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* 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.
171 lines
6.7 KiB
Python
171 lines
6.7 KiB
Python
"""Tests for the vision-aware image preprocessing in run_agent.py.
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Covers:
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* ``_prepare_anthropic_messages_for_api`` — passes image parts through
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unchanged when the active model reports ``supports_vision=True`` (the
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adapter handles them natively), and falls back to text-description
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replacement when the model lacks vision.
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* ``_prepare_messages_for_non_vision_model`` — the mirror method for the
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chat.completions / codex_responses paths. Same contract.
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"""
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from __future__ import annotations
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from unittest.mock import MagicMock, patch
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import pytest
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from run_agent import AIAgent
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def _make_agent() -> AIAgent:
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"""Build a bare-bones AIAgent instance without running __init__.
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Avoids the heavy provider/credential setup for these pure-method tests.
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"""
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agent = object.__new__(AIAgent)
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agent.provider = "anthropic"
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agent.model = "claude-sonnet-4"
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agent._anthropic_image_fallback_cache = {}
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return agent
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IMG_PARTS_USER_MSG = {
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"role": "user",
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"content": [
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{"type": "text", "text": "What's in this image?"},
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{"type": "image_url", "image_url": {"url": "data:image/png;base64,AAAA"}},
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],
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}
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PLAIN_USER_MSG = {"role": "user", "content": "hello, no images here"}
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# ─── _prepare_anthropic_messages_for_api ─────────────────────────────────────
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class TestPrepareAnthropicMessages:
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def test_no_images_passes_through(self):
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agent = _make_agent()
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msgs = [PLAIN_USER_MSG]
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out = agent._prepare_anthropic_messages_for_api(msgs)
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assert out is msgs # unchanged reference
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def test_vision_capable_passes_images_through(self):
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"""The Anthropic adapter handles image_url/input_image natively."""
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agent = _make_agent()
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with patch.object(agent, "_model_supports_vision", return_value=True):
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out = agent._prepare_anthropic_messages_for_api([IMG_PARTS_USER_MSG])
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# Passes through unchanged — image_url parts still present.
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assert out[0]["content"][1]["type"] == "image_url"
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def test_non_vision_replaces_images_with_text(self):
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agent = _make_agent()
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with patch.object(agent, "_model_supports_vision", return_value=False), \
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patch.object(
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agent,
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"_describe_image_for_anthropic_fallback",
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return_value="[Image description: a cat]",
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):
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out = agent._prepare_anthropic_messages_for_api([IMG_PARTS_USER_MSG])
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# Content collapsed to a string containing the description + user text.
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content = out[0]["content"]
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assert isinstance(content, str)
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assert "[Image description: a cat]" in content
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assert "What's in this image?" in content
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# No more image parts.
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assert "image_url" not in content
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# ─── _prepare_messages_for_non_vision_model ──────────────────────────────────
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class TestPrepareMessagesForNonVision:
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def test_no_images_passes_through(self):
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agent = _make_agent()
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msgs = [PLAIN_USER_MSG]
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out = agent._prepare_messages_for_non_vision_model(msgs)
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assert out is msgs
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def test_vision_capable_passes_through(self):
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"""For vision-capable models on chat.completions path, provider handles pixels."""
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agent = _make_agent()
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agent.provider = "openrouter"
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agent.model = "anthropic/claude-sonnet-4"
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with patch.object(agent, "_model_supports_vision", return_value=True):
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out = agent._prepare_messages_for_non_vision_model([IMG_PARTS_USER_MSG])
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assert out[0]["content"][1]["type"] == "image_url"
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def test_non_vision_strips_images(self):
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agent = _make_agent()
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agent.provider = "openrouter"
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agent.model = "qwen/qwen3-235b-a22b"
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with patch.object(agent, "_model_supports_vision", return_value=False), \
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patch.object(
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agent,
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"_describe_image_for_anthropic_fallback",
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return_value="[Image description: a dog]",
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):
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out = agent._prepare_messages_for_non_vision_model([IMG_PARTS_USER_MSG])
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content = out[0]["content"]
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assert isinstance(content, str)
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assert "[Image description: a dog]" in content
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assert "image_url" not in content
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def test_multiple_messages_with_mixed_content(self):
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agent = _make_agent()
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agent.model = "qwen/qwen3-235b"
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msgs = [
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{"role": "user", "content": "first turn"},
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{"role": "assistant", "content": "ack"},
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IMG_PARTS_USER_MSG,
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]
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with patch.object(agent, "_model_supports_vision", return_value=False), \
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patch.object(
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agent,
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"_describe_image_for_anthropic_fallback",
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return_value="[Image: thing]",
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):
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out = agent._prepare_messages_for_non_vision_model(msgs)
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# First two messages unchanged (no images), third stripped.
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assert out[0]["content"] == "first turn"
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assert out[1]["content"] == "ack"
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assert isinstance(out[2]["content"], str)
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assert "[Image: thing]" in out[2]["content"]
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# ─── _model_supports_vision ──────────────────────────────────────────────────
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class TestModelSupportsVision:
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def test_missing_provider_or_model_returns_false(self):
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agent = _make_agent()
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agent.provider = ""
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agent.model = "claude-sonnet-4"
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assert agent._model_supports_vision() is False
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agent.provider = "anthropic"
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agent.model = ""
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assert agent._model_supports_vision() is False
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def test_uses_get_model_capabilities(self):
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agent = _make_agent()
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fake_caps = MagicMock()
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fake_caps.supports_vision = True
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with patch("agent.models_dev.get_model_capabilities", return_value=fake_caps):
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assert agent._model_supports_vision() is True
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fake_caps.supports_vision = False
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with patch("agent.models_dev.get_model_capabilities", return_value=fake_caps):
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assert agent._model_supports_vision() is False
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def test_none_caps_returns_false(self):
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agent = _make_agent()
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with patch("agent.models_dev.get_model_capabilities", return_value=None):
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assert agent._model_supports_vision() is False
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def test_exception_returns_false(self):
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agent = _make_agent()
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with patch("agent.models_dev.get_model_capabilities", side_effect=RuntimeError("boom")):
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assert agent._model_supports_vision() is False
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