GPT-5.4 supports none/low/medium/high/xhigh but not 'minimal'.
Users may configure 'minimal' via OpenRouter conventions, which would
cause a 400 on native OpenAI. Clamp to 'low' in the codex_responses
path before sending.
* feat(skills): add fitness-nutrition skill to optional-skills
Cherry-picked from PR #9177 by @haileymarshall.
Adds a fitness and nutrition skill for gym-goers and health-conscious users:
- Exercise search via wger API (690+ exercises, free, no auth)
- Nutrition lookup via USDA FoodData Central (380K+ foods, DEMO_KEY fallback)
- Offline body composition calculators (BMI, TDEE, 1RM, macros, body fat %)
- Pure stdlib Python, no pip dependencies
Changes from original PR:
- Moved from skills/ to optional-skills/health/ (correct location)
- Fixed BMR formula in FORMULAS.md (removed confusing -5+10, now just +5)
- Fixed author attribution to match PR submitter
- Marked USDA_API_KEY as optional (DEMO_KEY works without signup)
Also adds optional env var support to the skill readiness checker:
- New 'optional: true' field in required_environment_variables entries
- Optional vars are preserved in metadata but don't block skill readiness
- Optional vars skip the CLI capture prompt flow
- Skills with only optional missing vars show as 'available' not 'setup_needed'
* fix: auto-correct close model name matches in /model validation
When a user types a model name with a minor typo (e.g. gpt5.3-codex instead
of gpt-5.3-codex), the validation now auto-corrects to the closest match
instead of accepting the wrong name with a warning.
Uses difflib get_close_matches with cutoff=0.9 to avoid false corrections
(e.g. gpt-5.3 should not silently become gpt-5.4). Applied consistently
across all three validation paths: codex provider, custom endpoints, and
generic API-probed providers.
The validate_requested_model() return dict gains an optional corrected_model
key that switch_model() applies before building the result.
Reported by Discord user — /model gpt5.3-codex was accepted with a warning
but would fail at the API level.
---------
Co-authored-by: haileymarshall <haileymarshall@users.noreply.github.com>
During custom endpoint setup, users are now asked for a display name
with the auto-generated name as the default. Typing 'Ollama' or
'LM Studio' replaces the generic 'Local (localhost:11434)' in the
provider menu.
Extracts _auto_provider_name() for reuse and adds a name= parameter
to _save_custom_provider() so the caller can pass through the
user-chosen label.
Models like MiniMax emit inline <think>...</think> reasoning blocks in
their content field. The CLI already suppresses these via a state machine
in _stream_delta, but the gateway's GatewayStreamConsumer had no
equivalent filtering — raw think blocks were streamed directly to
Discord/Telegram/Slack.
The fix adds a _filter_and_accumulate() method that mirrors the CLI's
approach: a state machine tracks whether we're inside a reasoning block
and silently discards the content. Includes the same block-boundary
check (tag must appear at line start or after whitespace-only prefix)
to avoid false positives when models mention <think> in prose.
Handles all tag variants: <think>, <thinking>, <THINKING>, <thought>,
<reasoning>, <REASONING_SCRATCHPAD>.
Also handles edge cases:
- Tags split across streaming deltas (partial tag buffering)
- Unclosed blocks (content suppressed until stream ends)
- Multiple consecutive blocks
- _flush_think_buffer on stream end for held-back partial tags
Adds 22 unit tests + 1 integration test covering all scenarios.
* feat(skills): add fitness-nutrition skill to optional-skills
Cherry-picked from PR #9177 by @haileymarshall.
Adds a fitness and nutrition skill for gym-goers and health-conscious users:
- Exercise search via wger API (690+ exercises, free, no auth)
- Nutrition lookup via USDA FoodData Central (380K+ foods, DEMO_KEY fallback)
- Offline body composition calculators (BMI, TDEE, 1RM, macros, body fat %)
- Pure stdlib Python, no pip dependencies
Changes from original PR:
- Moved from skills/ to optional-skills/health/ (correct location)
- Fixed BMR formula in FORMULAS.md (removed confusing -5+10, now just +5)
- Fixed author attribution to match PR submitter
- Marked USDA_API_KEY as optional (DEMO_KEY works without signup)
Also adds optional env var support to the skill readiness checker:
- New 'optional: true' field in required_environment_variables entries
- Optional vars are preserved in metadata but don't block skill readiness
- Optional vars skip the CLI capture prompt flow
- Skills with only optional missing vars show as 'available' not 'setup_needed'
* docs: add Hugging Face and Xiaomi MiMo to README provider list
---------
Co-authored-by: haileymarshall <haileymarshall@users.noreply.github.com>
Cherry-picked from PR #9177 by @haileymarshall.
Adds a fitness and nutrition skill for gym-goers and health-conscious users:
- Exercise search via wger API (690+ exercises, free, no auth)
- Nutrition lookup via USDA FoodData Central (380K+ foods, DEMO_KEY fallback)
- Offline body composition calculators (BMI, TDEE, 1RM, macros, body fat %)
- Pure stdlib Python, no pip dependencies
Changes from original PR:
- Moved from skills/ to optional-skills/health/ (correct location)
- Fixed BMR formula in FORMULAS.md (removed confusing -5+10, now just +5)
- Fixed author attribution to match PR submitter
- Marked USDA_API_KEY as optional (DEMO_KEY works without signup)
Also adds optional env var support to the skill readiness checker:
- New 'optional: true' field in required_environment_variables entries
- Optional vars are preserved in metadata but don't block skill readiness
- Optional vars skip the CLI capture prompt flow
- Skills with only optional missing vars show as 'available' not 'setup_needed'
- Add GET /api/model/info endpoint that resolves model metadata using the
same 10-step context-length detection chain the agent uses. Returns
auto-detected context length, config override, effective value, and
model capabilities (tools, vision, reasoning, max output, model family).
- Surface model.context_length as model_context_length virtual field in
the config normalize/denormalize cycle. 0 = auto-detect (default),
positive value overrides. Writing 0 removes context_length from the
model dict on disk.
- Add ModelInfoCard component showing resolved context window (e.g. '1M
auto-detected' or '500K override — auto: 1M'), max output tokens, and
colored capability badges (Tools, Vision, Reasoning, model family).
- Inject ModelInfoCard between model field and context_length override in
ConfigPage General tab. Card re-fetches on model change and after save.
- Insert model_context_length right after model in CONFIG_SCHEMA ordering
so the three elements (model input → info card → override) are adjacent.
Plugins can now return {"action": "block", "message": "reason"} from
their pre_tool_call hook to prevent a tool from executing. The error
message is returned to the model as a tool result so it can adjust.
Covers both execution paths: handle_function_call (model_tools.py) and
agent-level tools (run_agent.py _invoke_tool + sequential/concurrent).
Blocked tools skip all side effects (counter resets, checkpoints,
callbacks, read-loop tracker).
Adds skip_pre_tool_call_hook flag to avoid double-firing the hook when
run_agent.py already checked and then calls handle_function_call.
Salvaged from PR #5385 (gianfrancopiana) and PR #4610 (oredsecurity).
* Add hermes debug share instructions to all issue templates
- bug_report.yml: Add required Debug Report section with hermes debug share
and /debug instructions, make OS/Python/Hermes version optional (covered
by debug report), demote old logs field to optional supplementary
- setup_help.yml: Replace hermes doctor reference with hermes debug share,
add Debug Report section with fallback chain (debug share -> --local -> doctor)
- feature_request.yml: Add optional Debug Report section for environment context
All templates now guide users to run hermes debug share (or /debug in chat)
and paste the resulting paste.rs links, giving maintainers system info,
config, and recent logs in one step.
* feat: add openrouter/elephant-alpha to curated model lists
- Add to OPENROUTER_MODELS (free, positioned above GPT models)
- Add to _PROVIDER_MODELS["nous"] mirror list
- Add 256K context window fallback in model_metadata.py
Adds a CI workflow that blocks PRs introducing commits with
unmapped author emails. Checks each new commit's author email
against AUTHOR_MAP in scripts/release.py — GitHub noreply emails
auto-pass, but personal/work emails must be mapped.
Also adds --strict and --diff-base flags to contributor_audit.py
for programmatic use. --strict exits 1 when new unmapped emails
are found; --diff-base scopes the check to only flag emails from
commits after a given ref (grandfathers existing unknowns).
Prevention for the 97-unmapped-email gap found in the April 2026
contributor audit.
Audit of all external contributor PRs revealed 97 commit emails
not mapped in AUTHOR_MAP, meaning contributors weren't properly
credited in release notes. Cross-referenced via:
- GitHub API email search (9 resolved before rate limit)
- Salvage PR body mentions (@username in descriptions)
- Git noreply email cross-reference (same person, both emails)
- GH contributor list username matching
Also adds .mailmap for git shortlog/log display consistency.
Remaining 22 unmapped emails need GH API resolution when rate
limit resets — the contributor_audit.py script will flag them.
Addresses ColourfulWhite's report about missing contributor tags.
When the 5-second stream_task timeout in gateway/run.py expires (due to
slow Telegram API calls from rate limiting after several messages), the
stream consumer is cancelled via asyncio.CancelledError. The
CancelledError handler did a best-effort final edit but never set
final_response_sent, so the gateway fell through to the normal send path
and delivered the full response again as a reply — causing a duplicate.
The fix: in the CancelledError handler, set final_response_sent = True
when already_sent is True (i.e., the stream consumer had already
delivered content to the user). This tells the gateway's already_sent
check that the response was delivered, preventing the duplicate send.
Adds two tests verifying the cancellation behavior:
- Cancelled with already_sent=True → final_response_sent=True (no dup)
- Cancelled with already_sent=False → final_response_sent=False (normal
send path proceeds)
Reported by community user hume on Discord.
The generic 'gpt-5' fallback was set to 128,000 — which is the max
OUTPUT tokens, not the context window. GPT-5 base and most variants
(codex, mini) have 400,000 context. This caused /model to report
128k for models like gpt-5.3-codex when models.dev was unavailable.
Added specific entries for GPT-5 variants with different context sizes:
- gpt-5.4, gpt-5.4-pro: 1,050,000 (1.05M)
- gpt-5.4-mini, gpt-5.4-nano: 400,000
- gpt-5.3-codex-spark: 128,000 (reduced)
- gpt-5.1-chat: 128,000 (chat variant)
- gpt-5 (catch-all): 400,000
Sources: https://developers.openai.com/api/docs/models
Remove the two-tier (top/extended) provider picker that hid most
providers behind a 'More providers...' submenu. All providers now
appear in a single flat list.
- Remove tier field from ProviderEntry namedtuple
- Remove tier values from all CANONICAL_PROVIDERS entries
- Flatten the hermes model picker (no more 'More...' submenu)
- Move 'Custom endpoint' to the bottom of the main list
Port two improvements inspired by Kilo-Org/kilocode analysis:
1. Error classifier: add context overflow patterns for vLLM, Ollama,
and llama.cpp/llama-server. These local inference servers return
different error formats than cloud providers (e.g., 'exceeds the
max_model_len', 'context length exceeded', 'slot context'). Without
these patterns, context overflow errors from local servers are
misclassified as format errors, causing infinite retries instead
of triggering compression.
2. MCP initial connection retry: previously, if the very first
connection attempt to an MCP server failed (e.g., transient DNS
blip at startup), the server was permanently marked as failed with
no retry. Post-connect reconnection had 5 retries with exponential
backoff, but initial connection had zero. Now initial connections
retry up to 3 times with backoff before giving up, matching the
resilience of post-connect reconnection.
(Inspired by Kilo Code's MCP server disappearing fix in v1.3.3)
Tests: 6 new error classifier tests, 4 new MCP retry tests, 1
updated existing test. All 276 affected tests pass.
Adds Arcee AI as a standard direct provider (ARCEEAI_API_KEY) with
Trinity models: trinity-large-thinking, trinity-large-preview, trinity-mini.
Standard OpenAI-compatible provider checklist: auth.py, config.py,
models.py, main.py, providers.py, doctor.py, model_normalize.py,
model_metadata.py, setup.py, trajectory_compressor.py.
Based on PR #9274 by arthurbr11, simplified to a standard direct
provider without dual-endpoint OpenRouter routing.
- Nav: icons only on mobile, icon+label on sm+
- Brand: abbreviated "H A" on mobile, full "Hermes Agent" on sm+
- Content: reduced padding on mobile (px-3 vs px-6)
- StatusPage: session cards stack vertically on mobile, truncate
overflow text, strip model namespace for brevity
- ConfigPage: sidebar becomes horizontal scrollable pills on mobile
instead of fixed left column, search hidden on mobile
- SessionsPage: title + search stack vertically on mobile, search
goes full-width
- Card component: add overflow-hidden to prevent content bleed
- Body/root: add overflow-x-hidden to prevent horizontal scroll
- Footer: reduced font sizes on mobile
All changes use Tailwind responsive breakpoints (sm: prefix).
No logic changes — purely layout/CSS adjustments.
The /model picker currently renders one row per ``custom_providers``
entry. When several entries share the same provider name (e.g. four
``ollama-cloud`` entries for ``qwen3-coder``, ``glm-5.1``, ``kimi-k2``,
``minimax-m2.7``), users see four separate "Ollama Cloud" rows in the
picker, which is confusing UX — there is only one Ollama Cloud
provider, so there should be one row containing four models.
This PR groups ``custom_providers`` entries that share the same provider
name into a single picker row while keeping entries with distinct names
as separate rows. So:
* Four entries named ``Ollama Cloud`` → one "Ollama Cloud" row with
four models inside.
* One entry named ``Ollama Cloud`` and one named ``Moonshot`` → two
separate rows, one model each.
Implementation
--------------
Replaces the single-pass loop in ``list_authenticated_providers()`` with
a two-pass approach:
1. First pass: build an ``OrderedDict`` keyed by ``custom_provider_slug(name)``,
accumulating ``models`` per group while preserving discovery order.
2. Second pass: iterate the groups and append one result row per group,
skipping any slug that already appeared in an earlier provider source
(the existing ``seen_slugs`` guard).
Insertion order is preserved via ``OrderedDict``, so providers and
their models still appear in the order the user listed them in
``custom_providers``. No new dependencies.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
get_compatible_custom_providers() deduplicates by (name, base_url) which
collapses multiple models under the same provider into a single entry.
For example, 7 Ollama Cloud entries with different models become 1.
Adding model to the tuple preserves all entries.
The dashboard device-code flow (_nous_poller in web_server.py) saved
credentials to the credential pool only, while get_nous_auth_status()
only checked the auth store (auth.json). This caused the Keys tab to
show 'not connected' even when the backend was fully authenticated.
Two fixes:
1. get_nous_auth_status() now checks the credential pool first (like
get_codex_auth_status() already does), then falls back to the auth
store.
2. _nous_poller now also persists to the auth store after saving to
the credential pool, matching the CLI flow (_login_nous).
Adds 3 tests covering pool-only, auth-store-fallback, and empty-state
scenarios.
Three problems fixed:
1. bobashopcashier missing from v0.9.0 contributor list despite
authoring the gateway drain PR (#7290, salvaged into #7503).
Their email (kennyx102@gmail.com) was missing from AUTHOR_MAP.
2. release.py only scanned git commit authors, missing Co-authored-by
trailers. Now parse_coauthors() extracts trailers from commit bodies.
3. No mechanism to detect contributors from salvaged PRs (where original
author only appears in PR description, not git log).
Changes:
- scripts/release.py: add kennyx102@gmail.com to AUTHOR_MAP, enhance
get_commits() to parse Co-authored-by trailers, filter AI assistants
(Claude, Copilot, Cursor Agent) from co-author lists
- scripts/contributor_audit.py: new script that cross-references git
authors, co-author trailers, and salvaged PR descriptions. Reports
unknown emails and contributors missing from release notes.
- RELEASE_v0.9.0.md: add bobashopcashier to community contributors
Usage:
python scripts/contributor_audit.py --since-tag v2026.4.8
python scripts/contributor_audit.py --since-tag v2026.4.8 --release-file RELEASE_v0.9.0.md
- Use isinstance() with try/except import for CopilotACPClient check
in _to_async_client instead of fragile __class__.__name__ string check
- Restore accurate comment: GPT-5.x models *require* (not 'often require')
the Responses API on OpenAI/OpenRouter; ACP is the exception, not a
softening of the requirement
- Add inline comment explaining the ACP exclusion rationale
Plugin context engines loaded via load_context_engine() were never
given context_length, causing the CLI status bar to show "ctx --"
with an empty progress bar. Call update_model() immediately after
loading the plugin engine, mirroring what switch_model() already does.
FixesNousResearch/hermes-agent#9071
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Three separate hardcoded provider lists (/model, /provider, hermes model)
diverged over time, causing providers to be missing from some commands.
- Create CANONICAL_PROVIDERS in hermes_cli/models.py as the single source
of truth for all provider identity, labels, and TUI ordering
- Derive _PROVIDER_LABELS and list_available_providers() from canonical list
- Add step 2b in list_authenticated_providers() to cross-check canonical
list — catches providers with credentials that weren't found via
PROVIDER_TO_MODELS_DEV or HERMES_OVERLAYS mappings
- Derive hermes model TUI provider menus from canonical list
- Add deepseek and xai as first-class providers (were missing from TUI)
- Add grok/x-ai/x.ai aliases for xai provider
Fixes: /model command not showing all providers that hermes model shows
/stop was calling suspend_session() which marked the session for auto-reset
on the next message. This meant users lost their conversation history every
time they stopped a running agent — especially painful for untitled sessions
that can't be resumed by name.
Now /stop just interrupts the agent and cleans the session lock. The session
stays intact so users can continue the conversation.
The suspend behavior was introduced in #7536 to break stuck session resume
loops on gateway restart. That case is already handled by
suspend_recently_active() which runs at gateway startup, so removing it from
/stop doesn't regress the original fix.
