reactions on the bot's OWN messages; an explicit
emoji-name list routes those emojis from any message (handoff flows).
- reaction_removed events now route too, distinguished by the
cross-platform text convention reaction:added:<emoji> /
reaction:removed:<emoji> (matches the Feishu and Photon adapters, so
agents and skills see one shape everywhere).
- Authorization: the reactor becomes the synthesized message's user, so
the early _is_user_authorized gate and allowed_channels whitelist
apply exactly as for typed messages. _hermes_force_process only skips
the mention requirement (a reaction on the bot's own message is
definitionally addressed to the bot), mirroring Feishu/Photon.
- Gateway hooks (#33111 by @johnkattenhorn): every human reaction on a
message item fires reaction:added / reaction:removed through the new
BasePlatformAdapter.set_reaction_handler → GatewayRunner
._handle_reaction_event → HookRegistry.emit, independent of the
routing opt-in. Documented in hooks.md.
- Channel handoff (#45265 by @Kev-fs): slack.reaction_trigger_target
routes the reaction turn to a configured channel (top-level via
_hermes_no_thread_response + reply-anchor suppression in
gateway/platforms/base.py) or C123:<ts> thread.
- Manifest: reaction_removed event subscription added alongside
reaction_added/reactions:read.
- Docs: slack.md Reaction Triggers section; hooks.md event table rows.
Also credits #44508 by @harrisonmedmedmetrics (inbound reaction_added
handling — same plumbing class, superseded by this consolidated shape).
Co-authored-by: johnkattenhorn <john.kattenhorn.personal@gmail.com>
Co-authored-by: Kev-fs <kevin@fleetsmarts.net>
Co-authored-by: harrisonmedmedmetrics <harrison@medmetricsrx.com>
markVoicePlaybackInterrupted() / takeVoicePlaybackInterrupted() mirror
the backend latch in the renderer (the barge happens client-side, where
the audio plays). VAD barges and typing over playback mark it; the next
prompt.submit carries interrupted:true, which the TUI gateway latches
into the model note.
Removes Homebrew and PyPI wheel/sdist as Hermes distribution paths while
preserving the supported source, Docker, and Nix workflows.
Changes:
- Removes the Homebrew formula, PyPI publish workflow, sdist manifest
(MANIFEST.in), and wheel/sdist release-attachment logic from scripts/release.py.
- Keeps setuptools metadata and entry points required by editable installs
and Docker/Nix builds, but adds a setup.py guard that rejects wheel/sdist
builds outside a sealed Nix derivation (HERMES_NIX_BUILD=1).
- Removes pip/Homebrew install detection, PyPI update checks, the pip
self-update path, the deprecation-banner state, the postinstall subcommand,
wheel data-directory fallbacks in agent/i18n.py and hermes_constants.py,
and the ACP Registry manifest/version-lockstep release logic.
- Adds /nix/store/ path detection so `nix run` / `nix profile install`
installs (which don't set HERMES_MANAGED) are correctly identified as
"nix" rather than falling through to "git"/"unknown".
- Retired install-method values ("pip", "homebrew") in existing
.install_method stamps (both code-scoped and home-scoped) are ignored by
the allowlist reader and fall through to "unknown" instead of resurrecting
a retired enum value.
- Updates Nix packaging to ship bare runtime data (locales, optional-mcps)
through store symlinks and wrapper env vars instead of wheel data-files.
- Removes the ACP Registry manifest/icon and their version-lockstep tests.
- Deletes or rewrites packaging, pip-update, Homebrew, and ACP Registry
tests; adds parametrized coverage for the packaging build guard covering
BOTH sdist and wheel paths (the guards live in separate cmdclass entries
— a passing sdist test proves nothing about the wheel path).
- Updates installation/platform documentation and related user-facing copy.
- Adjusts the supply-chain scan so deleted install-hook files do not trigger
a finding, while additions or modifications still require the existing
ci-reviewed label gate.
Supported installation paths (unchanged):
- git installer (install.sh)
- Docker
- Nix/NixOS
- editable development installs (uv sync, uv pip install -e ., pip install -e .)
Children inherit the parent's env, repo, and toolsets but were denied
execute_code ('children should reason step-by-step, not write scripts').
That forces subagents doing mechanical multi-step work (batch file
reads, fetch-N-pages loops, filter-before-context reductions) to burn
reasoning iterations one tool call at a time.
- Remove execute_code from DELEGATE_BLOCKED_TOOLS
- Stop stripping the code_execution toolset from child bundles
- No recursion risk: the sandbox bridges only the 7 SANDBOX_ALLOWED_TOOLS
(web/file/terminal) — delegate_task and execute_code itself are not
reachable from inside a sandbox script
- Update schema text, AGENTS.md, and delegation docs
- Tests: blocked-constant, strip, and child-assembly tests updated;
new test pins execute_code as intentionally unblocked
Slow language servers (tsserver on large projects especially) publish
diagnostics long after an edit. The client's wait/report path had three
holes that together surfaced the PREVIOUS edit's errors as if they were
current ("ghost diagnostics"), sending the agent chasing errors it had
already fixed:
1. open_file only cleared the diagnostic stores on first open — on the
didChange path (every subsequent edit) stale push/pull entries
survived.
2. wait_for_diagnostics' predicates were satisfiable by that leftover
state (`path in _published`, `path in _pull_diagnostics`), so the
"wait" often returned instantly with old data.
3. diagnostics_for merged the stale push store unconditionally, so even
a fresh clean pull got the old error merged back in.
Fix: anchor freshness on a per-file didChange timestamp.
- Pull results record their request send-time and are dropped when a
didChange raced past them; the pull store is invalidated on every
change, not just first open.
- wait_for_diagnostics now returns bool (fresh data vs timeout), only
counts pushes published at/after the change (and version >= ours when
the server echoes versions), and accepts an explicit timeout — the
user's lsp.wait_timeout config now actually controls the inner wait
budget instead of only the outer thread-join.
- diagnostics_for(fresh_only=True) excludes stores that predate the
latest change; all manager report paths use it.
- On timeout the manager returns [] ("no data") instead of stale
state, logs a WARNING via eventlog, and does NOT mark the server
broken — slow is not dead.
- seed-on-first-push no longer marks the file published, so the TS
seed push can't satisfy a waiter.
Tests: new "stale" and "slow_push" mock-server scripts model the slow
tsserver, plus client- and service-level regression tests
(tests/agent/lsp/test_stale_diagnostics.py).
The copy-paste config sample had reasoning_effort: low active, which
would silently downshift effort for anyone pasting the block. Keep it
commented like other optional keys. Also add the contributor email
mapping for the salvage.
grok-4.5 is xAI's newest release (their versioning is non-monotonic:
4.5 > 4.20) and is the model xAI's own docs use for the server-side
x_search tool. Users who explicitly pinned x_search.model keep their
choice; everyone else picks up the new default via the config
deep-merge — no _config_version bump needed.
- tools/x_search_tool.py: DEFAULT_X_SEARCH_MODEL
- hermes_cli/config.py: DEFAULT_CONFIG x_search.model + comment
- agent/reasoning_timeouts.py: 300s stale-timeout floor entry for
grok-4.5 (grok-4.20-reasoning entry kept for pinned users)
- docs: x-search.md en + zh-Hans (config sample + troubleshooting)
- tests: default-model assertion + timeout-floor positive case
* feat(delegation): live-viewable subagent transcripts for delegate_task
Each child now streams an append-only, human-readable log to
<hermes_home>/cache/delegation/live/<delegation_id>/task-<n>.log while it
runs, and the dispatch return includes the paths so the caller can tail
them immediately instead of waiting blind for the consolidated summary.
- New tools/delegation_live_log.py: LiveTranscriptWriter (per-event append
+ flush, one-line rendering with truncation, never raises into the agent
loop), wrap_progress_callback (tees the child's existing
tool_progress_callback events into the log, preserves the _flush
contract), dispatch-time creation with pre-headered files so tail -f
attaches immediately, manifest.json (goals/task count/per-task status),
and 7-day retention pruning on new dispatches.
- delegate_task: wraps each child's progress callback with the writer;
sync results and background dispatch responses gain live_transcripts
(+ hint field on dispatch); per-task result entries carry
live_transcript; transcripts finalized with exit-reason markers.
- async_delegation: dispatch_async_delegation_batch accepts an optional
delegation_id so the live/ dir name matches the returned handle; the
completion event carries live_transcripts.
- process_registry: consolidated batch-completion block references each
task's live transcript path.
- Tool schema description documents the live_transcripts return surface;
docs gain a 'Live Transcripts' section with a tail -f example.
Placement under cache/delegation means the logs are mounted read-only
into remote terminal backends for free. Side-channel only: zero changes
to message content, so prompt caching is unaffected. Transcript-OUT only
— no overlap with the subagent control surfaces of PR #66046.
* fix(delegation): label the kickoff transcript line as user — it is the child's one user message
Add an end-to-end developer guide for extending the native Hermes Desktop
app introduced in #60638: the HermesPlugin contract, PluginContext, every
contribution area (panes, routes, sidebar nav, status/title bar, palette,
keybinds, themes, composer, mount-scoped Contribute), the host API, the
React Query + nanostores data layer, the UI kit + theme variables, the
scoped ctx.rest/ctx.socket backend (plugin_api.py under /api/plugins/<id>)
and its separate enable gate, Settings/defaultEnabled/storage, bundled
plugins, the security model, pitfalls, and a full reference.
- Register the page in the sidebar under Extending -> Plugins.
- Disambiguate from the unrelated web-dashboard plugin SDK from both
directions, and add a map-table row + a desktop user-guide pointer.
- Fill the gaps in the agent-facing hermes-desktop-plugins skill
(ctx.rest/socket + backend, React Query, defaultEnabled, Contribute)
and point it at the new reference so agents know the SDK when writing
addons.
The reported confusion was an unblocked task 'unpredictably' ending up in
triage. unblock itself only ever routes to ready/todo; a subsequent same-cause
re-block hitting BLOCK_RECURRENCE_LIMIT is what escalates to triage. Document
this deterministic loop-breaker at the human-facing lifecycle level so users
stop reading it as an LLM decision.
- codex-app-server-runtime.md: add a Live display section covering the
stream/reasoning/tool-card bridge and show_commentary gating.
- release.py: AUTHOR_MAP entries for HaiderSultanArc, jjadeo-oss, juanfradb
(the latter two for forthcoming follow-up salvages of #62396 / #18050).
Community feedback (@LSanapalli on X): the inline task-creation form is
cramped inside a ~280px column with no way to resize; board-level
workspace defaults can't be changed after board creation; and users
believe they must block a task, comment, then unblock just to talk to
a worker.
- Create-task dialog: replace the inline column form with a centered
modal (reuses hermes-kanban-dialog chrome, 36rem wide) with labeled
fields for title, assignee, priority, skills, workspace kind/path,
goal mode, and parent task. Same request shape; Enter/Escape behavior
preserved; submit disabled until a title is present.
- Board settings dialog: new Settings button in the board switcher opens
a modal to edit display name, description, and the board-level default
project directory (default_workdir). PATCH /boards/:slug now accepts
default_workdir (validated absolute existing dir; empty string clears;
omitted leaves unchanged) and returns the recomputed
default_workspace_kind so task-creation defaults follow immediately.
- Comment workflow hint: the task drawer's comment box now explains that
comments land on the thread immediately and reach the worker on its
next run/kanban_show() — no block/unblock dance needed — with a fuller
tooltip for when blocking IS the right tool.
- i18n: new keys optional in the kanban namespace with English fallbacks
in the bundle (established pattern; avoids churning 17 locale files).
- Docs: dashboard section updated for the dialog + Settings button.
* feat(browser): store full snapshots on truncation; make eval denylist opt-in
Two harness fixes motivated by BU_Bench results where fixed-verb + lossy
observation cost Hermes heavily vs code-driven browser agents:
1. Snapshot truncation no longer loses content. When a snapshot exceeds
the 8000-char threshold, the complete accessibility tree is saved to
cache/web (same truncate-and-store pattern as web_extract) and the
truncated view / LLM summary includes the file path plus a ready-made
read_file call. Element refs beyond the cut are recoverable without
re-snapshotting. Stored copies are force-redacted and capped at 2MB;
content-hash filenames dedupe repeated snapshots of the same page.
2. The browser_console(expression=...) sensitive-primitive denylist is
now opt-in via browser.restrict_evaluate (default false). The
names-based denylist blocked legitimate DOM extraction — any selector
or expression containing 'fetch', 'cookie', 'input', etc. — which
crippled the agent's only programmatic page-inspection path. The
SSRF/private-URL egress guards in _browser_eval are independent of
this policy and remain always-on. browser.allow_unsafe_evaluate keeps
its meaning (bypass the denylist) for configs that already set it.
* test: update None-guard test for stored-snapshot pointer in _extract_relevant_content
test_normal_content_returned pinned the exact return value; the summary
now carries a pointer to the stored full snapshot. Assert the summary
passes through and the pointer is present instead.
* feat(browser): align snapshot threshold with web_extract's 15k char budget
SNAPSHOT_SUMMARIZE_THRESHOLD 8000 -> 15000, matching
web_tools.DEFAULT_EXTRACT_CHAR_LIMIT so the snapshot and web_extract
truncate-and-store paths give the model the same per-page budget.
_truncate_snapshot's default max_chars now follows the constant.
Invariant test added; docs (en+zh) and CLI tip updated.
_clean_reasoning_effort kept its own whitelist that stopped at 'max',
silently dropping 'ultra' from MoA slot configs. Route it through
hermes_constants.parse_reasoning_effort — the same one-source-of-truth
fix the salvaged commit applies to the gateway — so future effort
levels can't drift here either. Docs updated to list ultra.
Follow-up to salvaged PR #64012.
Adds the proxied-callback option to the remote/headless OAuth section,
links the mcp-oauth-remote-gateway skill for fully headless gateways,
and documents the WAF pitfall behind redirect_host.
PR #36051's values went stale since May 31: session-store SCHEMA_VERSION
is now 21 (PR said 14), and the dashboard ships 8 built-in themes
(PR said 7). Also document the v16/v18/v20 data migrations added since.
Cross-checked website/docs against the source at main HEAD and corrected
documented commands, env vars, config keys, headers, and default values
that don't match the code. Docs-only; no behavioral changes.
Refs #36048
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Fixes#62452. Two amplifiers turned one slow auxiliary route into a
per-turn multi-minute stall:
1. Fallback candidates inherited the exact effective_timeout the primary
was called with. When the primary's deadline was short (tuned or
already burned), an independently healthy fallback died on the same
clock — the reporter's 163k-token compression needed ~90s on the
fallback and got the primary's 30s, every turn. fallback_chain
entries may now declare their own 'timeout' (seconds); both fallback
candidate call sites (sync + async) resolve it via
_fallback_entry_timeout, label-scoped so only configured-chain
candidates are affected. No entry timeout → task-level timeout,
preserving existing behavior.
2. A session whose transcript structurally cannot be summarized within
the deadline re-attempted every 60s, re-burning the full timeout on
every subsequent turn. Consecutive timeout-class failures now
escalate the cooldown 60s → 300s → 900s (capped); any successful
summary or session reset clears the streak. Timeout classification
takes precedence over the streaming-closed 30s rung ('timed out'
also matches _is_connection_error) and now recognizes the SDK's
'Request timed out.' phrasing.
Fail-safe behavior is unchanged: all messages are preserved when every
candidate fails; the cooldown only spaces out retries.
Document that safe-root violations are hard-blocked (not approval-gated),
add a security guide section for write_file/patch guards, and link cron
and verifier docs so users trust the footer over agent summaries.
- Nudge text now warns that repeated protocol violations will block the
task and require manual intervention, so the model understands the
consequence of ignoring the nudge.
- Kanban docs restructured to clearly separate the two defense layers:
agent-side prevention (nudge, from #64350) and dispatcher-side
recovery (bounded retry, from this PR).
- Two new integration tests verifying the nudge mentions blocking and
that the agent-side and dispatcher-side budgets are independent.
Fold in kevinb361's suggested lifecycle wording (#61817 conceded in favor of
this PR) and update the second stale site his sweep didn't cover: the
task-events table still said the dispatcher 'auto-blocks immediately instead
of retrying'. Both now describe the violation-only streak: protocol_violation
fires on every violation (its payload marker feeds the budget), below-budget
runs return the task to ready, and gave_up + auto-block happen only when the
consecutive streak reaches _PROTOCOL_VIOLATION_FAILURE_LIMIT (default 3,
per-task max_retries overriding).
Sherman
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Carlos Diosdado
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SHL0MS
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embwl0x
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ethernet
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