ProviderProfile.supports_vision is documented as "the API accepts image content
inside tool-result messages" -- a provider-wide wire capability, not a per-model
user-image verdict. Treating it as the latter flipped every model on the bundled
`router` relay (and models.dev-unknown meta-ai/xiaomi models) from text to native
image parts. Per-model vision now comes from ProviderProfile.model_capabilities
(#116570) through the existing models.dev probe.
The setup-catalog test mirrored the plugin through auth._register_plugin_provider,
which #116553 renames; build the ProviderConfig from public types instead so the
test passes on both trees.
`decide_image_input_mode` consulted config overrides, the managed runtime,
models.dev and Ollama, but never the registered profile's `supports_vision`
— the field the tool-result media path already trusts. A plugin that
declared vision was therefore native for tool results and text-only for
user-attached images. The declaration is now the last probe in
`_VISION_PROBES`: only an explicit True is a verdict, and per-model catalog
entries still win.
Part of #116408
Resolved toward the branch: PM provisions uv/python (main's install.ps1 uv-shim
salvage + its test and workflow steps dropped), the shim re-exec stays retired,
package.json carries no electron-builder block (afterExtract identity stamp wired
into electron-builder.config.cjs instead; after-pack.mjs keeps signing only),
Desktop workspace-deps helpers stay retired. Main's scratch-dir bootstrap
(export_scratch_tmp_env) is taken and re-run after profile resolution.
`gpt-5.6-sol-900k` is a Hermes picker alias of `gpt-5.6-sol`; the models.dev catalog only
knows the base, so `agent.image_input_mode: auto` resolved supports_vision=False for the
alias and routed images through the lossy text/vision_analyze path instead of native
attachment. `_probe_models_dev` now strips a VALID `-900k` suffix (openai-codex only) for
the lookup; the runtime model id is untouched and ineligible `-900k` strings pass through
unchanged, gaining no capabilities.
Fixes#102189
Co-authored-by: finn763 <165816600+finn763@users.noreply.github.com>
Reconcile plugin declarations and validation through PM's atomic generation publication; preserve external runtimes, target markers, and conflict refusal. Keep one source-update completion owner and port upstream lifecycle changes to the PM desktop/runtime paths.
Merge upstream b1f003e186 while preserving PM runtime ownership and
Python 3.14 worker startup, Windows signing, and macOS wait recovery.
Keep retired runtime modules deleted. Port upstream updater preflight
checks into the checkout strategy and preserve live build logging.
Carry checkpoint filename handling and process recovery into the current
module layout. Regenerate locks and adapt incoming platform test markers.
Focused Python and JavaScript tests, desktop and root-test typechecks,
conflict-path lint checks, lock validation, and retired-import checks pass.
The full test suite and packaged release builds were not run.
Extend #104477 to the native thinking, vision, metadata, and local header paths identified by #87641. Materialize only at probe boundaries; leave the chat callable and cache ownership untouched. Local-wire A/B: thinking and vision show requests change from 403 to 200, while static credentials and callable chat retain success. Target suites queued.
Co-authored-by: liuhao1024 <sunsky.lau@gmail.com>
Prepare dependency generations before selecting them. Keep shipped tool
bytes separate from writable additions, and store facts beside their entries.
Validate proposed plugin sets before config publication. Restore the previous
config if the facts write fails.
Consolidate duplicate updater, backup, setup, and voice helpers. Repair
launcher selection, dependency consumers, download ownership, update feeds,
and native Windows process and file handling.
Verification: 206 changed/prior-failing Python files reported 4630 passed,
one failed, and 330 skipped. Fix the remaining Hindsight fixture boundary.
The final targeted rerun reported 234 passed and two skipped. The store
review regression batch reported 83 passed and one skipped. Desktop
TypeScript checks, 56 selected Electron tests, 24 release tests, and the
removed-import/compatibility guards passed.
This is an integration checkpoint, not full audit acceptance. The complete
Python suite has not run on this fixed tree. Crash-atomic plugin publication,
generation cleanup, receipt correlation, and packaged lifecycle acceptance
remain open in docs/pm-audit-status.md.
For each issue anchor present in BASE 63279301bc non-test .py and absent on HEAD, the BASE comment/docstring block was re-attached at the HEAD location of the code it explained (matched by the distinctive code line / enclosing def). Sentences already covered by an existing HEAD comment were deduped; the issue number always survives. Insert-only: no code lines changed.
Run models locally as a first-class provider. The CLI grows a managed
llama.cpp runtime (engine install, model download, server supervision);
the desktop app grows the full setup and management story on top of it.
GUI surfaces ship behind the desktop --local launch flag (hermes desktop
--local, or the flag on the packaged app); backend routes and the CLI
are always live.
Runtime (hermes_cli/local_runtime/):
- curated GGUF catalog with per-machine variant selection: hardware
probe (VRAM/RAM/UMA), fit planning with spill accounting, quant choice
by context window
- derived recommendation: quality-ranked picks gated by a predicted
decode-speed floor, bandwidth-aware on unified memory; the decision
table is pinned as a test (pick AND reason per memory class), and the
Recommended badge explains its pick in a tooltip fed by the resolver's
actual branch
- engine install + model download with resumable split parts, cumulative
plan-level progress, and staged-model integrity (a split GGUF counts
only when every part is present)
- server supervision: spawn/adopt/stop, router mode with per-model load
progress relayed over SSE, abandoned-request cleanup
Desktop:
- Settings -> Providers -> Local models: one-click quickstart (install
engine, download the recommended model, boot) plus per-model download/
activate/eject, fit-ranked catalog with context pills
- model pickers (composer dropdown + Cmd+K) show staged local models,
in-flight downloads as live progress rows, and load-into-memory bars
- local-setup campaign tip for eligible hardware; System resources
statusbar widget (GPU/VRAM/RAM); in-chat load progress during sends
- friendly dead-server errors, and failed agent builds retry on the next
send instead of wedging the session
Co-developed with NVIDIA field feedback on RTX 5090 and DGX Spark.
Gate the POSIX-only and symlink-only tests with the linux_only and
require_symlinks markers. Fix the real cross-platform bugs:
- file_operations: use the translate_path flag, send snippets as base64, and
use sys.executable (the MS Store python3 stub and the list2cmdline
backslash collapse both broke snippets)
- approval: treat backslash as a Windows path separator, not an escape
- registry, browser_registry, secret_sources, plugins: normalize scope-key case
- checkpoint_manager, plugins_cmd: clear read-only bits before delete
- deadline: make MAX_SAFE_TIMEOUT_S fit the Windows limit
- image_routing, acp, cua_backend, daytona: fix Windows and POSIX paths
- hermes_state: match backslash in the retag LIKE clause
- kanban, disk-cleanup: match drive paths and split command arguments
- scripts: emit host separators through as_posix
207 test files are gated or isolated.
The image-routing vision path calls detect_local_server_type without
the provider's API key. Against a remote API-keyed endpoint (sglang /
vLLM with --api-key) every leg of the 5-request probe waterfall came
back 401 — and because a failed verdict was never written to the
in-memory cache (only positive verdicts were), the waterfall re-ran on
EVERY image-bearing turn (#89863: 51 detail-less busy-acks observed in
one Slack channel while the probe sprayed the user's own server).
Two changes:
- image_routing._should_probe_ollama_vision now takes the API key and
forwards it; a new _resolve_inference_api_key mirrors
_resolve_inference_base_url's resolution order (runtime value,
model.api_key, providers blocks) so the key always matches the URL
being probed.
- detect_local_server_type caches a None verdict in memory with a short
failure TTL (5 min, vs 1h for positives) so the next turn is served
from the negative entry instead of re-running the waterfall — while
a transient failure (server starting, key being fixed) recovers in
minutes. Negative verdicts are deliberately not written to the
cross-process disk cache.
Fixes#89863. With a custom: provider pointing at a remote, API-keyed
endpoint (sglang/vLLM/OpenAI-compat), every image turn triggered a 5-request
probe waterfall without Authorization, spraying 401s at the backend.
Two fixes:
1. _should_probe_ollama_vision now takes api_key and forwards it to
detect_local_server_type so keyed local servers don't 401.
2. When provider != 'ollama', remote endpoints (per is_local_endpoint) are
rejected early — server-fingerprint probing is only valid for local
boxes. Non-Ollama remotes expose Ollama-compat endpoints that can
misidentify and trigger unnecessary /api/show probes.
_lookup_supports_vision resolves the runtime api_key via
_runtime_main_value and forwards it to both helpers. New test class
TestShouldProbeOllamaVision covers the contract in both directions.
Pasting a .webp at a local vision model produced fluent, completely
wrong descriptions: llama.cpp decodes images with stb_image, which has
no WebP support, and an undecodable image part fails SILENTLY — no HTTP
error, no log line. The model receives a turn that mentions an image it
never saw and confabulates. Measured against the live server with the
same red square: PNG answered 'Red', WebP answered 'Unseen', and the
model's own reasoning discussed being unable to see the image while the
visible reply described an imaginary one.
Image attachment already had a transcode-to-PNG path for formats some
cloud providers reject (AVIF/HEIC/BMP); WebP was in the universal set
because every cloud provider takes it. When the active main model is
served by the managed runtime, narrow the accepted set to what its
decoder actually handles (PNG/JPEG) so WebP transcodes here instead of
vanishing server-side. Cloud providers keep native WebP — no transcode
tax where none is needed.
Live receipt: the transcoded WebP-as-PNG answers 'Red' through the real
server. Contract tests: webp->png for managed, webp passthrough for
cloud.
Pasting an image at a vision-capable local model failed with 'can't see
the image': capability lookup consults the user's config override, the
models.dev catalog, and an Ollama probe — and a cloud catalog has never
heard of a local GGUF, so every managed model read as text-only and
images detoured to the auxiliary vision model (or nothing). Wrong twice
for a local-first user: broken feature, and a screenshot silently
leaving the machine.
New hermes_cli/local_runtime/capabilities.py answers from ground truth,
best source first: the RUNNING child's /props modalities block (the
server that will receive the image says whether it can see), then the
catalog entry's vision projector — required to actually be on disk, so a
model staged without its mmproj honestly reads blind. Non-managed models
return None and the chain falls through unchanged.
Wired into _lookup_supports_vision between the config override (still
root of trust) and the cloud catalog; _main_model_supports_vision and
image routing inherit through the same resolver. No image conversion
needed anywhere — llama-server accepts standard image_url content parts
once its projector is loaded.
Contract tests: not-ours passes through, projector-on-disk sees,
projector-missing is blind, live /props beats the catalog, the chain
never consults the cloud catalog for a managed model, and the user
override still outranks everything.
- web_server CONFIG_SCHEMA: fold the one-field models_dev category
(models_dev.url) into the agent tab via _CATEGORY_MERGE, matching the
established pattern for single-field categories (slice 7,
test_no_single_field_categories).
- image_routing._lookup_supports_vision: pass allow_network=True to
get_model_capabilities. The vision-capability lookup runs when an
image actually needs routing (not per conversation turn), and the
#31179 text-only-main guard depends on catalog data — with the new
allow_network=False default a cold cache returned 'unknown', which
falls back to attempting the call and reintroduced the #31179
failure shape (slice 8, test_text_only_main_skipped_when_no_
aggregator). This preserves that path's historical
network-on-cold-cache behavior; the fetch stays 4h-TTL cached and
backoff-limited.
Extend the existing candidate-name resolver in _supports_vision_override
to accept 'vision' as an alias for 'supports_vision' on per-model config,
for both the providers.<name>.models dict and the legacy list-style
custom_providers form.
Per review feedback on #31912: this extends the current resolver rather
than replacing its candidate-name logic. Named custom providers resolve
to the runtime value provider='custom' while the config keeps the
user-declared name under model.provider; that lookup path is preserved.
Adds regression tests covering model.provider=my-vllm with runtime
provider='custom' for both config shapes.
When model.provider is set to custom:<name>, _supports_vision_override()
previously tried only the runtime provider key ('custom') and the raw
config value ('custom:my-proxy'). It did not try the stripped name
('my-proxy'), which is the actual key under providers: in config.yaml.
This caused native image routing to fall back to text mode even when the
user explicitly declared supports_vision: true on the named provider's
model entry.
Fixes#39963
When local Ollama models are absent from models.dev, probe the Ollama
server's /api/show capabilities so attached images are routed natively
instead of being stripped as non-vision input.
A document attached alongside an image in the same Discord message was
swept into the vision pipeline and 400'd the whole turn ("Could not
process image"), and was simultaneously never surfaced to the agent as a
readable file. Restores the "any file type works" contract for mixed
messages and fixes the HTTP 400.
Bug 1 — mixed attachments: the inbound routing loop keyed image/audio/video
classification off the message-level type (PHOTO/VOICE/AUDIO), so a doc in
a PHOTO message landed in image_paths and poisoned the vision call. The
document context-note path was gated on message_type == DOCUMENT, so that
same doc never reached the agent at all. Now classification is
per-attachment (trust each attachment's own MIME; fall back to the
message-level type only when MIME is unknown), via shared _event_media_is_*
helpers used by both _build_media_placeholder and the main inbound loop.
The document note now fires for any non-image/audio/video attachment
regardless of message-level type.
Bug 2 — uncommon formats: AVIF/HEIC/BMP/TIFF/ICO produced the same generic
400 because providers only accept PNG/JPEG/GIF/WEBP. image_routing now
transcodes those to PNG via Pillow before declaring media_type, skipping
cleanly (logged) if Pillow/plugins are missing. SVG is vector — Pillow
can't rasterize it — so it's skipped rather than transcoded.
Closes#25935.
Co-authored-by: LeonSGP43 <cine.dreamer.one@gmail.com>
Co-authored-by: cypres0099 <74935762+cypres0099@users.noreply.github.com>
_supports_vision_override() in image_routing.py checked model.supports_vision
and providers.<name>.models, but not the legacy list-style custom_providers
config. A custom provider entry like:
custom_providers:
- name: my-provider
models:
my-model:
supports_vision: true
was ignored, causing image_input_mode=auto to route through the auxiliary
vision_analyze path instead of natively attaching images.
Fix: added a lookup step for custom_providers list entries, matching by
provider name (including 'custom:<name>' variants at runtime).
providers.<name>.models still takes precedence over custom_providers.
13 new tests covering: true/false override, custom: prefix matching,
no-match fallback, non-dict entries, empty lists, models key missing.
Kanban workers now scan the task body for local image paths and
http(s) image URLs and attach them to the worker's first user turn —
matching the CLI/gateway behaviour for inbound images. Before, a
user pasting `/home/me/screenshot.png` or `https://example.com/img.png`
into a kanban task description had it sent to the model as plain
text and the pixels were never seen.
How it works:
* agent/image_routing.py gains extract_image_refs(text) → (paths, urls)
that mirrors gateway/platforms/base.py:extract_local_files (absolute /
~-relative paths, image extensions only, ignores fenced/inline code).
* build_native_content_parts() accepts an optional image_urls= kwarg
and emits passthrough image_url parts for remote URLs alongside the
base64 data: URLs used for local paths.
* cli.py (single-query/quiet branch — the path every dispatcher-spawned
worker takes) detects HERMES_KANBAN_TASK, reads the task body via
kanban_db.get_task, runs extract_image_refs, and threads the results
into the existing image-routing decision (native vs text). Best-effort:
enrichment failures never block worker startup.
Tested:
* tests/agent/test_image_routing.py — 22 new tests for extract_image_refs
and URL pass-through in build_native_content_parts.
* tests/hermes_cli/test_kanban_worker_image_extraction.py — 10 new tests
driving real kanban_db round-trip (create task → read body → extract
refs → build parts).
* E2E: created a fake kanban task with a body referencing both a local
PNG and an https URL; verified the worker pipeline produces a
multimodal user turn with 1 text part + 2 image_url parts (data URL
for the local file, passthrough URL for the remote).
The contributor PR (#17936) only patched the strip path in
`_model_supports_vision()`. The auto-mode router in
`agent/image_routing._lookup_supports_vision` still only read models.dev,
so a custom-provider model declared as vision-capable would still get its
images routed through vision_analyze in the default `agent.image_input_mode:
auto` setting. Users had to set both `supports_vision: true` AND
`image_input_mode: native` to bypass the text pipeline.
Single-knob behavior now: `supports_vision: true` alone is enough in auto
mode. The strip path and the routing path consult the same resolver.
- Extract override resolution into `_supports_vision_override()` in
agent/image_routing.py and wire it into `_lookup_supports_vision()`.
- Refactor `run_agent._model_supports_vision` to call the same helper
(DRY, single source of truth for the resolution order).
- Strict YAML boolean coercion: `supports_vision: "false"` (quoted —
a common YAML mistake) no longer coerces to True via bool() truthiness.
Recognised tokens: true/false/yes/no/on/off/1/0 plus real bools and 0/1.
Unrecognised values return None and fall through to models.dev.
- Add @CNSeniorious000 to AUTHOR_MAP for release attribution.
Tests: 26 new (TestCoerceCapabilityBool, TestSupportsVisionOverride,
TestLookupSupportsVisionOverride, TestAutoModeRespectsOverride). Existing
contributor tests + image_routing + vision_native_fast_path +
native_image_buffer_isolation all green (92/92).
Replace with for all literal-tuple
membership tests. Set lookup is O(1) vs O(n) for tuple — consistent
micro-optimization across the codebase.
608 instances fixed via `ruff --fix --unsafe-fixes`, 0 remaining.
133 files, +626/-626 (net zero).
Discord (and similar platforms) can serve a PNG image cached as
discord_xxx.webp because the CDN reports content_type=image/webp for
proxied stickers, custom emoji, and certain bot-uploaded images even
when the actual bytes are PNG. Hermes' agent.image_routing._guess_mime
trusted the file suffix and declared media_type=image/webp to
Anthropic, which strict-validates and returns:
HTTP 400 messages.N.content.M.image.source.base64:
The image was specified using the image/webp media type,
but the image appears to be a image/png image
The Discord image attachment never reaches the model; the whole turn
fails with no salvage path.
Fix: sniff magic bytes in _file_to_data_url before declaring MIME.
Suffix-based detection is kept as a fallback when bytes aren't
available. New helper _sniff_mime_from_bytes covers PNG, JPEG, GIF,
WEBP, BMP, and HEIC/HEIF.
Tests:
- Two existing tests asserted the old broken behaviour (PNG bytes in
a .jpg/.webp file should report jpeg/webp); rewritten with real
jpeg/webp magic bytes so they still cover suffix-aligned cases.
- New regression test test_mime_sniff_overrides_misleading_extension
reproduces the exact Discord scenario (PNG bytes, .webp suffix) and
asserts the data URL comes back as image/png.
All 28 tests in tests/agent/test_image_routing.py pass.
In native image mode (vision-capable models like gpt-4o, claude-sonnet-4),
build_native_content_parts() previously emitted only the user's caption
plus image_url parts. The local file path of each attached image never
appeared in the conversation text, so the model could see the pixels but
had no string handle for tools that take image_url: str (custom MCP
tools, vision_analyze on a re-look, attach-to-tracker workflows).
The text-mode path already injects an equivalent hint via
Runner._enrich_message_with_vision ("...vision_analyze using image_url:
<path>..."). This brings native mode to parity by appending one
"[Image attached at: <path>]" line per successfully attached image to
the user-text part of the multimodal turn. Skipped (unreadable) paths
are NOT advertised, so the model is never told a non-existent file is
attached.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* feat(image-input): native multimodal routing based on model vision capability
Attach user-sent images as OpenAI-style content parts on the user turn when
the active model supports native vision, so vision-capable models see real
pixels instead of a lossy text description from vision_analyze.
Routing decision (agent/image_routing.py::decide_image_input_mode):
agent.image_input_mode = auto | native | text (default: auto)
In auto mode:
- If auxiliary.vision.provider/model is explicitly configured, keep the
text pipeline (user paid for a dedicated vision backend).
- Else if models.dev reports supports_vision=True for the active
provider/model, attach natively.
- Else fall back to text (current behaviour).
Call sites updated: gateway/run.py (all messaging platforms), tui_gateway
(dashboard/Ink), cli.py (interactive /attach + drag-drop).
run_agent.py changes:
- _prepare_anthropic_messages_for_api now passes image parts through
unchanged when the model supports vision — the Anthropic adapter
translates them to native image blocks. Previous behaviour
(vision_analyze → text) only runs for non-vision Anthropic models.
- New _prepare_messages_for_non_vision_model mirrors the same contract
for chat.completions and codex_responses paths, so non-vision models
on any provider get text-fallback instead of failing at the provider.
- New _model_supports_vision() helper reads models.dev caps.
vision_analyze description rewritten: positions it as a tool for images
NOT already visible in the conversation (URLs, tool output, deeper
inspection). Prevents the model from redundantly calling it on images
already attached natively.
Config default: agent.image_input_mode = auto.
Tests: 35 new (test_image_routing.py + test_vision_aware_preprocessing.py),
all existing tests that reference _prepare_anthropic_messages_for_api
still pass (198 targeted + new tests green).
* feat(image-input): size-cap + resize oversized images, charge image tokens in compressor
Two follow-ups that make the native image routing safer for long / heavy
sessions:
1) Oversize handling in build_native_content_parts:
- 20 MB ceiling per image (matches vision_tools._MAX_BASE64_BYTES,
the most restrictive provider — Gemini inline data).
- Delegates to vision_tools._resize_image_for_vision (Pillow-based,
already battle-tested) to downscale to 5 MB first-try.
- If Pillow is missing or resize still overshoots, the image is
dropped and reported back in skipped[]; caller falls back to text
enrichment for that image.
2) Image-token accounting in context_compressor:
- New _IMAGE_TOKEN_ESTIMATE = 1600 (matches Claude Code's constant;
within the realistic range for Anthropic/GPT-4o/Gemini billing).
- _content_length_for_budget() helper: sums text-part lengths and
charges _IMAGE_CHAR_EQUIVALENT (1600 * 4 chars) per image/image_url/
input_image part. Base64 payload inside image_url is NOT counted
as chars — dimensions don't matter, only image-presence.
- Both tail-cut sites (_prune_old_tool_results L527 and
_find_tail_cut_by_tokens L1126) now call the helper so multi-image
conversations don't slip past compression budget.
Tests: 9 new in test_image_routing.py (oversize triggers resize,
resize-fails-returns-None, oversize-skipped-reported), 11 new in
test_compressor_image_tokens.py (flat charge per image, multiple images,
Responses-API / Anthropic-native / OpenAI-chat shapes, no-inflation on
raw base64, bounds-check on the constant, integration test that an
image-heavy tail actually gets trimmed).
* fix(image-input): replace blanket 20MB ceiling with empirically-verified per-provider limits
The previous commit imposed a hardcoded 20 MB base64 ceiling on all
providers, triggering auto-resize on anything larger. This was wrong in
both directions:
* Too loose for Anthropic — actual limit is 5 MB (returns HTTP 400
'image exceeds 5 MB maximum' above that).
* Too strict for OpenAI / Codex / OpenRouter — accept 49 MB+ without
complaint (empirically verified April 2026 with progressive PNG
sizes).
New behaviour:
* _PROVIDER_BASE64_CEILING table: only anthropic and bedrock have a
ceiling (5 MB, since bedrock-on-Claude shares Anthropic's decoder).
* Providers NOT in the table get no ceiling — images attach at native
size and we trust the provider to return its own error if it
disagrees. A provider-specific 400 message is clearer than us
guessing wrong and silently degrading image quality.
* build_native_content_parts() gains a keyword-only provider arg;
gateway/CLI/TUI pass the active provider so Anthropic users get
auto-resize protection while OpenAI users don't pay it.
* Resize target dropped from 5 MB to 4 MB to slide safely under
Anthropic's boundary with header overhead.
Empirical measurements (direct API, no Hermes in the loop):
image b64 anthropic openrouter/gpt5.5 codex-oauth/gpt5.5
0.19 MB ✓ ✓ ✓
12.37 MB ✗ 400 5MB ✓ ✓
23.85 MB ✗ 400 5MB ✓ ✓
49.46 MB ✗ 413 ✓ ✓
Tests: rewrote TestOversizeHandling (5 tests): no-ceiling pass-through,
Anthropic resize fires, Anthropic skip on resize-fail, build_native_parts
routes ceiling by provider, unknown provider gets no ceiling. All 52
targeted tests pass.
* refactor(image-input): attempt native, shrink-and-retry on provider reject
Replace proactive per-provider size ceilings with a reactive shrink path
on the provider's actual rejection. All providers now attempt native
full-size attachment first; if the provider returns an image-too-large
error, the agent silently shrinks and retries once.
Why the previous design was wrong: hardcoding provider ceilings
(anthropic=5MB, others=unlimited) meant OpenAI users on a 10MB image
paid no tax, but Anthropic users lost quality on anything >5MB even
though the empirical behaviour at provider-reject time is the same
(shrink + retry). Baking the table into the routing layer also
requires updating Hermes every time a provider's limit changes.
Reactive design:
- image_routing.py: _file_to_data_url encodes native size, no ceiling.
build_native_content_parts drops its provider kwarg.
- error_classifier.py: new FailoverReason.image_too_large + pattern
match ("image exceeds", "image too large", etc.) checked BEFORE
context_overflow so Anthropic's 5MB rejection lands in the right
bucket.
- run_agent.py: new _try_shrink_image_parts_in_messages walks api
messages in-place, re-encodes oversized data: URL image parts
through vision_tools._resize_image_for_vision to fit under 4MB,
handles both chat.completions (dict image_url) and Responses
(string image_url) shapes, ignores http URLs (provider-fetched).
New image_shrink_retry_attempted flag in the retry loop fires the
shrink exactly once per turn after credential-pool recovery but
before auth retries.
E2E verified live against Anthropic claude-sonnet-4-6:
- 17.9MB PNG (23.9MB b64) attached at native size
- Anthropic returns 400 "image exceeds 5 MB maximum"
- Agent logs '📐 Image(s) exceeded provider size limit — shrank and
retrying...'
- Retry succeeds, correct response delivered in 6.8s total.
Tests: 12 new (8 shrink-helper shapes + 4 classifier signals),
replaces 5 proactive-ceiling tests with 3 simpler 'native attach works'
tests. 181 targeted tests pass. test_enum_members_exist in
test_error_classifier.py updated for the new enum value.
