Revert the Vercel AI Gateway image-rejection guess

It also matched errors about one image form, e.g. Groq's
'Non-image file content parts' functionality not supported.

strix/core/execution.py

@@ -4,9 +4,12 @@ from __future__ import annotations
 
 import asyncio
 import contextlib
+import inspect
 import logging
+import re
 import uuid
 from collections.abc import Callable
+from dataclasses import replace
 from functools import cache
 from typing import TYPE_CHECKING, Any, cast
 
@@ -30,6 +33,7 @@ from strix.core.sessions import (
     enforce_image_budget,
     open_agent_session,
     replace_session_items,
+    scrub_images_from_items,
     seed_initial_input,
     strip_all_images_from_session,
 )
@@ -44,6 +48,7 @@ if TYPE_CHECKING:
     from agents.lifecycle import RunHooks
     from agents.memory import Session, SQLiteSession
     from agents.result import RunResultBase
+    from agents.run_config import CallModelData, ModelInputData
 
     from strix.core.agents import AgentCoordinator, Status
 
@@ -53,6 +58,17 @@ logger = logging.getLogger(__name__)
 StreamEventSink = Callable[[str, Any], None]
 
 _INPUT_REJECTION_CODES = frozenset({400, 404, 422})
+# Replies meaning "this model takes no images", not image errors in general: a
+# context overflow that counts "image/vision expansion" must not match.
+_IMAGE_REJECTION = re.compile(
+    r"no endpoints found that support image input"  # OpenRouter
+    r"|image_url is only supported by certain models"  # OpenAI
+    r"|is not a multimodal model|at most 0 image\(s\)"  # vLLM
+    r"|does not support image input"  # LiteLLM's Fireworks check
+    r"|doesn't support the image field"  # Bedrock Converse
+    r"|unknown variant `image_url`",  # DeepSeek, e.g. via Vercel AI Gateway
+    re.IGNORECASE,
+)
 _MAX_COMPACTIONS_PER_CYCLE = 2
 
 
@@ -120,6 +136,28 @@ async def _compact_session(
     )
 
 
+_TEXT_ONLY_IMAGE_TEXT = "[error: this model cannot view images; use `snapshot -i` instead]"
+
+
+def _with_image_scrub(run_config: RunConfig, context: dict[str, Any]) -> RunConfig:
+    if context.get("supports_images", True):
+        return run_config
+    # Chain any filter already set; it sees the scrubbed input.
+    inner = run_config.call_model_input_filter
+
+    async def _scrub(data: CallModelData[Any]) -> ModelInputData:
+        model_data = replace(
+            data.model_data,
+            input=scrub_images_from_items(data.model_data.input, text=_TEXT_ONLY_IMAGE_TEXT),
+        )
+        if inner is None:
+            return model_data
+        result = inner(replace(data, model_data=model_data))
+        return await result if inspect.isawaitable(result) else result
+
+    return replace(run_config, call_model_input_filter=_scrub)
+
+
 _MAX_TRANSIENT_MODEL_RETRIES = 5
 _TRANSIENT_MODEL_RETRY_BASE_DELAY_S = 2.0
 _TRANSIENT_MODEL_RETRY_MAX_DELAY_S = 90.0
@@ -130,6 +168,12 @@ def _model_error_status_code(exc: BaseException) -> int | None:
     return code if isinstance(code, int) else None
 
 
+def _is_image_rejection(exc: BaseException) -> bool:
+    return _model_error_status_code(exc) in _INPUT_REJECTION_CODES and bool(
+        _IMAGE_REJECTION.search(str(exc))
+    )
+
+
 def _is_transient_model_error(exc: BaseException) -> bool:
     if codex.is_content_guardrail_error(exc):
         return False
@@ -747,7 +791,7 @@ async def _run_cycle(  # noqa: PLR0912, PLR0915
             stream = Runner.run_streamed(
                 agent,
                 input=input_data,
-                run_config=run_config,
+                run_config=_with_image_scrub(run_config, context),
                 context=context,
                 max_turns=max_turns,
                 session=session,
@@ -805,11 +849,7 @@ async def _run_cycle(  # noqa: PLR0912, PLR0915
             await coordinator.trigger_budget_stop()
             raise
         except Exception as exc:
-            if (
-                image_strips < 3
-                and session is not None
-                and getattr(exc, "status_code", None) in _INPUT_REJECTION_CODES
-            ):
+            if image_strips < 3 and session is not None and _is_image_rejection(exc):
                 try:
                     stripped = await strip_all_images_from_session(session)
                 except Exception:

strix/core/runner.py

@@ -602,7 +602,7 @@ async def run_strix_scan(
         )
         if not interactive and result is not None:
             final = getattr(result, "final_output", None)
-            # Lifecycle tools mark the root completed. 
+            # Lifecycle tools mark the root completed.
             async with coordinator._lock:
                 root_completed = coordinator.statuses.get(root_id) == "completed"
             if not root_completed:

strix/core/sessions.py

@@ -199,13 +199,13 @@ async def enforce_image_budget(session: Session, max_images: int) -> bool:
     return await _rewrite_session(session, _transform)
 
 
-def scrub_images_from_items(items: list[Any]) -> list[Any]:
+def scrub_images_from_items(items: list[Any], *, text: str = _INHERITED_IMAGE_TEXT) -> list[Any]:
     """Return a copy of ``items`` with every image block replaced by text."""
 
     def _scrub(obj: Any) -> Any:
         if isinstance(obj, dict):
             if obj.get("type") == "input_image":
-                return {"type": "input_text", "text": _INHERITED_IMAGE_TEXT}
+                return {"type": "input_text", "text": text}
             return {k: _scrub(v) for k, v in obj.items()}
         if isinstance(obj, list):
             return [_scrub(v) for v in obj]

strix/skills/__init__.py

@@ -17,7 +17,7 @@ _FRONTMATTER_PATTERN = re.compile(r"^---\s*\n(?P<body>.*?)\n---\s*\n", re.DOTALL
 # Only skills with `template: jinja` in their frontmatter are rendered; the rest hold
 # literal `{{ }}` payloads.
 _SKILL_TEMPLATES = Environment(
-    autoescape=False,  # noqa: S701 - prompts, not HTML
+    autoescape=False,  # noqa: S701  # nosec B701 - prompts, not HTML
     undefined=StrictUndefined,
     trim_blocks=True,
     lstrip_blocks=True,

strix/tools/mcp/client.py

@@ -32,6 +32,7 @@ from agents.mcp import (
 )
 from mcp.client.stdio import stdio_client
 from mcp.shared._httpx_utils import create_mcp_http_client
+from mcp.types import TextContent
 
 from strix.tools.mcp.failures import HttpStatusRecorder
 from strix.tools.mcp.session import McpConnectionUnavailableError, SupervisedMcpSession
@@ -183,9 +184,19 @@ def _build_server(config: McpConnectionConfig) -> BuiltMcpServer:
     )
 
 
-def _mcp_result_to_tool_output(
-    server: MCPServer, result: Any, *, supports_images: bool = True
-) -> Any:
+def _without_images(result: Any) -> Any:
+    content = [
+        TextContent(
+            type="text", text=f"[{item.mimeType} image omitted: this model cannot view images]"
+        )
+        if item.type == "image"
+        else item
+        for item in result.content
+    ]
+    return result.model_copy(update={"content": content})
+
+
+def _mcp_result_to_tool_output(server: MCPServer, result: Any) -> Any:
     """Serialize a ``CallToolResult`` to a tool output, mirroring the agents SDK.
 
     This reproduces the serialization in ``agents.mcp.util.MCPUtil.invoke_mcp_tool``
@@ -201,13 +212,6 @@ def _mcp_result_to_tool_output(
     for item in result.content:
         if item.type == "text":
             outputs.append({"type": "text", "text": item.text})
-        elif item.type == "image" and not supports_images:
-            outputs.append(
-                {
-                    "type": "text",
-                    "text": f"[{item.mimeType} image omitted: this model cannot view images]",
-                }
-            )
         elif item.type == "image":
             outputs.append(
                 {"type": "image", "image_url": f"data:{item.mimeType};base64,{item.data}"}
@@ -243,9 +247,11 @@ async def dispatch_mcp_call(
       corrupt the content the agent receives).
     """
     result = await server.call_tool(tool_name, arguments)
+    if not supports_images:
+        result = _without_images(result)
     if result_transform is not None:
         return result_transform(label, result.model_dump(mode="json"))
-    tool_output = _mcp_result_to_tool_output(server, result, supports_images=supports_images)
+    tool_output = _mcp_result_to_tool_output(server, result)
     if getattr(result, "isError", False):
         return _errored_tool_output(tool_output)
     return tool_output

tests/test_execution.py

@@ -9,9 +9,11 @@ from typing import Any, cast
 from unittest.mock import MagicMock
 
 import pytest
+from agents import RunConfig
 from agents.exceptions import MaxTurnsExceeded
 from agents.items import MessageOutputItem
 from agents.memory import SQLiteSession
+from agents.run_config import CallModelData, ModelInputData
 from agents.tool_context import ToolContext
 from openai.types.responses import ResponseOutputMessage, ResponseOutputRefusal, ResponseOutputText
 
@@ -1531,3 +1533,32 @@ async def test_autonomous_nudge_does_not_offer_the_user() -> None:
     )
 
     assert "wait_for_user" not in items[0]["content"]
+
+
+@pytest.mark.asyncio
+async def test_text_only_filter_scrubs_images_then_chains_existing_filter() -> None:
+    run_config = RunConfig(model="m")
+    assert execution._with_image_scrub(run_config, {"supports_images": True}) is run_config
+
+    seen: list[ModelInputData] = []
+
+    def _inner(data: CallModelData[Any]) -> ModelInputData:
+        seen.append(data.model_data)
+        return data.model_data
+
+    run_config = RunConfig(model="m", call_model_input_filter=_inner)
+    scrub: Any = execution._with_image_scrub(run_config, {"supports_images": False})
+    image = {"type": "input_image", "image_url": "data:image/png;base64,aGk="}
+    output = {"type": "function_call_output", "call_id": "c1", "output": [image]}
+    data = CallModelData(
+        model_data=ModelInputData(input=[cast("Any", output)], instructions="sys"),
+        agent=MagicMock(),
+        context=None,
+    )
+
+    result = await scrub.call_model_input_filter(data)
+    assert result is seen[0]
+    assert result.input[0]["output"] == [
+        {"type": "input_text", "text": execution._TEXT_ONLY_IMAGE_TEXT}
+    ]
+    assert data.model_data.input[0]["output"] == [image]

tests/test_execution_transient_retry.py

@@ -109,6 +109,7 @@ def _patch_fast_backoff(monkeypatch: pytest.MonkeyPatch) -> None:
 async def _run_once(
     monkeypatch: pytest.MonkeyPatch,
     streams: list[_FakeStream],
+    session: Any = None,
 ) -> Any:
     _patch_fast_backoff(monkeypatch)
     calls = {"n": 0}
@@ -131,7 +132,7 @@ async def _run_once(
         run_config=cast("RunConfig", object()),
         context={},
         max_turns=5,
-        session=None,
+        session=session,
         interactive=False,
         event_sink=None,
         hooks=None,
@@ -172,3 +173,74 @@ async def test_run_cycle_does_not_retry_permanent_error(
     streams = [_FakeStream(exc=bad_request), _FakeStream()]
     with pytest.raises(BadRequestError):
         await _run_once(monkeypatch, streams)
+
+
+@pytest.mark.parametrize(
+    ("status", "message", "expected"),
+    [
+        (
+            404,
+            "litellm.NotFoundError: NotFoundError: OpenrouterException - "
+            '{"error":{"message":"No endpoints found that support image input","code":404,'
+            '"metadata":{"failed_routing_step":"Filter by Image Support"}}}',
+            True,
+        ),
+        (400, "Invalid content type. image_url is only supported by certain models.", True),
+        (400, "my-model is not a multimodal model", True),
+        (400, "This model doesn't support the image field for user messages.", True),
+        (422, "messages[3]: unknown variant `image_url`, expected `text`", True),
+        (
+            400,
+            "OpenRouterException: Message: This model's maximum context length is 1048576 "
+            "tokens, but the request requires 1073484 tokens (942412 input including "
+            "image/vision expansion + 131072 for the completion). Reduce the input length "
+            "or max_tokens.",
+            False,
+        ),
+        (400, "At most 5 image(s) may be provided in one prompt.", False),
+        (
+            400,
+            "Upstream error from Relace: Generation stopped with an incomplete tool call",
+            False,
+        ),
+        (500, "No endpoints found that support image input", False),
+    ],
+)
+def test_is_image_rejection(status: int, message: str, expected: bool) -> None:
+    exc = APIStatusError(message, response=httpx.Response(status, request=_request()), body=None)
+    assert execution._is_image_rejection(exc) is expected
+
+
+async def _run_with_image_session(
+    monkeypatch: pytest.MonkeyPatch, streams: list[_FakeStream]
+) -> tuple[Any, int, int]:
+    strips = {"n": 0}
+
+    async def _fake_strip(_session: Any) -> bool:
+        strips["n"] += 1
+        return True
+
+    async def _no_compact(*_args: Any, **_kwargs: Any) -> bool:
+        return False
+
+    monkeypatch.setattr(execution, "strip_all_images_from_session", _fake_strip)
+    monkeypatch.setattr(execution, "_compact_session", _no_compact)
+    result, attempts, _coordinator = await _run_once(monkeypatch, streams, session=object())
+    return result, attempts, strips["n"]
+
+
+@pytest.mark.asyncio
+async def test_run_cycle_strips_images_on_image_rejection(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    rejection = APIStatusError(
+        "No endpoints found that support image input",
+        response=httpx.Response(status_code=404, request=_request()),
+        body=None,
+    )
+    streams = [_FakeStream(exc=rejection), _FakeStream()]
+    result, attempts, strips = await _run_with_image_session(monkeypatch, streams)
+
+    assert result is streams[1]
+    assert attempts == 2
+    assert strips == 1

tests/test_mcp_client.py

@@ -917,6 +917,17 @@ async def test_call_mcp_replaces_images_for_a_text_only_model() -> None:
         {"type": "text", "text": "[image/png image omitted: this model cannot view images]"},
     ]
 
+    transformed: list[Any] = []
+    registry.add(
+        name="pro",
+        server=_ImageServer("pro", [_mcp_tool("shot")]),
+        purpose=None,
+        tool_count=1,
+        result_transform=lambda _label, result: transformed.append(result),
+    )
+    await call_mcp.on_invoke_tool(ctx, json.dumps({"connection": "pro", "tool": "shot"}))
+    assert [block["type"] for block in transformed[0]["content"]] == ["text", "text"]
+
 
 # --- generic MCP tools are the only MCP surface every agent gets -------------
 

tests/test_runner_root_prompt.py

@@ -13,6 +13,7 @@ from typing import Any
 import httpx
 import pytest
 from agents import ModelSettings
+from agents.tool_context import ToolContext
 from openai import RateLimitError
 
 import strix.tools.mcp as mcp_pkg
@@ -24,6 +25,7 @@ from strix.core import runner
 from strix.core.agents import AgentCoordinator
 from strix.core.inputs import make_model_settings
 from strix.runtime import session_manager
+from strix.tools.load_skill.tool import load_skill
 from strix.tools.mcp import BearerAuth, McpConnectionConfig, McpConnectionRequest
 
 
@@ -296,6 +298,20 @@ def test_text_only_prompt_drops_screenshot_guidance() -> None:
     assert "<!--" not in prompt
 
 
+@pytest.mark.asyncio
+async def test_text_only_load_skill_drops_screenshot_guidance() -> None:
+    ctx = ToolContext(
+        context={"supports_images": False},
+        tool_name="load_skill",
+        tool_call_id="call-1",
+        tool_arguments="{}",
+    )
+
+    out = await load_skill.on_invoke_tool(ctx, '{"skills": ["agent_browser"]}')
+    assert "view_image" not in out
+    assert "text-only model" in out
+
+
 def test_scope_is_sent_as_its_own_system_message_on_cache_point_routes() -> None:
     settings = make_model_settings(None, model_name="anthropic/claude-sonnet-5-5")
     prompt = render_system_prompt(