* 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().
Adds OpenAI's new GPT Image 2 model via FAL.ai, selectable through
`hermes tools` → Image Generation. SOTA text rendering (including CJK)
and world-aware photorealism.
- FAL_MODELS entry with image_size_preset style
- 4:3 presets on all aspect ratios — 16:9 (1024x576) falls below
GPT-Image-2's 655,360 min-pixel floor and would be rejected
- quality pinned to medium (same rule as gpt-image-1.5) for
predictable Nous Portal billing
- BYOK (openai_api_key) deliberately omitted from supports so all
users stay on shared FAL billing
- 6 new tests covering preset mapping, quality pinning, and
supports-whitelist integrity
- Docs table + aspect-ratio map updated
Live-tested end-to-end: 39.9s cold request, clean 1024x768 PNG
Follow-up to PR #2504. The original fix covered the two direct FAL_KEY
checks in image_generation_tool but left four other call sites intact,
including the managed-gateway gate where a whitespace-only FAL_KEY
falsely claimed 'user has direct FAL' and *skipped* the Nous managed
gateway fallback entirely.
Introduce fal_key_is_configured() in tools/tool_backend_helpers.py as a
single source of truth (consults os.environ, falls back to .env for
CLI-setup paths) and route every FAL_KEY presence check through it:
- tools/image_generation_tool.py : _resolve_managed_fal_gateway,
image_generate_tool's upfront check, check_fal_api_key
- hermes_cli/nous_subscription.py : direct_fal detection, selected
toolset gating, tools_ready map
- hermes_cli/tools_config.py : image_gen needs-setup check
Verified by extending tests/tools/test_image_generation_env.py and by
E2E exercising whitespace + managed-gateway composition directly.
Treat whitespace-only FAL_KEY the same as unset so users who export
FAL_KEY=" " (or CI that leaves a blank token) get the expected
'not set' error path instead of a confusing downstream fal_client
failure.
Applied to the two direct FAL_KEY checks in image_generation_tool.py:
image_generate_tool's upfront credential check and check_fal_api_key().
Both keep the existing managed-gateway fallback intact.
Adapted the original whitespace/valid tests to pin the managed gateway
to None so the whitespace assertion exercises the direct-key path
rather than silently relying on gateway absence.
Full AST-based scan of all .py files to find every case where a module
or name is imported locally inside a function body but is already
available at module level. This is the second pass — the first commit
handled the known cases from the lint report; this one catches
everything else.
Files changed (19):
cli.py — 16 removals: time as _time/_t/_tmod (×10),
re / re as _re (×2), os as _os, sys,
partial os from combo import,
from model_tools import get_tool_definitions
gateway/run.py — 8 removals: MessageEvent as _ME /
MessageType as _MT (×3), os as _os2,
MessageEvent+MessageType (×2), Platform,
BasePlatformAdapter as _BaseAdapter
run_agent.py — 6 removals: get_hermes_home as _ghh,
partial (contextlib, os as _os),
cleanup_vm, cleanup_browser,
set_interrupt as _sif (×2),
partial get_toolset_for_tool
hermes_cli/main.py — 4 removals: get_hermes_home, time as _time,
logging as _log, shutil
hermes_cli/config.py — 1 removal: get_hermes_home as _ghome
hermes_cli/runtime_provider.py
— 1 removal: load_config as _load_bedrock_config
hermes_cli/setup.py — 2 removals: importlib.util (×2)
hermes_cli/nous_subscription.py
— 1 removal: from hermes_cli.config import load_config
hermes_cli/tools_config.py
— 1 removal: from hermes_cli.config import load_config, save_config
cron/scheduler.py — 3 removals: concurrent.futures, json as _json,
from hermes_cli.config import load_config
batch_runner.py — 1 removal: list_distributions as get_all_dists
(kept print_distribution_info, not at top level)
tools/send_message_tool.py
— 2 removals: import os (×2)
tools/skills_tool.py — 1 removal: logging as _logging
tools/browser_camofox.py
— 1 removal: from hermes_cli.config import load_config
tools/image_generation_tool.py
— 1 removal: import fal_client
environments/tool_context.py
— 1 removal: concurrent.futures
gateway/platforms/bluebubbles.py
— 1 removal: httpx as _httpx
gateway/platforms/whatsapp.py
— 1 removal: import asyncio
tui_gateway/server.py — 2 removals: from datetime import datetime,
import time
All alias references (_time, _t, _tmod, _re, _os, _os2, _json, _ghh,
_ghome, _sif, _ME, _MT, _BaseAdapter, _load_bedrock_config, _httpx,
_logging, _log, get_all_dists) updated to use the top-level names.
* 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.
Replace the HERMES_ENABLE_NOUS_MANAGED_TOOLS env-var feature flag with
subscription-based detection. The Tool Gateway is now available to any
paid Nous subscriber without needing a hidden env var.
Core changes:
- managed_nous_tools_enabled() checks get_nous_auth_status() +
check_nous_free_tier() instead of an env var
- New use_gateway config flag per tool section (web, tts, browser,
image_gen) records explicit user opt-in and overrides direct API
keys at runtime
- New prefers_gateway(section) shared helper in tool_backend_helpers.py
used by all 4 tool runtimes (web, tts, image gen, browser)
UX flow:
- hermes model: after Nous login/model selection, shows a curses
prompt listing all gateway-eligible tools with current status.
User chooses to enable all, enable only unconfigured tools, or skip.
Defaults to Enable for new users, Skip when direct keys exist.
- hermes tools: provider selection now manages use_gateway flag —
selecting Nous Subscription sets it, selecting any other provider
clears it
- hermes status: renamed section to Nous Tool Gateway, added
free-tier upgrade nudge for logged-in free users
- curses_radiolist: new description parameter for multi-line context
that survives the screen clear
Runtime behavior:
- Each tool runtime (web_tools, tts_tool, image_generation_tool,
browser_use) checks prefers_gateway() before falling back to
direct env-var credentials
- get_nous_subscription_features() respects use_gateway flags,
suppressing direct credential detection when the user opted in
Removed:
- HERMES_ENABLE_NOUS_MANAGED_TOOLS env var and all references
- apply_nous_provider_defaults() silent TTS auto-set
- get_nous_subscription_explainer_lines() static text
- Override env var warnings (use_gateway handles this properly now)
- add managed modal and gateway-backed tool integrations\n- improve CLI setup, auth, and configuration for subscriber flows\n- expand tests and docs for managed tool support
- Add 'emoji' field to ToolEntry and 'get_emoji()' to ToolRegistry
- Add emoji= to all 50+ registry.register() calls across tool files
- Add get_tool_emoji() helper in agent/display.py with 3-tier resolution:
skin override → registry default → hardcoded fallback
- Replace hardcoded emoji maps in run_agent.py, delegate_tool.py, and
gateway/run.py with centralized get_tool_emoji() calls
- Add 'tool_emojis' field to SkinConfig so skins can override per-tool
emojis (e.g. ares skin could use swords instead of wrenches)
- Add 11 tests (5 registry emoji, 6 display/skin integration)
- Update AGENTS.md skin docs table
Based on the approach from PR #1061 by ForgingAlex (emoji centralization
in registry). This salvage fixes several issues from the original:
- Does NOT split the cronjob tool (which would crash on missing schemas)
- Does NOT change image_generate toolset/requires_env/is_async
- Does NOT delete existing tests
- Completes the centralization (gateway/run.py was missed)
- Hooks into the skin system for full customizability
Adds full stack traces to error logs in _upscale_image() and
image_generate_tool() for better debugging. Matches the pattern
used across the rest of the codebase.
Cherry-picked from PR #868 by aydnOktay.
Co-authored-by: aydnOktay <aydnOktay@users.noreply.github.com>
Root cause: fal_client.AsyncClient uses @cached_property for its
httpx.AsyncClient, creating it once and caching forever. In the gateway,
the agent runs in a thread pool where _run_async() calls asyncio.run()
which creates a temporary event loop. The first call works, but
asyncio.run() closes that loop. On the next call, a new loop is created
but the cached httpx.AsyncClient still references the old closed loop,
causing 'Event loop is closed'.
Fix: Switch from async fal_client API (submit_async/handler.get with
await) to sync API (submit/handler.get). The sync API uses httpx.Client
which has no event loop dependency. Since the tool already runs in a
thread pool via the gateway, async adds no benefit here.
Changes:
- image_generate_tool: async def -> def
- _upscale_image: async def -> def
- fal_client.submit_async -> fal_client.submit
- await handler.get() -> handler.get()
- is_async=True -> is_async=False in registry
- Remove unused asyncio import
- Introduced a new DebugSession class in tools/debug_helpers.py to centralize debug logging functionality, replacing duplicated code across various tool modules.
- Updated image_generation_tool.py, mixture_of_agents_tool.py, vision_tools.py, web_tools.py, and others to utilize the new DebugSession for logging tool calls and saving debug logs.
- Enhanced maintainability and consistency in debug logging practices across the codebase.
- Introduced logging functionality in cli.py, run_agent.py, scheduler.py, and various tool modules to replace print statements with structured logging.
- Enhanced error handling and informational messages to improve debugging and monitoring capabilities.
- Ensured consistent logging practices across the codebase, facilitating better traceability and maintenance.
- Updated batch processing to include robust resume functionality by scanning completed prompts based on content rather than indices, improving recovery from failures.
- Implemented retry logic for image downloads with exponential backoff to handle transient failures effectively.
- Refined image generation tool to utilize the FLUX 2 Pro model, updating descriptions and parameters for clarity and consistency.
- Added new configuration scripts for GLM 4.7 and Imagen tasks, enhancing usability and logging capabilities.
- Removed outdated scripts and test files to streamline the codebase.
