feat(vision): vision_analyze returns pixels to vision-capable models, not aux text (#22955)

When the active main model has native vision and the provider supports
multimodal tool results (Anthropic, OpenAI Chat, Codex Responses, Gemini
3, OpenRouter, Nous), vision_analyze loads the image bytes and returns
them to the model as a multimodal tool-result envelope. The model then
sees the pixels directly on its next turn instead of receiving a lossy
text description from an auxiliary LLM.

Falls back to the legacy aux-LLM text path for non-vision models and
unverified providers.

Mirrors the architecture used in OpenCode, Claude Code, Codex CLI, and
Cline. All four converge on the same pattern: tool results carry image
content blocks for vision-capable provider/model combinations.

Changes
- tools/vision_tools.py: _vision_analyze_native fast path + provider
  capability table (_supports_media_in_tool_results). Schema description
  updated to reflect new behaviour.
- agent/codex_responses_adapter.py: function_call_output.output now
  accepts the array form for multimodal tool results (was string-only).
  Preflight validates input_text/input_image parts.
- agent/auxiliary_client.py: _RUNTIME_MAIN_PROVIDER/_MODEL globals so
  tools see the live CLI/gateway override, not the stale config.yaml
  default. set_runtime_main()/clear_runtime_main() helpers.
- run_agent.py: AIAgent.run_conversation calls set_runtime_main at turn
  start so vision_analyze's fast-path check sees the actual runtime.
- tests/conftest.py: clear runtime-main override between tests.

Tests
- tests/tools/test_vision_native_fast_path.py: provider capability
  table, envelope shape, fast-path gating (vision-capable model uses
  fast path; non-vision model falls through to aux).
- tests/run_agent/test_codex_multimodal_tool_result.py: list tool
  content becomes function_call_output.output array; preflight
  preserves arrays and drops unknown part types.

Live verified
- Opus 4.6 + Sonnet 4.6 on OpenRouter: model calls vision_analyze on a
  typed filepath, gets pixels back, reads exact text from images that
  no aux description could capture (font color irony, multi-line
  fruit-count list, etc.).

PR replaces the closed prior efforts (#16506 shipped the inbound user-
attached path; this PR closes the gap for tool-discovered images).

agent/auxiliary_client.py

@@ -1463,7 +1463,16 @@ def _read_main_model() -> str:
 
     config.yaml model.default is the single source of truth for the active
     model. Environment variables are no longer consulted.
+
+    Runtime override: when an AIAgent is active with a CLI/gateway-provided
+    model that differs from config.yaml, ``set_runtime_main()`` records the
+    override in a process-local global. This is consulted FIRST so tools
+    that gate on "the active main model" (e.g. ``vision_analyze``'s native
+    fast path) see the live runtime, not the persisted config default.
     """
+    override = _RUNTIME_MAIN_MODEL
+    if isinstance(override, str) and override.strip():
+        return override.strip()
     try:
         from hermes_cli.config import load_config
         cfg = load_config()
@@ -1484,7 +1493,13 @@ def _read_main_provider() -> str:
 
     Returns the lowercase provider id (e.g. "alibaba", "openrouter") or ""
     if not configured.
+
+    Runtime override: see ``_read_main_model`` — same mechanism for the
+    provider half of the runtime tuple.
     """
+    override = _RUNTIME_MAIN_PROVIDER
+    if isinstance(override, str) and override.strip():
+        return override.strip().lower()
     try:
         from hermes_cli.config import load_config
         cfg = load_config()
@@ -1498,6 +1513,32 @@ def _read_main_provider() -> str:
     return ""
 
 
+# Process-local override set by AIAgent at session/turn start. Single-threaded
+# per turn — no lock needed. Cleared by ``clear_runtime_main()``.
+_RUNTIME_MAIN_PROVIDER: str = ""
+_RUNTIME_MAIN_MODEL: str = ""
+
+
+def set_runtime_main(provider: str, model: str) -> None:
+    """Record the live runtime provider/model for the current AIAgent.
+
+    Called by ``run_agent.AIAgent._sync_runtime_main_for_aux_routing`` (or
+    equivalent setter) at the top of each turn so that
+    ``_read_main_provider`` / ``_read_main_model`` reflect CLI/gateway
+    overrides instead of the stale config.yaml default.
+    """
+    global _RUNTIME_MAIN_PROVIDER, _RUNTIME_MAIN_MODEL
+    _RUNTIME_MAIN_PROVIDER = (provider or "").strip().lower()
+    _RUNTIME_MAIN_MODEL = (model or "").strip()
+
+
+def clear_runtime_main() -> None:
+    """Clear the runtime override (e.g. on session end)."""
+    global _RUNTIME_MAIN_PROVIDER, _RUNTIME_MAIN_MODEL
+    _RUNTIME_MAIN_PROVIDER = ""
+    _RUNTIME_MAIN_MODEL = ""
+
+
 def _resolve_custom_runtime() -> Tuple[Optional[str], Optional[str], Optional[str]]:
     """Resolve the active custom/main endpoint the same way the main CLI does.
 

agent/codex_responses_adapter.py

@@ -410,10 +410,29 @@ def _chat_messages_to_responses_input(messages: List[Dict[str, Any]]) -> List[Di
                     call_id = raw_tool_call_id.strip()
             if not isinstance(call_id, str) or not call_id.strip():
                 continue
+
+            # Multimodal tool result: convert OpenAI-style content list into
+            # Responses ``function_call_output.output`` array. The Responses
+            # API accepts ``output`` as either a string or an array of
+            # ``input_text``/``input_image`` items. See
+            # https://developers.openai.com/api/reference/python/resources/responses/.
+            tool_content = msg.get("content")
+            output_value: Any
+            if isinstance(tool_content, list):
+                converted = _chat_content_to_responses_parts(
+                    tool_content, role="user",
+                )
+                if converted:
+                    output_value = converted
+                else:
+                    output_value = ""
+            else:
+                output_value = str(tool_content or "")
+
             items.append({
                 "type": "function_call_output",
                 "call_id": call_id,
-                "output": str(msg.get("content", "") or ""),
+                "output": output_value,
             })
 
     return items
@@ -466,6 +485,38 @@ def _preflight_codex_input_items(raw_items: Any) -> List[Dict[str, Any]]:
             output = item.get("output", "")
             if output is None:
                 output = ""
+            # Output may be a string OR an array of structured content
+            # items (input_text / input_image) for multimodal tool results.
+            # Both shapes are accepted by the Responses API. We preserve
+            # the array form when present.
+            if isinstance(output, list):
+                # Validate each item is a recognised content shape; drop
+                # anything else to avoid 4xx from the API.
+                cleaned: List[Dict[str, Any]] = []
+                for part in output:
+                    if not isinstance(part, dict):
+                        continue
+                    ptype = part.get("type")
+                    if ptype == "input_text":
+                        text = part.get("text")
+                        if isinstance(text, str) and text:
+                            cleaned.append({"type": "input_text", "text": text})
+                    elif ptype == "input_image":
+                        url = part.get("image_url")
+                        if isinstance(url, str) and url:
+                            entry: Dict[str, Any] = {"type": "input_image", "image_url": url}
+                            detail = part.get("detail")
+                            if isinstance(detail, str) and detail.strip():
+                                entry["detail"] = detail.strip()
+                            cleaned.append(entry)
+                normalized.append(
+                    {
+                        "type": "function_call_output",
+                        "call_id": call_id.strip(),
+                        "output": cleaned if cleaned else "",
+                    }
+                )
+                continue
             if not isinstance(output, str):
                 output = str(output)
 

run_agent.py

@@ -11119,6 +11119,20 @@ class AIAgent:
 
         self._ensure_db_session()
 
+        # Tell auxiliary_client what the live main provider/model are for
+        # this turn. Used by tools whose behaviour depends on the active
+        # main model (e.g. vision_analyze's native fast path) so they see
+        # the CLI/gateway override instead of the stale config.yaml
+        # default. Idempotent — fine to call every turn.
+        try:
+            from agent.auxiliary_client import set_runtime_main
+            set_runtime_main(
+                getattr(self, "provider", "") or "",
+                getattr(self, "model", "") or "",
+            )
+        except Exception:
+            pass
+
         # Tag all log records on this thread with the session ID so
         # ``hermes logs --session <id>`` can filter a single conversation.
         from hermes_logging import set_session_context

tests/conftest.py

@@ -427,6 +427,15 @@ def _reset_module_state():
     except Exception:
         pass
 
+    # --- agent.auxiliary_client — runtime main provider/model override ---
+    # Set per-turn by AIAgent.run_conversation; tests that import it must
+    # see a clean state so config.yaml fallback works as expected.
+    try:
+        from agent import auxiliary_client as _aux_mod
+        _aux_mod.clear_runtime_main()
+    except Exception:
+        pass
+
     # --- tools.file_tools — per-task read history + file-ops cache ---
     # _read_tracker accumulates per-task_id read history for loop detection,
     # capped by _READ_HISTORY_CAP. If entries from a prior test persist, the

tests/run_agent/test_codex_multimodal_tool_result.py

@@ -0,0 +1,173 @@
+"""Tests for codex_responses_adapter multimodal tool-result handling.
+
+Tool messages can contain a list of OpenAI-style content parts
+(``[{type:"text"...}, {type:"image_url"...}]``) when the
+``vision_analyze`` native fast path returns image bytes for the main model.
+This file verifies the Codex Responses adapter:
+
+  1. Converts that list into ``function_call_output.output`` as an array of
+     ``input_text``/``input_image`` items (not a stringified blob).
+  2. Preserves array-shaped output through the preflight validator.
+"""
+
+from __future__ import annotations
+
+from agent.codex_responses_adapter import (
+    _chat_messages_to_responses_input,
+    _preflight_codex_input_items,
+)
+
+
+def _build_messages_with_multimodal_tool_result():
+    return [
+        {"role": "user", "content": "What's in /tmp/foo.png?"},
+        {
+            "role": "assistant",
+            "content": "",
+            "tool_calls": [{
+                "id": "call_abc",
+                "type": "function",
+                "function": {
+                    "name": "vision_analyze",
+                    "arguments": '{"image_url": "/tmp/foo.png", "question": "describe"}',
+                },
+            }],
+        },
+        {
+            "role": "tool",
+            "name": "vision_analyze",
+            "tool_call_id": "call_abc",
+            "content": [
+                {"type": "text", "text": "Image loaded."},
+                {"type": "image_url", "image_url": {"url": "data:image/png;base64,XYZ"}},
+            ],
+        },
+    ]
+
+
+class TestMultimodalToolResultConversion:
+    def test_list_content_becomes_output_array(self):
+        items = _chat_messages_to_responses_input(
+            _build_messages_with_multimodal_tool_result()
+        )
+        # Find the function_call_output item
+        outputs = [it for it in items if it.get("type") == "function_call_output"]
+        assert len(outputs) == 1
+        out = outputs[0]
+        assert out["call_id"] == "call_abc"
+        # Output should be a LIST (array form), not a string
+        assert isinstance(out["output"], list), \
+            f"Expected array output for multimodal tool result, got {type(out['output']).__name__}: {out['output']!r}"
+        types = [p.get("type") for p in out["output"]]
+        assert "input_text" in types
+        assert "input_image" in types
+
+    def test_input_image_preserves_data_url(self):
+        items = _chat_messages_to_responses_input(
+            _build_messages_with_multimodal_tool_result()
+        )
+        out = next(it for it in items if it.get("type") == "function_call_output")
+        image_parts = [p for p in out["output"] if p.get("type") == "input_image"]
+        assert len(image_parts) == 1
+        assert image_parts[0]["image_url"] == "data:image/png;base64,XYZ"
+
+    def test_string_tool_content_still_string_output(self):
+        msgs = [
+            {"role": "user", "content": "hi"},
+            {
+                "role": "assistant", "content": "",
+                "tool_calls": [{
+                    "id": "call_x", "type": "function",
+                    "function": {"name": "terminal", "arguments": "{}"},
+                }],
+            },
+            {
+                "role": "tool", "name": "terminal", "tool_call_id": "call_x",
+                "content": "ls output here",
+            },
+        ]
+        items = _chat_messages_to_responses_input(msgs)
+        out = next(it for it in items if it.get("type") == "function_call_output")
+        assert isinstance(out["output"], str)
+        assert out["output"] == "ls output here"
+
+
+class TestPreflightAcceptsArrayOutput:
+    def test_preflight_passes_array_through(self):
+        raw = [
+            {
+                "type": "function_call",
+                "call_id": "call_abc",
+                "name": "vision_analyze",
+                "arguments": "{}",
+            },
+            {
+                "type": "function_call_output",
+                "call_id": "call_abc",
+                "output": [
+                    {"type": "input_text", "text": "Image loaded."},
+                    {"type": "input_image", "image_url": "data:image/png;base64,ABC"},
+                ],
+            },
+        ]
+        normalized = _preflight_codex_input_items(raw)
+        out = [it for it in normalized if it.get("type") == "function_call_output"][0]
+        assert isinstance(out["output"], list)
+        assert len(out["output"]) == 2
+        assert out["output"][1]["type"] == "input_image"
+        assert out["output"][1]["image_url"] == "data:image/png;base64,ABC"
+
+    def test_preflight_drops_unknown_part_types(self):
+        raw = [
+            {
+                "type": "function_call",
+                "call_id": "call_abc", "name": "vision_analyze", "arguments": "{}",
+            },
+            {
+                "type": "function_call_output",
+                "call_id": "call_abc",
+                "output": [
+                    {"type": "input_text", "text": "ok"},
+                    {"type": "garbage", "data": "nope"},  # unknown — should be dropped
+                    {"type": "input_image", "image_url": "data:image/png;base64,ZZ"},
+                ],
+            },
+        ]
+        normalized = _preflight_codex_input_items(raw)
+        out = [it for it in normalized if it.get("type") == "function_call_output"][0]
+        # The "garbage" part is dropped; valid parts remain
+        types = [p.get("type") for p in out["output"]]
+        assert types == ["input_text", "input_image"]
+
+    def test_preflight_empty_array_becomes_empty_string(self):
+        # Defensive: an array with no valid parts shouldn't break the API call
+        raw = [
+            {
+                "type": "function_call",
+                "call_id": "call_x", "name": "vision_analyze", "arguments": "{}",
+            },
+            {
+                "type": "function_call_output",
+                "call_id": "call_x",
+                "output": [{"type": "garbage"}],  # all dropped
+            },
+        ]
+        normalized = _preflight_codex_input_items(raw)
+        out = [it for it in normalized if it.get("type") == "function_call_output"][0]
+        assert out["output"] == ""
+
+    def test_preflight_string_output_unchanged(self):
+        raw = [
+            {
+                "type": "function_call",
+                "call_id": "call_x", "name": "terminal", "arguments": "{}",
+            },
+            {
+                "type": "function_call_output",
+                "call_id": "call_x",
+                "output": "plain text output",
+            },
+        ]
+        normalized = _preflight_codex_input_items(raw)
+        out = [it for it in normalized if it.get("type") == "function_call_output"][0]
+        assert out["output"] == "plain text output"

tests/tools/test_vision_native_fast_path.py

@@ -0,0 +1,207 @@
+"""Tests for the native-vision fast path inside vision_analyze.
+
+When the active main model supports native vision AND the provider supports
+image content inside tool-result messages, ``_handle_vision_analyze`` skips
+the auxiliary LLM and returns a multimodal envelope so the main model sees
+the pixels directly on its next turn.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import base64
+import json
+from pathlib import Path
+from unittest.mock import patch
+
+import pytest
+
+from tools.vision_tools import (
+    _build_native_vision_tool_result,
+    _handle_vision_analyze,
+    _supports_media_in_tool_results,
+    _vision_analyze_native,
+)
+
+
+# Minimal valid 1x1 PNG bytes.
+_TINY_PNG = base64.b64decode(
+    b"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII="
+)
+
+
+# ─── _supports_media_in_tool_results ─────────────────────────────────────────
+
+
+class TestSupportsMediaInToolResults:
+    def test_anthropic_native_yes(self):
+        assert _supports_media_in_tool_results("anthropic", "claude-opus-4-6") is True
+
+    def test_openrouter_yes(self):
+        assert _supports_media_in_tool_results("openrouter", "anthropic/claude-opus-4.6") is True
+
+    def test_nous_yes(self):
+        assert _supports_media_in_tool_results("nous", "anthropic/claude-sonnet-4.6") is True
+
+    def test_openai_chat_yes(self):
+        assert _supports_media_in_tool_results("openai", "gpt-5.4") is True
+
+    def test_openai_codex_yes(self):
+        assert _supports_media_in_tool_results("openai-codex", "gpt-5-codex") is True
+
+    def test_gemini_3_yes(self):
+        assert _supports_media_in_tool_results("google", "gemini-3-flash-preview") is True
+
+    def test_gemini_2_no(self):
+        assert _supports_media_in_tool_results("google", "gemini-2.5-pro") is False
+
+    def test_unknown_provider_conservative_no(self):
+        assert _supports_media_in_tool_results("brand-new-provider", "any-model") is False
+
+    def test_empty_provider_no(self):
+        assert _supports_media_in_tool_results("", "anything") is False
+        assert _supports_media_in_tool_results(None, "anything") is False  # type: ignore[arg-type]
+
+
+# ─── _build_native_vision_tool_result ────────────────────────────────────────
+
+
+class TestBuildNativeVisionToolResult:
+    def test_envelope_shape(self):
+        env = _build_native_vision_tool_result(
+            image_url="/tmp/foo.png",
+            question="what does it say?",
+            image_data_url="data:image/png;base64,XYZ",
+            image_size_bytes=1024,
+        )
+        assert env["_multimodal"] is True
+        assert isinstance(env["content"], list)
+        assert len(env["content"]) == 2
+        assert env["content"][0]["type"] == "text"
+        assert env["content"][1]["type"] == "image_url"
+        assert env["content"][1]["image_url"]["url"] == "data:image/png;base64,XYZ"
+        assert "what does it say?" in env["content"][0]["text"]
+        assert "Image attached natively" in env["text_summary"]
+
+    def test_no_question_omits_question_section(self):
+        env = _build_native_vision_tool_result(
+            image_url="/tmp/foo.png",
+            question="",
+            image_data_url="data:image/png;base64,XYZ",
+            image_size_bytes=512,
+        )
+        text = env["content"][0]["text"]
+        assert "Question:" not in text
+        assert "Image loaded" in text
+
+
+# ─── _vision_analyze_native ──────────────────────────────────────────────────
+
+
+class TestVisionAnalyzeNative:
+    def test_local_file_returns_multimodal_envelope(self, tmp_path):
+        img = tmp_path / "test.png"
+        img.write_bytes(_TINY_PNG)
+        result = asyncio.get_event_loop().run_until_complete(
+            _vision_analyze_native(str(img), "what is this?")
+        )
+        assert isinstance(result, dict)
+        assert result.get("_multimodal") is True
+        parts = result["content"]
+        assert any(p.get("type") == "image_url" for p in parts)
+        assert any(p.get("type") == "text" for p in parts)
+        url = next(p["image_url"]["url"] for p in parts if p.get("type") == "image_url")
+        assert url.startswith("data:image/")
+
+    def test_missing_file_returns_error_string(self, tmp_path):
+        result = asyncio.get_event_loop().run_until_complete(
+            _vision_analyze_native(str(tmp_path / "nope.png"), "?")
+        )
+        # tool_error returns a JSON string, not the multimodal envelope
+        assert isinstance(result, str)
+        parsed = json.loads(result)
+        assert parsed.get("success") is False
+        assert "Invalid image source" in parsed.get("error", "")
+
+    def test_empty_image_url_returns_error(self):
+        result = asyncio.get_event_loop().run_until_complete(
+            _vision_analyze_native("", "?")
+        )
+        assert isinstance(result, str)
+        parsed = json.loads(result)
+        assert parsed.get("success") is False
+        assert "image_url is required" in parsed.get("error", "")
+
+    def test_file_url_scheme_resolves(self, tmp_path):
+        img = tmp_path / "t.png"
+        img.write_bytes(_TINY_PNG)
+        result = asyncio.get_event_loop().run_until_complete(
+            _vision_analyze_native(f"file://{img}", "?")
+        )
+        assert isinstance(result, dict)
+        assert result.get("_multimodal") is True
+
+
+# ─── _handle_vision_analyze fast-path gating ─────────────────────────────────
+
+
+class TestHandleVisionAnalyzeFastPath:
+    """Verify the dispatcher chooses fast-path vs aux-LLM correctly."""
+
+    def test_vision_capable_main_model_uses_fast_path(self, tmp_path, monkeypatch):
+        """Main model supports native vision → fast path returns multimodal."""
+        img = tmp_path / "x.png"
+        img.write_bytes(_TINY_PNG)
+
+        # Set runtime override so the handler thinks we're on opus@openrouter
+        from agent.auxiliary_client import set_runtime_main, clear_runtime_main
+        set_runtime_main("openrouter", "anthropic/claude-opus-4.6")
+        try:
+            coro = _handle_vision_analyze({"image_url": str(img), "question": "?"})
+            result = asyncio.get_event_loop().run_until_complete(coro)
+        finally:
+            clear_runtime_main()
+
+        assert isinstance(result, dict), \
+            f"Expected multimodal envelope, got {type(result).__name__}: {str(result)[:200]}"
+        assert result.get("_multimodal") is True
+
+    def test_non_vision_main_model_falls_through_to_aux(self, tmp_path, monkeypatch):
+        """Non-vision main model → fast path skipped, aux LLM path attempted."""
+        img = tmp_path / "x.png"
+        img.write_bytes(_TINY_PNG)
+
+        async def _aux_sentinel(*args, **kwargs):
+            return '{"sentinel": "aux-path"}'
+
+        from agent.auxiliary_client import set_runtime_main, clear_runtime_main
+        set_runtime_main("openrouter", "qwen/qwen3-coder")
+        try:
+            with patch("tools.vision_tools.vision_analyze_tool", side_effect=_aux_sentinel):
+                coro = _handle_vision_analyze({"image_url": str(img), "question": "?"})
+                result = asyncio.get_event_loop().run_until_complete(coro)
+        finally:
+            clear_runtime_main()
+
+        assert not (isinstance(result, dict) and result.get("_multimodal") is True), \
+            "Fast path fired for non-vision model; should have fallen through to aux LLM"
+
+    def test_fast_path_disabled_for_unsupported_provider(self, tmp_path, monkeypatch):
+        """Even with vision-capable model, unknown provider → fall through."""
+        img = tmp_path / "x.png"
+        img.write_bytes(_TINY_PNG)
+
+        async def _aux_sentinel(*args, **kwargs):
+            return '{"sentinel": "aux-path"}'
+
+        from agent.auxiliary_client import set_runtime_main, clear_runtime_main
+        set_runtime_main("brand-new-provider", "anthropic/claude-opus-4.6")
+        try:
+            with patch("tools.vision_tools.vision_analyze_tool", side_effect=_aux_sentinel):
+                coro = _handle_vision_analyze({"image_url": str(img), "question": "?"})
+                result = asyncio.get_event_loop().run_until_complete(coro)
+        finally:
+            clear_runtime_main()
+
+        assert not (isinstance(result, dict) and result.get("_multimodal") is True), \
+            "Fast path fired for unknown provider; should have fallen through"

tools/vision_tools.py

@@ -403,6 +403,232 @@ def _resize_image_for_vision(image_path: Path, mime_type: Optional[str] = None,
     return data_url or _image_to_base64_data_url(image_path, mime_type=mime_type)
 
 
+# ---------------------------------------------------------------------------
+# Native fast path: short-circuit the auxiliary LLM when the active main model
+# supports native vision. Instead of asking a separate LLM to describe the
+# image and returning text, we load the image, base64-encode it, and return a
+# multimodal tool-result envelope. The agent loop unwraps the envelope into an
+# OpenAI-style content list on the `tool` role; provider adapters (anthropic,
+# codex_responses, chat_completions) translate that into Anthropic
+# tool_result image blocks / Responses input_image / OpenAI image_url tool
+# content. The main model then "sees" the pixels directly on its next turn.
+# ---------------------------------------------------------------------------
+
+
+def _supports_media_in_tool_results(provider: str, model: str) -> bool:
+    """Whether the given provider+model combination accepts image content
+    inside a tool-result message.
+
+    Providers covered today (per spec docs verified Apr-2026):
+
+      * Anthropic Messages API (``anthropic`` provider, plus aggregators that
+        proxy Claude — ``openrouter``, ``nous``, ``vertex``, ``bedrock``):
+        ``tool_result`` blocks accept ``image`` content blocks.
+      * OpenAI Chat Completions: tool messages accept array content with
+        ``image_url`` parts.
+      * OpenAI Responses (``openai-codex``): ``function_call_output.output``
+        accepts an array of ``input_text``/``input_image`` items.
+      * Gemini 3 (and proxied via aggregators): supports multimodal tool
+        results. Older Gemini does NOT.
+
+    For unknown / legacy providers we conservatively return False — the
+    caller falls back to the legacy aux-LLM text path.
+    """
+    if not isinstance(provider, str):
+        return False
+    p = provider.strip().lower()
+    if not p:
+        return False
+
+    # Aggregators that route to multiple vendors — assume support since
+    # users on these aggregators are typically using vision-capable
+    # frontier models. Falling back to text would be a regression for
+    # them.
+    _AGGREGATORS = {
+        "openrouter", "nous", "vertex", "bedrock", "anthropic-vertex",
+        "google-vertex",
+    }
+    if p in _AGGREGATORS:
+        return True
+
+    # Native Anthropic
+    if p in {"anthropic", "claude", "anthropic-direct"}:
+        return True
+
+    # OpenAI Chat Completions and Responses
+    if p in {"openai", "openai-chat", "openai-codex", "azure-openai"}:
+        return True
+
+    # Gemini — gate on model name; older Gemini variants did not support
+    # multimodal functionResponse. Gemini 3.x does.
+    if p in {"google", "gemini", "google-gemini", "google-vertex-gemini"}:
+        if not isinstance(model, str):
+            return False
+        m = model.strip().lower()
+        if "gemini-3" in m or "gemini-pro-3" in m or "gemini-flash-3" in m:
+            return True
+        return False
+
+    # Other vision-capable provider stacks. Conservative default: False.
+    # Add explicit entries here as we verify each provider's tool-result
+    # multimodal support empirically.
+    return False
+
+
+def _build_native_vision_tool_result(
+    image_url: str,
+    question: str,
+    image_data_url: str,
+    image_size_bytes: int,
+) -> Dict[str, Any]:
+    """Build the multimodal tool-result envelope returned by the fast path.
+
+    Shape:
+      {
+        "_multimodal": True,
+        "content": [
+          {"type": "text", "text": "<short note + the user's question>"},
+          {"type": "image_url", "image_url": {"url": "data:image/png;base64,..."}}
+        ],
+        "text_summary": "<plain-text fallback>",
+        "meta": {"image_url": ..., "size_bytes": N},
+      }
+
+    The text part exists for two reasons: (1) it gives the model an
+    instruction to act on now that the pixels are in context, and
+    (2) providers that don't support multimodal tool results can fall back
+    to ``text_summary``.
+    """
+    # The tool-result text part is intentionally minimal. The model already
+    # has the user's original question in context; this just acknowledges
+    # the image is now visible and reminds it what it was asked.
+    text_part = (
+        "Image loaded into your context — you can see it natively now. "
+        "Use your built-in vision to answer the user."
+    )
+    if isinstance(question, str) and question.strip():
+        text_part += f"\n\nQuestion: {question.strip()}"
+
+    summary = (
+        f"Image attached natively for the main model "
+        f"({image_size_bytes / 1024:.1f} KB). "
+        "Answer using built-in vision."
+    )
+
+    return {
+        "_multimodal": True,
+        "content": [
+            {"type": "text", "text": text_part},
+            {"type": "image_url", "image_url": {"url": image_data_url}},
+        ],
+        "text_summary": summary,
+        "meta": {
+            "image_url": image_url[:200],
+            "size_bytes": image_size_bytes,
+            "native_vision": True,
+        },
+    }
+
+
+async def _vision_analyze_native(
+    image_url: str,
+    question: str,
+) -> Any:
+    """Fast path for vision-capable main models.
+
+    Loads the image (local file OR remote URL), base64-encodes it, and
+    returns a multimodal tool-result envelope. The agent loop unwraps it;
+    provider adapters serialize it into the right tool-result-with-image
+    shape for each backend.
+
+    Returns:
+        A ``_multimodal`` envelope dict on success.
+        A JSON error string on failure (matches the existing tool-result
+        contract so the agent loop displays errors normally).
+    """
+    if not isinstance(image_url, str) or not image_url.strip():
+        return tool_error("image_url is required", success=False)
+
+    temp_image_path: Optional[Path] = None
+    should_cleanup = False
+    try:
+        from tools.interrupt import is_interrupted
+        if is_interrupted():
+            return tool_error("Interrupted", success=False)
+
+        # Resolve the image source (mirrors vision_analyze_tool's logic
+        # exactly so behaviour is consistent).
+        resolved_url = image_url
+        if resolved_url.startswith("file://"):
+            resolved_url = resolved_url[len("file://"):]
+        local_path = Path(os.path.expanduser(resolved_url))
+
+        if local_path.is_file():
+            temp_image_path = local_path
+            should_cleanup = False
+        elif _validate_image_url(image_url):
+            blocked = check_website_access(image_url)
+            if blocked:
+                return tool_error(blocked["message"], success=False)
+            temp_dir = get_hermes_dir("cache/vision", "temp_vision_images")
+            temp_image_path = temp_dir / f"temp_image_{uuid.uuid4()}.jpg"
+            await _download_image(image_url, temp_image_path)
+            should_cleanup = True
+        else:
+            return tool_error(
+                "Invalid image source. Provide an HTTP/HTTPS URL or a "
+                "valid local file path.",
+                success=False,
+            )
+
+        image_size_bytes = temp_image_path.stat().st_size
+        detected_mime_type = _detect_image_mime_type(temp_image_path)
+        if not detected_mime_type:
+            return tool_error(
+                "Only real image files are supported for vision analysis.",
+                success=False,
+            )
+
+        image_data_url = _image_to_base64_data_url(
+            temp_image_path, mime_type=detected_mime_type,
+        )
+
+        # Honour the same hard cap as the legacy path. Resize if needed.
+        if len(image_data_url) > _MAX_BASE64_BYTES:
+            image_data_url = _resize_image_for_vision(
+                temp_image_path, mime_type=detected_mime_type,
+            )
+            if len(image_data_url) > _MAX_BASE64_BYTES:
+                return tool_error(
+                    f"Image too large for vision API: base64 payload is "
+                    f"{len(image_data_url) / (1024 * 1024):.1f} MB "
+                    f"(limit {_MAX_BASE64_BYTES / (1024 * 1024):.0f} MB) "
+                    f"even after resizing. Install Pillow "
+                    f"(`pip install Pillow`) for better auto-resize, "
+                    f"or compress the image manually.",
+                    success=False,
+                )
+
+        return _build_native_vision_tool_result(
+            image_url=image_url,
+            question=question,
+            image_data_url=image_data_url,
+            image_size_bytes=image_size_bytes,
+        )
+
+    except Exception as exc:
+        logger.warning("Native vision fast path failed: %s", exc)
+        return tool_error(f"Native vision failed: {exc}", success=False)
+    finally:
+        # Only delete temp files we created — never user-provided paths.
+        if should_cleanup and temp_image_path is not None:
+            try:
+                if temp_image_path.exists():
+                    temp_image_path.unlink()
+            except Exception:
+                pass
+
+
 async def vision_analyze_tool(
     image_url: str,
     user_prompt: str,
@@ -758,24 +984,25 @@ from tools.registry import registry, tool_error
 VISION_ANALYZE_SCHEMA = {
     "name": "vision_analyze",
     "description": (
-        "Inspect an image from a URL, file path, or tool output when you need "
+        "Load an image into the conversation so you can see it. Accepts a "
-        "closer detail than what's visible in the conversation. If the user's "
+        "URL, local file path, or data URL. When your active model has "
-        "image is already attached to the conversation and you can see it, "
+        "native vision, the image is attached to your context directly "
-        "just answer directly — only call this tool for images referenced by "
+        "and you read the pixels yourself on the next turn — call this "
-        "URL/path, images returned inside other tool results (browser "
+        "any time the user references an image (filepath in their message, "
-        "screenshots, search thumbnails), or when you need a deeper look at "
+        "URL in tool output, screenshot from the browser, etc.). For "
-        "a specific region the main model's vision may have missed."
+        "non-vision models, falls back to an auxiliary vision model that "
+        "returns a text description."
     ),
     "parameters": {
         "type": "object",
         "properties": {
             "image_url": {
                 "type": "string",
-                "description": "Image URL (http/https) or local file path to analyze."
+                "description": "Image URL (http/https), local file path, or data: URL to load."
             },
             "question": {
                 "type": "string",
-                "description": "Your specific question or request about the image to resolve. The AI will automatically provide a complete image description AND answer your specific question."
+                "description": "Your specific question or request about the image. Optional context the model uses on the next turn after seeing the image."
             }
         },
         "required": ["image_url", "question"]
@@ -786,6 +1013,31 @@ VISION_ANALYZE_SCHEMA = {
 def _handle_vision_analyze(args: Dict[str, Any], **kw: Any) -> Awaitable[str]:
     image_url = args.get("image_url", "")
     question = args.get("question", "")
+
+    # Fast path: when the active main model supports native vision AND the
+    # provider supports image content inside tool results, short-circuit
+    # the auxiliary LLM and return the image bytes as a multimodal
+    # tool-result envelope. The main model sees the pixels directly on its
+    # next turn — no aux call, no information loss, no extra latency.
+    try:
+        from agent.auxiliary_client import _read_main_provider, _read_main_model
+        from agent.image_routing import decide_image_input_mode
+        from hermes_cli.config import load_config
+
+        _provider = _read_main_provider()
+        _model = _read_main_model()
+        _cfg = load_config()
+        _mode = decide_image_input_mode(_provider, _model, _cfg)
+        if _mode == "native" and _supports_media_in_tool_results(_provider, _model):
+            logger.info(
+                "vision_analyze: native fast path (provider=%s, model=%s)",
+                _provider, _model,
+            )
+            return _vision_analyze_native(image_url, question)
+    except Exception as exc:
+        logger.debug("Native vision fast-path check failed; using aux LLM: %s", exc)
+
+    # Legacy path: aux LLM describes the image and we return its text.
     full_prompt = (
         "Fully describe and explain everything about this image, then answer the "
         f"following question:\n\n{question}"