The quality-first default (OpenAI image via OpenRouter) is slow, and a full
hatch fans out ~8 rows with up to 3 retries each (300s/call) across 2 parallel
waves, so the absolute backend worst case is ~30 min. The old ceilings fired
mid-run:
- per-image HTTP call: 180s -> 300s (a single cold row can exceed 3 min)
- drafts RPC: 240s -> 420s (single wave, no retries — 7 min is ample)
- hatch RPC: 420s -> 1hr (sits above the ~30 min backend worst case)
The hatch ceiling is intentionally well above the realistic max so the frontend
never throws "request timed out" before the backend has exhausted its own
retries. The background-resumable notification path remains the real UX safety
net — the user can close the modal and get pinged on completion.
OpenRouter/Nous image gen now runs a quality-first model chain by default:
attempt the highest-fidelity OpenAI image model first, then fall back to
Gemini 3 Pro Image when it's access-gated/unavailable/times out. An explicit
OPENROUTER_IMAGE_MODEL / config model override pins one model with no fallback.
Atlas validation rejects malformed model output instead of shipping it: adds a
per-state collapse guard (a single sliver/fragment row no longer passes because
other rows are healthy), on top of the existing postage-stamp + multi-pose
checks.
Desktop: pet-gen native notifications are now "global" (not tied to a chat
session), so a background generation started from the command center fires an
OS notification when the user is away even with no active session. Adds a
neutral "This can take up to 5 minutes." banner on step 1, and lets the
provider picker auto-size.
Tests updated/added for the OpenRouter fallback chain, the collapse guard, and
the global notification path.
Reference-grounded image provider over the OpenRouter-compatible
chat-completions image protocol (Gemini Flash Image et al.). Nous Portal
proxies OpenRouter, so one provider serves both — giving pet generation a
reference-capable backend beyond OpenAI gpt-image.
* feat(image-gen): add image-to-image / editing to image_generate
Brings image generation to parity with video generation: the unified
image_generate tool now edits/transforms a source image (image-to-image)
when given image_url / reference_image_urls, routing to each backend's
edit endpoint, exactly as video_generate routes to image-to-video.
- ImageGenProvider ABC: generate() gains keyword-only image_url +
reference_image_urls; new capabilities() declares modalities +
max_reference_images (defaults to text-only, backward compatible).
success_response gains a modality field; adds normalize_reference_images.
- image_generate tool: schema exposes image_url + reference_image_urls;
dynamic schema reflects the active model's actual edit capability so the
agent knows when image_url is honored. Handler + plugin dispatch forward
the new inputs; legacy/text-only providers get a clear modality_unsupported
error instead of silently dropping the source image.
- In-tree FAL: 7 models gain edit endpoints (flux-2-klein, flux-2-pro,
nano-banana-pro, gpt-image-1.5, gpt-image-2, ideogram/v3, qwen-image)
with per-model edit_supports whitelists + reference caps; routes to the
/edit endpoint and skips the upscaler for edits.
- Plugins: openai (images.edit, 16 refs), xai (/v1/images/edits via
grok-imagine-image-quality, JSON body per xAI docs), krea
(image_style_references, 10 refs). openai-codex stays text-only and
rejects edits with an actionable error.
- Tests: 15 new (payload, routing, dispatch forwarding, dynamic schema,
capabilities); updated 2 change-detector/lambda tests for the new schema.
- Docs: image-generation feature page, image-gen provider plugin guide,
tools reference.
* fix(image-gen): preserve legacy passthrough in fal/krea plugin tests
Two existing plugin tests asserted pre-image-to-image behavior:
- fal: forward image_url/reference_image_urls only when supplied, so a
text-to-image delegation stays byte-identical (no None kwargs).
- krea: keep dict-shaped image_style_references refs verbatim (the unified
string refs go through normalize_reference_images; legacy non-string ref
objects pass through unchanged) — fixes KeyError when callers pass the
richer Krea ref-object shape.
* fix(image-gen): clearer not-capable message for text-to-image-only models
When a text-to-image-only model (incl. gpt-image-2 on the Codex OAuth path,
which can't do editing through the Responses image_generation tool) gets a
source image, say 'this model is not capable of image-to-image / editing —
provide a text-only prompt' rather than sending the user shopping for other
backends. Applies to the openai-codex guard, the in-tree FAL no-edit-endpoint
error, and the dynamic tool-schema text-only line.
* feat(image_gen): add Krea provider plugin (Krea 2 Medium + Large)
New built-in image_gen backend wrapping Krea's Krea 2 foundation
image model family. Auto-discovered like the other image_gen plugins
and appears in 'hermes tools' → Image Generation → Krea.
Krea's API is asynchronous — submit returns a job_id, poll /jobs/{id}
until terminal. The provider hides that behind the synchronous
ImageGenProvider.generate() contract: submit, poll every 2s with
light backoff (max 5s), 3-minute ceiling matching Krea's hosted-tool
timeout. Result URL is materialised to $HERMES_HOME/cache/images/
to avoid CDN-expiry 404s downstream (same fix as xAI #26942).
Models:
- krea-2-medium (default — Krea's 'start here' recommendation)
- krea-2-large
Aspect ratios map landscape→16:9, square→1:1, portrait→9:16.
Resolution: 1K (Krea's only current option).
Kwarg passthrough: seed, creativity (raw/low/medium/high), styles,
image_style_references (capped 10), moodboards (capped 1) — matches
Krea's per-request limits. Unknown kwargs are ignored.
Config knobs (config.yaml):
image_gen.provider: krea
image_gen.krea.model: krea-2-medium | krea-2-large
image_gen.krea.creativity: raw | low | medium | high
Env overrides: KREA_API_KEY (required), KREA_IMAGE_MODEL.
KREA_API_KEY is registered in OPTIONAL_ENV_VARS so 'hermes setup'
prompts for it.
31 new tests; image_gen suite + picker + tools_config: 211/211.
* fix(image_gen/krea): address review feedback
- Update KREA_API_KEY setup URL to the canonical token-creation page
(https://www.krea.ai/app/api/tokens). The previous URL returned 404.
- Fail fast on non-retryable HTTP statuses during poll. The previous
loop retried every HTTPError for the full 180s deadline, so an auth
(401), billing (402), forbidden (403), or not-found (404) response
would make image_generate hang for three minutes. Only retry
transient statuses (408/409/425/429/5xx); surface everything else
immediately.
- Add 5 tests covering fail-fast on 401/403/404 and retry on 429/503.
* fix(krea): point users at the real API token dashboard URL
Three call sites linked users to dashboard pages that don't exist:
- hermes_cli/config.py: https://www.krea.ai/app/api/tokens
- plugins/image_gen/krea/__init__.py get_setup_schema: https://www.krea.ai/api-keys
- plugins/image_gen/krea/__init__.py auth_required error: https://www.krea.ai/api-keys
Per Krea's own docs (https://docs.krea.ai/developers/api-keys-and-billing),
the real dashboard URL is https://www.krea.ai/settings/api-tokens. All three
sites now point there.
xAI's grok-imagine-image API returns ephemeral imgen.x.ai/xai-tmp-* URLs
that 404 within minutes — long before downstream consumers (Telegram
send_photo, browser preview, multi-tier delivery fallback) get a chance
to fetch them. The xAI image_gen provider was passing those URLs
through unchanged on the elif url: branch; b64 responses were already
cached locally via save_b64_image. Result: every image_generate call
on a Telegram-routed xai-oauth profile delivered no image, falling
through to text-only.
Adds agent.image_gen_provider.save_url_image() — a sibling helper to
save_b64_image that downloads URL bytes to $HERMES_HOME/cache/images/.
Content-type-aware extension inference with URL-suffix fallback;
oversize cap (25MB default) with partial-write cleanup; empty-body
refusal. Mirrors the audio_cache pattern used by text_to_speech.
Wires save_url_image into both the xAI and OpenAI providers' URL
branches. When the download fails (network blip, 404 in-flight) we
log a warning and fall back to the bare URL rather than turning the
tool call into a hard error — the gateway's existing URL-send fallback
then gets a chance to surface the original error legibly.
Test plan:
- tests/agent/test_save_url_image.py — 8 direct tests against a real
in-process HTTP server: bytes round-trip, content-type → extension,
URL-suffix fallback, default-to-png, 404 propagation, empty-body
refusal, oversize cap + cleanup, filename uniqueness.
- tests/plugins/image_gen/test_xai_provider.py — flip
test_successful_url_response (was asserting the bug), add
test_url_response_falls_back_to_bare_url_when_download_fails.
- tests/plugins/image_gen/test_openai_provider.py — symmetric pair.
160/160 in the broader image_gen test surface.
Mirrors the architecture established by the web (#25182), browser
(#25214), and video_gen (#25126) plugin migrations:
* `tools/fal_common.py` — stateless atoms shared by both FAL-backed
plugins (image_gen + video_gen). Holds the lazy `fal_client` import
helper, `_ManagedFalSyncClient`, `_normalize_fal_queue_url_format`,
`_extract_http_status`. Stateful pieces (`fal_client` module global,
`_managed_fal_client*` cache, `_submit_fal_request`,
`_resolve_managed_fal_gateway`, `_get_managed_fal_client`)
intentionally stay on `tools.image_generation_tool` so the existing
`monkeypatch.setattr(image_tool, ...)` patch sites keep working
unchanged.
* `plugins/video_gen/fal/__init__.py` — drops its inline
`_load_fal_client` duplicate; consumes `tools.fal_common.import_fal_client`.
* `plugins/image_gen/fal/{plugin.yaml,__init__.py}` — new plugin.
`FalImageGenProvider` is a thin registration adapter that resolves
the legacy module via `import tools.image_generation_tool as _it`
and calls `_it.image_generate_tool` + `_it._resolve_fal_model` at
call time. The 18-model catalog, `_build_fal_payload`, managed-
gateway selection, and Clarity Upscaler chaining all remain in
`tools.image_generation_tool` as the single source of truth —
the plugin is a registration adapter, not a parallel implementation.
* `tools/image_generation_tool.py::_dispatch_to_plugin_provider` —
drops the `configured == "fal"` skip. Setting `image_gen.provider:
fal` now routes through the registry like any other provider; the
plugin re-enters this module's pipeline so behavior is identical.
Unset `image_gen.provider` still falls through to the in-tree
pipeline (preserves no-config-with-FAL_KEY UX from #15696).
* `hermes_cli/tools_config.py` — drops the hardcoded "FAL.ai" row from
`TOOL_CATEGORIES["image_gen"]["providers"]` (now injected by
`_plugin_image_gen_providers` like every other backend) and the
`getattr(provider, "name") == "fal"` skip that protected against
duplication with the hardcoded row. The "Nous Subscription" row
stays as a setup-flow entry — same shape browser kept "Nous
Subscription (Browser Use cloud)" after #25214.
* `tests/plugins/image_gen/test_fal_provider.py` — 14 cases covering
the ABC surface, call-time indirection (verifying
`monkeypatch.setattr(image_tool, "image_generate_tool", ...)` takes
effect through the plugin), response-shape stamping, exception
handling, and registry wiring.
* `tests/plugins/image_gen/check_parity_vs_main.py` — subprocess
harness mirroring `tests/plugins/browser/check_parity_vs_main.py`.
Pins one path to origin/main, one to the worktree; runs six
scenarios (unset, explicit-fal-no-creds, explicit-fal-with-creds,
explicit-fal-with-model, typo provider, managed-gateway-only) and
diffs the reduced shape `{dispatch_kind, provider_name, model}`
per scenario. The only acceptable diff is "legacy_fal → plugin
(fal)" for explicit-FAL paths — every other delta is flagged as
a regression.
* `tests/hermes_cli/test_image_gen_picker.py::test_fal_surfaced_alongside_other_plugins`
— flips the previous `test_fal_skipped_to_avoid_duplicate` to
match the new shape (FAL is a plugin now, no dedup needed).
Verified: 195/195 tests across
`tests/{tools/test_image_generation*,tools/test_managed_media_gateways,plugins/image_gen,plugins/video_gen,hermes_cli/test_image_gen_picker}.py`
pass on this branch with no test patches modified outside the picker
test that asserted the old skip behaviour.
Fixes#26241
Adds a new authentication provider that lets SuperGrok subscribers sign
in to Hermes with their xAI account via the standard OAuth 2.0 PKCE
loopback flow, instead of pasting a raw API key from console.x.ai.
Highlights
----------
* OAuth 2.0 PKCE loopback login against accounts.x.ai with discovery,
state/nonce, and a strict CORS-origin allowlist on the callback.
* Authorize URL carries `plan=generic` (required for non-allowlisted
loopback clients) and `referrer=hermes-agent` for best-effort
attribution in xAI's OAuth server logs.
* Token storage in `auth.json` with file-locked atomic writes; JWT
`exp`-based expiry detection with skew; refresh-token rotation
synced both ways between the singleton store and the credential
pool so multi-process / multi-profile setups don't tear each other's
refresh tokens.
* Reactive 401 retry: on a 401 from the xAI Responses API, the agent
refreshes the token, swaps it back into `self.api_key`, and retries
the call once. Guarded against silent account swaps when the active
key was sourced from a different (manual) pool entry.
* Auxiliary tasks (curator, vision, embeddings, etc.) route through a
dedicated xAI Responses-mode auxiliary client instead of falling back
to OpenRouter billing.
* Direct HTTP tools (`tools/xai_http.py`, transcription, TTS, image-gen
plugin) resolve credentials through a unified runtime → singleton →
env-var fallback chain so xai-oauth users get them for free.
* `hermes auth add xai-oauth` and `hermes auth remove xai-oauth N` are
wired through the standard auth-commands surface; remove cleans up
the singleton loopback_pkce entry so it doesn't silently reinstate.
* `hermes model` provider picker shows
"xAI Grok OAuth (SuperGrok Subscription)" and the model-flow falls
back to pool credentials when the singleton is missing.
Hardening
---------
* Discovery and refresh responses validate the returned
`token_endpoint` host against the same `*.x.ai` allowlist as the
authorization endpoint, blocking MITM persistence of a hostile
endpoint.
* Discovery / refresh / token-exchange `response.json()` calls are
wrapped to raise typed `AuthError` on malformed bodies (captive
portals, proxy error pages) instead of leaking JSONDecodeError
tracebacks.
* `prompt_cache_key` is routed through `extra_body` on the codex
transport (sending it as a top-level kwarg trips xAI's SDK with a
TypeError).
* Credential-pool sync-back preserves `active_provider` so refreshing
an OAuth entry doesn't silently flip the active provider out from
under the running agent.
Testing
-------
* New `tests/hermes_cli/test_auth_xai_oauth_provider.py` (~63 tests)
covers JWT expiry, OAuth URL params (plan + referrer), CORS origins,
redirect URI validation, singleton↔pool sync, concurrency races,
refresh error paths, runtime resolution, and malformed-JSON guards.
* Extended `test_credential_pool.py`, `test_codex_transport.py`, and
`test_run_agent_codex_responses.py` cover the pool sync-back,
`extra_body` routing, and 401 reactive refresh paths.
* 165 tests passing on this branch via `scripts/run_tests.sh`.
The xAI /v1/images/generations endpoint expects resolution as a
literal string ('1k' or '2k'), not the numeric value ('1024').
- Change _XAI_RESOLUTIONS from a dict mapping to a validation set
- Use the resolution key directly instead of the mapped value
- Fall back to DEFAULT_RESOLUTION on invalid config values
Fixes 422 Unprocessable Entity errors when resolution was sent.
The agent-facing image_generate tool only passes prompt + aspect_ratio to
provider.generate() (see tools/image_generation_tool.py:953). The editing
block (reference_images / edit_image kwargs) could never fire from the
tool surface, and the xAI edits endpoint is /images/edits with a
different payload shape anyway — not /images/generations as submitted.
- Remove reference_images / edit_image kwargs handling from generate()
- Remove matching test_with_reference_images case
- Update docstring + plugin.yaml description to text-to-image only
- Surface resolution in the success extras
Follow-up to PR #14547. Tests: 18/18 pass.
New built-in image_gen backend at plugins/image_gen/openai-codex/ that
exposes the same gpt-image-2 low/medium/high tier catalog as the
existing 'openai' plugin, but routes generation through the ChatGPT/
Codex Responses image_generation tool path. Available whenever the user
has Codex OAuth signed in; no OPENAI_API_KEY required.
The two plugins are independent — users select between them via
'hermes tools' → Image Generation, and image_gen.provider in
config.yaml. The existing 'openai' (API-key) plugin is unchanged.
Reuses _read_codex_access_token() and _codex_cloudflare_headers() from
agent.auxiliary_client so token expiry / cred-pool / Cloudflare
originator handling stays in one place.
Inspired by #14047 by @Hygaard, but re-implemented as a separate
plugin instead of an in-place fork of the openai plugin.
Closes#11195
* 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().
