chore: ruff auto-fix PLR6201 resweep — tuple → set in membership tests (#27355)
Six days after #23937 (608 fixes) the codebase had accumulated 241 new
PLR6201 violations. Same mechanical x in (...) → x in {...} fix,
same zero-risk profile: set lookup is O(1) vs O(n) for tuple and the
two are semantically equivalent for hashable scalar membership tests.
All 241 instances fixed via `ruff check --select PLR6201 --fix
--unsafe-fixes`, zero remaining. Every changed value is a hashable
scalar (str/int/None/enum/signal); no risk of unhashable runtime
errors. No behavior change.
Test plan:
- 119 files changed, +244/-244 (net zero) — exactly one-line edits
- ruff check clean afterward
- Compile checks pass on the largest touched files (cli.py, run_agent.py,
gateway/run.py, gateway/platforms/discord.py, model_tools.py)
- Subset broad test run on tests/gateway/ tests/hermes_cli/ tests/agent/
tests/tools/: 18187 passed, 59 pre-existing failures (verified against
origin/main with the same shape — identical failure count, identical
category — all xdist test-order flakes unrelated to this change)
Follows the same template as PR #23937 ([tracker: #23972](https://github.com/NousResearch/hermes-agent/issues/23972)).
fix(xai): restore encrypted reasoning replay across turns
xAI partner integration requires Hermes to thread encrypted_content
reasoning items back to the Responses API on every turn so Grok can
maintain cross-turn reasoning coherence. PR #26644 (May 15) gated this
off for is_xai_responses on the theory that the OAuth/SuperGrok
surface rejected replayed encrypted blobs and produced the multi-turn
"Expected to have received \response.created\ before \error\"
failure. That diagnosis was wrong — the prelude-SSE fallback added in
the same PR is what actually fixed that failure mode. Suppressing the
replay was an unnecessary side-effect that broke the whole point of
xAI's partnership integration.
Changes:
- agent/codex_responses_adapter.py — drop the is_xai_responses gate
in _chat_messages_to_responses_input. Keep the kwarg in the
signature for transport compatibility; update the docstring to
document the May 2026 reversal.
- agent/transports/codex.py — restore
kwargs["include"] = ["reasoning.encrypted_content"] on the xAI
Responses path so xAI echoes encrypted reasoning back to us.
- tests/run_agent/test_codex_xai_oauth_recovery.py — flip the three
xAI assertions (now: xAI MUST receive replayed reasoning AND we MUST
include encrypted_content in the request).
- tests/agent/transports/test_codex_transport.py — flip the
include assertions on test_xai_reasoning_effort_passed and
test_xai_grok_4_omits_reasoning_effort; update the allowlist
block comment.
The prelude-SSE fallback and the entitlement-403 surfacing fixes from
#26644 are untouched — they were independent fixes that happened to
ride along with the reasoning-replay gate.
Validation:
- Targeted: tests/run_agent/test_codex_xai_oauth_recovery.py +
tests/agent/transports/test_codex_transport.py → 65/65 pass
- Broader: tests/agent/transports/ + tests/run_agent/ →
1674 passed, 3 skipped, 0 failures
- E2E (real imports, isolated HERMES_HOME, ResponsesApiTransport
build_kwargs): turn-1 request carries
include: ["reasoning.encrypted_content"]; turn-2 input replays
the encrypted_content blob from turn-1's
codex_reasoning_items; native Codex unchanged.
chore: ruff auto-fix PLR6201 — tuple → set in membership tests (#23937)
Replace with for all literal-tuple
membership tests. Set lookup is O(1) vs O(n) for tuple — consistent
micro-optimization across the codebase.
608 instances fixed via ruff --fix --unsafe-fixes, 0 remaining.
133 files, +626/-626 (net zero).
feat(sessions): opt-in per-session JSON snapshot writer
PR #29182 deleted the per-session JSON snapshot writer outright because
state.db is canonical and the snapshots had no in-tree consumer. Some
users have external tooling that reads ~/.hermes/sessions/session_{sid}.json
directly, so reintroduce the writer behind a config flag that defaults
to off.
- Add sessions.write_json_snapshots (default False) to DEFAULT_CONFIG
- Restore AIAgent._save_session_log + _clean_session_content as
gated methods. When the flag is off the call is a fast no-op; when
on, the writer behaves as before (atomic write, truncation guard
preserved, REASONING_SCRATCHPAD → think tag normalization)
- Re-derive the target path from agent.session_id on each call so
/branch and /compress re-points happen automatically — no need
to restore the explicit re-point bookkeeping at call sites
- Wire the single call site in _persist_session (the cleanup-on-exit
hook). Did NOT restore the 7 intra-turn calls the original PR deleted
— those were redundant writes within the same turn that doubled disk
I/O without adding any persistence guarantee _persist_session does
not already provide
- Read the flag once at agent init via load_config(), cache as
agent._session_json_enabled
- Update TestNoSessionJsonSnapshot → TestSessionJsonSnapshotOptIn
to pin behavior: default off (no file), opt-in true (file written),
no-op method on default agents, logs_dir retained unconditionally
- Update CONTRIBUTING.md and the bundled hermes-agent skill to
document the flag and its default
fix(async): close unscheduled coroutines in all threadsafe bridges (#26584)
Wraps every sync->async coroutine-scheduling site in the codebase with a
new agent.async_utils.safe_schedule_threadsafe() helper that closes the
coroutine on scheduling failure (closed loop, shutdown race, etc.)
instead of leaking it as 'coroutine was never awaited' RuntimeWarnings
plus reference leaks.
22 production call sites migrated across the codebase:
- acp_adapter/events.py, acp_adapter/permissions.py
- agent/lsp/manager.py
- cron/scheduler.py (media + text delivery paths)
- gateway/platforms/feishu.py (5 sites, via existing _submit_on_loop helper
which now delegates to safe_schedule_threadsafe)
- gateway/run.py (10 sites: telegram rename, agent:step hook, status
callback, interim+bg-review, clarify send, exec-approval button+text,
temp-bubble cleanup, channel-directory refresh)
- plugins/memory/hindsight, plugins/platforms/google_chat
- tools/browser_supervisor.py (3), browser_cdp_tool.py,
computer_use/cua_backend.py, slash_confirm.py
- tools/environments/modal.py (_AsyncWorker)
- tools/mcp_tool.py (2 + 8 _run_on_mcp_loop callers converted to
factory-style so the coroutine is never constructed on a dead loop)
- tui_gateway/ws.py
Tests: new tests/agent/test_async_utils.py covers helper behavior under
live loop, dead loop, None loop, and scheduling exceptions. Regression
tests added at three PR-original sites (acp events, acp permissions,
mcp loop runner) mirroring contributor's intent.
Live-tested end-to-end:
- Helper stress test: 1500 schedules across live/dead/race scenarios,
zero leaked coroutines
- Race exercised: 5000 schedules with loop killed mid-flight, 100 ok /
4900 None returns, zero leaks
- hermes chat -q with terminal tool call (exercises step_callback bridge)
- MCP probe against failing subprocess servers + factory path
- Real gateway daemon boot + SIGINT shutdown across multiple platform
adapter inits
- WSTransport 100 live + 50 dead-loop writes
- Cron delivery path live + dead loop
Salvages PR #2657 — adopts contributor's intent over a much wider site
list and a single centralized helper instead of inline try/except at
each site. 3 of the original PR's 6 sites no longer exist on main
(environments/patches.py deleted, DingTalk refactored to native async);
the equivalent fix lives in tools/environments/modal.py instead.
Co-authored-by: JithendraNara <jithendranaidunara@gmail.com>
fix(xai-oauth): pin inference base_url to x.ai origin (#28952)
XAI_BASE_URL / HERMES_XAI_BASE_URL let users repoint the OAuth-authenticated
inference endpoint, but the env override was an unguarded credential-leak
vector: a tampered .env or hostile shell init setting
XAI_BASE_URL=https://attacker.example/v1 would silently ship the SuperGrok
OAuth bearer to a third party on every request.
Add _xai_validate_inference_base_url() that pins the host to x.ai or a
*.x.ai subdomain and rejects non-HTTPS. On rejection, fall back to the
default with a warning rather than raise — a bad env var should not
deadlock auth, but should never leak the bearer either.
Apply at all three sites that read the env override for xai-oauth:
- hermes_cli/auth.py resolve_xai_oauth_runtime_credentials (main path)
- hermes_cli/auth.py _xai_oauth_loopback_login (initial login)
- agent/auxiliary_client.py _resolve_xai_oauth_for_aux (aux client)
E2E validated against four scenarios: attacker.example, lookalike
api.x.ai.evil.com, http:// downgrade on api.x.ai, and legit custom.x.ai
subdomain (which still resolves correctly).
Discovered while comparing against the opencode-grok-auth plugin
(github.com/ysnock404/opencode-grok-auth), which highlighted the same
guard on the OpenCode side.
fix(xai): restore encrypted reasoning replay across turns
xAI partner integration requires Hermes to thread encrypted_content
reasoning items back to the Responses API on every turn so Grok can
maintain cross-turn reasoning coherence. PR #26644 (May 15) gated this
off for is_xai_responses on the theory that the OAuth/SuperGrok
surface rejected replayed encrypted blobs and produced the multi-turn
"Expected to have received \response.created\ before \error\"
failure. That diagnosis was wrong — the prelude-SSE fallback added in
the same PR is what actually fixed that failure mode. Suppressing the
replay was an unnecessary side-effect that broke the whole point of
xAI's partnership integration.
Changes:
- agent/codex_responses_adapter.py — drop the is_xai_responses gate
in _chat_messages_to_responses_input. Keep the kwarg in the
signature for transport compatibility; update the docstring to
document the May 2026 reversal.
- agent/transports/codex.py — restore
kwargs["include"] = ["reasoning.encrypted_content"] on the xAI
Responses path so xAI echoes encrypted reasoning back to us.
- tests/run_agent/test_codex_xai_oauth_recovery.py — flip the three
xAI assertions (now: xAI MUST receive replayed reasoning AND we MUST
include encrypted_content in the request).
- tests/agent/transports/test_codex_transport.py — flip the
include assertions on test_xai_reasoning_effort_passed and
test_xai_grok_4_omits_reasoning_effort; update the allowlist
block comment.
The prelude-SSE fallback and the entitlement-403 surfacing fixes from
#26644 are untouched — they were independent fixes that happened to
ride along with the reasoning-replay gate.
Validation:
- Targeted: tests/run_agent/test_codex_xai_oauth_recovery.py +
tests/agent/transports/test_codex_transport.py → 65/65 pass
- Broader: tests/agent/transports/ + tests/run_agent/ →
1674 passed, 3 skipped, 0 failures
- E2E (real imports, isolated HERMES_HOME, ResponsesApiTransport
build_kwargs): turn-1 request carries
include: ["reasoning.encrypted_content"]; turn-2 input replays
the encrypted_content blob from turn-1's
codex_reasoning_items; native Codex unchanged.
fix(xai): surface provider 'error' SSE frame in Codex fallback stream (#27184)
Original commit 2b193907d by Teknium added a new module-level
_StreamErrorEvent class and threaded its raise into
_run_codex_create_stream_fallback in pre-refactor run_agent.py.
- _StreamErrorEvent class → run_agent.py (module-level, next to
_qwen_portal_headers; class needs to be top-level for the codex
runtime to import it)
- The fallback event-loop's 'type=error' handler → agent/codex_runtime.py
where run_codex_create_stream_fallback now lives. Imports
_StreamErrorEvent lazily from run_agent to avoid circular import.
Co-authored-by: Teknium <127238744+teknium1@users.noreply.github.com>
fix(compress): make abort-on-summary-failure opt-in via config flag (#28117)
PR #28102 made the summary-failure abort path the unconditional default,
changing established behavior. Gate it behind config.yaml flag
compression.abort_on_summary_failure (default False = historical
fallback-placeholder behavior).
- hermes_cli/config.py: new compression.abort_on_summary_failure key,
default False, documented inline.
- agent/agent_init.py: read the flag from compression config and pass to
ContextCompressor.
- agent/context_compressor.py: __init__ accepts abort_on_summary_failure
(default False). compress() failure branch gates the abort on the
flag; when False, falls through to the restored legacy fallback path
(static "summary unavailable" placeholder + drop middle window).
- tests: restore original fallback expectations as default; add new
TestAbortOnSummaryFailure class for the opt-in mode.
Gateway/CLI plumbing (force=True on /compress, hygiene/handler abort
detection, locale gateway.compress.aborted key) from PR #28102 stays
intact — those paths only fire when _last_compress_aborted is True,
which now only happens when the flag is enabled.
fix(compression): keep default protect_first_n at 3 + align ABC
Follow-up on the salvaged feat commit:
- Keep the constructor / config / yaml-example default at 3 so existing
gateway and CLI users see no behavioural change. PR #13754 (which this
builds on) had lowered the default to 2 to chase pre-feature parity in
the system-prompt-present case, at the cost of quietly halving the
protected head for the gateway path (which strips the system prompt
before calling compress()). With the new "system prompt is implicit"
semantics, default 3 gives every caller a stable head shape.
- agent/context_engine.py: bring the ABC's protect_first_n docstring in
line with the new semantics so plugin context engines interpret the
config key the same way the built-in compressor does.
- tests: adjust the default-value test (3, not 2) and a stale comment;
per-test protect_first_n=2/3/1 values added in PR #13754 stay as-is
since those tests fix concrete head shapes.
feat(curator): hint at hermes curator pin in the rename block (#23212)
Surfaces the pin command at the moment users care about it: when a
consolidation just landed against their skill library and they're
looking at the umbrella name in the curator output. Previously `hermes
curator pin` existed but had no discovery surface — users only learned
it existed by reading docs or stumbling onto hermes curator --help.
The hint:
archived 3 skill(s):
• docx-extraction → document-tools
• pdf-extraction → document-tools
• old-stale — pruned (stale)
full report: hermes curator status
keep an umbrella stable: hermes curator pin document-tools
Gated on having at least one consolidation that produced an umbrella.
Pruned-only runs (nothing surviving to pin) skip the hint. When
multiple umbrellas were produced, picks alphabetically first as a
concrete example rather than listing them all.
3 new tests in tests/agent/test_curator_classification.py covering:
consolidation produces hint with real umbrella name, pruned-only run
omits it, multi-umbrella picks one example.
fix(curator): authoritative absorbed_into on delete + restore cron skill links on rollback (#18671) (#18731)
* fix(curator): authoritative absorbed_into declarations on skill delete
Closes #18671. The classification pipeline that feeds cron-ref rewriting
used to infer consolidation vs pruning from two brittle signals: the
curator model's post-hoc YAML summary block, and a substring heuristic
scanning other tool calls for the removed skill's name. Both miss in
real consolidations — the model forgets the YAML under reasoning
pressure, and the heuristic misses when the umbrella's patch content
describes the absorbed behavior abstractly instead of naming the old
slug. When both miss, the skill falls through to 'no-evidence fallback'
pruned, and #18253's cron rewriter drops the cron ref entirely instead
of mapping it to the umbrella. Same observable symptom as pre-#18253:
'Skill(s) not found and skipped' at the next cron run.
The fix makes the model declare intent at the moment of deletion.
skill_manage(action='delete') now accepts absorbed_into:
- absorbed_into='<umbrella>' -> consolidated, target must exist on disk
- absorbed_into='' -> explicit prune, no forwarding target
- missing -> legacy path, falls through to heuristic/YAML
The curator reconciler reads these declarations off llm_meta.tool_calls
BEFORE either the YAML block or the substring heuristic. Declaration
wins. Fallback logic stays intact for backward compat with any caller
(human or older curator conversation) that doesn't populate the arg.
Changes
- tools/skill_manager_tool.py: add absorbed_into param to skill_manage
+ _delete_skill. Validate target exists when non-empty. Reject
absorbed_into=<self>. Wire through dispatcher + registry + schema.
- agent/curator.py: new _extract_absorbed_into_declarations() walks
tool calls for skill_manage(delete) with the arg. _reconcile_classification
accepts absorbed_declarations= and treats them as authoritative. Curator
prompt updated to require the arg on every delete.
- Tests: 7 new skill_manager tests covering the tool contract (valid
target, empty string, nonexistent target, self-reference, whitespace,
backward compat, dispatcher plumbing). 11 new curator tests covering
the extractor + authoritative reconciler path + mixed-legacy-and-
declared runs.
Validation
- 307/307 targeted tests pass (curator + cron + skill_manager suites).
- E2E #18671 repro: 3 narrow skills, 1 umbrella, cron job referencing
all 3. Model emits NO YAML block. Heuristic misses (patch prose
doesn't name old slugs). Delete calls carry absorbed_into. Result:
both PR skills correctly classified 'consolidated' + cron rewritten
['pr-review-format', 'pr-review-checklist', 'stale-junk'] ->
['hermes-agent-dev']; stale-junk pruned via absorbed_into=''.
- E2E backward-compat: delete without absorbed_into, model emits YAML
-> routed via existing 'model' source, cron still rewritten correctly.
* feat(curator): capture + restore cron skill links across snapshot/rollback
Before this, rolling back a curator run restored the skills tree but cron
jobs still pointed at the umbrella skills the curator had rewritten them
to. The user would see their old narrow skills back on disk but their
cron jobs still configured with the merged umbrella — not actually 'back
to how it was'.
Snapshot side: snapshot_skills() now captures ~/.hermes/cron/jobs.json
alongside the skills tarball, as cron-jobs.json. The manifest gets a new
'cron_jobs' block with {backed_up, jobs_count} so rollback (and the CLI
confirm dialog) can surface what's in the snapshot. If jobs.json is
missing/unreadable/malformed, snapshot proceeds without cron data — the
skills backup is the core guarantee; cron is additive.
Rollback side: after the skills extract succeeds, the new
_restore_cron_skill_links() reconciles the backed-up jobs into the live
jobs.json SURGICALLY. Only 'skills' and 'skill' fields are restored, and
only on jobs matched by id. Everything else about a cron job — schedule,
last_run_at, next_run_at, enabled, prompt, workdir, hooks — is live
state the user or scheduler has modified since the snapshot; overwriting
it would regress unrelated activity.
Reconciliation rules:
- Job in backup AND live, skills differ → skills restored.
- Job in backup AND live, skills match → no-op.
- Job in backup, NOT in live → skipped (user deleted it
after snapshot; their choice
is later than the snapshot).
- Job in live, NOT in backup → untouched (user created it
after snapshot).
- Snapshot missing cron-jobs.json at all → rollback still succeeds,
reports 'not captured'
(older pre-feature snapshots
keep working).
Writes go through cron.jobs.save_jobs under the same _jobs_file_lock the
scheduler uses, so rollback doesn't race tick().
Also:
- hermes_cli/curator.py: rollback confirm dialog now shows
'cron jobs: N (will be restored for skill-link fields only)' when the
snapshot has cron data, or 'not in snapshot (<reason>)' otherwise.
- rollback()'s message string includes a 'cron links: ...' clause
summarizing the reconciliation outcome.
Tests
- 9 new cases: snapshot-with-cron, snapshot-without-cron, malformed-json
captured-as-raw, full rollback-restores-skills-and-cron, rollback
touches only skill fields, rollback skips user-deleted jobs, rollback
leaves user-created jobs untouched, rollback still works with
pre-feature snapshot that has no cron-jobs.json, standalone unit test
on _restore_cron_skill_links exercising the full report shape.
Validation
- 484/484 targeted tests pass (curator + cron + skill_manager suites).
- E2E: real snapshot_skills, real cron rewrite, real rollback. Before:
['pr-review-format', 'pr-review-checklist', 'pr-triage-salvage'].
After curator: ['hermes-agent-dev']. After rollback: ['pr-review-format',
'pr-review-checklist', 'pr-triage-salvage']. Non-skill fields (id,
name, prompt) preserved across the round trip.
security(file-safety): also write-deny <root>/.env when running under a profile (#15981)
build_write_denied_paths() resolved the protected .env via
get_hermes_home(), which is profile-aware. When a profile is active
HERMES_HOME points at <root>/profiles/<name> and hermes_home / ".env"
expands to the *profile* env file only — the global <root>/.env is left
off the deny list and a write_file call against it succeeds. Since the
top-level .env supplies credentials inherited by every profile, this is a
P0 credential-exfiltration / overwrite path.
Add a parallel _hermes_root_path() helper that returns the Hermes root
(via the existing get_default_hermes_root() constant) and include
<root>/.env in the deny list alongside <active_profile>/.env. Both
paths now refuse write_file/patch regardless of profile state. The active
HERMES_HOME .env entry is preserved so the protection in non-profile mode
is unchanged.
A regression test exercises the profile-active scenario by pointing
HERMES_HOME at <tmp>/profiles/coder and asserting that <tmp>/.env
is denied.
Fixes #15981
fix(auxiliary): evict async wrappers on poisoned client (follow-up to #23482)
#23482 fixed cache poisoning in the sync path: when a Codex auxiliary
timeout closes the underlying OpenAI client, _evict_cached_client_instance
walks CodexAuxiliaryClient wrappers via their _real_client attribute and
drops the cache entry so the next aux call rebuilds.
The cache key includes async_mode (see _client_cache_key), so the sync and
async clients for the same provider live in two distinct entries pointing
at the same underlying transport. The fix walked the sync wrapper's
_real_client correctly but the async wrappers
(AsyncCodexAuxiliaryClient, AsyncAnthropicAuxiliaryClient,
AsyncGeminiNativeClient) never exposed _real_client at all, so the async
entry survived eviction and kept handing out the poisoned client.
Effect on async aux callers: one timeout now poisons every subsequent
async aux call (compression, vision, session_search, title_generation)
with 'Connection error' until gateway restart -- even while the sync
route recovered as designed in #23482.
Mirror the sync wrapper's _real_client onto each async wrapper so the
existing eviction helper finds them. Three changes, one per wrapper:
- AsyncCodexAuxiliaryClient: self._real_client = sync_wrapper._real_client
(the underlying OpenAI client)
- AsyncAnthropicAuxiliaryClient: same shape
- AsyncGeminiNativeClient: self._real_client = sync_client (Gemini's
native facade is itself the leaf; no OpenAI client beneath it)
Update _evict_cached_client_instance docstring to reflect that it now
covers both sync and async wrappers via the same attribute walk.
Test: TestAuxiliaryClientPoisonedCacheEviction.test_evict_cached_client_instance_walks_async_wrapper
seeds both sync and async cache entries pointing at the same leaf and
asserts both are dropped on a single eviction call. Verified the test
fails without the wrapper changes ("async cache entry survived
eviction -- wrapper is missing _real_client") and passes with them.
Refs #23482, #23432
chore: remove unused imports and dead locals (ruff F401, F841) (#17010)
Mechanical cleanup across 43 files — removes 46 unused imports
(F401) and 14 unused local variables (F841) detected by
ruff check --select F401,F841. Net: -49 lines.
Also fixes a latent NameError in rl_cli.py where get_hermes_home()
was called at module line 32 before its import at line 65 — the
module never imported successfully on main. The ruff audit surfaced
this because it correctly saw the symbol as imported-but-unused
(the call happened before the import ran); the fix moves the import
to the top of the file alongside other stdlib imports.
One # noqa: F401 kept in hermes_cli/status.py for subprocess:
tests monkeypatch hermes_cli.status.subprocess as a regression
guard that systemctl isn't called on Termux, so the name must
exist at module scope even though the module body doesn't reference
it. Docstring explains the reason.
Also fixes an invalid # noqa: directive in
gateway/platforms/discord.py:308 that lacked a rule code.
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
chore: remove unused imports and dead locals (ruff F401, F841) (#17010)
Mechanical cleanup across 43 files — removes 46 unused imports
(F401) and 14 unused local variables (F841) detected by
ruff check --select F401,F841. Net: -49 lines.
Also fixes a latent NameError in rl_cli.py where get_hermes_home()
was called at module line 32 before its import at line 65 — the
module never imported successfully on main. The ruff audit surfaced
this because it correctly saw the symbol as imported-but-unused
(the call happened before the import ran); the fix moves the import
to the top of the file alongside other stdlib imports.
One # noqa: F401 kept in hermes_cli/status.py for subprocess:
tests monkeypatch hermes_cli.status.subprocess as a regression
guard that systemctl isn't called on Termux, so the name must
exist at module scope even though the module body doesn't reference
it. Docstring explains the reason.
Also fixes an invalid # noqa: directive in
gateway/platforms/discord.py:308 that lacked a rule code.
Co-authored-by: teknium1 <teknium@users.noreply.github.com>
feat(i18n): localize all gateway commands + web dashboard, add 8 new locales (16 total) (#22914)
* feat(i18n): localize /model command output
Reported by @tianma8888: when Chinese users run /model, the labels
("Provider:", "Context:", "_session only_", etc.) are still English.
This routes the static prose through the existing i18n catalog so it
follows display.language / HERMES_LANGUAGE.
Changes:
- locales/{en,zh,ja,de,es,fr,tr,uk}.yaml: add 17 keys under
gateway.model.* covering switched/provider/context/max_output/cost/
capabilities/prompt_caching/warning/saved_global/session_only_hint/
current_label/current_tag/more_models_suffix/usage_*.
- gateway/run.py _handle_model_command: replace hardcoded f-strings in
the picker callback, the text-list fallback, and the direct-switch
confirmation block with t("gateway.model.<key>", ...).
What stays English:
- model IDs, provider slugs, capability strings, cost figures, and the
"[Note: model was just switched...]" prepended to the model's next
prompt (LLM-facing, not user-facing).
- The two slightly-different session-only hints unify on a single key
with the em-dash phrasing.
Validation: tests/agent/test_i18n.py 27/27 passing (parity contract
holds), tests/gateway/ -k 'model or i18n' 74/74 passing.
* feat(i18n): localize all gateway slash command outputs
Expands the i18n catalog from 7 strings to 234 keys across 35 gateway
slash command handlers, so non-English users see localized output for
\/profile\, \/status\, \/help\, \/personality\, \/voice\, \/reset\,
\/agents\, \/restart\, \/commands\, \/goal\, \/retry\, \/undo\,
\/sethome\, \/title\, \/yolo\, \/background\, \/approve\, \/deny\,
\/insights\, \/debug\, \/rollback\, \/reasoning\, \/fast\,
\/verbose\, \/footer\, \/compress\, \/topic\, \/kanban\,
\/resume\, \/branch\, \/usage\, \/reload-mcp\, \/reload-skills\,
\/update\, \/stop\ (plus the \/model\ block already added in the
previous commit).
Reported by @tianma8888 — Chinese users want command output prose in
their language, not just the labels we already had.
Translations are hand-written for all 8 supported locales (en, zh, ja,
de, es, fr, tr, uk), matching each catalog's existing style: full-width
punctuation in zh, em-dashes in zh/ja/uk, French spaced colons,
German noun capitalization, etc.
What stays English (unchanged):
- Identifiers/values: model IDs, file paths, profile names, session IDs,
command flag names like --global, URLs, config keys.
- Backtick code spans: \/foo\, \config.yaml\.
- Log messages (logger.info/warning/error).
- LLM-facing system notes prepended to next prompt (e.g. [Note: model
was just switched...]).
- Strings produced by external modules (gateway_help_lines,
format_gateway, manual_compression_feedback) — those have their
own surfaces.
New shared keys for cross-handler boilerplate:
- gateway.shared.session_db_unavailable (5 call sites: branch, title,
resume, topic, _disable_telegram_topic_mode_for_chat)
- gateway.shared.session_not_found (1 site)
- gateway.shared.warn_passthrough (2 sites in /title's f"⚠️ {e}" pattern)
YAML gotcha fixed: \yolo.on\ and \yolo.off\ were originally written
unquoted, which YAML 1.1 parses as boolean True/False keys. Renamed to
\yolo.enabled\ / \yolo.disabled\ for both safety and clarity.
Test fix: tests/agent/test_i18n.py::test_t_missing_key_in_non_english_falls_back_to_english
now resets the catalog cache on teardown, so the fake "foo: English Foo"
locale doesn't poison the module-level cache for subsequent tests in
the same xdist worker. (Without this, every gateway slash command test
that shares a worker with the i18n suite would see the fake catalog.)
Validation:
- tests/agent/test_i18n.py: 27/27 (parity contract — every key in every
locale, matching placeholder tokens).
- tests/gateway/: 5077 passed, 0 failed (full gateway suite).
- 180 t() call sites added across 35 handlers; 1872 catalog entries
total (234 keys × 8 locales).
* feat(i18n): add 8 new locales — af, ko, it, ga, zh-hant, pt, ru, hu
Expands the static-message catalog from 8 → 16 languages, each with full
270-key parity against the English source-of-truth. Every locale now
covers the same surface PR #22914 added: approval prompts plus all 35
gateway slash command outputs.
New locales:
- af Afrikaans (community ask in #21961 by @GodsBoy; PRs #21962, #21970)
- ko Korean (PRs #20297 by @tmdgusya, #22285 by @project820)
- it Italian (PR #20371 by @leprincep35700)
- ga Irish/Gaeilge (PR #20962 by @ryanmcc09-dot)
- zh-hant Traditional Chinese (PRs #20523 by @jackey8616, #13140 by @anomixer)
- pt Portuguese (PRs #20443 by @pedroborges, #15737 by @carloshenriquecarniatto, #22063 by @Magaav)
- ru Russian (PR #22770 by @DrMaks22)
- hu Hungarian (PR #22336 by @lunasec007)
Each locale uses native-quality translations matching the existing tone
and conventions of the older 8 locales:
- zh-hant uses 繁體 characters with TW/HK technical vocabulary (軟體
not 软件, 連線 not 连接, 設定 not 设置, 訊息 not 消息, 工作階段 not 会话, 程式
not 程序, 預設 not 默认, 伺服器 not 服务器), full-width punctuation 「：（）」.
- ko uses formal 합니다체 (습니다/합니다) register throughout.
- pt uses European Portuguese as baseline with neutral PT/BR vocabulary
where possible.
- ga uses standard An Caighdeán Oifigiúil; English loanwords retained
for tech terms without good Irish equivalents (gateway, API, JSON).
- All preserve {placeholder} tokens, backtick code spans, slash commands,
brand names (Hermes, MCP, TTS, YOLO, OpenAI, Telegram, etc.), and emoji.
Aliases added in agent/i18n.py:
- af-za, Afrikaans → af
- ko-kr, Korean, 한국어 → ko
- it-it, italiano → it
- ga-ie, Irish, Gaeilge → ga
- zh-tw, zh-hk, zh-mo, traditional-chinese → zh-hant (note: zh-tw used to
alias to zh; now aliases to its own zh-hant catalog)
- zh-cn, zh-hans, zh-sg → zh (unchanged from before)
- pt-pt, pt-br, brazilian, portuguese → pt
- ru-ru, Russian, русский → ru
- hu-hu, Magyar → hu
The zh-tw alias re-routing is intentional: previously typing 'zh-TW' got
the Simplified Chinese catalog (wrong vocabulary for Taiwan/HK users).
Now those users get the proper Traditional Chinese catalog.
Validation:
- tests/agent/test_i18n.py: 43/43 (parity contract holds for all 16
languages × 270 keys = 4320 catalog entries, with matching placeholder
tokens).
- E2E alias resolution verified for all 19 alias inputs (Afrikaans, ko-KR,
한국어, italiano, Gaeilge, zh-TW, zh-HK, traditional-chinese, pt-BR,
brazilian, Magyar, etc.).
- tests/gateway/: 5198 passed (3 pre-existing TTS routing failures
unrelated to i18n).
Credit to all contributors whose PRs surfaced these language requests.
Their original PRs may now be closed as superseded with credit.
* feat(dashboard-i18n): add 14 web dashboard locales matching the static catalog
Brings the React dashboard (web/src/) up to the same 16-language
coverage the static catalog already has after the previous commits in
this PR. The Translations interface is TypeScript-typed, so every new
locale must provide every key — tsc -b is the parity guard.
Languages added (each is a complete 429-line locale file):
- af Afrikaans
- ja Japanese (PR #22513 by @snuffxxx surfaced this)
- de German (PR #21749 by @mag1art)
- es Spanish (PR #21749)
- fr French (PRs #21749, #10310 by @foXaCe)
- tr Turkish
- uk Ukrainian
- ko Korean (PRs #21749, #18894 by @ovstng, #22285 by @project820)
- it Italian
- ga Irish (Gaeilge)
- zh-hant Traditional Chinese (PR #13140 by @anomixer)
- pt Portuguese (PRs #22063 by @Magaav, #22182 by @wesleysimplicio, #15737 by @carloshenriquecarniatto)
- ru Russian (PRs #21749, #22770 by @DrMaks22)
- hu Hungarian (PR #22336 by @lunasec007)
Each translation covers all 15 namespaces with full key parity vs en.ts,
preserves every {placeholder} token verbatim, keeps identifiers
untranslated (brand names, file paths, cron expressions, code spans),
translates the language.switchTo tooltip into the target language, and
matches existing tone conventions (zh-hant uses TW/HK vocab; ja uses
formal desu/masu; ko uses formal seumnida register; ga uses An
Caighdean Oifigiuil with English loanwords for tech vocab without good
Irish equivalents).
Plumbing:
- web/src/i18n/types.ts: Locale union expanded to all 16 codes.
- web/src/i18n/context.tsx: imports all 16 catalogs; exports
LOCALE_META (endonym + flag per locale); isLocale() type guard.
- web/src/i18n/index.ts: re-export LOCALE_META.
- web/src/components/LanguageSwitcher.tsx: replaced two-state EN-ZH
toggle with a click-to-open dropdown listing all 16 languages.
Note: zh-hant.ts exports zhHant (camelCase) since hyphen is invalid in
a JS identifier; the canonical 'zh-hant' string keys it in TRANSLATIONS.
Validation:
- npx tsc -b: 0 errors. Every locale satisfies Translations.
- npm run build (tsc + vite production): green, 2062 modules.
- Each locale file is exactly 429 lines.
Out of scope: plugin dashboards (kanban/achievements ship as prebuilt
bundles with no source in repo); Docusaurus docs (separate surface);
TUI (no i18n yet).
* feat(plugin-i18n): localize achievements + kanban plugin dashboards across all 16 locales
Brings the two shipped plugin dashboards (hermes-achievements, kanban)
under the same i18n umbrella as the core dashboard PR #22914 just
established. Both bundles now read user-facing strings from the host's
i18n catalog via SDK.useI18n() instead of hardcoded English.
## Approach
Plugin dashboards ship as prebuilt IIFE bundles in
plugins/<name>/dashboard/dist/index.js — no build step, no source in
repo (upstream-authored, vendored as compiled JS). Earlier contributor
PRs (#22594, #22595, #18747) tried direct edits but didn't actually
wire the bundles to read translations.
This change does the wiring properly:
1. Each bundle gets a useI18n shim at IIFE scope:
const useI18n = SDK.useI18n
|| function () { return { t: { kanban: null }, locale: "en" }; };
Older host SDKs without useI18n still load the bundle and render
English fallbacks.
2. A small tx(t, path, fallback, vars) helper resolves dotted keys
under the plugin's namespace (t.kanban.* or t.achievements.*) and
interpolates {placeholder} tokens.
3. Every React component starts with const { t } = useI18n() and
each user-visible string is wrapped in tx(t, "key", "English fallback").
Helpers called outside React components (window.prompt callers,
constants used during init) take t as a parameter.
4. Top-level constants that were English dictionaries (COLUMN_LABEL,
COLUMN_HELP, DESTRUCTIVE_TRANSITIONS, DIAGNOSTIC_EVENT_LABELS in
kanban) become getColumnLabel(t, status)-style functions backed by
FALLBACK_* dictionaries.
## Translations added
Two new top-level namespaces added to the dashboard's TypeScript-typed
Translations interface:
- achievements: ~70 keys covering the hero, scan banner, achievement
card, share dialog, stats, filters, and empty states.
- kanban: ~145 keys covering the board, columns (with nested
columnLabels and columnHelp sub-dicts), card detail panel,
bulk-actions toolbar, dependency editor, board switcher, and
diagnostic callouts.
Each key is provided across all 16 supported locales:
en, zh, zh-hant, ja, de, es, fr, tr, uk, af, ko, it, ga, pt, ru, hu.
Total new translation entries: ~3,440 (215 keys × 16 locales).
## What stays English (deliberate)
- API paths, CSS class names, data-* attributes, JSON keys, regex
strings, URLs, file paths (~/.hermes/kanban.db, boards/_archived/).
- State identifier strings used as lookup keys (triage / todo / ready /
running / blocked / done / archived) — labels translate, key strings
don't.
- The PNG share-card text rendered to canvas in the achievements
ShareDialog (HERMES AGENT watermark, UNLOCKED stamp, tier names) —
these become part of a globally-shared image and stay English.
- localStorage keys (hermes.kanban.selectedBoard).
- Brand names (Kanban, Hermes, WebSocket, Nous Research).
## Contributor credit
PR #22594 by @02356abc and PR #22595 by @02356abc supplied the
en + zh kanban namespace skeleton (145 keys); used as the en source-
of-truth in this commit and translated to the other 14 locales.
PR #18747 by @laolaoshiren first surfaced the achievements
localization request.
## Validation
- npx tsc -b: 0 errors. All 16 locale .ts files satisfy the
Translations type with full key parity.
- npm run build (tsc + vite production build): green, 2062 modules,
1.56MB JS / 95KB CSS, ~2.5s build.
- node --check on both plugin bundles: parse cleanly.
- 126 tx() call sites in kanban, 46 in achievements.
## Out of scope
- TUI (ui-tui/) has no i18n infrastructure yet.
- Docusaurus docs (website/i18n/) — already had zh-Hans; expanding
is a separate translation workstream (Thai / Korean / Hindi PRs).
feat(plugins): pluggable image_gen backends + OpenAI provider (#13799)
* feat(plugins): pluggable image_gen backends + OpenAI provider
Adds a ImageGenProvider ABC so image generation backends register as
bundled plugins under plugins/image_gen/<name>/. The plugin scanner
gains three primitives to make this work generically:
- kind: manifest field (standalone | backend | exclusive).
Bundled kind: backend plugins auto-load — no plugins.enabled
incantation. User-installed backends stay opt-in.
- Path-derived keys: plugins/image_gen/openai/ gets key
image_gen/openai, so a future tts/openai cannot collide.
- Depth-2 recursion into category namespaces (parent dirs without a
plugin.yaml of their own).
Includes OpenAIImageGenProvider as the first consumer (gpt-image-1.5
default, plus gpt-image-1, gpt-image-1-mini, DALL-E 3/2). Base64
responses save to $HERMES_HOME/cache/images/; URL responses pass
through.
FAL stays in-tree for this PR — a follow-up ports it into
plugins/image_gen/fal/ so the in-tree image_generation_tool.py
slims down. The dispatch shim in _handle_image_generate only fires
when image_gen.provider is explicitly set to a non-FAL value, so
existing FAL setups are untouched.
- 41 unit tests (scanner recursion, kind parsing, gate logic,
registry, OpenAI payload shapes)
- E2E smoke verified: bundled plugin autoloads, registers, and
_handle_image_generate routes to OpenAI when configured
* fix(image_gen/openai): don't send response_format to gpt-image-*
The live API rejects it: 'Unknown parameter: response_format'
(verified 2026-04-21 with gpt-image-1.5). gpt-image-* models return
b64_json unconditionally, so the parameter was both unnecessary and
actively broken.
* feat(image_gen/openai): gpt-image-2 only, drop legacy catalog
gpt-image-2 is the latest/best OpenAI image model (released 2026-04-21)
and there's no reason to expose the older gpt-image-1.5 / gpt-image-1 /
dall-e-3 / dall-e-2 alongside it — slower, lower quality, or awkward
(dall-e-2 squares only). Trim the catalog down to a single model.
Live-verified end-to-end: landscape 1536x1024 render of a Moog-style
synth matches prompt exactly, 2.4MB PNG saved to cache.
* feat(image_gen/openai): expose gpt-image-2 as three quality tiers
Users pick speed/fidelity via the normal model picker instead of a
hidden quality knob. All three tier IDs resolve to the single underlying
gpt-image-2 API model with a different quality parameter:
gpt-image-2-low ~15s fast iteration
gpt-image-2-medium ~40s default
gpt-image-2-high ~2min highest fidelity
Live-measured on OpenAI's API today: 15.4s / 40.8s / 116.9s for the
same 1024x1024 prompt.
Config:
image_gen.openai.model: gpt-image-2-high
# or
image_gen.model: gpt-image-2-low
# or env var for scripts/tests
OPENAI_IMAGE_MODEL=gpt-image-2-medium
Live-verified end-to-end with the low tier: 18.8s landscape render of a
golden retriever in wildflowers, vision-confirmed exact match.
* feat(tools_config): plugin image_gen providers inject themselves into picker
'hermes tools' → Image Generation now shows plugin-registered backends
alongside Nous Subscription and FAL.ai without tools_config.py needing
to know about them. OpenAI appears as a third option today; future
backends appear automatically as they're added.
Mechanism:
- ImageGenProvider gains an optional get_setup_schema() hook
(name, badge, tag, env_vars). Default derived from display_name.
- tools_config._plugin_image_gen_providers() pulls the schemas from
every registered non-FAL plugin provider.
- _visible_providers() appends those rows when rendering the Image
Generation category.
- _configure_provider() handles the new image_gen_plugin_name marker:
writes image_gen.provider and routes to the plugin's list_models()
catalog for the model picker.
- _toolset_needs_configuration_prompt('image_gen') stops demanding a
FAL key when any plugin provider reports is_available().
FAL is skipped in the plugin path because it already has hardcoded
TOOL_CATEGORIES rows — when it gets ported to a plugin in a follow-up
PR the hardcoded rows go away and it surfaces through the same path
as OpenAI.
Verified live: picker shows Nous Subscription / FAL.ai / OpenAI.
Picking OpenAI prompts for OPENAI_API_KEY, then shows the
gpt-image-2-low/medium/high model picker sourced from the plugin.
397 tests pass across plugins/, tools_config, registry, and picker.
* fix(image_gen): close final gaps for plugin-backend parity with FAL
Two small places that still hardcoded FAL:
- hermes_cli/setup.py status line: an OpenAI-only setup showed
'Image Generation: missing FAL_KEY'. Now probes plugin providers
and reports '(OpenAI)' when one is_available() — or falls back to
'missing FAL_KEY or OPENAI_API_KEY' if nothing is configured.
- image_generate tool schema description: said 'using FAL.ai, default
FLUX 2 Klein 9B'. Rewrote provider-neutral — 'backend and model are
user-configured' — and notes the 'image' field can be a URL or an
absolute path, which the gateway delivers either way via
extract_local_files().
fix(agent): consult supports_vision override in auto-mode routing
The contributor PR (#17936) only patched the strip path in
_model_supports_vision(). The auto-mode router in
agent/image_routing._lookup_supports_vision still only read models.dev,
so a custom-provider model declared as vision-capable would still get its
images routed through vision_analyze in the default `agent.image_input_mode:
auto setting. Users had to set both supports_vision: true` AND
image_input_mode: native to bypass the text pipeline.
Single-knob behavior now: supports_vision: true alone is enough in auto
mode. The strip path and the routing path consult the same resolver.
- Extract override resolution into _supports_vision_override() in
agent/image_routing.py and wire it into _lookup_supports_vision().
- Refactor run_agent._model_supports_vision to call the same helper
(DRY, single source of truth for the resolution order).
- Strict YAML boolean coercion: supports_vision: "false" (quoted —
a common YAML mistake) no longer coerces to True via bool() truthiness.
Recognised tokens: true/false/yes/no/on/off/1/0 plus real bools and 0/1.
Unrecognised values return None and fall through to models.dev.
- Add @CNSeniorious000 to AUTHOR_MAP for release attribution.
Tests: 26 new (TestCoerceCapabilityBool, TestSupportsVisionOverride,
TestLookupSupportsVisionOverride, TestAutoModeRespectsOverride). Existing
contributor tests + image_routing + vision_native_fast_path +
native_image_buffer_isolation all green (92/92).
fix(compression): include system prompt + tool schemas in token estimates (#18265)
The user-visible /compress banner and the post-compression last_prompt_tokens
writeback both counted only the raw message transcript (chars/4). With a 15KB
system prompt and 30 tool schemas (~26KB), a 4-message transcript that looks
like ~45 tokens to the transcript-only estimator is really ~10.5K tokens of
request pressure — a 234x gap.
Two user-facing consequences:
- Banner shows 'Compressing … (~45 tokens)…' while compression is actually
firing on 10K+ tokens of real pressure, confusing users about why
compression triggered (reported by @codecovenant on X; #6217).
- Post-compression last_prompt_tokens writeback omits tool schemas, so the
next should_compress() check compares real usage against a stale
underestimate — compression triggers late, potentially past the model's
context limit on small-context models (#14695).
Swap estimate_messages_tokens_rough() for estimate_request_tokens_rough()
at every user-visible banner and at the post-compression writeback.
estimate_request_tokens_rough() already existed for exactly this purpose
and includes system prompt + tool schemas.
Touched call sites:
- run_agent.py: post-compression last_prompt_tokens writeback, post-tool
call should_compress() fallback when provider usage is missing
- cli.py: /compress banner + summary
- gateway/run.py: gateway /compress banner + summary
- tui_gateway/server.py: TUI /compress status + summary
- acp_adapter/server.py: ACP /compact before/after
Left intentionally alone:
- Session-hygiene fallback and the 'no agent' /status path in gateway/run.py
— no agent instance is in scope to query for system prompt/tools, and the
existing 30-50% overestimate wobble on hygiene is safety-accepted.
- Verbose-mode 'Request size' logging — informational only, already counts
system prompt via api_messages[0].
Also relabels the feedback line from 'Rough transcript estimate' to
'Approx request size' so the metric label matches what it actually measures.
Credits: diagnoses from @devilardis (#14695) and @Jackten (#6217);
user report @codecovenant on X (2026-04-30).
Closes #14695
Closes #6217
fix(cli): vertical fallback for markdown tables wider than terminal (#23948)
Follow-up to #23863 (CJK table alignment). The realigner was
correctly padding pipes to identical column offsets, but when a
table's natural width exceeds terminal cells it produced lines that
the terminal soft-wrapped mid-cell, destroying column alignment
visually even though the bytes were perfectly padded. Reported as
'columns are not aligned' on tables containing one long row alongside
several short rows.
Approach mirrors Claude Code's MarkdownTable.tsx narrow-terminal
fallback: when realign_markdown_tables is given an available_width
budget and the rebuilt horizontal table exceeds it, render each body
row as 'Header: value' lines separated by a thin ─ rule. Word-wraps
oversize values at the budget with a 2-space continuation indent.
- agent/markdown_tables.py: realign_markdown_tables(text, available_width=None);
threshold check at the top of _render_block flips into a new
_render_vertical fallback. Includes _wrap_to_width with hard-break
for tokens longer than the budget.
- cli.py: helper _terminal_width_for_streaming() returns
shutil.get_terminal_size().columns minus _STREAM_PAD and a 2-cell
safety margin; passed to all three realign call sites
(_render_final_assistant_content for strip+render Panel paths, and
the streaming flushers in _emit_stream_text / _flush_stream).
- tests/agent/test_markdown_tables.py: 4 new tests covering the
overflow-vertical fallback for ASCII + CJK content, the
'fits → keep horizontal' case, and the long-cell wrap with indent.
Live-verified: with COLUMNS=100, the user's reported 'long row in
ASCII table' case now renders as vertical key-value rows that all fit
the panel; the 6-column CJK comparison table still renders as an
aligned horizontal table because it fits inside 100 cols.
fix(metadata): qwen3.6-plus has a 1M context window (#27008)
qwen3.6-plus did not have an explicit entry in DEFAULT_CONTEXT_LENGTHS,
so the longest-substring fallback matched the generic 'qwen': 131072
catch-all. That dropped the effective context limit from 1,048,576
tokens to 131,072, prematurely lowered the compression threshold, and
produced misleading warnings about main/compression context mismatch
in long sessions.
Add an explicit 'qwen3.6-plus': 1048576 entry before the catch-all and
cover it with a regression test (bare, qwen/, and dashscope/ prefixes).
Note: PR #6599 also mentions touching model_metadata.py but the actual
diff only edits hermes_cli/models.py, so this fix is independent and
not duplicated by that PR.
Closes #27008
fix(moonshot): strip $ref siblings and collapse tuple items in tool schemas (#27104)
Port from anomalyco/opencode#24730: Moonshot's JSON Schema validator rejects
two shapes that the rest of the JSON Schema ecosystem accepts:
1. $ref nodes with sibling keywords. Moonshot expands the reference before
validation and then rejects the node if keys like description, type,
or default appear alongside $ref. MCP-sourced tool schemas commonly
put a description on $ref-typed properties so the model sees the
field hint — which worked on every provider except Moonshot.
2. Tuple-style items arrays (positional element schemas). Moonshot's
engine requires ONE schema applied to every array element. Common in
tool schemas generated from Go/Protobuf that model fixed-length arrays
as [{type:number}, {type:number}].
Repairs applied in agent/moonshot_schema.py:
- Rule 3: when a node has $ref, return {"$ref": <value>} only
(strip every sibling). The referenced definition still carries its own
description on the target node, which Moonshot accepts.
- Rule 4: when items is a list, collapse to the first element schema
(falling back to {} which is then filled by the generic missing-type
rule). Preserves minItems / maxItems / other siblings.
Tests: 10 new cases across TestRefSiblingStripping + TestTupleItems,
plus the existing TestMissingTypeFilled::test_ref_node_is_not_given_synthetic_type
still passes (it asserted plain $ref passes through; now it passes through
as exactly {"$ref": "..."} which is strictly compatible).
All 35 tests in test_moonshot_schema.py pass.
docs(onboarding): lead OpenClaw residue banner with migrate, warn that cleanup breaks OpenClaw (#17507)
The ~/.openclaw/ detection banner (#16327) had two problems flagged in #16629:
1. It only pitched 'hermes claw cleanup' (destructive archive) and never
mentioned 'hermes claw migrate' — the actual non-destructive path that
ports config/memory/skills into Hermes.
2. The copy anthropomorphized the bug ('the agent can still get confused',
'dutifully reads') and framed OpenClaw as a competitor to eliminate
('instead of Hermes's').
Rewrite so migrate leads, cleanup is a clearly-labelled follow-up with a
warning that archiving breaks OpenClaw for users still running it.
Closes #16629
feat(plugins): run any LLM call from inside a plugin via ctx.llm (#23194)
* feat(plugins): host-owned LLM access via ctx.llm
Plugins can now ask the host to run a one-shot chat or structured
completion against the user's active model and auth, without ever
seeing an OAuth token or API key. Closes the gap where plugins that
needed bounded structured inference (receipts, CRM extraction,
support classification) had to either bring their own provider keys
or register a tool the agent had to call.
New surface on PluginContext:
- ctx.llm.complete(messages, ...)
- ctx.llm.complete_structured(instructions, input, json_schema, ...)
- async siblings ctx.llm.acomplete / acomplete_structured
Backed by the existing auxiliary_client.call_llm pipeline — every
provider, fallback chain, vision routing, and timeout policy Hermes
already supports applies automatically.
Trust gate (fail-closed by default):
- plugins.entries.<id>.llm.allow_model_override
- plugins.entries.<id>.llm.allowed_models (allowlist; '*' = any)
- plugins.entries.<id>.llm.allow_agent_id_override
- plugins.entries.<id>.llm.allow_profile_override
Embedded model@profile shorthand goes through the same gate as
explicit profile=, so it can't bypass the auth-profile policy.
Conflicting explicit and embedded profiles fail closed.
Also lands:
- plugins/plugin-llm-example/ — reference plugin that registers
/receipt-extract, demonstrating image+text structured input,
jsonschema validation, and the trust-gate config.
- website/docs/developer-guide/plugin-llm-access.md — full API docs.
- 45 unit tests covering trust gates, JSON parsing, schema
validation, image encoding, async surface, and config loading.
Validation:
- 2628 tests pass in tests/agent/
- E2E: bundled plugin loaded with isolated HERMES_HOME, slash
command produced parsed JSON via stubbed call_llm
- response_format extra_body wired correctly for both json_object
and json_schema modes
* docs(plugin-llm): rewrite quickstart and framing
The quickstart now uses a meeting-notes-to-tasks example instead of
a receipt extractor, and the page leads with hook-time / gateway
pre-filter / scheduled-job framing rather than the OpenClaw
KB/support/CRM/finance/migration enumeration that the original
upstream PR used. Receipt example moved to a separate worked
example link so the docs page itself doesn't echo any of the
upstream framing.
Also clarifies where ctx.llm fits in the broader plugin surface
(table comparing register_tool / register_platform / register_hook
/ etc.) and what makes this lane different from auxiliary_client
internals.
No code change.
* docs(plugin-llm): reframe as any LLM call, not just structured output
The original draft leaned heavily on complete_structured() and made
the chat lane (complete() / acomplete()) feel like a footnote.
Restructure so:
- The page title and description say 'any LLM call.'
- The lead shows BOTH a plain chat call (error rewriter) AND a
structured call (triage scorer) up top.
- Quick start has two complete plugin examples — /tldr (chat) and
/paste-to-tasks (structured).
- New 'When to use which' table for choosing complete() vs
complete_structured() vs the async siblings.
- Trust-gate sections explicitly note 'all four methods,' and the
request-shaping list calls out chat-only fields (messages) and
structured-only fields (instructions, input, json_schema)
alongside each other.
- The 'Where this fits' section now says 'for any reason,
structured or not.'
The receipt-extractor reference plugin still exists under
plugins/plugin-llm-example/ — but the docs page no longer treats
it as the canonical surface example. It's now described as 'a third
worked example, this time with image input.'
No code change.
* feat(plugin-llm): split provider/model into independent explicit kwargs
The first cut accepted a single 'provider/model' slug on every method
and split it internally. That looked clean but broke under live test:
the model-override path tried to use the slug's vendor prefix as a
literal Hermes provider id, which silently switched the user off
their aggregator (e.g. plugin asks for 'openai/gpt-4o-mini' on a user
who routes through OpenRouter — host attempted to call the 'openai'
provider directly, failed because OPENAI_API_KEY wasn't set).
New shape mirrors the host's main config:
ctx.llm.complete(
messages=[...],
provider='openrouter', # gated, optional
model='openai/gpt-4o-mini', # gated, optional
profile='work', # gated, optional
...
)
Each is independently gated by its own allow_*_override flag.
Granting model-override does NOT auto-grant provider-override.
Allowlists are now per-axis (allowed_providers, allowed_models)
matched literally against whatever string the plugin sends.
Dropped 'model@profile' embedded-suffix shorthand entirely. Hermes
doesn't use that pattern anywhere else; profile= is its own kwarg.
Live E2E (against real OpenRouter via Teknium's config) confirms:
- zero-config call works
- default-deny blocks each override with a helpful error
- model-only override stays on user's active provider (the bug)
- provider+model override switches cleanly
- allowlist refuses non-listed entries
- structured output round-trip parses + schema-validates
Tests: 49 cases (up from 45); all green. Docs updated to match the
new shape, including a 'most plugins never need this section' callout
on the trust-gate config block.
* fix+cleanup(plugin-llm): real attribution, hook-mode coverage, move example out of core
Three integration fixes for the ctx.llm surface:
1. Attribution bug — result.provider and result.model now reflect
what call_llm actually used, not placeholder fallbacks ('auto',
'default'). New _resolve_attribution() helper:
- explicit overrides win (what the call targeted)
- response.model wins for the recorded model (provider
canonicalisation: 'gpt-4o' → 'gpt-4o-2024-08-06' etc.)
- falls back to _read_main_provider() / _read_main_model()
when no override is set, so audit logs reflect the user's
active main provider/model
- 'auto' / 'default' only when EVERYTHING is empty
Live verified: zero-config call now records
provider='openrouter', model='anthropic/claude-4.7-opus-20260416'
instead of provider='auto', model='default'.
2. Hook-mode coverage — TestHookMode confirms ctx.llm.complete
works from inside a registered post_tool_call callback. The
docs page promised hook integration; now there's a test that
exercises the lazy-import path through the real invoke_hook
machinery. Two cases: traceback-rewrite hook with conditional
ctx.llm.complete, and minimal hook regression for the
sync-hook + sync-llm path.
3. Reference plugin moved out of core. plugins/plugin-llm-example/
is gone from hermes-agent — it now lives in the new
NousResearch/hermes-example-plugins companion repo. The docs
page links there. Hermes' bundled plugins should be plugins
users actually run; reference / docs-companion plugins live
externally.
Test count: 56 (up from 49). Wider sweep on tests/hermes_cli/
+ tests/gateway/ + tests/tools/ + tests/agent/ shows 16770
passing; the 12 failures are all pre-existing on origin/main
(verified by stashing this branch's changes and re-running) —
kanban-boards, delegate-task, gateway-restart, tts-routing —
none touch the plugin_llm surface.
* chore(plugins): move all example plugins to companion repo
Reference / docs-companion plugins now live exclusively in
NousResearch/hermes-example-plugins, not bundled with the core repo:
- example-dashboard
- strike-freedom-cockpit
A new fourth example, plugin-llm-async-example, was added to that
repo demonstrating ctx.llm's async surface (acomplete()) with
asyncio.gather() — registers /translate <lang>: <text> which fires
forward translation + sentiment classifier in parallel, then a
back-translation for QA. Live-tested at 2.5s for three real
provider round-trips (would be ~5-6s sequential).
Docs updated:
- developer-guide/plugin-llm-access.md links both sync and async
examples in the Reference section
- user-guide/features/extending-the-dashboard.md repoints both demo
sections to the companion repo with corrected install paths
- user-guide/features/built-in-plugins.md drops the two demo rows
- AGENTS.md notes that example plugins live in the companion repo
Net: hermes-agent's plugins/ directory now contains only plugins
users actually run (memory providers, dashboard tabs that ship real
features, the disk-cleanup hook, platform adapters). All four
demo / reference plugins live externally where they can be cloned
on demand instead of inflating the core install.
feat(nous): unified client=hermes-client-v<version> tag on every Portal request (#24779)
* feat(nous): unified client=hermes-client-v<version> tag on every Portal request
Every Hermes request to Nous Portal now carries the same
client=hermes-client-v<__version__> tag (e.g. client=hermes-client-v0.13.0
on this release), sourced live from hermes_cli.__version__. The release
script's regex bump auto-aligns it on every release.
Centralized in agent/portal_tags.py and wired into all four call sites:
- NousProfile.build_extra_body (main agent loop, every chat completion)
- auxiliary_client.NOUS_EXTRA_BODY + _build_call_kwargs (aux client)
- run_agent.py compression-summary fallback path
- tools/web_tools.py web_extract fallback
Replaces the client=aux marker added in #24194 with the unified version
tag. Tests assert against the helper output (invariant) rather than the
literal string, so they don't need updating on every release.
* feat(nous): cover /goal judge and kanban specify aux paths
Two aux-using surfaces bypassed call_llm by invoking
client.chat.completions.create() directly without extra_body, so they
were missing the unified Portal client tag:
- hermes_cli/goals.py — /goal standing-goal judge
- hermes_cli/kanban_specify.py — kanban triage specifier
Both now pass extra_body=get_auxiliary_extra_body() or None so they
inherit the version tag when the aux client points at Nous Portal, and
emit nothing otherwise (no tag leak to OpenRouter/Anthropic auxes).
fix(kanban): stale reclaim must not tick failure counter (#28680)
Follow-up to #28452. detect_stale_running() was calling
_record_task_failure() on every reclaim, which ticked the
consecutive_failures counter. With the default failure_limit=2,
two legitimately long-running tasks (>4 h without explicit
heartbeat) would auto-block via the spawn-failure circuit
breaker — even though no worker actually failed.
Stale reclaim is dispatcher-side absence-of-heartbeat detection,
not a worker fault. Removed the _record_task_failure() call;
the 'stale' event in task_events is still the audit surface,
but the failure counter is now reserved for spawn_failed /
timed_out / crashed (real failures).
Also documents the heartbeat requirement:
- KANBAN_GUIDANCE in agent/prompt_builder.py now states the
rule ('call kanban_heartbeat at least once an hour for tasks
running longer than 1 hour') so workers learn the contract.
- kanban.md adds the stale event row to the events table and
flags the heartbeat requirement in the worker lifecycle list.
New regression test: test_detect_stale_does_not_tick_failure_counter
locks in the new behaviour.
fix(cache): kill long-lived prefix layout — system prompt is now byte-static within a session (#24778)
The long-lived prefix-cache layout split the system prompt into stable/
context/volatile blocks and re-derived them on every API call. The
volatile tier (timestamp + memory snapshot + USER profile) ticks per
turn, so the system message bytes mutated mid-conversation and broke
upstream prompt caches (OpenRouter, Nous Portal, Anthropic).
Diagnosed via live wire-format diffing: an 8-turn conversation showed
OLD layout flipping system block[1] sha mid-session at the minute
boundary, dropping cached_tokens to 0 on that turn (cumulative
66.6% vs 83.3% for the single-block layout). Hermes invariant:
history (system + all but the last 1-2 messages) must be static.
Fix: drop the long-lived layout entirely. Single layout everywhere —
system_and_3 with one cached system string built once on first turn,
replayed verbatim on every subsequent turn. Loses cross-session 1h
prefix caching for Claude (the feature that motivated the split), but
within-session caching now actually works on every provider.
Removed:
- run_agent.py: _use_long_lived_prefix_cache flag, _long_lived_cache_ttl,
_supports_long_lived_anthropic_cache method, the long-lived branch in
run_conversation, mark_tools_for_long_lived_cache call site
- agent/prompt_caching.py: apply_anthropic_cache_control_long_lived,
mark_tools_for_long_lived_cache, _mark_system_stable_block helper
- hermes_cli/config.py: prompt_caching.long_lived_prefix and
prompt_caching.long_lived_ttl config keys
- tests/agent/test_prompt_caching_live.py (entire file)
- tests/agent/test_prompt_caching.py: TestMarkToolsForLongLivedCache,
TestApplyAnthropicCacheControlLongLived
- tests/run_agent/test_anthropic_prompt_cache_policy.py:
TestSupportsLongLivedAnthropicCache
Targeted tests: 62/62 pass.
perf(agent-loop): cut 47% of per-conversation function calls via 3 targeted hot-path optimizations (#28866)
* perf(config): add load_config_readonly() fast path for hot agent loop
load_config() is called from the agent loop's per-API-call hot path via
get_provider_request_timeout() and get_provider_stale_timeout() —
both invoked once per turn from _resolved_api_call_timeout() in
run_agent.py.
Profiling a synthetic 20-tool-call agent run revealed:
- 21 invocations of load_config() cumulating 56ms (~17% of agent loop)
- 34,398 deepcopy calls totaling 37ms (config defensive deepcopy + chain)
- 8,652 _expand_env_vars invocations (~412 per turn)
Microbench (cache-hit, real config.yaml present):
load_config() 265us/call (125us deepcopy + 140us infra)
load_config_readonly() 138us/call (~48% faster)
load_config_readonly() returns the cached dict directly without the
defensive deepcopy. Documented contract: caller must not mutate. Returns
plain dict (not MappingProxyType) so downstream isinstance(x, dict)
guards keep working — caught during initial implementation when
MappingProxyType broke get_provider_request_timeout's guard logic.
Wired into hermes_cli/timeouts.py (the two functions called per agent
turn). load_config() is unchanged for the 263 other call sites that
mutate the result before save_config(), are not in the hot path, or
where the safety guarantee matters more than the perf.
Profile A/B (cached config, 21-turn agent loop):
BEFORE AFTER delta
get_provider_request_timeout 55ms 16ms -71%
total function calls 399k 160k -60%
deepcopy calls (in hotspots) 34,398 ~0 ~elim
Verified:
- isinstance(load_config_readonly(), dict) is True
- timeout/stale resolutions correct
- load_config() still returns isolated mutable deepcopies
- tests/hermes_cli/test_config*.py / test_timeouts.py: 102/102 pass
- tests/cli/ + tests/agent/test_auxiliary_client.py: 883/883 pass
* perf(redact): substring pre-screens skip non-matching regex chains
Every log record passes through RedactingFormatter.format which calls
redact_sensitive_text, which historically ran ALL 13 secret-pattern
regexes against every line — including DB connection strings, JWTs,
Discord mentions, Signal phone numbers, etc. — even for typical clean
log records like 'INFO run_agent: API call completed'.
Add cheap substring pre-checks before each regex pass. False positives
still run the regex (which then matches nothing); false negatives are
impossible because every pattern requires the gated substring to match
its leading anchor:
- _PREFIX_RE gated on any of 33 known credential prefix substrings
- _ENV_ASSIGN_RE gated on = in text
- _JSON_FIELD_RE gated on : and " in text
- _AUTH_HEADER_RE gated on uthorization/UTHORIZATION in text
- _TELEGRAM_RE gated on : in text
- _PRIVATE_KEY_RE gated on BEGIN and -----
- _DB_CONNSTR_RE gated on :// in text
- _JWT_RE gated on eyJ in text
- URL userinfo/query gated on ://
- _redact_form_body gated on & and =
- _DISCORD_MENTION_RE gated on <@
- _SIGNAL_PHONE_RE gated on +
Microbench (5 typical log records, 20k iterations each):
BEFORE AFTER delta
redact_sensitive_text per call 5.63us 1.79us -68%
Real-world impact: ~244 log records emitted in a 30-turn agent loop, so
the chain saves ~1ms of CPU per conversation. Bigger win is the
reduction in regex execution and GC pressure during heavy logging
sessions (verbose logging, gateway message processing).
Security regression test: 30 secret-containing inputs (sk-/ghp_/JWT/DB
connstr/Auth-Bearer/private key/URL userinfo/Discord/Signal/etc.)
verified to produce identical redacted output before/after. All 75
existing tests/agent/test_redact.py cases pass.
The ?access_token=foo&code=bar (bare query string, no scheme) case
that 'leaks' is pre-existing behavior — the URL query redaction
requires a well-formed URL with scheme+host. Not a regression.
* perf(run_agent): cache _needs_thinking_reasoning_pad result per (provider, model, base_url)
Profile of a 31-turn synthetic agent run shows _needs_thinking_reasoning_pad
fires 495 times (~16 per turn) and each call ran 3 helper methods, each
hitting base_url_host_matches 1-4 times via urlparse. Total cost:
3,342 base_url_host_matches calls + 3,373 urlparse calls accounting for
~36ms of agent-loop overhead (~7% of the entire post-network work).
Provider / model / base_url don't change during a conversation except via
switch_model and fallback activation — both of which already overwrite
those attributes atomically. Cache the result on a tuple key; since the
key is derived from the very fields that would change, the cache
auto-invalidates on the next read after a switch. No manual invalidation
needed in switch_model / _try_activate_fallback.
Profile A/B (31-turn cached-config agent run):
BEFORE AFTER delta
_needs_thinking_reasoning_pad cum 18ms 1ms -94%
_copy_reasoning_content_for_api cum 17ms 1ms -94%
base_url_host_matches calls 3,342 372 -89%
urlparse calls 3,373 403 -88%
total function calls 296k 223k -25%
Verified:
- tests/run_agent/test_deepseek_reasoning_content_echo.py: 36/36 pass
- tests/run_agent/ (full): 1383/1383 pass + 3 skipped
feat(skills): add skill bundles — alias /<name> loads multiple skills (#28373)
Skill bundles are tiny YAML files in ~/.hermes/skill-bundles/ that
group several skills under one slash command. Invoking /<bundle-name>
from any surface (CLI, TUI, dashboard, any gateway platform) loads
every referenced skill into a single combined user message.
Use cases:
- /backend-dev → loads github-code-review + test-driven-development
+ github-pr-workflow as one bundle.
- /research → loads several research skills together.
- Team task profiles shared via dotfiles.
Behavior:
- Bundles take precedence over individual skills when slugs collide.
- Missing skills are skipped with a note, not fatal.
- No system-prompt mutation — bundles generate a fresh user message
at invocation time, the same way /<skill> does. Prompt cache stays
intact.
- Works in CLI dispatch, gateway dispatch, autocomplete (CLI + TUI),
/help display.
Schema (~/.hermes/skill-bundles/<slug>.yaml):
name: backend-dev
description: Backend feature work.
skills:
- github-code-review
- test-driven-development
instruction: |
Optional extra guidance prepended to the loaded skills.
New module: agent/skill_bundles.py — load, scan, resolve, build
invocation message, save, delete. yaml.safe_load only; broken
bundles log a warning and are skipped, never raise.
New CLI subcommand: hermes bundles {list,show,create,delete,reload}.
Implementation in hermes_cli/bundles.py; wired in hermes_cli/main.py.
'bundles' added to _BUILTIN_SUBCOMMANDS so plugin discovery skips it.
New in-session slash command: /bundles lists installed bundles in
both CLI and gateway. /<bundle-name> dispatch added to CLI (cli.py)
and gateway (gateway/run.py) before the existing /<skill-name> path.
Autocomplete: SlashCommandCompleter gained an optional
skill_bundles_provider parameter that defaults to None — the prompt
shows '▣ <description> (N skills)' for bundles vs '⚡' for skills.
Tests:
- tests/agent/test_skill_bundles.py — 33 tests covering slugify,
scan/cache freshness, resolve (including underscore→hyphen
Telegram alias), build_bundle_invocation_message (loading, missing
skills, user/bundle instruction injection, dedup), save/delete,
reload diff, list sort.
- tests/hermes_cli/test_bundles.py — 8 tests for the CLI
subcommand (create/list/show/delete/reload, --force, missing
bundle errors).
- tests/gateway/test_bundles_command.py — 4 tests for the gateway
handler and bundle resolution priority.
Live E2E: verified subprocess invocations of hermes bundles
{list,create,show,reload,delete} round-trip correctly against an
isolated HERMES_HOME.
Docs:
- website/docs/user-guide/features/skills.md — new 'Skill Bundles'
section with quick example, YAML schema, management commands,
behavior notes.
- website/docs/reference/cli-commands.md — 'hermes bundles' added to
the top-level command table and given its own subcommand section.
perf(cli): cut ~19s from 'hermes' cold start (skills cache + lazy Feishu + no Nous HTTP) (#22138)
Interactive hermes launch drops from ~21s to ~2.5s. Three independent
fixes, each targets a distinct hot spot in the banner / tool-registration
path that fires on every CLI invocation.
1. get_external_skills_dirs() in-process mtime cache (~10s saved)
The function re-read + YAML-parsed the full ~/.hermes/config.yaml on
every call. Banner build invokes it once per skill to resolve the
category column, which on a 120-skill install meant ~120 reparses of
a 15 KB config (~85 ms each). Added a
(config_path, mtime_ns) -> list[Path] memo; stat() is ~2 us vs
~85 ms for the parse. Edits to config.yaml invalidate the cache on
the next call via mtime.
2. Feishu availability probe uses importlib.util.find_spec (~5.2s saved)
tools/feishu_doc_tool.py::_check_feishu and the identical helper in
feishu_drive_tool.py were calling import lark_oapi purely to
detect whether the SDK was installed. Executing the real import pulls
in websockets + dispatcher + every v2 API model — ~5 seconds of work
that fires at every tool-registry bootstrap. find_spec answers the
same question ("is lark_oapi importable?") without executing the
module. The actual tool handlers still do the real import on invoke,
so runtime behavior is unchanged.
3. _web_requires_env no longer triggers Nous portal refresh (~800ms saved)
tools/web_tools.py::_web_requires_env used
managed_nous_tools_enabled() to gate four gateway env-var names in
the returned list. The gate called get_nous_auth_status() ->
resolve_nous_runtime_credentials() -> live HTTP POST to the portal
on every tool-registry bootstrap. But the list is pure metadata — if
the env var is set at runtime, the tool lights up; otherwise it
doesn't. Including the four names unconditionally is harmless for
unsubscribed users (vars just aren't set) and eliminates the sync
HTTP round trip from startup.
Test:
- tests/agent/test_external_skills_dirs_cache.py (new, 6 cases):
returns config'd dir, caches on second call (yaml_load patched to
raise — never invoked), invalidates on mtime bump, empty when config
missing, returned list is a defensive copy, per-HERMES_HOME cache key
isolation.
- Existing tests/agent/test_external_skills.py and tests/tools/
continue to pass modulo pre-existing flakes on main (test_delegate,
test_send_message — unrelated, pass in isolation).
Measured: bare hermes (cold → REPL ready) 21,519ms -> 2,618ms on
Teknium's install (119 skills, 15 KB config.yaml, Nous auth logged in,
lark_oapi installed). 8x faster.
perf(prompt): cache kanban worker guidance at session init
Salvages #24402 by @RyanRana. The KANBAN_GUIDANCE block (~835 tokens)
is session-static — the dispatcher decides at spawn time whether the
process is a kanban worker via the kanban_show tool's check_fn (gated
on HERMES_KANBAN_TASK env var). Re-checking 'kanban_show' in
valid_tool_names and re-loading the reference on every system-prompt
rebuild (init + each context compression) is wasted work.
Caches the resolved string on agent._kanban_worker_guidance once in
agent_init and consumes it in system_prompt.build_system_prompt(),
with a getattr fallback for code paths that bypass agent_init.
fix(agent): stateful streaming scrubber for reasoning-block leaks (#17924) (#20184)
* revert(gateway): remove stale-code self-check and auto-restart
Removes the _detect_stale_code / _trigger_stale_code_restart mechanism
introduced in #17648 and iterated in #19740. On every incoming message
the gateway compared the boot-time git HEAD SHA to the current SHA on
disk, and if they differed it would reply with
Gateway code was updated in the background --
restarting this gateway so your next message runs
on the new code. Please retry in a moment.
and then kick off a graceful restart. This is unwanted behaviour:
users who run a long-lived gateway and do their own ad-hoc git
operations on the checkout end up with their chat interrupted and
the current message dropped every time HEAD moves, with no way to
opt out.
If an operator really needs the old protection against stale
sys.modules after "hermes update", the SIGKILL-survivor sweep in
hermes update (hermes_cli/main.py, also tagged #17648) already
handles the supervisor-respawn case on its own.
Removed:
gateway/run.py:
- _STALE_CODE_SENTINELS, _GIT_SHA_CACHE_TTL_SECS
- _read_git_head_sha(), _compute_repo_mtime() module helpers
- class-level _boot_wall_time / _boot_repo_mtime / _boot_git_sha /
_stale_code_restart_triggered defaults
- __init__ boot-snapshot block (_boot_*, _cached_current_sha*,
_repo_root_for_staleness, _stale_code_notified)
- _current_git_sha_cached(), _detect_stale_code(),
_trigger_stale_code_restart() methods
- stale-code check + user-facing restart notice at the top of
_handle_message()
tests/gateway/test_stale_code_self_check.py (deleted, 412 lines)
No new logic added. Zero remaining references to any removed
symbol. Gateway test suite passes the same 4589 tests it passed
before; the 3 pre-existing unrelated failures (discord free-channel,
feishu bot admission, teams typing) are unchanged by this commit.
* fix(agent): stateful streaming scrubber for reasoning-block leaks (#17924)
Per-delta _strip_think_blocks ran at _fire_stream_delta and destroyed
downstream state. When MiniMax-M2.7 / DeepSeek / Qwen3 streamed a tag
split across deltas (delta1='<think>', delta2='Let me check'), the
regex case-2 match erased delta1 entirely, so CLI/gateway state
machines never learned a block was open and leaked delta2 as content.
Raw consumers (ACP, api_server, TTS) had no downstream defense at all.
Replace the per-delta regex with a stateful StreamingThinkScrubber
that survives delta boundaries:
- Closed <tag>X</tag> pairs always stripped (matches _strip_think_blocks
case 1).
- Unterminated open at block boundary enters a block; content
discarded until close tag arrives. At end-of-stream, held
content is dropped.
- Orphan close tags stripped without boundary gating.
- Partial tags at delta boundaries held back until resolved.
- Block-boundary rule (start-of-stream, after \n, or
whitespace-only since last \n) preserves prose that mentions
tag names.
Reset at turn start alongside the existing context scrubber; flush at
turn end so a benign '<' held back at end-of-stream reaches the UI.
E2E-verified on live OpenRouter->MiniMax-m2 streams: closed pairs
strip cleanly, first word of post-block content is preserved, pure
content passes through unchanged. Stefan's screenshot case (#17924)
— 'Let me check' getting chopped to ' me check' — no longer happens.
Final _strip_think_blocks calls on completed strings (final_response,
replay, compression) are preserved; only the streaming per-delta call
site switched to the scrubber.
feat(video_gen): unified video_generate tool with pluggable provider backends (#25126)
* feat(video_gen): unified video_generate tool with pluggable provider backends
One core video_generate tool, every backend a plugin. Mirrors the
image_gen + memory_provider + context_engine architecture: ABC, registry,
plugin-context registration hook, and per-plugin model catalogs surfaced
through hermes tools.
Surface (one schema, every backend):
- operation: generate / edit / extend
- modalities: text-to-video (prompt only), image-to-video (prompt +
image_url), video edit (prompt + video_url), video extend (video_url)
- reference_image_urls, duration, aspect_ratio, resolution,
negative_prompt, audio, seed, model override
- Providers ignore unknown kwargs and declare what they support via
VideoGenProvider.capabilities() — backend-specific quirks stay in the
backend, the agent learns one tool
Backends shipped:
- plugins/video_gen/xai/ — Grok-Imagine, full generate/edit/extend +
image-to-video + reference images (salvaged from PR #10600 by
@Jaaneek, reshaped into the plugin interface)
- plugins/video_gen/fal/ — Veo 3.1 (t2v + i2v), Kling O3 i2v,
Pixverse v6 i2v with model-aware payload building that drops keys a
model doesn't declare
Wiring:
- agent/video_gen_provider.py — VideoGenProvider ABC, normalize_operation,
success_response / error_response, save_b64_video / save_bytes_video,
$HERMES_HOME/cache/videos/
- agent/video_gen_registry.py — thread-safe register/get/list +
get_active_provider() reading video_gen.provider from config.yaml
- hermes_cli/plugins.py — PluginContext.register_video_gen_provider()
- hermes_cli/tools_config.py — Video Generation category in
hermes tools, plugin-only providers list, model picker per plugin,
config write to video_gen.{provider,model}
- toolsets.py — new video_gen toolset
- tests: 31 new tests covering ABC, registry, tool dispatch, both plugins
- docs: developer-guide/video-gen-provider-plugin.md (parallel to the
image-gen guide), sidebar + toolsets-reference + plugin guides updated
Supersedes: #25035 (FAL), #17972 (FAL), #14543 (xAI), #13847 (HappyHorse),
#10458 (provider categories), #10786 (xAI media+search bundle), #2984
(FAL duplicate), #19086 (Google Veo standalone — easy port to plugin
interface).
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
* feat(video_gen): dynamic schema reflects active backend's capabilities
Address the 'capability variance' question — instead of one tool with a
static schema that lies about what every backend supports, the
video_generate tool now rebuilds its description at get_definitions()
time based on the configured video_gen.provider and video_gen.model.
The agent sees backend-specific guidance up-front:
- 'fal-ai/veo3.1/image-to-video': 'image-to-video only — image_url is
REQUIRED; text-only prompts will be rejected'
- 'fal-ai/veo3.1' (t2v): no image_url restriction shown
- xAI grok-imagine-video: 'operations: generate, edit, extend; up to 7
reference_image_urls'
- Backends without edit/extend: 'not supported on this backend — surface
that they need to switch backends via hermes tools'
This is the same pattern PR #22694 used for delegate_task self-capping —
documented in the dynamic-tool-schemas skill. Cache invalidation is
free: get_tool_definitions() already memoizes on config.yaml mtime, so a
mid-session backend swap rebuilds the schema automatically.
Tested:
- Empirical FAL OpenAPI schema check confirms image-to-video models
require image_url (FAL returns HTTP 422 otherwise) — client-side
rejection in FALVideoGenProvider.generate() now prevents the wasted
round-trip
- Live E2E: fal-ai/veo3.1/image-to-video + prompt-only → clean
missing_image_url error; fal-ai/veo3.1 + prompt-only → dispatches
- 6 new tests cover the builder (no config / image-only / full-surface /
text-only / unknown provider / registry wiring), all passing
- 37/37 in the slice, 134/134 in the broader regression set
* test(video_gen/xai): full surface integration tests + cleaner schema
Verified end-to-end that the xAI plugin handles every documented mode
from PR #10600's surface: text-to-video, image-to-video,
reference-images-to-video, video edit, video extend (with and without
prompt). All five modes route to the correct xAI endpoint
(/videos/generations, /videos/edits, /videos/extensions) with the right
payload shape (image / reference_images / video keys), and all five
client-side rejections fire before the network: edit-without-prompt,
extend-without-video_url, image+refs conflict, >7 references, and
duration/aspect_ratio clamping.
15 new integration tests grouped into four classes (endpoint routing,
modalities, validation, clamping). httpx is stubbed via a small fake
AsyncClient that records POSTs so the tests assert the actual payload
the plugin would send to xAI — not just the success/error envelope.
Also cleaned up a description redundancy: when a model's operations
match the backend's overall set, we no longer print the duplicate
'operations supported by this model' line. xAI's description now reads:
Active backend: xAI . model: grok-imagine-video
- operations supported by this backend: edit, extend, generate
- modalities supported by this backend: image, reference_images, text
- aspect_ratio choices: 16:9, 1:1, 2:3, 3:2, 3:4, 4:3, 9:16
- resolution choices: 480p, 720p
- duration range: 1-15s
- reference_image_urls: up to 7 images
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
* feat(video_gen): collapse surface to t2v + i2v, family-based auto-routing
Two design changes per Teknium:
1) Drop edit/extend from the tool surface entirely. Only text-to-video
and image-to-video remain. The agent sees a clean tool with two
modalities; backend-specific quirks like xAI's edit/extend endpoints
stay out of the unified schema.
2) FAL: pick a model FAMILY once, the plugin routes between the
family's text-to-video and image-to-video endpoints based on whether
image_url was passed. Users no longer pick 'fal-ai/veo3.1' AND
'fal-ai/veo3.1/image-to-video' as separate options — they pick
'veo3.1', and the plugin handles the rest.
Catalog rewritten as families:
veo3.1 fal-ai/veo3.1 / fal-ai/veo3.1/image-to-video
pixverse-v6 fal-ai/pixverse/v6/text-to-video / fal-ai/pixverse/v6/image-to-video
kling-o3-standard fal-ai/kling-video/o3/standard/text-to-video / fal-ai/kling-video/o3/standard/image-to-video
xAI uses a single endpoint (/videos/generations) for both modes,
routed by the presence of the 'image' field in the payload — no
edit/extend exposure.
Schema changes:
- VIDEO_GENERATE_SCHEMA: drop operation, drop video_url. Final params:
prompt (required), image_url, reference_image_urls, duration,
aspect_ratio, resolution, negative_prompt, audio, seed, model.
- VideoGenProvider ABC: drop normalize_operation, VALID_OPERATIONS,
DEFAULT_OPERATION. capabilities() drops 'operations' key.
- success_response: add 'modality' field ('text' | 'image') so the
agent and logs can see which endpoint was actually hit.
Dynamic schema builder simplified — no operations bullet, no
'switch backends if you need edit/extend' guidance. When the active
backend supports both modalities (the common case), description reads:
Active backend: FAL . model: pixverse-v6
- supports both text-to-video (omit image_url) and image-to-video
(pass image_url) - routes automatically
- aspect_ratio choices: 16:9, 9:16, 1:1
- resolution choices: 360p, 540p, 720p, 1080p
- duration range: 1-15s
- audio: pass audio=true to enable native audio (pricing tier)
- negative_prompt: supported
Tests: 51 in the video_gen slice, 216 across the broader image+video
sweep, all passing. New FAL routing tests prove pixverse-v6 + no image
hits text-to-video endpoint, pixverse-v6 + image_url hits
image-to-video endpoint, same for veo3.1 and kling-o3-standard.
Docs updated: developer-guide page rewrites the 'model families' pattern
as a first-class section so external plugin authors know the convention.
toolsets-reference and toolsets.py descriptions match the new surface.
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
* feat(video_gen/fal): expand catalog to 6 families, cheap + premium tiers
Catalog now covers everything Teknium specced from FAL:
Cheap tier:
ltx-2.3 fal-ai/ltx-2.3-22b/text-to-video / image-to-video
pixverse-v6 fal-ai/pixverse/v6/text-to-video / image-to-video
Premium tier:
veo3.1 fal-ai/veo3.1 / fal-ai/veo3.1/image-to-video
seedance-2.0 bytedance/seedance-2.0/text-to-video / image-to-video
kling-v3-4k fal-ai/kling-video/v3/4k/text-to-video / image-to-video
happy-horse fal-ai/happy-horse/text-to-video / image-to-video
DEFAULT_MODEL moved from veo3.1 (premium) to pixverse-v6 (cheap, sane
defaults, both modalities) — better first-run UX for users who haven't
explicitly picked a model.
New family-entry knob: image_param_key. Kling v3 4K's image-to-video
endpoint expects start_image_url instead of image_url; declaring
image_param_key='start_image_url' on the family lets _build_payload
remap correctly. Other families default to plain image_url.
Per-family capability flags reflect each model's docs:
- LTX 2.3 + Happy Horse: minimal payloads (no duration/aspect/resolution
enum exposed by FAL — let endpoint apply defaults)
- Seedance: 6 aspect ratios incl 21:9, durations 4-15, audio supported,
negative prompts NOT supported per docs
- Kling v3 4K: 16:9/9:16/1:1, 3-15s, audio + negative
- Veo 3.1: unchanged, 16:9/9:16, 4/6/8s
Tests: +5 covering the new families (full catalog, Kling 4K
start_image_url remap, Seedance routing, LTX payload minimality, Happy
Horse minimality). 56/56 in the slice green.
Note: I did NOT add the FAL-hosted xAI Grok-Imagine variant. Hermes
already has a direct xAI plugin that talks to xAI's own API; routing
the same model through FAL's wrapper would duplicate the surface
without adding capabilities. Users on FAL who want Grok-Imagine should
use the xAI plugin directly; flag if you want both routes available.
* test(video_gen): tool-surface routing matrix — every model x modality
End-to-end matrix test driven through _handle_video_generate() — the
actual function the agent's video_generate tool call lands in. Writes
config.yaml, invokes the registered handler with a raw args dict, then
asserts the outbound HTTP/SDK call hit the right endpoint with the right
payload shape.
Parametrized over FAL_FAMILIES.keys() so the matrix auto-discovers new
families as they're added (add a family to FAL_FAMILIES and you get
both modalities tested for free).
Coverage:
- All 6 FAL families x {text-only, text+image} = 12 cases
- xAI x {text-only, text+image} = 2 cases
- tool-level model= arg overrides config = 2 cases
For each case, verifies:
- result['success'] is True
- result['modality'] matches input shape ('text' if no image_url, 'image' otherwise)
- outbound endpoint URL matches the family's text_endpoint or image_endpoint
- text-only payloads carry no image-shaped keys
- text+image payloads carry the family's image key (image_url for most,
start_image_url for kling-v3-4k, wrapped 'image' object for xAI)
All 16 cases passing. Confirms the tool surface routes every
(provider, model, modality) combination correctly with zero leakage.
* feat(video_gen): keep video_gen out of first-run setup, surface in status
Two changes:
1. video_gen joins _DEFAULT_OFF_TOOLSETS, so it is NOT pre-selected in
the first-run toolset checklist. Video gen is niche, paid, and slow —
most users don't want it nagging them during initial setup. Anyone
who wants it opts in via 'hermes tools' -> Video Generation, which
already routes to the provider+model picker.
2. The 'hermes setup' status panel learns about video_gen — but only
shows the row when a plugin reports available. Users without
FAL_KEY/XAI_API_KEY see nothing about video gen; users with one of
those keys see 'Video Generation (FAL) ✓' as confirmation it's wired.
Verified live:
- Fresh install (no creds): zero video_gen mentions in wizard.
- With FAL_KEY: status row appears with active backend name.
- 160/160 in the setup + tools_config + video_gen test slice.
Rationale: image_gen is on by default because it's a featured creative
tool used in casual chat (telegrams, etc). Video gen is heavier — long
wait, paid per-second pricing. Default-off matches user intent better.
---------
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
feat(video_gen): unified video_generate tool with pluggable provider backends (#25126)
* feat(video_gen): unified video_generate tool with pluggable provider backends
One core video_generate tool, every backend a plugin. Mirrors the
image_gen + memory_provider + context_engine architecture: ABC, registry,
plugin-context registration hook, and per-plugin model catalogs surfaced
through hermes tools.
Surface (one schema, every backend):
- operation: generate / edit / extend
- modalities: text-to-video (prompt only), image-to-video (prompt +
image_url), video edit (prompt + video_url), video extend (video_url)
- reference_image_urls, duration, aspect_ratio, resolution,
negative_prompt, audio, seed, model override
- Providers ignore unknown kwargs and declare what they support via
VideoGenProvider.capabilities() — backend-specific quirks stay in the
backend, the agent learns one tool
Backends shipped:
- plugins/video_gen/xai/ — Grok-Imagine, full generate/edit/extend +
image-to-video + reference images (salvaged from PR #10600 by
@Jaaneek, reshaped into the plugin interface)
- plugins/video_gen/fal/ — Veo 3.1 (t2v + i2v), Kling O3 i2v,
Pixverse v6 i2v with model-aware payload building that drops keys a
model doesn't declare
Wiring:
- agent/video_gen_provider.py — VideoGenProvider ABC, normalize_operation,
success_response / error_response, save_b64_video / save_bytes_video,
$HERMES_HOME/cache/videos/
- agent/video_gen_registry.py — thread-safe register/get/list +
get_active_provider() reading video_gen.provider from config.yaml
- hermes_cli/plugins.py — PluginContext.register_video_gen_provider()
- hermes_cli/tools_config.py — Video Generation category in
hermes tools, plugin-only providers list, model picker per plugin,
config write to video_gen.{provider,model}
- toolsets.py — new video_gen toolset
- tests: 31 new tests covering ABC, registry, tool dispatch, both plugins
- docs: developer-guide/video-gen-provider-plugin.md (parallel to the
image-gen guide), sidebar + toolsets-reference + plugin guides updated
Supersedes: #25035 (FAL), #17972 (FAL), #14543 (xAI), #13847 (HappyHorse),
#10458 (provider categories), #10786 (xAI media+search bundle), #2984
(FAL duplicate), #19086 (Google Veo standalone — easy port to plugin
interface).
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
* feat(video_gen): dynamic schema reflects active backend's capabilities
Address the 'capability variance' question — instead of one tool with a
static schema that lies about what every backend supports, the
video_generate tool now rebuilds its description at get_definitions()
time based on the configured video_gen.provider and video_gen.model.
The agent sees backend-specific guidance up-front:
- 'fal-ai/veo3.1/image-to-video': 'image-to-video only — image_url is
REQUIRED; text-only prompts will be rejected'
- 'fal-ai/veo3.1' (t2v): no image_url restriction shown
- xAI grok-imagine-video: 'operations: generate, edit, extend; up to 7
reference_image_urls'
- Backends without edit/extend: 'not supported on this backend — surface
that they need to switch backends via hermes tools'
This is the same pattern PR #22694 used for delegate_task self-capping —
documented in the dynamic-tool-schemas skill. Cache invalidation is
free: get_tool_definitions() already memoizes on config.yaml mtime, so a
mid-session backend swap rebuilds the schema automatically.
Tested:
- Empirical FAL OpenAPI schema check confirms image-to-video models
require image_url (FAL returns HTTP 422 otherwise) — client-side
rejection in FALVideoGenProvider.generate() now prevents the wasted
round-trip
- Live E2E: fal-ai/veo3.1/image-to-video + prompt-only → clean
missing_image_url error; fal-ai/veo3.1 + prompt-only → dispatches
- 6 new tests cover the builder (no config / image-only / full-surface /
text-only / unknown provider / registry wiring), all passing
- 37/37 in the slice, 134/134 in the broader regression set
* test(video_gen/xai): full surface integration tests + cleaner schema
Verified end-to-end that the xAI plugin handles every documented mode
from PR #10600's surface: text-to-video, image-to-video,
reference-images-to-video, video edit, video extend (with and without
prompt). All five modes route to the correct xAI endpoint
(/videos/generations, /videos/edits, /videos/extensions) with the right
payload shape (image / reference_images / video keys), and all five
client-side rejections fire before the network: edit-without-prompt,
extend-without-video_url, image+refs conflict, >7 references, and
duration/aspect_ratio clamping.
15 new integration tests grouped into four classes (endpoint routing,
modalities, validation, clamping). httpx is stubbed via a small fake
AsyncClient that records POSTs so the tests assert the actual payload
the plugin would send to xAI — not just the success/error envelope.
Also cleaned up a description redundancy: when a model's operations
match the backend's overall set, we no longer print the duplicate
'operations supported by this model' line. xAI's description now reads:
Active backend: xAI . model: grok-imagine-video
- operations supported by this backend: edit, extend, generate
- modalities supported by this backend: image, reference_images, text
- aspect_ratio choices: 16:9, 1:1, 2:3, 3:2, 3:4, 4:3, 9:16
- resolution choices: 480p, 720p
- duration range: 1-15s
- reference_image_urls: up to 7 images
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
* feat(video_gen): collapse surface to t2v + i2v, family-based auto-routing
Two design changes per Teknium:
1) Drop edit/extend from the tool surface entirely. Only text-to-video
and image-to-video remain. The agent sees a clean tool with two
modalities; backend-specific quirks like xAI's edit/extend endpoints
stay out of the unified schema.
2) FAL: pick a model FAMILY once, the plugin routes between the
family's text-to-video and image-to-video endpoints based on whether
image_url was passed. Users no longer pick 'fal-ai/veo3.1' AND
'fal-ai/veo3.1/image-to-video' as separate options — they pick
'veo3.1', and the plugin handles the rest.
Catalog rewritten as families:
veo3.1 fal-ai/veo3.1 / fal-ai/veo3.1/image-to-video
pixverse-v6 fal-ai/pixverse/v6/text-to-video / fal-ai/pixverse/v6/image-to-video
kling-o3-standard fal-ai/kling-video/o3/standard/text-to-video / fal-ai/kling-video/o3/standard/image-to-video
xAI uses a single endpoint (/videos/generations) for both modes,
routed by the presence of the 'image' field in the payload — no
edit/extend exposure.
Schema changes:
- VIDEO_GENERATE_SCHEMA: drop operation, drop video_url. Final params:
prompt (required), image_url, reference_image_urls, duration,
aspect_ratio, resolution, negative_prompt, audio, seed, model.
- VideoGenProvider ABC: drop normalize_operation, VALID_OPERATIONS,
DEFAULT_OPERATION. capabilities() drops 'operations' key.
- success_response: add 'modality' field ('text' | 'image') so the
agent and logs can see which endpoint was actually hit.
Dynamic schema builder simplified — no operations bullet, no
'switch backends if you need edit/extend' guidance. When the active
backend supports both modalities (the common case), description reads:
Active backend: FAL . model: pixverse-v6
- supports both text-to-video (omit image_url) and image-to-video
(pass image_url) - routes automatically
- aspect_ratio choices: 16:9, 9:16, 1:1
- resolution choices: 360p, 540p, 720p, 1080p
- duration range: 1-15s
- audio: pass audio=true to enable native audio (pricing tier)
- negative_prompt: supported
Tests: 51 in the video_gen slice, 216 across the broader image+video
sweep, all passing. New FAL routing tests prove pixverse-v6 + no image
hits text-to-video endpoint, pixverse-v6 + image_url hits
image-to-video endpoint, same for veo3.1 and kling-o3-standard.
Docs updated: developer-guide page rewrites the 'model families' pattern
as a first-class section so external plugin authors know the convention.
toolsets-reference and toolsets.py descriptions match the new surface.
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>
* feat(video_gen/fal): expand catalog to 6 families, cheap + premium tiers
Catalog now covers everything Teknium specced from FAL:
Cheap tier:
ltx-2.3 fal-ai/ltx-2.3-22b/text-to-video / image-to-video
pixverse-v6 fal-ai/pixverse/v6/text-to-video / image-to-video
Premium tier:
veo3.1 fal-ai/veo3.1 / fal-ai/veo3.1/image-to-video
seedance-2.0 bytedance/seedance-2.0/text-to-video / image-to-video
kling-v3-4k fal-ai/kling-video/v3/4k/text-to-video / image-to-video
happy-horse fal-ai/happy-horse/text-to-video / image-to-video
DEFAULT_MODEL moved from veo3.1 (premium) to pixverse-v6 (cheap, sane
defaults, both modalities) — better first-run UX for users who haven't
explicitly picked a model.
New family-entry knob: image_param_key. Kling v3 4K's image-to-video
endpoint expects start_image_url instead of image_url; declaring
image_param_key='start_image_url' on the family lets _build_payload
remap correctly. Other families default to plain image_url.
Per-family capability flags reflect each model's docs:
- LTX 2.3 + Happy Horse: minimal payloads (no duration/aspect/resolution
enum exposed by FAL — let endpoint apply defaults)
- Seedance: 6 aspect ratios incl 21:9, durations 4-15, audio supported,
negative prompts NOT supported per docs
- Kling v3 4K: 16:9/9:16/1:1, 3-15s, audio + negative
- Veo 3.1: unchanged, 16:9/9:16, 4/6/8s
Tests: +5 covering the new families (full catalog, Kling 4K
start_image_url remap, Seedance routing, LTX payload minimality, Happy
Horse minimality). 56/56 in the slice green.
Note: I did NOT add the FAL-hosted xAI Grok-Imagine variant. Hermes
already has a direct xAI plugin that talks to xAI's own API; routing
the same model through FAL's wrapper would duplicate the surface
without adding capabilities. Users on FAL who want Grok-Imagine should
use the xAI plugin directly; flag if you want both routes available.
* test(video_gen): tool-surface routing matrix — every model x modality
End-to-end matrix test driven through _handle_video_generate() — the
actual function the agent's video_generate tool call lands in. Writes
config.yaml, invokes the registered handler with a raw args dict, then
asserts the outbound HTTP/SDK call hit the right endpoint with the right
payload shape.
Parametrized over FAL_FAMILIES.keys() so the matrix auto-discovers new
families as they're added (add a family to FAL_FAMILIES and you get
both modalities tested for free).
Coverage:
- All 6 FAL families x {text-only, text+image} = 12 cases
- xAI x {text-only, text+image} = 2 cases
- tool-level model= arg overrides config = 2 cases
For each case, verifies:
- result['success'] is True
- result['modality'] matches input shape ('text' if no image_url, 'image' otherwise)
- outbound endpoint URL matches the family's text_endpoint or image_endpoint
- text-only payloads carry no image-shaped keys
- text+image payloads carry the family's image key (image_url for most,
start_image_url for kling-v3-4k, wrapped 'image' object for xAI)
All 16 cases passing. Confirms the tool surface routes every
(provider, model, modality) combination correctly with zero leakage.
* feat(video_gen): keep video_gen out of first-run setup, surface in status
Two changes:
1. video_gen joins _DEFAULT_OFF_TOOLSETS, so it is NOT pre-selected in
the first-run toolset checklist. Video gen is niche, paid, and slow —
most users don't want it nagging them during initial setup. Anyone
who wants it opts in via 'hermes tools' -> Video Generation, which
already routes to the provider+model picker.
2. The 'hermes setup' status panel learns about video_gen — but only
shows the row when a plugin reports available. Users without
FAL_KEY/XAI_API_KEY see nothing about video gen; users with one of
those keys see 'Video Generation (FAL) ✓' as confirmation it's wired.
Verified live:
- Fresh install (no creds): zero video_gen mentions in wizard.
- With FAL_KEY: status row appears with active backend name.
- 160/160 in the setup + tools_config + video_gen test slice.
Rationale: image_gen is on by default because it's a featured creative
tool used in casual chat (telegrams, etc). Video gen is heavier — long
wait, paid per-second pricing. Default-off matches user intent better.
---------
Co-authored-by: Jaaneek <Jaaneek@users.noreply.github.com>