diff --git a/artemis/services/llm.py b/artemis/services/llm.py
index ec91e6fa0..79fc2d167 100644
--- a/artemis/services/llm.py
+++ b/artemis/services/llm.py
@@ -624,6 +624,12 @@ def _endpoint_key(self) -> str:
         return type(self.base_model).__name__
 
     def _traced_call(self, args: tuple, kwargs: dict) -> tuple[tuple, dict, Any]:
+        # Every request leaves through here: keep parallel tool results
+        # contiguous so OpenAI-compatible providers accept image carriers.
+        from artemis.tools.tool_wrapper import order_tool_batch_messages
+
+        if args:
+            args = (order_tool_batch_messages(args[0]), *args[1:])
         trace_id = None
         if self.ctx and self.ctx.data_engine:
             trace_id = CURRENT_TRACE_ID.get()
diff --git a/artemis/tools/tool_wrapper.py b/artemis/tools/tool_wrapper.py
index 89f0908a6..c2e3932cb 100644
--- a/artemis/tools/tool_wrapper.py
+++ b/artemis/tools/tool_wrapper.py
@@ -20,7 +20,7 @@
 from typing import Annotated, Any, Literal, get_args, get_origin
 import uuid
 
-from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, ToolMessage
 from langchain_core.tools import BaseTool
 from langchain_core.tools.base import InjectedToolCallId
 from langgraph.prebuilt import InjectedState
@@ -268,3 +268,33 @@ def tool_result_messages(
         ToolMessage(tool_call_id=tool_call_id, content=text, status=status, **extra),
         HumanMessage(content=[{"type": "text", "text": caption}, *images]),
     ]
+
+
+def order_tool_batch_messages(messages: list) -> list:
+    """Move "human" image carriers after every tool result of their turn.
+
+    Agent loops extend history with ``tool_result_messages`` once per tool
+    call, so a turn with parallel screenshot calls becomes Tool, Human, Tool,
+    Human. OpenAI-compatible APIs require all tool results to directly follow
+    the assistant turn and reject that with a 400, so each run of tool results
+    and carriers after a tool-calling AIMessage is reordered to all
+    ToolMessages first, then the HumanMessages in their original order.
+    """
+    if not isinstance(messages, list):
+        return messages
+    ordered: list = []
+    i = 0
+    while i < len(messages):
+        msg = messages[i]
+        ordered.append(msg)
+        i += 1
+        if not (isinstance(msg, AIMessage) and msg.tool_calls):
+            continue
+        tools: list = []
+        humans: list = []
+        while i < len(messages) and isinstance(messages[i], (ToolMessage, HumanMessage)):
+            (tools if isinstance(messages[i], ToolMessage) else humans).append(messages[i])
+            i += 1
+        ordered.extend(tools)
+        ordered.extend(humans)
+    return ordered
diff --git a/tests/unit/tools/history/test_result_messages.py b/tests/unit/tools/history/test_result_messages.py
index 59829339d..6424afa7e 100644
--- a/tests/unit/tools/history/test_result_messages.py
+++ b/tests/unit/tools/history/test_result_messages.py
@@ -118,3 +118,80 @@ def test_explicit_carrier_wins_over_the_llm():
     assert len(msgs) == 2
     msgs = tool_result_messages("tc", SHOT, image_carrier="tool", llm=_llm("openai"))
     assert len(msgs) == 1
+
+
+# --- Parallel tool calls: image carriers must follow the whole tool batch -------
+
+
+def _parallel_turn(n):
+    from langchain_core.messages import AIMessage
+
+    calls = [{"name": "get_step_screenshot", "args": {}, "id": f"call_{i}"} for i in range(n)]
+    return AIMessage(content="", tool_calls=calls)
+
+
+def test_order_tool_batch_moves_image_carriers_after_all_tool_results():
+    """OpenAI-compatible APIs reject a user turn between the tool results of
+    one assistant turn (400: tool_call_ids did not have response messages)."""
+    from artemis.tools.tool_wrapper import order_tool_batch_messages
+
+    ai = _parallel_turn(2)
+    per_call = [
+        tool_result_messages(f"call_{i}", SHOT, name="get_step_screenshot", llm=_llm("openai"))
+        for i in range(2)
+    ]
+    interleaved = [ai, *per_call[0], *per_call[1]]
+
+    ordered = order_tool_batch_messages(interleaved)
+
+    assert [type(m) for m in ordered] == [
+        type(ai),
+        ToolMessage,
+        ToolMessage,
+        HumanMessage,
+        HumanMessage,
+    ]
+    assert [m.tool_call_id for m in ordered[1:3]] == ["call_0", "call_1"]
+
+
+def test_order_tool_batch_keeps_valid_history_unchanged():
+    from artemis.tools.tool_wrapper import order_tool_batch_messages
+
+    ai = _parallel_turn(2)
+    history = [
+        HumanMessage(content="check"),
+        ai,
+        ToolMessage(tool_call_id="call_0", content="a"),
+        ToolMessage(tool_call_id="call_1", content="b"),
+        HumanMessage(content="This is your final iteration."),
+    ]
+
+    assert order_tool_batch_messages(history) == history
+
+
+@pytest.mark.asyncio
+async def test_wrapper_sends_tool_batch_before_image_carriers():
+    from artemis.services.llm import RobustChatModelWrapper
+
+    seen = {}
+
+    class _Model:
+        async def ainvoke(self, messages, **kwargs):
+            seen["messages"] = messages
+            return "ok"
+
+    ai = _parallel_turn(2)
+    interleaved = [ai]
+    for i in range(2):
+        interleaved += tool_result_messages(
+            f"call_{i}", SHOT, name="get_step_screenshot", llm=_llm("openai")
+        )
+
+    await RobustChatModelWrapper(_Model()).ainvoke(interleaved)
+
+    assert [type(m).__name__ for m in seen["messages"][1:]] == [
+        "ToolMessage",
+        "ToolMessage",
+        "HumanMessage",
+        "HumanMessage",
+    ]