fix: preserve exploration tool history and cap context budget

tests/unit/agents/test_operator_transcript.py

@@ -30,7 +30,7 @@ from types import SimpleNamespace
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
-from langchain_core.messages import HumanMessage, SystemMessage
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 
 from artemis.agents.operator.operator import OperatorNode
 from artemis.agents.operator.prompts import load_operator_prompts
@@ -141,6 +141,67 @@ def _transcript_state(**overrides):
     return state
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "exploration,plugins", [(False, False), (True, False), (False, True), (True, True)]
+)
+async def test_injected_prefix_and_compression_preserve_tool_pairs(
+    monkeypatch, exploration, plugins
+):
+    from langchain_openai import ChatOpenAI
+
+    ctx = _transcript_ctx()
+    ctx.exploration_id = "test-exploration" if exploration else None
+    ctx.plugins = SimpleNamespace(prompt=lambda: "Plugin instructions") if plugins else None
+    monkeypatch.setattr(
+        "artemis.exploration.runtime.exploration_messages",
+        lambda _: [
+            SystemMessage(content="Exploration instructions"),
+            HumanMessage(content="User guidance"),
+        ],
+    )
+    monkeypatch.setattr("artemis.agents.operator.operator.get_llm", lambda **_: MagicMock())
+    node = OperatorNode(ctx, transcript_config=MemoryTranscriptConfig(enabled=True))
+    requests = []
+    wire_model = ChatOpenAI(model="test", api_key="test", use_responses_api=True, store=False)
+
+    async def tool_loop(current_messages, **_kwargs):
+        messages = current_messages
+        payload = wire_model._get_request_payload(messages)
+        requests.append(payload["input"])
+        call_id = f"call_{len(requests)}"
+        messages.extend(
+            [
+                AIMessage(
+                    content="", tool_calls=[{"name": "click", "args": {"target": 1}, "id": call_id}]
+                ),
+                ToolMessage(content="Action Recorded", tool_call_id=call_id),
+            ]
+        )
+        return [{"action": "tap"}], None, None, False
+
+    monkeypatch.setattr(node, "_invoke_llm_loop", tool_loop)
+    for turn in range(8):
+        await node(_transcript_state(current_step_id=f"step-{turn}"))
+        if turn == 4:
+            ctx.transcript_ledger.freeze_turns(2, [HumanMessage(content="Summarized first turns")])
+
+    for items in requests:
+        pending, seen = set(), set()
+        for item in items:
+            if item.get("type") == "function_call":
+                assert item["call_id"] not in seen
+                pending.add(item["call_id"])
+                seen.add(item["call_id"])
+            elif item.get("type") == "function_call_output":
+                assert item["call_id"] in pending
+                pending.remove(item["call_id"])
+        assert not pending
+    assert {item["call_id"] for item in requests[-1] if item.get("type") == "function_call"} == {
+        f"call_{n}" for n in range(3, 8)
+    }
+
+
 def _action_llm(captured: list):
     mock_llm = MagicMock()
     mock_response = MagicMock()