Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license

tests/agent/test_moa_aggregator_cache_control.py

@@ -0,0 +1,153 @@
+"""Regression test: the MoA aggregator's one-shot synthesis call
+(``aggregate_moa_context``, used by the ``/moa <prompt>`` command) must get
+the same Anthropic-style prompt-caching decoration as the acting-aggregator
+turn (``MoAChatCompletions.create``) and the advisor fan-out
+(``_run_reference``).
+
+22c5048d9 ("fix(moa): restore prompt caching for the aggregator and
+advisors") fixed the other two MoA call paths but never touched
+``aggregate_moa_context`` — a third, independent call path with its own
+``call_llm(task="moa_aggregator", ...)`` invocation. Without this fix, every
+``/moa <prompt>`` one-shot call re-bills its full input (system-less prompt
+containing all joined reference outputs) with zero cache_control breakpoints,
+even when the resolved aggregator slot is a cache-honoring route.
+"""
+
+from __future__ import annotations
+
+import copy
+from types import SimpleNamespace
+
+import pytest
+
+
+def _response(content="synthesized guidance"):
+    message = SimpleNamespace(content=content, tool_calls=[])
+    choice = SimpleNamespace(message=message, finish_reason="stop")
+    return SimpleNamespace(choices=[choice], usage=None, model="fake")
+
+
+@pytest.fixture
+def captured_calls(monkeypatch):
+    calls = []
+
+    def fake_call_llm(**kwargs):
+        calls.append(kwargs)
+        return _response()
+
+    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)
+    monkeypatch.setattr(
+        "agent.moa_loop._run_references_parallel",
+        lambda *a, **k: [("advisor-a", "advice from a", None)],
+    )
+    return calls
+
+
+def _aggregator_kwargs(calls):
+    return next(c for c in calls if c.get("task") == "moa_aggregator")
+
+
+def test_aggregator_synthesis_gets_cache_control_on_native_anthropic_route(
+    captured_calls, monkeypatch
+):
+    """A cache-honoring aggregator slot (native Anthropic) must get
+    cache_control breakpoints on its synthesis call."""
+    from agent import moa_loop
+
+    monkeypatch.setattr(
+        moa_loop,
+        "_slot_runtime",
+        lambda slot: {
+            "provider": "anthropic",
+            "model": "claude-opus-4.8",
+            "base_url": "",
+            "api_mode": "anthropic_messages",
+        },
+    )
+
+    moa_loop.aggregate_moa_context(
+        user_prompt="what should I do next?",
+        api_messages=[{"role": "user", "content": "help me plan"}],
+        reference_models=[{"provider": "openrouter", "model": "openai/gpt-5.5"}],
+        aggregator={"provider": "anthropic", "model": "claude-opus-4.8"},
+    )
+
+    agg_kwargs = _aggregator_kwargs(captured_calls)
+    synth_message = agg_kwargs["messages"][0]
+    assert synth_message["role"] == "user"
+    content = synth_message["content"]
+    # Native Anthropic layout places cache_control on inner content blocks,
+    # so a cached message's content is a list of blocks rather than a bare
+    # string once decorated.
+    assert isinstance(content, list), "expected native cache_control block layout"
+    assert any(
+        isinstance(block, dict) and "cache_control" in block for block in content
+    ), "aggregator synthesis message must carry a cache_control breakpoint"
+
+
+def test_aggregator_synthesis_untouched_on_non_caching_route(
+    captured_calls, monkeypatch
+):
+    """A non-cache-honoring aggregator slot (plain OpenAI) must not be
+    decorated — proves the guard doesn't over-fire."""
+    from agent import moa_loop
+
+    monkeypatch.setattr(
+        moa_loop,
+        "_slot_runtime",
+        lambda slot: {
+            "provider": "openai",
+            "model": "gpt-5.5",
+            "base_url": "",
+            "api_mode": "chat_completions",
+        },
+    )
+
+    moa_loop.aggregate_moa_context(
+        user_prompt="what should I do next?",
+        api_messages=[{"role": "user", "content": "help me plan"}],
+        reference_models=[{"provider": "openrouter", "model": "openai/gpt-5.5"}],
+        aggregator={"provider": "openai", "model": "gpt-5.5"},
+    )
+
+    agg_kwargs = _aggregator_kwargs(captured_calls)
+    synth_message = agg_kwargs["messages"][0]
+    assert isinstance(synth_message["content"], str), "must stay undecorated (plain string content)"
+
+
+def test_prepared_aggregator_plans_tools_without_decorating_prepared_state(monkeypatch):
+    from agent import moa_loop
+
+    calls = []
+    monkeypatch.setattr(moa_loop, "call_llm", lambda **kwargs: calls.append(kwargs) or _response())
+    monkeypatch.setattr(
+        moa_loop,
+        "_slot_runtime",
+        lambda slot: {
+            "provider": "anthropic",
+            "model": "claude-sonnet-4-6",
+            "base_url": "https://api.anthropic.com",
+            "api_mode": "anthropic_messages",
+        },
+    )
+    completions = moa_loop.MoAChatCompletions.__new__(moa_loop.MoAChatCompletions)
+    completions._pending_trace = None
+    prepared = {
+        "messages": [
+            {"role": "system", "content": "system"},
+            {"role": "user", "content": "lookup"},
+            {"role": "assistant", "content": "", "tool_calls": [{"id": "lookup", "function": {"name": "lookup", "arguments": "{}"}}]},
+            {"role": "tool", "tool_call_id": "lookup", "content": "result"},
+        ],
+        "guidance": None,
+        "aggregator": {"provider": "anthropic", "model": "claude-sonnet-4-6"},
+        "aggregator_temperature": None,
+    }
+    canonical_prepared = copy.deepcopy(prepared)
+    tools = [{"type": "function", "function": {"name": "lookup", "parameters": {"type": "object", "properties": {}}}}]
+
+    completions._call_prepared_aggregator(prepared, {"tools": tools})
+
+    assert "cache_control" in calls[0]["tools"][-1]
+    assert "cache_control" not in tools[-1]
+    assert prepared == canonical_prepared