When a user edits a bundled skill, sync flags it as user_modified and
skips it forever. The problem: if the user later tries to undo the edit
by copying the current bundled version back into ~/.hermes/skills/, the
manifest still holds the old origin hash from the last successful
sync, so the fresh bundled hash still doesn't match and the skill stays
stuck as user_modified.
Adds an escape hatch for this case.
hermes skills reset <name>
Drops the skill's entry from ~/.hermes/skills/.bundled_manifest and
re-baselines against the user's current copy. Future 'hermes update'
runs accept upstream changes again. Non-destructive.
hermes skills reset <name> --restore
Also deletes the user's copy and re-copies the bundled version.
Use when you want the pristine upstream skill back.
Also available as /skills reset in chat.
- tools/skills_sync.py: new reset_bundled_skill(name, restore=False)
- hermes_cli/skills_hub.py: do_reset() + wired into skills_command and
handle_skills_slash; added to the slash /skills help panel
- hermes_cli/main.py: argparse entry for 'hermes skills reset'
- tests/tools/test_skills_sync.py: 5 new tests covering the stuck-flag
repro, --restore, unknown-skill error, upstream-removed-skill, and
no-op on already-clean state
- website/docs/user-guide/features/skills.md: new 'Bundled skill updates'
section explaining the origin-hash mechanic + reset usage
* feat(image_gen): upgrade Recraft V3 → V4 Pro, Nano Banana → Pro
Upstream asked for these two upgrades ASAP — the old entries show
stale models when newer, higher-quality versions are available on FAL.
Recraft V3 → Recraft V4 Pro
ID: fal-ai/recraft-v3 → fal-ai/recraft/v4/pro/text-to-image
Price: $0.04/image → $0.25/image (6x — V4 Pro is premium tier)
Schema: V4 dropped the required `style` enum entirely; defaults
handle taste now. Added `colors` and `background_color`
to supports for brand-palette control. `seed` is not
supported by V4 per the API docs.
Nano Banana → Nano Banana Pro
ID: fal-ai/nano-banana → fal-ai/nano-banana-pro
Price: $0.08/image → $0.15/image (1K); $0.30 at 4K
Schema: Aspect ratio family unchanged. Added `resolution`
(1K/2K/4K, default 1K for billing predictability),
`enable_web_search` (real-time info grounding, +$0.015),
and `limit_generations` (force exactly 1 image).
Architecture: Gemini 2.5 Flash → Gemini 3 Pro Image. Quality
and reasoning depth improved; slower (~6s → ~8s).
Migration: users who had the old IDs in `image_gen.model` will
fall through the existing 'unknown model → default' warning path
in `_resolve_fal_model()` and get the Klein 9B default on the next
run. Re-run `hermes tools` → Image Generation to pick the new
version. No silent cost-upgrade aliasing — the 2-6x price jump
on these tiers warrants explicit user re-selection.
Portal note: both new model IDs need to be allowlisted on the
Nous fal-queue-gateway alongside the previous 7 additions, or
users on Nous Subscription will see the 'managed gateway rejected
model' error we added previously (which is clear and
self-remediating, just noisy).
* docs: wrap '<1s' in backticks to unblock MDX compilation
Docusaurus's MDX parser treats unquoted '<' as the start of JSX, and
'<1s' fails because '1' isn't a valid tag-name start character. This
was broken on main since PR #11265 (never noticed because
docs-site-checks was failing on OTHER issues at the time and we
admin-merged through it).
Wrapping in backticks also gives the cell monospace styling which
reads more cleanly alongside the inline-code model ID in the same row.
The other '<1s' occurrence (line 52) is inside a fenced code block
and is already safe — code fences bypass MDX parsing.
* feat(image_gen): multi-model FAL support with picker in hermes tools
Adds 8 FAL text-to-image models selectable via `hermes tools` →
Image Generation → (FAL.ai | Nous Subscription) → model picker.
Models supported:
- fal-ai/flux-2/klein/9b (new default, <1s, $0.006/MP)
- fal-ai/flux-2-pro (previous default, kept backward-compat upscaling)
- fal-ai/z-image/turbo (Tongyi-MAI, bilingual EN/CN)
- fal-ai/nano-banana (Gemini 2.5 Flash Image)
- fal-ai/gpt-image-1.5 (with quality tier: low/medium/high)
- fal-ai/ideogram/v3 (best typography)
- fal-ai/recraft-v3 (vector, brand styles)
- fal-ai/qwen-image (LLM-based)
Architecture:
- FAL_MODELS catalog declares per-model size family, defaults, supports
whitelist, and upscale flag. Three size families handled uniformly:
image_size_preset (flux family), aspect_ratio (nano-banana), and
gpt_literal (gpt-image-1.5).
- _build_fal_payload() translates unified inputs (prompt + aspect_ratio)
into model-specific payloads, merges defaults, applies caller overrides,
wires GPT quality_setting, then filters to the supports whitelist — so
models never receive rejected keys.
- IMAGEGEN_BACKENDS registry in tools_config prepares for future imagegen
providers (Replicate, Stability, etc.); each provider entry tags itself
with imagegen_backend: 'fal' to select the right catalog.
- Upscaler (Clarity) defaults off for new models (preserves <1s value
prop), on for flux-2-pro (backward-compat). Per-model via FAL_MODELS.
Config:
image_gen.model = fal-ai/flux-2/klein/9b (new)
image_gen.quality_setting = medium (new, GPT only)
image_gen.use_gateway = bool (existing)
Agent-facing schema unchanged (prompt + aspect_ratio only) — model
choice is a user-level config decision, not an agent-level arg.
Picker uses curses_radiolist (arrow keys, auto numbered-fallback on
non-TTY). Column-aligned: Model / Speed / Strengths / Price.
Docs: image-generation.md rewritten with the model table and picker
walkthrough. tools-reference, tool-gateway, overview updated to drop
the stale "FLUX 2 Pro" wording.
Tests: 42 new in tests/tools/test_image_generation.py covering catalog
integrity, all 3 size families, supports filter, default merging, GPT
quality wiring, model resolution fallback. 8 new in
tests/hermes_cli/test_tools_config.py for picker wiring (registry,
config writes, GPT quality follow-up prompt, corrupt-config repair).
* feat(image_gen): translate managed-gateway 4xx to actionable error
When the Nous Subscription managed FAL proxy rejects a model with 4xx
(likely portal-side allowlist miss or billing gate), surface a clear
message explaining:
1. The rejected model ID + HTTP status
2. Two remediation paths: set FAL_KEY for direct access, or
pick a different model via `hermes tools`
5xx, connection errors, and direct-FAL errors pass through unchanged
(those have different root causes and reasonable native messages).
Motivation: new FAL models added to this release (flux-2-klein-9b,
z-image-turbo, nano-banana, gpt-image-1.5, ideogram-v3, recraft-v3,
qwen-image) are untested against the Nous Portal proxy. If the portal
allowlists model IDs, users on Nous Subscription will hit cryptic
4xx errors without guidance on how to work around it.
Tests: 8 new cases covering status extraction across httpx/fal error
shapes and 4xx-vs-5xx-vs-ConnectionError translation policy.
Docs: brief note in image-generation.md for Nous subscribers.
Operator action (Nous Portal side): verify that fal-queue-gateway
passes through these 7 new FAL model IDs. If the proxy has an
allowlist, add them; otherwise Nous Subscription users will see the
new translated error and fall back to direct FAL.
* feat(image_gen): pin GPT-Image quality to medium (no user choice)
Previously the tools picker asked a follow-up question for GPT-Image
quality tier (low / medium / high) and persisted the answer to
`image_gen.quality_setting`. This created two problems:
1. Nous Portal billing complexity — the 22x cost spread between tiers
($0.009 low / $0.20 high) forces the gateway to meter per-tier per
user, which the portal team can't easily support at launch.
2. User footgun — anyone picking `high` by mistake burns through
credit ~6x faster than `medium`.
This commit pins quality at medium by baking it into FAL_MODELS
defaults for gpt-image-1.5 and removes all user-facing override paths:
- Removed `_resolve_gpt_quality()` runtime lookup
- Removed `honors_quality_setting` flag on the model entry
- Removed `_configure_gpt_quality_setting()` picker helper
- Removed `_GPT_QUALITY_CHOICES` constant
- Removed the follow-up prompt call in `_configure_imagegen_model()`
- Even if a user manually edits `image_gen.quality_setting` in
config.yaml, no code path reads it — always sends medium.
Tests:
- Replaced TestGptQualitySetting (6 tests) with TestGptQualityPinnedToMedium
(5 tests) — proves medium is baked in, config is ignored, flag is
removed, helper is removed, non-gpt models never get quality.
- Replaced test_picker_with_gpt_image_also_prompts_quality with
test_picker_with_gpt_image_does_not_prompt_quality — proves only 1
picker call fires when gpt-image is selected (no quality follow-up).
Docs updated: image-generation.md replaces the quality-tier table
with a short note explaining the pinning decision.
* docs(image_gen): drop stale 'wires GPT quality tier' line from internals section
Caught in a cleanup sweep after pinning quality to medium. The
"How It Works Internally" walkthrough still described the removed
quality-wiring step.
Adds Google Gemini TTS as the seventh voice provider, with 30 prebuilt
voices (Zephyr, Puck, Kore, Enceladus, Gacrux, etc.) and natural-language
prompt control. Integrates through the existing provider chain:
- tools/tts_tool.py: new _generate_gemini_tts() calls the
generativelanguage REST endpoint with responseModalities=[AUDIO],
wraps the returned 24kHz mono 16-bit PCM (L16) in a WAV RIFF header,
then ffmpeg-converts to MP3 or Opus depending on output extension.
For .ogg output, libopus is forced explicitly so Telegram voice
bubbles get Opus (ffmpeg defaults to Vorbis for .ogg).
- hermes_cli/tools_config.py: exposes 'Google Gemini TTS' as a provider
option in the curses-based 'hermes tools' UI.
- hermes_cli/setup.py: adds gemini to the setup wizard picker, tool
status display, and API key prompt branch (accepts existing
GEMINI_API_KEY or GOOGLE_API_KEY, falls back to Edge if neither set).
- tests/tools/test_tts_gemini.py: 15 unit tests covering WAV header
wrap correctness, env var fallback (GEMINI/GOOGLE), voice/model
overrides, snake_case vs camelCase inlineData handling, HTTP error
surfacing, and empty-audio edge cases.
- docs: TTS features page updated to list seven providers with the new
gemini config block and ffmpeg notes.
Live-tested against api key against gemini-2.5-flash-preview-tts: .wav,
.mp3, and Telegram-compatible .ogg (Opus codec) all produce valid
playable audio.
llm-wiki was the only shipped skill using metadata.hermes.config, which
caused 'hermes update' and 'hermes config migrate' to prompt for a wiki
directory on every run — even for users who have never touched the skill
— because 'enabled' is opt-out (all shipped skills count as enabled unless
explicitly disabled). Declining the prompt didn't persist anything, so
the nag fired again on every update.
Switch llm-wiki to the env var + runtime default pattern that obsidian and
google-workspace already use: WIKI_PATH env var, default $HOME/wiki. No
prompting infrastructure, no config.yaml touch, no nag loop.
Changes:
- skills/research/llm-wiki/SKILL.md: remove metadata.hermes.config,
document WIKI_PATH env var in the Wiki Location section, update the
orientation snippet and initialization guidance.
- Docs: replace llm-wiki's wiki.path examples with a generic 'myplugin.path'
placeholder across configuration.md, features/skills.md, and
creating-skills.md so users don't try to set skills.config.wiki.path
expecting llm-wiki to use it.
- skills-catalog.md: mention WIKI_PATH instead of skills.config.wiki.path.
E2E verified: discover_all_skill_config_vars() and get_missing_skill_config_vars()
both return 0 entries after this change, so the prompt branch in migrate_config()
no longer fires.
The metadata.hermes.config feature stays in place for third-party skills
that genuinely need structured config, but built-ins now prefer env vars.
- New page: user-guide/features/tool-gateway.md covering eligibility,
setup (hermes model, hermes tools, manual config), how use_gateway
works, precedence, switching back, status checking, self-hosted
gateway env vars, and FAQ
- Added to sidebar under Features (top-level, before Core category)
- Cross-references from: overview.md, tools.md, browser.md,
image-generation.md, tts.md, providers.md, environment-variables.md
- Added Nous Tool Gateway subsection to env vars reference with
TOOL_GATEWAY_DOMAIN, TOOL_GATEWAY_SCHEME, TOOL_GATEWAY_USER_TOKEN,
and FIRECRAWL_GATEWAY_URL
Camofox automatically maps each userId to a persistent Firefox profile
on the server side — no CAMOFOX_PROFILE_DIR env var exists. Our docs
incorrectly told users to configure this on the server.
Removed the fabricated env var from:
- browser docs (:::note block)
- config.py DEFAULT_CONFIG comment
- test docstring
* feat: implement register_command() on plugin context
Complete the half-built plugin slash command system. The dispatch
code in cli.py and gateway/run.py already called
get_plugin_command_handler() but the registration side was never
implemented.
Changes:
- Add register_command() to PluginContext — stores handler,
description, and plugin name; normalizes names; rejects conflicts
with built-in commands
- Add _plugin_commands dict to PluginManager
- Add commands_registered tracking on LoadedPlugin
- Add get_plugin_command_handler() and get_plugin_commands()
module-level convenience functions
- Fix commands.py to use actual plugin description in Telegram
bot menu (was hardcoded 'Plugin command')
- Add plugin commands to SlashCommandCompleter autocomplete
- Show command count in /plugins display
- 12 new tests covering registration, conflict detection,
normalization, handler dispatch, and introspection
Closes#10495
* docs: add register_command() to plugin guides
- Build a Plugin guide: new 'Register slash commands' section with
full API reference, comparison table vs register_cli_command(),
sync/async examples, and conflict protection docs
- Features/Plugins page: add slash commands to capabilities table
and plugin types summary
- Matrix docs: full Proxy Mode section with architecture diagram,
step-by-step setup (host + Docker), docker-compose.yml/Dockerfile
examples, configuration reference, and limitations notes
- API Server docs: add Proxy Mode section explaining the api_server
serves as the backend for gateway proxy mode
- Environment variables reference: add GATEWAY_PROXY_URL and
GATEWAY_PROXY_KEY entries
Add ctx.register_skill() API so plugins can ship SKILL.md files under
a 'plugin:skill' namespace, preventing name collisions with built-in
Hermes skills. skill_view() detects the ':' separator and routes to
the plugin registry while bare names continue through the existing
flat-tree scan unchanged.
Key additions:
- agent/skill_utils: parse_qualified_name(), is_valid_namespace()
- hermes_cli/plugins: PluginContext.register_skill(), PluginManager
skill registry (find/list/remove)
- tools/skills_tool: qualified name dispatch in skill_view(),
_serve_plugin_skill() with full guards (disabled, platform,
injection scan), bundle context banner with sibling listing,
stale registry self-heal
- Hoisted _INJECTION_PATTERNS to module level (dedup)
- Updated skill_view schema description
Based on PR #9334 by N0nb0at. Lean P1 salvage — omits autogen shim
(P2) for a simpler first merge.
Closes#8422
Add a second light-mode skin option with warm brown/parchment tones,
adapted from ygd58's contribution in PR #4811. Includes completion
menu and status bar color keys for full light-terminal support.
Co-authored-by: buray <78954051+ygd58@users.noreply.github.com>
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.
- New docs page: user-guide/features/web-dashboard.md covering
quick start, prerequisites, all three pages (Status, Config, API Keys),
the /reload slash command, REST API endpoints, CORS config, and
development workflow
- Added 'Management' category in sidebar for web-dashboard
- Added 'hermes web' to CLI commands reference with options table
- Added '/reload' to slash commands reference (both CLI and gateway tables)
The check_interval parameter on terminal_tool sent periodic output
updates to the gateway chat, but these were display-only — the agent
couldn't see or act on them. This added schema bloat and introduced
a bug where notify_on_complete=True was silently dropped when
check_interval was also set (the not-check_interval guard skipped
fast-watcher registration, and the check_interval watcher dict
was missing the notify_on_complete key).
Removing check_interval entirely:
- Eliminates the notify_on_complete interaction bug
- Reduces tool schema size (one fewer parameter for the model)
- Simplifies the watcher registration path
- notify_on_complete (agent wake-on-completion) still works
- watch_patterns (output alerting) still works
- process(action='poll') covers manual status checking
Closes#7947 (root cause eliminated rather than patched).
Add is_network_accessible() helper using Python's ipaddress module to
robustly classify bind addresses (IPv4/IPv6 loopback, wildcards,
mapped addresses, hostname resolution with DNS-failure-fails-closed).
The API server connect() now refuses to start when the bind address is
network-accessible and no API_SERVER_KEY is set, preventing RCE from
other machines on the network.
Co-authored-by: entropidelic <entropidelic@users.noreply.github.com>
- Add HERMES_CRON_TIMEOUT and HERMES_CRON_SCRIPT_TIMEOUT to env vars reference
- Add script timeout and provider recovery sections to cron features page
- Add timeout resolution chain and credential pool details to cron internals
* feat: API server model name derived from profile name
For multi-user setups (e.g. OpenWebUI), each profile's API server now
advertises a distinct model name on /v1/models:
- Profile 'lucas' -> model ID 'lucas'
- Profile 'admin' -> model ID 'admin'
- Default profile -> 'hermes-agent' (unchanged)
Explicit override via API_SERVER_MODEL_NAME env var or
platforms.api_server.model_name config for custom names.
Resolves friction where OpenWebUI couldn't distinguish multiple
hermes-agent connections all advertising the same model name.
* docs: multi-user setup with profiles for API server + Open WebUI
- api-server.md: added Multi-User Setup section, API_SERVER_MODEL_NAME
to config table, updated /v1/models description
- open-webui.md: added Multi-User Setup with Profiles section with
step-by-step guide, updated model name references
- environment-variables.md: added API_SERVER_MODEL_NAME entry
Port missing features from the hindsight-hermes external integration
package into the native plugin. Only touches plugin files — no core
changes.
Features:
- Tags on retain/recall (tags, recall_tags, recall_tags_match)
- Recall config (recall_max_tokens, recall_max_input_chars, recall_types,
recall_prompt_preamble)
- Retain controls (retain_every_n_turns, auto_retain, auto_recall,
retain_async via aretain_batch, retain_context)
- Bank config via Banks API (bank_mission, bank_retain_mission)
- Structured JSON retain with per-message timestamps
- Full session accumulation with document_id for dedup
- Custom post_setup() wizard with curses picker
- Mode-aware dep install (hindsight-client for cloud, hindsight-all for local)
- local_external mode and openai_compatible LLM provider
- OpenRouter support with auto base URL
- Auto-upgrade of hindsight-client to >=0.4.22 on session start
- Comprehensive debug logging across all operations
- 46 unit tests
- Updated README and website docs
Users were setting model.provider to "main" after reading the auxiliary
provider docs, causing "Unknown provider" errors. The "main" alias is
only valid inside auxiliary:, compression:, and fallback_model: configs
where it means "use the same provider as my main agent chat."
Added warning admonitions and inline clarifications to:
- configuration.md: Auxiliary Models provider list and Provider Options table
- fallback-providers.md: Provider Options for Auxiliary Tasks table
Reported by community member cn on Discord.
Add documentation for cocktailpeanut's hermes-mod community tool —
a web UI for creating and managing Hermes skins visually. Covers
installation (Pinokio, npx, manual), usage walkthrough, and feature
overview including ASCII art generation from images.
Ref: https://github.com/cocktailpeanut/hermes-mod
Based on PR #5413 spec by MaheshtheDev (Mahesh Sanikommu).
Changes:
- Add search_mode config (hybrid/memories/documents) passed to SDK
- Add {identity} template support in container_tag for profile-scoped containers
- Add SUPERMEMORY_CONTAINER_TAG env var override (priority over config)
- Add multi-container mode: enable_custom_container_tags, custom_containers,
custom_container_instructions in supermemory.json
- Dynamic tool schemas when multi-container enabled (optional container_tag param)
- Whitelist validation for custom container tags in tool calls
- Simplify get_config_schema() to only prompt for API key during setup
- Defer container_tag sanitization to initialize() (after template resolution)
- Add custom_id support to documents.add calls
- Update README with multi-container docs, search_mode, identity template,
support links (Discord, email)
- Update memory-providers.md with new features and multi-container example
- Update memory-provider-plugin.md with minimal vs full schema guidance
- Add 12 new tests covering identity template, search_mode, multi-container,
config schema, and env var override
* refactor: remove browser_close tool — auto-cleanup handles it
The browser_close tool was called in only 9% of browser sessions (13/144
navigations across 66 sessions), always redundantly — cleanup_browser()
already runs via _cleanup_task_resources() at conversation end, and the
background inactivity reaper catches anything else.
Removing it saves one tool schema slot in every browser-enabled API call.
Also fixes a latent bug: cleanup_browser() now handles Camofox sessions
too (previously only Browserbase). Camofox sessions were never auto-cleaned
per-task because they live in a separate dict from _active_sessions.
Files changed (13):
- tools/browser_tool.py: remove function, schema, registry entry; add
camofox cleanup to cleanup_browser()
- toolsets.py, model_tools.py, prompt_builder.py, display.py,
acp_adapter/tools.py: remove browser_close from all tool lists
- tests/: remove browser_close test, update toolset assertion
- docs/skills: remove all browser_close references
* fix: repeat browser_scroll 5x per call for meaningful page movement
Most backends scroll ~100px per call — barely visible on a typical
viewport. Repeating 5x gives ~500px (~half a viewport), making each
scroll tool call actually useful.
Backend-agnostic approach: works across all 7+ browser backends without
needing to configure each one's scroll amount individually. Breaks
early on error for the agent-browser path.
* feat: auto-return compact snapshot from browser_navigate
Every browser session starts with navigate → snapshot. Now navigate
returns the compact accessibility tree snapshot inline, saving one
tool call per browser task.
The snapshot captures the full page DOM (not viewport-limited), so
scroll position doesn't affect it. browser_snapshot remains available
for refreshing after interactions or getting full=true content.
Both Browserbase and Camofox paths auto-snapshot. If the snapshot
fails for any reason, navigation still succeeds — the snapshot is
a bonus, not a requirement.
Schema descriptions updated to guide models: navigate mentions it
returns a snapshot, snapshot mentions it's for refresh/full content.
* refactor: slim cronjob tool schema — consolidate model/provider, drop unused params
Session data (151 calls across 67 sessions) showed several schema
properties were never used by models. Consolidated and cleaned up:
Removed from schema (still work via backend/CLI):
- skill (singular): use skills array instead
- reason: pause-only, unnecessary
- include_disabled: now defaults to true
- base_url: extreme edge case, zero usage
- provider (standalone): merged into model object
Consolidated:
- model + provider → single 'model' object with {model, provider} fields.
If provider is omitted, the current main provider is pinned at creation
time so the job stays stable even if the user changes their default.
Kept:
- script: useful data collection feature
- skills array: standard interface for skill loading
Schema shrinks from 14 to 10 properties. All backend functionality
preserved — the Python function signature and handler lambda still
accept every parameter.
* fix: remove mixture_of_agents from core toolsets — opt-in only via hermes tools
MoA was in _HERMES_CORE_TOOLS and composite toolsets (hermes-cli,
hermes-messaging, safe), which meant it appeared in every session
for anyone with OPENROUTER_API_KEY set. The _DEFAULT_OFF_TOOLSETS
gate only works after running 'hermes tools' explicitly.
Now MoA only appears when a user explicitly enables it via
'hermes tools'. The moa toolset definition and check_fn remain
unchanged — it just needs to be opted into.
- Add full Supermemory section to memory-providers.md with config table,
tools, setup instructions, and key features
- Update provider count from 7 to 8 across memory.md and memory-providers.md
- Add SUPERMEMORY_API_KEY to environment-variables.md
- Add Supermemory to integrations/providers.md optional API keys table
- Add supermemory to cli-commands.md provider list
- Add Supermemory to profile isolation section (config file providers)
Two fixes:
1. Replace all stale 'hermes login' references with 'hermes auth' across
auth.py, auxiliary_client.py, delegate_tool.py, config.py, run_agent.py,
and documentation. The 'hermes login' command was deprecated; 'hermes auth'
now handles OAuth credential management.
2. Fix credential removal not persisting for singleton-sourced credentials
(device_code for openai-codex/nous, hermes_pkce for anthropic).
auth_remove_command already cleared env vars for env-sourced credentials,
but singleton credentials stored in the auth store were re-seeded by
_seed_from_singletons() on the next load_pool() call. Now clears the
underlying auth store entry when removing singleton-sourced credentials.
Updates the plugin build guide and features page to reflect the
interactive env var prompting added in PR #5470. Documents the rich
manifest format (name/description/url/secret) alongside the simple
string format.
* feat(tools): add Firecrawl cloud browser provider
Adds Firecrawl (https://firecrawl.dev) as a cloud browser provider
alongside Browserbase and Browser Use. All browser tools route through
Firecrawl's cloud browser via CDP when selected.
- tools/browser_providers/firecrawl.py — FirecrawlProvider
- tools/browser_tool.py — register in _PROVIDER_REGISTRY
- hermes_cli/tools_config.py — add to onboarding provider picker
- hermes_cli/setup.py — add to setup summary
- hermes_cli/config.py — add FIRECRAWL_BROWSER_TTL config
- website/docs/ — browser docs and env var reference
Based on #4490 by @developersdigest.
Co-Authored-By: Developers Digest <124798203+developersdigest@users.noreply.github.com>
* refactor: simplify FirecrawlProvider.emergency_cleanup
Use self._headers() and self._api_url() instead of duplicating
env-var reads and header construction.
* fix: recognize Firecrawl in subscription browser detection
_resolve_browser_feature_state() now handles "firecrawl" as a direct
browser provider (same pattern as "browser-use"), so hermes setup
summary correctly shows "Browser Automation (Firecrawl)" instead of
misreporting as "Local browser".
Also fixes test_config_version_unchanged assertion (11 → 12).
---------
Co-authored-by: Developers Digest <124798203+developersdigest@users.noreply.github.com>
Skills can now declare config.yaml settings via metadata.hermes.config
in their SKILL.md frontmatter. Values are stored under skills.config.*
namespace, prompted during hermes config migrate, shown in hermes config
show, and injected into the skill context at load time.
Also adds the llm-wiki skill (Karpathy's LLM Wiki pattern) as the first
skill to use the new config interface, declaring wiki.path.
Skill config interface (new):
- agent/skill_utils.py: extract_skill_config_vars(), discover_all_skill_config_vars(),
resolve_skill_config_values(), SKILL_CONFIG_PREFIX
- agent/skill_commands.py: _inject_skill_config() injects resolved values
into skill messages as [Skill config: ...] block
- hermes_cli/config.py: get_missing_skill_config_vars(), skill config
prompting in migrate_config(), Skill Settings in show_config()
LLM Wiki skill (skills/research/llm-wiki/SKILL.md):
- Three-layer architecture (raw sources, wiki pages, schema)
- Three operations (ingest, query, lint)
- Session orientation, page thresholds, tag taxonomy, update policy,
scaling guidance, log rotation, archiving workflow
Docs: creating-skills.md, configuration.md, skills.md, skills-catalog.md
Closes#5100
As the agent navigates into subdirectories via tool calls (read_file,
terminal, search_files, etc.), automatically discover and load project
context files (AGENTS.md, CLAUDE.md, .cursorrules) from those directories.
Previously, context files were only loaded from the CWD at session start.
If the agent moved into backend/, frontend/, or any subdirectory with its
own AGENTS.md, those instructions were never seen.
Now, SubdirectoryHintTracker watches tool call arguments for file paths
and shell commands, resolves directories, and loads hint files on first
access. Discovered hints are appended to the tool result so the model
gets relevant context at the moment it starts working in a new area —
without modifying the system prompt (preserving prompt caching).
Features:
- Extracts paths from tool args (path, workdir) and shell commands
- Loads AGENTS.md, CLAUDE.md, .cursorrules (first match per directory)
- Deduplicates — each directory loaded at most once per session
- Ignores paths outside the working directory
- Truncates large hint files at 8K chars
- Works on both sequential and concurrent tool execution paths
Inspired by Block/goose SubdirectoryHintTracker.
