hermes-agent/tests/run_agent/test_moa_loop_mode.py

from types import SimpleNamespace
from unittest.mock import MagicMock

import pytest

from run_agent import AIAgent


def _response(content="done", *, tool_calls=None):
    message = SimpleNamespace(content=content, tool_calls=tool_calls or [])
    choice = SimpleNamespace(message=message, finish_reason="stop")
    return SimpleNamespace(choices=[choice], usage=None, model="fake-model")


def test_moa_virtual_provider_aggregator_is_actor(monkeypatch, tmp_path):
    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      reference_models:
        - provider: openai-codex
          model: gpt-5.5
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))
    calls = []

    def fake_call_llm(**kwargs):
        calls.append(kwargs)
        if kwargs["task"] == "moa_reference":
            return _response("reference advice")
        return _response("aggregator acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    agent = AIAgent(
        api_key="moa-virtual-provider",
        base_url="http://127.0.0.1/v1",
        model="review",
        provider="moa",
        quiet_mode=True,
        skip_context_files=True,
        skip_memory=True,
        enabled_toolsets=["file"],
        max_iterations=1,
    )
    monkeypatch.setattr(
        agent,
        "_create_request_openai_client",
        lambda *_args, **_kwargs: (_ for _ in ()).throw(
            AssertionError("MoA calls must use MoAClient, not a request OpenAI client")
        ),
    )

    result = agent.run_conversation("solve this")

    assert result["final_response"] == "aggregator acted"
    assert agent.base_url == "moa://local"
    assert [(c["task"], c["provider"], c["model"]) for c in calls] == [
        ("moa_reference", "openai-codex", "gpt-5.5"),
        ("moa_aggregator", "openrouter", "anthropic/claude-opus-4.8"),
    ]
    assert calls[1]["tools"] is not None


def test_moa_runtime_provider_uses_virtual_endpoint():
    from hermes_cli.runtime_provider import resolve_runtime_provider

    runtime = resolve_runtime_provider(requested="moa", target_model="review")

    assert runtime["provider"] == "moa"
    assert runtime["base_url"] == "moa://local"
    assert runtime["api_key"] == "moa-virtual-provider"


def test_moa_does_not_cap_output_tokens(monkeypatch, tmp_path):
    """MoA must not inject an output cap on reference or aggregator calls.

    The preset's old hardcoded max_tokens=4096 truncated long aggregator
    syntheses. MoA now passes max_tokens=None (no caller cap), so call_llm
    omits the parameter and each model uses its real maximum. Regression for
    the "no limit on MoA models" fix.
    """
    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      max_tokens: 4096
      reference_models:
        - provider: openai-codex
          model: gpt-5.5
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))
    calls = []

    def fake_call_llm(**kwargs):
        calls.append(kwargs)
        if kwargs["task"] == "moa_reference":
            return _response("reference advice")
        return _response("aggregator acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    agent = AIAgent(
        api_key="moa-virtual-provider",
        base_url="moa://local",
        model="review",
        provider="moa",
        quiet_mode=True,
        skip_context_files=True,
        skip_memory=True,
        enabled_toolsets=["file"],
        max_iterations=1,
    )
    agent.run_conversation("solve this")

    # Even with a preset max_tokens: 4096 present in config, neither the
    # reference nor the aggregator call carries a cap — MoA passes None and
    # call_llm omits the parameter so the model uses its full output budget.
    ref_call = next(c for c in calls if c["task"] == "moa_reference")
    agg_call = next(c for c in calls if c["task"] == "moa_aggregator")
    assert ref_call.get("max_tokens") is None
    assert agg_call.get("max_tokens") is None


def test_moa_slots_routed_through_resolve_runtime_provider(monkeypatch):
    """Reference + aggregator slots must be called via their provider's real
    runtime (resolve_runtime_provider), not a bare provider/model call.

    This is the "call any model the way it's called elsewhere" contract: each
    slot's resolved base_url/api_key is passed through to call_llm so the
    provider's actual API surface (anthropic_messages, max_completion_tokens,
    custom endpoints) applies — same as if the model were the acting model.
    """
    from agent import moa_loop

    resolved = []

    def fake_resolve(*, requested, target_model=None):
        resolved.append((requested, target_model))
        return {
            "provider": requested,
            "api_mode": "chat_completions",
            "base_url": f"https://{requested}.example/v1",
            "api_key": f"key-for-{requested}",
        }

    monkeypatch.setattr(
        "hermes_cli.runtime_provider.resolve_runtime_provider", fake_resolve
    )

    rt = moa_loop._slot_runtime({"provider": "minimax", "model": "MiniMax-M2"})
    assert ("minimax", "MiniMax-M2") in resolved
    assert rt["provider"] == "minimax"
    assert rt["model"] == "MiniMax-M2"
    assert rt["base_url"] == "https://minimax.example/v1"
    assert rt["api_key"] == "key-for-minimax"


def test_moa_codex_slot_preserves_provider_identity(monkeypatch):
    """Codex slots must not become custom chat-completions endpoints.

    _slot_runtime forwards the resolved base_url/api_key/api_mode; the single
    chokepoint that must NOT collapse openai-codex to provider=custom is
    _resolve_task_provider_model (via _preserve_provider_with_base_url). If it
    collapsed, the Codex auxiliary branch — Cloudflare headers + Responses
    adapter for chatgpt.com/backend-api/codex — would be bypassed.
    """
    from agent import moa_loop
    from agent.auxiliary_client import _resolve_task_provider_model

    def fake_resolve(*, requested, target_model=None):
        return {
            "provider": requested,
            "api_mode": "codex_responses",
            "base_url": "https://chatgpt.com/backend-api/codex",
            "api_key": "codex-oauth-token",
        }

    monkeypatch.setattr(
        "hermes_cli.runtime_provider.resolve_runtime_provider", fake_resolve
    )

    rt = moa_loop._slot_runtime({"provider": "openai-codex", "model": "gpt-5.5"})
    # _slot_runtime forwards the resolved endpoint unconditionally now.
    assert rt["provider"] == "openai-codex"
    assert rt["model"] == "gpt-5.5"
    assert rt["base_url"] == "https://chatgpt.com/backend-api/codex"

    # The chokepoint preserves openai-codex identity despite the explicit
    # base_url (api_mode is forwarded to call_llm directly, not the resolver).
    resolver_kwargs = {k: v for k, v in rt.items() if k != "api_mode"}
    resolved_provider, _model, base_url, _api_key, _mode = _resolve_task_provider_model(
        task="moa_reference",
        **resolver_kwargs,
    )
    assert resolved_provider == "openai-codex"
    assert base_url == "https://chatgpt.com/backend-api/codex"


@pytest.mark.parametrize("provider", ["minimax-oauth", "qwen-oauth"])
def test_moa_provider_backed_slot_survives_aux_resolution(monkeypatch, provider):
    """MoA can pass resolved endpoints for provider-backed slots without
    call_llm flattening them to generic custom endpoints.

    ``_slot_runtime`` resolves a provider-backed slot to ``provider`` plus a
    concrete ``base_url``/``api_key``/``api_mode``; ``_run_reference`` then
    forwards that dict to ``call_llm``. ``call_llm`` resolves the routing tuple
    via ``_resolve_task_provider_model`` (which takes everything except
    ``api_mode``, handled separately). The provider identity must survive that
    resolution rather than being flattened to ``custom``.

    NOTE: providers in the ``_slot_runtime`` name-preservation set (anthropic,
    bedrock, nous, openai-codex, xai-oauth) are intentionally NOT forwarded —
    they're covered by their own dedicated tests. This case covers the
    forward-the-resolved-endpoint path for providers that are NOT in the set.
    """
    from agent import moa_loop
    from agent.auxiliary_client import _resolve_task_provider_model

    def fake_resolve(*, requested, target_model=None):
        return {
            "provider": requested,
            "api_mode": "anthropic_messages",
            "base_url": f"https://{requested}.example/v1",
            "api_key": f"token-for-{requested}",
        }

    monkeypatch.setattr(
        "hermes_cli.runtime_provider.resolve_runtime_provider", fake_resolve
    )

    rt = moa_loop._slot_runtime({"provider": provider, "model": "test-model"})
    # api_mode is forwarded to call_llm directly, not to _resolve_task_provider_model.
    resolver_kwargs = {k: v for k, v in rt.items() if k != "api_mode"}
    resolved_provider, model, base_url, api_key, _mode = _resolve_task_provider_model(
        task="moa_reference",
        **resolver_kwargs,
    )

    assert resolved_provider == provider
    assert model == "test-model"
    assert base_url == f"https://{provider}.example/v1"
    assert api_key == f"token-for-{provider}"


def test_moa_slot_runtime_falls_back_on_resolution_error(monkeypatch):
    """A slot whose provider can't be resolved still attempts the call with the
    bare provider/model rather than aborting the whole MoA turn."""
    from agent import moa_loop

    def boom(*, requested, target_model=None):
        raise RuntimeError("unknown provider")

    monkeypatch.setattr(
        "hermes_cli.runtime_provider.resolve_runtime_provider", boom
    )

    rt = moa_loop._slot_runtime({"provider": "mystery", "model": "x"})
    assert rt == {"provider": "mystery", "model": "x"}
    assert "base_url" not in rt
    assert "api_key" not in rt


def test_reference_messages_drops_system_but_renders_tools_as_text():
    """System prompt is dropped, but tool calls + results are RENDERED as text.

    A reference must see what the agent did (tool calls) and what came back
    (tool results) to give an informed judgement — so neither is stripped. They
    are flattened to text so the view carries zero tool-role messages / no
    tool_calls arrays (strict providers reject those), while the reference
    still has the full picture. The view ends on a user turn.
    """
    from agent.moa_loop import _reference_messages

    messages = [
        {"role": "system", "content": "huge hermes system prompt"},
        {"role": "user", "content": "do the thing"},
        {
            "role": "assistant",
            "content": "",
            "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": "{}"}}],
        },
        {"role": "tool", "tool_call_id": "c1", "content": "tool result"},
        {"role": "assistant", "content": "here is my answer"},
    ]

    view = _reference_messages(messages)

    # Wire-format safety: only user/assistant text, no tool roles / tool_calls.
    assert all(m["role"] in ("user", "assistant") for m in view)
    assert all("tool_calls" not in m for m in view)
    # System prompt is gone.
    assert all("huge hermes system prompt" not in m["content"] for m in view)
    # The agent's action and the tool result are PRESERVED as text.
    joined = "\n".join(m["content"] for m in view)
    assert "[called tool: f(" in joined
    assert "[tool result: tool result]" in joined
    assert "here is my answer" in joined
    # Ends on a user turn (advisory request appended after the final assistant).
    assert view[-1]["role"] == "user"


def test_reference_messages_ends_with_user_not_assistant_prefill():
    """Advisory reference views must never end on an assistant turn.

    Mid-tool-loop the conversation ends on an assistant/tool exchange. Anthropic
    (and OpenRouter→Anthropic) treat a trailing assistant turn as an assistant
    prefill to continue, and no-prefill models (e.g. Claude Opus 4.8) reject it
    with ``400 ... must end with a user message``. We append a synthetic user
    turn asking for judgement rather than DELETING the agent's latest context —
    the reference must still see the current state to advise on it.
    """
    from agent.moa_loop import _reference_messages

    messages = [
        {"role": "user", "content": "q1"},
        {"role": "assistant", "content": "a1"},
        {"role": "user", "content": "q2 current"},
        {
            "role": "assistant",
            "content": "let me reason then call a tool",
            "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": "{}"}}],
        },
        {"role": "tool", "tool_call_id": "c1", "content": "the tool output"},
    ]

    view = _reference_messages(messages)

    assert view, "advisory view should not be empty"
    assert view[-1]["role"] == "user"
    joined = "\n".join(m["content"] for m in view)
    # The agent's latest action and its result are preserved, not dropped.
    assert "let me reason then call a tool" in joined
    assert "[called tool: f(" in joined
    assert "[tool result: the tool output]" in joined
    # Earlier context preserved too.
    assert "q1" in joined and "a1" in joined and "q2 current" in joined


def test_reference_messages_truncates_large_tool_results():
    """Large tool results are previewed head+tail, not replayed verbatim."""
    from agent.moa_loop import _REFERENCE_TOOL_RESULT_BUDGET, _reference_messages

    huge = "A" * (_REFERENCE_TOOL_RESULT_BUDGET * 3)
    messages = [
        {"role": "user", "content": "q"},
        {
            "role": "assistant",
            "content": "",
            "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": "{}"}}],
        },
        {"role": "tool", "tool_call_id": "c1", "content": huge},
    ]

    view = _reference_messages(messages)
    joined = "\n".join(m["content"] for m in view)
    assert "chars omitted" in joined
    # The folded result is far smaller than the raw payload.
    assert len(joined) < len(huge)


def test_reference_messages_fresh_user_turn_ends_on_that_user():
    """A fresh user prompt with no agent action yet ends on that user turn."""
    from agent.moa_loop import _reference_messages

    messages = [
        {"role": "system", "content": "sys"},
        {"role": "user", "content": "q1"},
        {"role": "assistant", "content": "a1"},
        {"role": "user", "content": "q2 current"},
    ]

    view = _reference_messages(messages)
    assert view[-1] == {"role": "user", "content": "q2 current"}


def test_run_reference_prepends_advisory_system_prompt(monkeypatch):
    """Each reference call gets the advisory-role system prompt first.

    Without it the reference assumes it is the acting agent and refuses ("I
    can't access repositories/URLs from here") or tries to call tools it
    doesn't have. The system prompt reframes it as an analyst advising the
    aggregator, and the advisory transcript still ends on a user turn.
    """
    from agent.moa_loop import _REFERENCE_SYSTEM_PROMPT, _run_reference

    captured = {}

    def fake_call_llm(**kwargs):
        captured.update(kwargs)
        return _response("advice")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    label, text, _acct = _run_reference(
        {"provider": "openai-codex", "model": "gpt-5.5"},
        [{"role": "user", "content": "review this PR"}],
    )

    assert text == "advice"
    msgs = captured["messages"]
    assert msgs[0] == {"role": "system", "content": _REFERENCE_SYSTEM_PROMPT}
    assert msgs[-1]["role"] == "user"


def test_moa_facade_references_get_trimmed_messages(monkeypatch, tmp_path):
    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      reference_models:
        - provider: openai-codex
          model: gpt-5.5
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))
    calls = []

    def fake_call_llm(**kwargs):
        calls.append(kwargs)
        return _response("ok")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    from agent.moa_loop import MoAChatCompletions

    facade = MoAChatCompletions("review")
    facade.create(
        messages=[
            {"role": "system", "content": "system prompt"},
            {"role": "user", "content": "question"},
            {
                "role": "assistant",
                "content": "checking",
                "tool_calls": [{"id": "x", "function": {"name": "lookup", "arguments": "{}"}}],
            },
            {"role": "tool", "tool_call_id": "x", "content": "tool output"},
        ],
        tools=[{"type": "function"}],
    )

    ref_call = next(c for c in calls if c["task"] == "moa_reference")
    ref_msgs = ref_call["messages"]
    # Advisory-role system prompt first; the agent's own system prompt is gone.
    assert ref_msgs[0]["role"] == "system"
    assert "reference advisor" in ref_msgs[0]["content"].lower()
    assert "system prompt" not in ref_msgs[0]["content"]
    # No tool-role messages and no tool_calls arrays leak to the reference.
    assert all(m["role"] in ("system", "user", "assistant") for m in ref_msgs)
    assert all("tool_calls" not in m for m in ref_msgs)
    # The agent's action + tool result ARE preserved, rendered as text.
    joined = "\n".join(m["content"] for m in ref_msgs[1:])
    assert "[called tool: lookup(" in joined
    assert "[tool result: tool output]" in joined
    # Ends on a user turn (advisory request after the final assistant block).
    assert ref_msgs[-1]["role"] == "user"
    assert ref_call.get("tools") in (None, [])
    # Aggregator still receives the original messages + tool schema.
    agg_call = next(c for c in calls if c["task"] == "moa_aggregator")
    assert agg_call["tools"] is not None


def test_moa_disabled_preset_skips_references(monkeypatch, tmp_path):
    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      enabled: false
      reference_models:
        - provider: openai-codex
          model: gpt-5.5
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))
    calls = []

    def fake_call_llm(**kwargs):
        calls.append(kwargs)
        return _response("aggregator only")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    from agent.moa_loop import MoAChatCompletions

    facade = MoAChatCompletions("review")
    facade.create(messages=[{"role": "user", "content": "question"}], tools=[{"type": "function"}])

    tasks = [c["task"] for c in calls]
    # No reference fan-out — only the aggregator runs.
    assert tasks == ["moa_aggregator"]
    # Aggregator gets the unmodified user message (no MoA guidance appended).
    agg_call = calls[0]
    assert agg_call["messages"][-1]["content"] == "question"


def test_references_run_in_parallel(monkeypatch):
    """References fan out concurrently (delegate-batch semantics), not serially.

    Each reference sleeps; wall-time must approximate the slowest single call,
    not the sum. Order is preserved and a failing reference is isolated.
    """
    import time

    from agent import moa_loop

    # Force _extract_text down its fallback path (no transport normalize).
    monkeypatch.setattr(moa_loop, "get_transport", lambda *_a, **_k: None)

    barrier_hits = []

    def slow_call_llm(**kwargs):
        barrier_hits.append(time.monotonic())
        model = kwargs["model"]
        if model == "boom":
            raise RuntimeError("kaboom")
        time.sleep(0.5)
        return _response(f"resp-{kwargs['provider']}")

    monkeypatch.setattr(moa_loop, "call_llm", slow_call_llm)

    refs = [
        {"provider": "p1", "model": "ok"},
        {"provider": "moa", "model": "preset"},  # recursion guard, not dispatched
        {"provider": "p2", "model": "boom"},  # failure isolated
        {"provider": "p3", "model": "ok"},
    ]

    start = time.monotonic()
    out = moa_loop._run_references_parallel(
        refs, [{"role": "user", "content": "hi"}], temperature=0.6, max_tokens=64
    )
    elapsed = time.monotonic() - start

    # Two 0.5s sleeps run concurrently → well under the 1.0s serial floor.
    assert elapsed < 0.9, f"references did not run in parallel (took {elapsed:.2f}s)"
    # Output order matches input order (stable Reference N labelling).
    assert [label for label, _, _ in out] == ["p1:ok", "moa:preset", "p2:boom", "p3:ok"]
    assert "recursively reference MoA" in out[1][1]
    assert out[2][1].startswith("[failed:")
    assert out[0][1] == "resp-p1"


def _ref_config(home):
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      reference_models:
        - provider: openai-codex
          model: gpt-5.5
        - provider: openrouter
          model: anthropic/claude-opus-4.8
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )


def test_moa_facade_emits_reference_then_aggregating(monkeypatch, tmp_path):
    """The facade reports each reference's output, then an aggregating signal,
    so frontends can render reference blocks before the aggregator acts."""
    home = tmp_path / ".hermes"
    _ref_config(home)
    monkeypatch.setenv("HERMES_HOME", str(home))

    def fake_call_llm(**kwargs):
        if kwargs["task"] == "moa_reference":
            return _response(f"advice from {kwargs['model']}")
        return _response("aggregator acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    from agent.moa_loop import MoAChatCompletions

    events = []
    facade = MoAChatCompletions("review", reference_callback=lambda ev, **kw: events.append((ev, kw)))
    facade.create(messages=[{"role": "user", "content": "q"}], tools=[{"type": "function"}])

    ref_events = [e for e in events if e[0] == "moa.reference"]
    agg_events = [e for e in events if e[0] == "moa.aggregating"]
    # One block per reference model, labelled by source, with index/count.
    assert len(ref_events) == 2
    assert ref_events[0][1]["label"] == "openai-codex:gpt-5.5"
    assert ref_events[0][1]["index"] == 1 and ref_events[0][1]["count"] == 2
    assert "advice from" in ref_events[0][1]["text"]
    # Exactly one aggregating signal, after the references, naming the aggregator.
    assert len(agg_events) == 1
    assert agg_events[0][1]["aggregator"] == "openrouter:anthropic/claude-opus-4.8"
    assert agg_events[0][1]["ref_count"] == 2


def test_moa_facade_reruns_references_on_new_tool_result(monkeypatch, tmp_path):
    """References re-run when a new tool result advances the task state.

    The agent loop calls create() once per tool-loop iteration. References must
    judge the LATEST state, so a new tool result is a cache MISS and re-runs the
    references — but a redundant create() call with the SAME state is a cache
    HIT (no re-run, no re-emit), so we don't fire on a pure no-op re-call.
    """
    home = tmp_path / ".hermes"
    _ref_config(home)
    monkeypatch.setenv("HERMES_HOME", str(home))

    ref_runs = []

    def fake_call_llm(**kwargs):
        if kwargs["task"] == "moa_reference":
            ref_runs.append(kwargs["model"])
            return _response("advice")
        return _response("acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    from agent.moa_loop import MoAChatCompletions

    events = []
    facade = MoAChatCompletions("review", reference_callback=lambda ev, **kw: events.append(ev))

    base_msgs = [{"role": "user", "content": "do the thing"}]
    # Iteration 1: fresh user turn — references run (2 models).
    facade.create(messages=base_msgs, tools=[{"type": "function"}])
    after_tool = base_msgs + [
        {"role": "assistant", "content": "", "tool_calls": [{"id": "c1", "function": {"name": "f", "arguments": "{}"}}]},
        {"role": "tool", "tool_call_id": "c1", "content": "result"},
    ]
    # Iteration 2: a NEW tool result advanced the state → references re-run.
    facade.create(messages=after_tool, tools=[{"type": "function"}])
    # Iteration 3: identical state (no new tool/user input) → cache hit, no re-run.
    facade.create(messages=after_tool, tools=[{"type": "function"}])

    # 2 models × 2 distinct states (fresh turn + new tool result) = 4 runs.
    # The redundant 3rd call adds none.
    assert len(ref_runs) == 4
    assert events.count("moa.reference") == 4
    assert events.count("moa.aggregating") == 2


def test_moa_facade_reruns_references_on_new_turn(monkeypatch, tmp_path):
    """A genuinely new user message invalidates the cache and re-runs refs."""
    home = tmp_path / ".hermes"
    _ref_config(home)
    monkeypatch.setenv("HERMES_HOME", str(home))

    ref_runs = []

    def fake_call_llm(**kwargs):
        if kwargs["task"] == "moa_reference":
            ref_runs.append(kwargs["model"])
            return _response("advice")
        return _response("acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)

    from agent.moa_loop import MoAChatCompletions

    facade = MoAChatCompletions("review")
    facade.create(messages=[{"role": "user", "content": "turn one"}], tools=[])
    facade.create(messages=[{"role": "user", "content": "turn two"}], tools=[])

    # 2 references × 2 distinct turns = 4 reference runs.
    assert len(ref_runs) == 4


def test_slot_runtime_anthropic_oauth_routes_through_provider_branch(monkeypatch):
    """Native anthropic slots must keep their provider identity, not collapse to custom.

    anthropic OAuth setup-tokens (sk-ant-oat*) require Bearer auth + the
    ``anthropic-beta: oauth-*`` header, which only the anthropic provider branch
    of call_llm adds. _slot_runtime forwards the resolved base_url/api_key for
    every provider now; the single chokepoint that must NOT collapse anthropic
    to provider=custom (which would send the token as x-api-key → bare 429) is
    _resolve_task_provider_model via _preserve_provider_with_base_url.
    """
    from agent import moa_loop
    from agent.auxiliary_client import _resolve_task_provider_model

    def fake_resolve(*, requested, target_model=None):
        return {
            "provider": requested,
            "base_url": "https://resolved.example/v1",
            "api_key": "resolved-key",
        }

    monkeypatch.setattr(
        "hermes_cli.runtime_provider.resolve_runtime_provider", fake_resolve
    )

    # _slot_runtime forwards the resolved endpoint for anthropic like any slot.
    anthropic_rt = moa_loop._slot_runtime(
        {"provider": "anthropic", "model": "claude-opus-4-8"}
    )
    assert anthropic_rt["provider"] == "anthropic"
    assert anthropic_rt["base_url"] == "https://resolved.example/v1"

    # The chokepoint preserves anthropic identity despite the explicit base_url,
    # so call_llm routes through the anthropic provider branch (not custom).
    resolved_provider, _model, base_url, _api_key, _mode = _resolve_task_provider_model(
        task="moa_reference",
        provider="anthropic",
        model="claude-opus-4-8",
        base_url="https://resolved.example/v1",
        api_key="resolved-key",
    )
    assert resolved_provider == "anthropic"

    # A generic provider (openrouter) is likewise forwarded and preserved.
    other_rt = moa_loop._slot_runtime(
        {"provider": "openrouter", "model": "some-model"}
    )
    assert other_rt["provider"] == "openrouter"
    assert other_rt["model"] == "some-model"
    assert other_rt["base_url"] == "https://resolved.example/v1"
    assert other_rt["api_key"] == "resolved-key"


def _response_with_usage(content="advice", *, prompt=100, completion=50, cached=0):
    """A fake response carrying OpenAI-style usage so normalize_usage works."""
    details = SimpleNamespace(cached_tokens=cached, cache_write_tokens=0)
    usage = SimpleNamespace(
        prompt_tokens=prompt,
        completion_tokens=completion,
        prompt_tokens_details=details,
        output_tokens_details=None,
    )
    message = SimpleNamespace(content=content, tool_calls=[])
    choice = SimpleNamespace(message=message, finish_reason="stop")
    return SimpleNamespace(choices=[choice], usage=usage, model="fake-model")


def test_run_reference_captures_usage_and_cost(monkeypatch):
    """A reference call returns per-advisor CanonicalUsage + priced cost.

    Before this, _run_reference discarded response.usage entirely, so the
    advisor fan-out was invisible to cost tracking.
    """
    from agent.moa_loop import _RefAccounting, _run_reference
    from agent.usage_pricing import CanonicalUsage

    monkeypatch.setattr(
        "agent.moa_loop.call_llm",
        lambda **kw: _response_with_usage(prompt=1000, completion=200, cached=400),
    )
    # Keep runtime resolution + pricing deterministic.
    monkeypatch.setattr(
        "agent.moa_loop._slot_runtime",
        lambda slot: {"provider": "openrouter", "model": slot.get("model")},
    )
    monkeypatch.setattr(
        "agent.usage_pricing.estimate_usage_cost",
        lambda *a, **k: SimpleNamespace(amount_usd=0.0123, status="estimated", source="table"),
    )

    label, text, acct = _run_reference(
        {"provider": "openrouter", "model": "vendor/adv-model"},
        [{"role": "user", "content": "state?"}],
    )

    assert text == "advice"
    assert isinstance(acct, _RefAccounting)
    assert isinstance(acct.usage, CanonicalUsage)
    # prompt_tokens=1000 with 400 cached → 600 fresh input + 400 cache_read.
    assert acct.usage.input_tokens == 600
    assert acct.usage.cache_read_tokens == 400
    assert acct.usage.output_tokens == 200
    assert acct.cost_usd == 0.0123


def test_references_parallel_sum_and_consume(monkeypatch, tmp_path):
    """create() sums advisor usage + cost once per turn; consume clears it.

    Repeat tool-iterations within a turn reuse the cache and contribute ZERO
    additional advisor spend (otherwise advisor cost multiplies by iteration
    count).
    """
    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      reference_models:
        - provider: openrouter
          model: adv-a
        - provider: openrouter
          model: adv-b
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))

    def fake_call_llm(**kwargs):
        if kwargs["task"] == "moa_reference":
            return _response_with_usage(prompt=1000, completion=100, cached=0)
        return _response("aggregator acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)
    monkeypatch.setattr(
        "agent.moa_loop._slot_runtime",
        lambda slot: {"provider": "openrouter", "model": slot.get("model")},
    )
    monkeypatch.setattr(
        "agent.usage_pricing.estimate_usage_cost",
        lambda *a, **k: SimpleNamespace(amount_usd=0.01, status="estimated", source="table"),
    )

    from agent.moa_loop import MoAChatCompletions

    facade = MoAChatCompletions("review")
    facade.create(messages=[{"role": "user", "content": "turn one"}], tools=[])

    usage, cost = facade.consume_reference_usage()
    # Two advisors × (1000 input, 100 output) = 2000 input, 200 output.
    assert usage.input_tokens == 2000
    assert usage.output_tokens == 200
    # Two advisors × $0.01 each = $0.02.
    assert cost == pytest.approx(0.02)

    # consume clears — a second consume with no new create() is zeroed.
    usage2, cost2 = facade.consume_reference_usage()
    assert usage2.input_tokens == 0
    assert cost2 is None

    # A repeat create() with the SAME advisory view is a cache HIT: advisors
    # do not re-run, so pending advisor spend is zero (no double-charge).
    facade.create(messages=[{"role": "user", "content": "turn one"}], tools=[])
    usage3, cost3 = facade.consume_reference_usage()
    assert usage3.input_tokens == 0
    assert cost3 is None


def test_canonical_usage_add():
    """CanonicalUsage sums per bucket (used to fold advisor tokens in)."""
    from agent.usage_pricing import CanonicalUsage

    a = CanonicalUsage(input_tokens=100, output_tokens=20, cache_read_tokens=5)
    b = CanonicalUsage(input_tokens=50, output_tokens=10, cache_write_tokens=3)
    total = a + b
    assert total.input_tokens == 150
    assert total.output_tokens == 30
    assert total.cache_read_tokens == 5
    assert total.cache_write_tokens == 3
    assert total.request_count == 2


def test_moa_full_trace_written_when_enabled(monkeypatch, tmp_path):
    """With moa.save_traces on, a full MoA turn is written to JSONL.

    Asserts the record captures each reference's FULL input messages + output
    and the aggregator's FULL input (incl. injected reference guidance) +
    output — the true full turn, auditable offline.
    """
    import json

    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  save_traces: true
  default_preset: review
  presets:
    review:
      reference_models:
        - provider: openrouter
          model: adv-a
        - provider: openrouter
          model: adv-b
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))

    def fake_call_llm(**kwargs):
        if kwargs["task"] == "moa_reference":
            # Echo the model so we can prove per-reference output is captured.
            model = kwargs.get("model", "?")
            return _response_with_usage(content=f"advice from {model}", prompt=500, completion=80)
        return _response("AGGREGATOR FINAL ANSWER")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)
    monkeypatch.setattr(
        "agent.moa_loop._slot_runtime",
        lambda slot: {"provider": "openrouter", "model": slot.get("model")},
    )
    monkeypatch.setattr(
        "agent.usage_pricing.estimate_usage_cost",
        lambda *a, **k: SimpleNamespace(amount_usd=0.001, status="estimated", source="table"),
    )

    from agent.moa_loop import MoAChatCompletions

    facade = MoAChatCompletions("review")
    # Non-streaming create() → aggregator output captured inline.
    facade.create(messages=[{"role": "user", "content": "please review the plan"}], tools=[])
    facade.consume_and_save_trace(session_id="sess-xyz")

    trace_file = home / "moa-traces" / "sess-xyz.jsonl"
    assert trace_file.exists(), "trace file not written"
    lines = trace_file.read_text(encoding="utf-8").strip().splitlines()
    assert len(lines) == 1
    rec = json.loads(lines[0])

    # Turn framing.
    assert rec["session_id"] == "sess-xyz"
    assert rec["preset"] == "review"

    # Both references captured, each with FULL input messages + output.
    assert len(rec["references"]) == 2
    for ref in rec["references"]:
        assert ref["model"] in ("adv-a", "adv-b")
        assert ref["provider"] == "openrouter"
        # Full input messages present (system advisory prompt + advisory view).
        assert isinstance(ref["input_messages"], list) and len(ref["input_messages"]) >= 2
        assert ref["input_messages"][0]["role"] == "system"
        # Full output present and model-specific.
        assert ref["output"] == f"advice from {ref['model']}"
        assert ref["usage"]["input_tokens"] == 500
        assert ref["cost_usd"] == 0.001

    # Aggregator: full input (with injected reference guidance) + inline output.
    agg = rec["aggregator"]
    assert agg["model"] == "anthropic/claude-opus-4.8"
    assert agg["streamed"] is False
    assert agg["output"] == "AGGREGATOR FINAL ANSWER"
    agg_text = json.dumps(agg["input_messages"])
    assert "Mixture of Agents reference context" in agg_text
    assert "advice from adv-a" in agg_text and "advice from adv-b" in agg_text


def test_moa_trace_not_written_when_disabled(monkeypatch, tmp_path):
    """Default (save_traces off) writes nothing."""
    home = tmp_path / ".hermes"
    home.mkdir()
    (home / "config.yaml").write_text(
        """
moa:
  default_preset: review
  presets:
    review:
      reference_models:
        - provider: openrouter
          model: adv-a
      aggregator:
        provider: openrouter
        model: anthropic/claude-opus-4.8
""".strip(),
        encoding="utf-8",
    )
    monkeypatch.setenv("HERMES_HOME", str(home))

    def fake_call_llm(**kwargs):
        if kwargs["task"] == "moa_reference":
            return _response_with_usage(content="advice")
        return _response("acted")

    monkeypatch.setattr("agent.moa_loop.call_llm", fake_call_llm)
    monkeypatch.setattr(
        "agent.moa_loop._slot_runtime",
        lambda slot: {"provider": "openrouter", "model": slot.get("model")},
    )

    from agent.moa_loop import MoAChatCompletions

    facade = MoAChatCompletions("review")
    facade.create(messages=[{"role": "user", "content": "hi"}], tools=[])
    facade.consume_and_save_trace(session_id="sess-off")

    assert not (home / "moa-traces").exists()


def test_reference_guidance_appended_at_end_in_tool_loop():
    """In an agentic loop the reference block must land at the END of the prompt.

    The most recent user turn is the original task near the top of the context;
    merging the per-turn (volatile) reference block into it would diverge the
    prompt prefix early and defeat the server's KV-cache reuse, forcing a full
    re-prefill of the whole conversation on every tool-loop step.
    """
    from agent.moa_loop import _attach_reference_guidance

    messages = [
        {"role": "system", "content": "system prompt"},
        {"role": "user", "content": "ORIGINAL TASK"},
        {"role": "assistant", "content": "", "tool_calls": [{"id": "1"}]},
        {"role": "tool", "content": "tool result", "tool_call_id": "1"},
    ]
    _attach_reference_guidance(messages, "REFERENCE BLOCK")

    # The original (top-of-context) user turn is untouched, so the prefix stays
    # cache-reusable across steps.
    assert messages[1]["content"] == "ORIGINAL TASK"
    # The reference block is appended as a new trailing turn, not merged upstream.
    assert messages[-1]["role"] == "user"
    assert messages[-1]["content"] == "REFERENCE BLOCK"
    assert len(messages) == 5


def test_reference_guidance_merges_into_trailing_user_in_plain_chat():
    """Plain chat ends on the user turn, so the block merges there (still at end)."""
    from agent.moa_loop import _attach_reference_guidance

    messages = [
        {"role": "system", "content": "system prompt"},
        {"role": "user", "content": "hello"},
    ]
    _attach_reference_guidance(messages, "REFERENCE BLOCK")

    # No extra message; the block joins the trailing user turn (which is the end).
    assert len(messages) == 2
    assert messages[-1]["role"] == "user"
    assert messages[-1]["content"] == "hello\n\nREFERENCE BLOCK"