chore: import upstream snapshot with attribution

tests/llm/test_fallback.py

@@ -0,0 +1,813 @@
+"""Integration tests for LLM fallback behaviour.
+
+Verifies that when a primary model fails (immediately or mid-stream), the
+per-agent backup model is used before the global FALLBACK_* settings.
+"""
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from application.llm.base import BaseLLM
+
+
+# Concrete LLM stubs
+
+
+class FakeLLM(BaseLLM):
+    """Minimal concrete BaseLLM for testing."""
+
+    def __init__(self, responses=None, stream_chunks=None, fail_at=None, **kwargs):
+        # Accept and discard api_key / user_api_key so LLMCreator.create_llm
+        # signatures work without errors.
+        kwargs.pop("api_key", None)
+        kwargs.pop("user_api_key", None)
+        super().__init__(**kwargs)
+        self.responses = responses or ["fake response"]
+        self.stream_chunks = stream_chunks or ["chunk1", "chunk2"]
+        self.fail_at = fail_at  # None = no failure, 0 = immediate, N = after N chunks
+        self.user_api_key = None
+        self.gen_called = False
+        self.gen_stream_called = False
+        self.last_model_received = None  # tracks the model kwarg passed to gen/gen_stream
+
+    # Track at the raw-method level. _execute_with_fallback applies
+    # decorators to the fallback's raw method directly and
+    # never calls .gen() / .gen_stream() on it, so a public-method
+    # override would not register fallback hops.
+    def _raw_gen(self, baseself, model, messages, stream, tools=None, **kwargs):
+        self.gen_called = True
+        self.last_model_received = model
+        if self.fail_at is not None:
+            raise RuntimeError("primary model unavailable")
+        return self.responses[0]
+
+    def _raw_gen_stream(self, baseself, model, messages, stream, tools=None, **kwargs):
+        self.gen_stream_called = True
+        self.last_model_received = model
+        yielded = 0
+        for chunk in self.stream_chunks:
+            if self.fail_at is not None and yielded >= self.fail_at:
+                raise RuntimeError("mid-stream failure")
+            yield chunk
+            yielded += 1
+
+
+# Helpers
+
+
+def _noop_decorator(func):
+    """Pass-through decorator that replaces cache / token-usage wrappers."""
+
+    def wrapper(self_llm, model, messages, stream, tools=None, **kwargs):
+        return func(self_llm, model, messages, stream, tools, **kwargs)
+
+    return wrapper
+
+
+def _noop_stream_decorator(func):
+    """Pass-through generator decorator for streaming wrappers."""
+
+    def wrapper(self_llm, model, messages, stream, tools=None, **kwargs):
+        yield from func(self_llm, model, messages, stream, tools, **kwargs)
+
+    return wrapper
+
+
+@pytest.fixture(autouse=True)
+def _patch_decorators(monkeypatch):
+    """Replace cache & token-usage decorators with no-ops so tests focus on
+    fallback logic without needing Redis or token-counting infra."""
+    monkeypatch.setattr("application.llm.base.gen_cache", _noop_decorator)
+    monkeypatch.setattr("application.llm.base.gen_token_usage", _noop_decorator)
+    monkeypatch.setattr("application.llm.base.stream_cache", _noop_stream_decorator)
+    monkeypatch.setattr(
+        "application.llm.base.stream_token_usage", _noop_stream_decorator
+    )
+
+
+@pytest.fixture
+def patch_model_utils(monkeypatch):
+    """Patch model_utils functions used by fallback_llm property."""
+
+    def _apply(get_provider=None, get_api_key=None, create_llm=None):
+        if get_provider:
+            monkeypatch.setattr(
+                "application.core.model_utils.get_provider_from_model_id",
+                get_provider,
+            )
+        if get_api_key:
+            monkeypatch.setattr(
+                "application.core.model_utils.get_api_key_for_provider",
+                get_api_key,
+            )
+        if create_llm:
+            monkeypatch.setattr(
+                "application.llm.llm_creator.LLMCreator.create_llm",
+                create_llm,
+            )
+
+    return _apply
+
+
+CALL_ARGS = dict(model="test-model", messages=[{"role": "user", "content": "hi"}])
+
+
+# Tests — fallback_llm property resolution
+
+
+@pytest.mark.integration
+class TestFallbackLLMResolution:
+
+    def test_backup_model_preferred_over_global_fallback(self, patch_model_utils):
+        """When agent has backup models configured, the first valid one is used
+        as fallback — not the global FALLBACK_* settings."""
+        backup_llm = FakeLLM(responses=["backup response"])
+
+        patch_model_utils(
+            get_provider=lambda mid, **_kwargs: "openai",
+            get_api_key=lambda prov: "fake-key",
+            create_llm=lambda type, **kw: backup_llm,
+        )
+
+        primary = FakeLLM(backup_models=["backup-model-id"])
+        fallback = primary.fallback_llm
+
+        assert fallback is backup_llm
+
+    def test_global_fallback_used_when_no_backup_models(
+        self, monkeypatch, patch_model_utils
+    ):
+        """When no per-agent backup models exist, global FALLBACK_* is used."""
+        global_fallback = FakeLLM(responses=["global fallback"])
+
+        patch_model_utils(
+            create_llm=lambda type, **kw: global_fallback,
+        )
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(
+                FALLBACK_LLM_PROVIDER="openai",
+                FALLBACK_LLM_NAME="gpt-4o",
+                FALLBACK_LLM_API_KEY="key",
+                API_KEY="key",
+            ),
+        )
+
+        primary = FakeLLM(backup_models=[])
+        fallback = primary.fallback_llm
+
+        assert fallback is global_fallback
+
+    def test_skips_unresolvable_backup_model_tries_next(self, patch_model_utils):
+        """If the first backup model can't be resolved, skip it and try the next."""
+        good_backup = FakeLLM(responses=["good backup"])
+        call_count = {"n": 0}
+
+        def fake_get_provider(model_id, **_kwargs):
+            call_count["n"] += 1
+            if model_id == "bad-model":
+                return None  # unresolvable
+            return "openai"
+
+        patch_model_utils(
+            get_provider=fake_get_provider,
+            get_api_key=lambda prov: "key",
+            create_llm=lambda type, **kw: good_backup,
+        )
+
+        primary = FakeLLM(backup_models=["bad-model", "good-model"])
+        fallback = primary.fallback_llm
+
+        assert fallback is good_backup
+        assert call_count["n"] == 2  # tried both
+
+    def test_no_fallback_when_nothing_configured(self, monkeypatch):
+        """No backup models + no global FALLBACK_* → fallback_llm is None."""
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(FALLBACK_LLM_PROVIDER=None),
+        )
+        primary = FakeLLM(backup_models=[])
+        assert primary.fallback_llm is None
+
+
+# Tests — non-streaming fallback (gen)
+
+
+@pytest.mark.integration
+class TestNonStreamingFallback:
+
+    def test_primary_success_no_fallback(self):
+        """When primary succeeds, fallback is never touched."""
+        primary = FakeLLM(responses=["primary ok"])
+        result = primary.gen(**CALL_ARGS)
+        assert result == "primary ok"
+
+    def test_primary_fails_uses_backup_model(self, patch_model_utils):
+        """Primary fails immediately → backup model from agent config is used."""
+        backup = FakeLLM(responses=["backup ok"])
+
+        patch_model_utils(
+            get_provider=lambda mid, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["backup-model"])
+        result = primary.gen(**CALL_ARGS)
+        assert result == "backup ok"
+        assert backup.gen_called
+
+    def test_no_fallback_raises(self, monkeypatch):
+        """Primary fails and no fallback configured → exception propagates."""
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(FALLBACK_LLM_PROVIDER=None),
+        )
+        primary = FakeLLM(fail_at=0, backup_models=[])
+        with pytest.raises(RuntimeError, match="primary model unavailable"):
+            primary.gen(**CALL_ARGS)
+
+
+# Tests — streaming fallback (gen_stream)
+
+
+@pytest.mark.integration
+class TestStreamingFallback:
+
+    def test_stream_primary_success(self):
+        """Full stream completes without triggering fallback."""
+        primary = FakeLLM(stream_chunks=["a", "b", "c"])
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+        assert chunks == ["a", "b", "c"]
+
+    def test_stream_immediate_failure_uses_backup(self, patch_model_utils):
+        """Primary fails before yielding anything → entire backup stream returned."""
+        backup = FakeLLM(stream_chunks=["fallback1", "fallback2"])
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["x", "y"],
+            fail_at=0,  # fail before first chunk
+            backup_models=["backup-model"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+        assert chunks == ["fallback1", "fallback2"]
+        assert backup.gen_stream_called
+
+    def test_stream_mid_stream_failure_uses_backup(self, patch_model_utils):
+        """Primary yields some chunks then fails → backup stream follows partial output."""
+        backup = FakeLLM(stream_chunks=["recovery1", "recovery2"])
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["ok1", "ok2", "ok3"],
+            fail_at=2,  # yields ok1, ok2, then fails before ok3
+            backup_models=["backup-model"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+        # First two from primary, then full backup stream
+        assert chunks == ["ok1", "ok2", "recovery1", "recovery2"]
+
+    def test_stream_no_fallback_raises(self, monkeypatch):
+        """Primary stream fails and no fallback → exception propagates."""
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(FALLBACK_LLM_PROVIDER=None),
+        )
+        primary = FakeLLM(stream_chunks=["x"], fail_at=0, backup_models=[])
+        with pytest.raises(RuntimeError, match="mid-stream failure"):
+            list(primary.gen_stream(**CALL_ARGS))
+
+    def test_fallback_emits_stream_start_with_fallback_provider(
+        self, patch_model_utils, caplog
+    ):
+        # The fallback raw-stream path bypasses ``gen_stream``, so it must
+        # emit its own ``llm_stream_start`` event tagged with the fallback
+        # vendor — otherwise dashboards record only the failed primary
+        # even when the response came from the backup.
+        import logging as _logging
+
+        class FallbackProvider(FakeLLM):
+            provider_name = "fallback-vendor"
+
+        backup = FallbackProvider(
+            stream_chunks=["b1"], model_id="backup-model-id"
+        )
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        class PrimaryProvider(FakeLLM):
+            provider_name = "primary-vendor"
+
+        primary = PrimaryProvider(
+            stream_chunks=["x"],
+            fail_at=0,
+            backup_models=["backup-model-id"],
+        )
+
+        with caplog.at_level(_logging.INFO, logger="root"):
+            list(
+                primary.gen_stream(
+                    model="primary-model",
+                    messages=[{"role": "user", "content": "hi"}],
+                )
+            )
+
+        starts = [r for r in caplog.records if r.message == "llm_stream_start"]
+        assert len(starts) == 2
+        assert starts[0].provider == "primary-vendor"
+        assert starts[0].model == "primary-model"
+        assert starts[1].provider == "fallback-vendor"
+        assert starts[1].model == "backup-model-id"
+
+
+# Tests — fallback never re-enters the orchestrator (Option B regression)
+
+
+@pytest.mark.integration
+class TestFallbackNoRecursion:
+    """When the primary fails, _execute_with_fallback applies decorators to
+    the fallback's raw method directly. The fallback's own ``fallback_llm``
+    property must never be accessed — otherwise a fallback failure would
+    re-enter the orchestrator and walk the global FALLBACK_LLM_* chain
+    unboundedly."""
+
+    def test_backup_fallback_llm_property_never_accessed_on_gen_failure(
+        self, monkeypatch, patch_model_utils
+    ):
+        backup = FakeLLM(fail_at=0)  # backup also fails
+
+        accessed_on = []
+        original_property = BaseLLM.fallback_llm
+
+        def tracked_fallback_llm(self_llm):
+            accessed_on.append(self_llm)
+            return original_property.fget(self_llm)
+
+        monkeypatch.setattr(
+            BaseLLM, "fallback_llm", property(tracked_fallback_llm)
+        )
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["backup-model"])
+        with pytest.raises(RuntimeError, match="primary model unavailable"):
+            primary.gen(**CALL_ARGS)
+
+        assert primary in accessed_on  # primary lazy-loaded its fallback
+        assert backup not in accessed_on  # backup's chain was never walked
+
+    def test_backup_fallback_llm_property_never_accessed_on_stream_failure(
+        self, monkeypatch, patch_model_utils
+    ):
+        backup = FakeLLM(stream_chunks=["x"], fail_at=0)
+
+        accessed_on = []
+        original_property = BaseLLM.fallback_llm
+
+        def tracked_fallback_llm(self_llm):
+            accessed_on.append(self_llm)
+            return original_property.fget(self_llm)
+
+        monkeypatch.setattr(
+            BaseLLM, "fallback_llm", property(tracked_fallback_llm)
+        )
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["y"], fail_at=0, backup_models=["backup-model"]
+        )
+        with pytest.raises(RuntimeError, match="mid-stream failure"):
+            list(primary.gen_stream(**CALL_ARGS))
+
+        assert primary in accessed_on
+        assert backup not in accessed_on
+
+    def test_fallback_failure_propagates_without_chain(self, patch_model_utils):
+        """When both primary and fallback fail, the fallback's exception
+        propagates cleanly — no third hop, no extra retries."""
+        backup = FakeLLM(fail_at=0)
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["backup-model"])
+        with pytest.raises(RuntimeError, match="primary model unavailable"):
+            primary.gen(**CALL_ARGS)
+
+        assert backup.gen_called  # confirms fallback raw method WAS invoked
+
+
+# Tests — backup model priority over global fallback
+
+
+@pytest.mark.integration
+class TestBackupModelPriority:
+
+    def test_agent_backup_tried_before_global_on_gen_failure(self, patch_model_utils):
+        """On gen() failure, agent's backup model is used — not the global fallback."""
+        backup = FakeLLM(responses=["agent backup"])
+        created_models = []
+
+        def fake_create_llm(type, **kw):
+            created_models.append(kw.get("model_id"))
+            return backup
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["agent-backup-model"])
+        result = primary.gen(**CALL_ARGS)
+
+        assert result == "agent backup"
+        assert "agent-backup-model" in created_models
+
+    def test_agent_backup_tried_before_global_on_stream_failure(
+        self, patch_model_utils
+    ):
+        """On gen_stream() failure, agent's backup model is used — not the global."""
+        backup = FakeLLM(stream_chunks=["agent-stream"])
+        created_models = []
+
+        def fake_create_llm(type, **kw):
+            created_models.append(kw.get("model_id"))
+            return backup
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["x"], fail_at=0, backup_models=["agent-backup-model"]
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+
+        assert chunks == ["agent-stream"]
+        assert "agent-backup-model" in created_models
+
+    def test_global_fallback_used_when_all_backup_models_fail(
+        self, monkeypatch, patch_model_utils
+    ):
+        """If every agent backup model fails to initialize, fall through to global."""
+        global_fallback = FakeLLM(responses=["global ok"])
+        call_order = []
+
+        def fake_get_provider(mid, **_kwargs):
+            if mid == "broken-backup":
+                return "nonexistent_provider"
+            return "openai"
+
+        def fake_create_llm(type, **kw):
+            model_id = kw.get("model_id")
+            call_order.append(model_id)
+            if model_id == "broken-backup":
+                raise ValueError("provider init failed")
+            return global_fallback
+
+        patch_model_utils(
+            get_provider=fake_get_provider,
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(
+                FALLBACK_LLM_PROVIDER="openai",
+                FALLBACK_LLM_NAME="global-model",
+                FALLBACK_LLM_API_KEY="gk",
+                API_KEY="gk",
+            ),
+        )
+
+        primary = FakeLLM(fail_at=0, backup_models=["broken-backup"])
+        result = primary.gen(**CALL_ARGS)
+
+        assert result == "global ok"
+        # Tried broken-backup first, then fell through to global-model
+        assert call_order == ["broken-backup", "global-model"]
+
+
+# Tests — fallback uses its own model_id, not the primary's
+
+
+@pytest.mark.integration
+class TestFallbackModelIdOverride:
+    """The fallback LLM must be called with its own model_id — not the
+    primary's.  Otherwise providers like Groq receive an unknown model name
+    (e.g. a Qwen model_id) and return 404."""
+
+    def test_gen_fallback_receives_own_model_id(self, patch_model_utils):
+        """Non-streaming: fallback.gen() is called with fallback.model_id."""
+        backup = FakeLLM(
+            responses=["backup ok"], model_id="groq-gpt-oss-120b"
+        )
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "groq",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            fail_at=0,
+            model_id="qwen/qwen3-4b-2507",
+            backup_models=["groq-gpt-oss-120b"],
+        )
+        result = primary.gen(**CALL_ARGS)
+
+        assert result == "backup ok"
+        assert backup.last_model_received == "groq-gpt-oss-120b"
+
+    def test_gen_stream_fallback_receives_own_model_id(self, patch_model_utils):
+        """Streaming: fallback.gen_stream() is called with fallback.model_id."""
+        backup = FakeLLM(
+            stream_chunks=["ok"], model_id="groq-gpt-oss-120b"
+        )
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "groq",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["x"],
+            fail_at=0,
+            model_id="qwen/qwen3-4b-2507",
+            backup_models=["groq-gpt-oss-120b"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+
+        assert chunks == ["ok"]
+        assert backup.last_model_received == "groq-gpt-oss-120b"
+
+    def test_mid_stream_fallback_receives_own_model_id(self, patch_model_utils):
+        """Mid-stream failure: fallback still gets its own model_id, not the
+        primary's that was already partially streaming."""
+        backup = FakeLLM(
+            stream_chunks=["recovered"], model_id="groq-gpt-oss-120b"
+        )
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "groq",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            stream_chunks=["partial1", "partial2", "boom"],
+            fail_at=2,
+            model_id="qwen/qwen3-4b-2507",
+            backup_models=["groq-gpt-oss-120b"],
+        )
+        chunks = list(primary.gen_stream(**CALL_ARGS))
+
+        assert chunks == ["partial1", "partial2", "recovered"]
+        assert backup.last_model_received == "groq-gpt-oss-120b"
+
+
+# Tests — model_user_id (BYOM owner scope) propagates into fallback resolution
+
+
+@pytest.mark.integration
+class TestFallbackModelUserIdScope:
+    """A shared agent dispatched by user B but owned by user A stores
+    A's BYOM UUIDs as backup_models. Without the P2 fix the fallback
+    property looks up those UUIDs against ``decoded_token['sub']`` (B,
+    the caller), which can't see A's per-user layer — backups are
+    silently skipped and the global FALLBACK_* settings are used
+    instead. These tests pin down that ``model_user_id`` (the owner)
+    is used both for the registry lookup and for the recursive
+    ``LLMCreator.create_llm`` call."""
+
+    def test_backup_lookup_uses_model_user_id_not_caller(
+        self, patch_model_utils
+    ):
+        captured = {"user_id": None}
+
+        def fake_get_provider(model_id, **kwargs):
+            captured["user_id"] = kwargs.get("user_id")
+            return "openai"
+
+        backup = FakeLLM(responses=["ok"])
+        patch_model_utils(
+            get_provider=fake_get_provider,
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+
+        primary = FakeLLM(
+            decoded_token={"sub": "caller-bob"},
+            model_user_id="owner-alice",
+            backup_models=["alice-byom-uuid"],
+        )
+        _ = primary.fallback_llm
+        assert captured["user_id"] == "owner-alice"
+
+    def test_backup_create_llm_receives_model_user_id(self, patch_model_utils):
+        backup = FakeLLM(responses=["ok"])
+        captured = {}
+
+        def fake_create_llm(type, **kw):
+            captured["model_user_id"] = kw.get("model_user_id")
+            captured["model_id"] = kw.get("model_id")
+            return backup
+
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=fake_create_llm,
+        )
+
+        primary = FakeLLM(
+            decoded_token={"sub": "caller-bob"},
+            model_user_id="owner-alice",
+            backup_models=["alice-byom-uuid"],
+        )
+        _ = primary.fallback_llm
+        assert captured["model_user_id"] == "owner-alice"
+        assert captured["model_id"] == "alice-byom-uuid"
+
+    def test_global_fallback_create_llm_receives_model_user_id(
+        self, monkeypatch, patch_model_utils
+    ):
+        """The global FALLBACK_LLM_NAME path must also forward
+        ``model_user_id`` — operators can configure it to a BYOM UUID
+        that's owned by the same user as the primary model."""
+        backup = FakeLLM(responses=["ok"])
+        captured = {}
+
+        def fake_create_llm(type, **kw):
+            captured["model_user_id"] = kw.get("model_user_id")
+            return backup
+
+        patch_model_utils(create_llm=fake_create_llm)
+        monkeypatch.setattr(
+            "application.llm.base.settings",
+            MagicMock(
+                FALLBACK_LLM_PROVIDER="openai",
+                FALLBACK_LLM_NAME="some-uuid",
+                FALLBACK_LLM_API_KEY="k",
+                API_KEY="k",
+            ),
+        )
+
+        primary = FakeLLM(
+            decoded_token={"sub": "caller-bob"},
+            model_user_id="owner-alice",
+            backup_models=[],
+        )
+        _ = primary.fallback_llm
+        assert captured["model_user_id"] == "owner-alice"
+
+    def test_falls_back_to_caller_when_model_user_id_unset(
+        self, patch_model_utils
+    ):
+        """Built-in models / pre-P2 callers don't pass model_user_id.
+        In that case the caller's sub is still used — preserving
+        existing behaviour."""
+        captured = {}
+
+        def fake_get_provider(model_id, **kwargs):
+            captured["user_id"] = kwargs.get("user_id")
+            return "openai"
+
+        patch_model_utils(
+            get_provider=fake_get_provider,
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: FakeLLM(responses=["ok"]),
+        )
+
+        primary = FakeLLM(
+            decoded_token={"sub": "caller-bob"},
+            model_user_id=None,
+            backup_models=["some-builtin-id"],
+        )
+        _ = primary.fallback_llm
+        assert captured["user_id"] == "caller-bob"
+
+
+# Tests — LLMCreator wires model_user_id through to BaseLLM
+
+
+@pytest.mark.unit
+class TestLLMCreatorPassesModelUserId:
+    """End-to-end through ``LLMCreator.create_llm``: the constructed
+    LLM must store ``model_user_id`` so its fallback property can
+    resolve under the right scope."""
+
+    def test_model_user_id_set_on_constructed_llm(self, monkeypatch):
+        from application.llm.llm_creator import LLMCreator
+        from application.llm.providers import PROVIDERS_BY_NAME
+
+        captured = {}
+
+        class _CapturingLLM:
+            def __init__(self, api_key, user_api_key, *args, **kwargs):
+                captured["model_user_id"] = kwargs.get("model_user_id")
+
+        # Pick any registered provider — we only need the constructor
+        # call to land in our fake.
+        monkeypatch.setattr(
+            PROVIDERS_BY_NAME["openai"], "llm_class", _CapturingLLM
+        )
+
+        LLMCreator.create_llm(
+            type="openai",
+            api_key="k",
+            user_api_key=None,
+            decoded_token={"sub": "caller-bob"},
+            model_id=None,
+            model_user_id="owner-alice",
+        )
+
+        assert captured["model_user_id"] == "owner-alice"
+
+
+# Tests — responding-provider tracking (cross-provider fallback handler fix)
+
+
+class _Google(FakeLLM):
+    provider_name = "google"
+
+
+class _OpenAI(FakeLLM):
+    provider_name = "openai"
+
+
+@pytest.mark.integration
+class TestRespondingProviderTracking:
+    """The handler that parses a response must follow the model that
+    actually produced it. ``BaseLLM`` exposes ``_responding_provider`` so
+    the handler layer can re-route ``parse_response`` after a fallback to a
+    different-provider model (Google primary -> OpenAI backup), instead of
+    silently dropping the backup's tool calls."""
+
+    def test_defaults_to_own_provider_before_any_call(self):
+        assert _Google()._responding_provider == "google"
+
+    def test_stream_success_keeps_primary_provider(self):
+        primary = _Google(stream_chunks=["a", "b"])
+        list(primary.gen_stream(**CALL_ARGS))
+        assert primary._responding_provider == "google"
+
+    def test_stream_fallback_records_backup_provider(self, patch_model_utils):
+        backup = _OpenAI(stream_chunks=["x"])
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+        primary = _Google(
+            stream_chunks=["a"], fail_at=0, backup_models=["backup-model"]
+        )
+        list(primary.gen_stream(**CALL_ARGS))
+        assert primary._responding_provider == "openai"
+
+    def test_gen_success_keeps_primary_provider(self):
+        primary = _Google(responses=["ok"])
+        primary.gen(**CALL_ARGS)
+        assert primary._responding_provider == "google"
+
+    def test_gen_fallback_records_backup_provider(self, patch_model_utils):
+        backup = _OpenAI(responses=["backup ok"])
+        patch_model_utils(
+            get_provider=lambda m, **_kwargs: "openai",
+            get_api_key=lambda p: "k",
+            create_llm=lambda type, **kw: backup,
+        )
+        primary = _Google(fail_at=0, backup_models=["backup-model"])
+        primary.gen(**CALL_ARGS)
+        assert primary._responding_provider == "openai"