crewAI/lib/crewai/tests/test_human_feedback_integration.py

"""Integration tests for the @human_feedback decorator with Flow.

This module tests the integration of @human_feedback with @listen,
routing behavior, multi-step flows, and state management.
"""

from __future__ import annotations

import asyncio
from datetime import datetime
from typing import Any
from unittest.mock import MagicMock, patch

import pytest
from pydantic import BaseModel

from crewai.flow import Flow, HumanFeedbackResult, human_feedback, listen, or_, start
from crewai.flow.flow import FlowState


class TestRoutingIntegration:
    """Tests for routing integration with @listen decorators."""

    @patch("builtins.input", return_value="I approve")
    @patch("builtins.print")
    def test_routes_to_matching_listener(self, mock_print, mock_input):
        """Test that collapsed outcome routes to the matching @listen method."""
        execution_order = []

        class ReviewFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm="gpt-4o-mini",
            )
            def generate(self):
                execution_order.append("generate")
                return "content"

            @listen("approved")
            def on_approved(self):
                execution_order.append("on_approved")
                return "published"

            @listen("rejected")
            def on_rejected(self):
                execution_order.append("on_rejected")
                return "discarded"

        flow = ReviewFlow()

        with (
            patch.object(flow, "_request_human_feedback", return_value="Approved!"),
            patch.object(flow, "_collapse_to_outcome", return_value="approved"),
        ):
            result = flow.kickoff()

        assert "generate" in execution_order
        assert "on_approved" in execution_order
        assert "on_rejected" not in execution_order

    @patch("builtins.input", return_value="")
    @patch("builtins.print")
    def test_default_outcome_routes_correctly(self, mock_print, mock_input):
        """Test that default_outcome routes when no feedback provided."""
        executed_listener = []

        class ReviewFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "needs_work"],
                llm="gpt-4o-mini",
                default_outcome="needs_work",
            )
            def generate(self):
                return "content"

            @listen("approved")
            def on_approved(self):
                executed_listener.append("approved")

            @listen("needs_work")
            def on_needs_work(self):
                executed_listener.append("needs_work")

        flow = ReviewFlow()

        with patch.object(flow, "_request_human_feedback", return_value=""):
            flow.kickoff()

        assert "needs_work" in executed_listener
        assert "approved" not in executed_listener


class TestMultiStepFlows:
    """Tests for multi-step flows with multiple @human_feedback decorators."""

    @patch("builtins.input", side_effect=["Good draft", "Final approved"])
    @patch("builtins.print")
    def test_multiple_feedback_steps(self, mock_print, mock_input):
        """Test a flow with multiple human feedback steps."""

        class MultiStepFlow(Flow):
            @start()
            @human_feedback(message="Review draft:")
            def draft(self):
                return "Draft content"

            @listen(draft)
            @human_feedback(message="Final review:")
            def final_review(self, prev_result: HumanFeedbackResult):
                return f"Final content based on: {prev_result.feedback}"

        flow = MultiStepFlow()

        with patch.object(
            flow, "_request_human_feedback", side_effect=["Good draft", "Approved"]
        ):
            flow.kickoff()

        # Both feedbacks should be recorded
        assert len(flow.human_feedback_history) == 2
        assert flow.human_feedback_history[0].method_name == "draft"
        assert flow.human_feedback_history[0].feedback == "Good draft"
        assert flow.human_feedback_history[1].method_name == "final_review"
        assert flow.human_feedback_history[1].feedback == "Approved"

    @patch("builtins.input", return_value="feedback")
    @patch("builtins.print")
    def test_mixed_feedback_and_regular_methods(self, mock_print, mock_input):
        """Test flow with both @human_feedback and regular methods."""
        execution_order = []

        class MixedFlow(Flow):
            @start()
            def generate(self):
                execution_order.append("generate")
                return "generated"

            @listen(generate)
            @human_feedback(message="Review:")
            def review(self):
                execution_order.append("review")
                return "reviewed"

            @listen(review)
            def finalize(self, result):
                execution_order.append("finalize")
                return "finalized"

        flow = MixedFlow()

        with patch.object(flow, "_request_human_feedback", return_value="feedback"):
            flow.kickoff()

        assert execution_order == ["generate", "review", "finalize"]

    def test_chained_router_feedback_steps(self):
        """Test that a router outcome can trigger another router method.

        Regression test: @listen("outcome") combined with @human_feedback(emit=...)
        creates a method that is both a listener and a router. The flow must find
        and execute it when the upstream router emits the matching outcome.
        """
        execution_order: list[str] = []

        class ChainedRouterFlow(Flow):
            @start()
            @human_feedback(
                message="First review:",
                emit=["approved", "rejected"],
                llm="gpt-4o-mini",
            )
            def draft(self):
                execution_order.append("draft")
                return "draft content"

            @listen("approved")
            @human_feedback(
                message="Final review:",
                emit=["publish", "revise"],
                llm="gpt-4o-mini",
            )
            def final_review(self, prev: HumanFeedbackResult):
                execution_order.append("final_review")
                return "final content"

            @listen("rejected")
            def on_rejected(self, prev: HumanFeedbackResult):
                execution_order.append("on_rejected")
                return "rejected"

            @listen("publish")
            def on_publish(self, prev: HumanFeedbackResult):
                execution_order.append("on_publish")
                return "published"

            @listen("revise")
            def on_revise(self, prev: HumanFeedbackResult):
                execution_order.append("on_revise")
                return "revised"

        flow = ChainedRouterFlow()

        with (
            patch.object(
                flow,
                "_request_human_feedback",
                side_effect=["looks good", "ship it"],
            ),
            patch.object(
                flow,
                "_collapse_to_outcome",
                side_effect=["approved", "publish"],
            ),
        ):
            result = flow.kickoff()

        assert execution_order == ["draft", "final_review", "on_publish"]
        assert result == "published"
        assert len(flow.human_feedback_history) == 2
        assert flow.human_feedback_history[0].outcome == "approved"
        assert flow.human_feedback_history[1].outcome == "publish"

    def test_chained_router_rejected_path(self):
        """Test that a start-router outcome routes to a non-router listener."""
        execution_order: list[str] = []

        class ChainedRouterFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm="gpt-4o-mini",
            )
            def draft(self):
                execution_order.append("draft")
                return "draft"

            @listen("approved")
            @human_feedback(
                message="Final:",
                emit=["publish", "revise"],
                llm="gpt-4o-mini",
            )
            def final_review(self, prev: HumanFeedbackResult):
                execution_order.append("final_review")
                return "final"

            @listen("rejected")
            def on_rejected(self, prev: HumanFeedbackResult):
                execution_order.append("on_rejected")
                return "rejected"

        flow = ChainedRouterFlow()

        with (
            patch.object(
                flow, "_request_human_feedback", return_value="bad"
            ),
            patch.object(
                flow, "_collapse_to_outcome", return_value="rejected"
            ),
        ):
            result = flow.kickoff()

        assert execution_order == ["draft", "on_rejected"]
        assert result == "rejected"
        assert len(flow.human_feedback_history) == 1
        assert flow.human_feedback_history[0].outcome == "rejected"

    def test_hitl_self_loop_routes_back_to_same_method(self):
        """Test that a HITL router can loop back to itself via its own emit outcome.

        Pattern: review_work listens to or_("do_work", "review") and emits
        ["review", "approved"]. When the human rejects (outcome="review"),
        the method should re-execute. When approved, the flow should continue
        to the approve_work listener.
        """
        execution_order: list[str] = []

        class SelfLoopFlow(Flow):
            @start()
            def initial_func(self):
                execution_order.append("initial_func")
                return "initial"

            @listen(initial_func)
            def do_work(self):
                execution_order.append("do_work")
                return "work output"

            @human_feedback(
                message="Do you approve this content?",
                emit=["review", "approved"],
                llm="gpt-4o-mini",
                default_outcome="approved",
            )
            @listen(or_("do_work", "review"))
            def review_work(self):
                execution_order.append("review_work")
                return "content for review"

            @listen("approved")
            def approve_work(self):
                execution_order.append("approve_work")
                return "published"

        flow = SelfLoopFlow()

        # First call: human rejects (outcome="review") -> self-loop
        # Second call: human approves (outcome="approved") -> continue
        with (
            patch.object(
                flow,
                "_request_human_feedback",
                side_effect=["needs changes", "looks good"],
            ),
            patch.object(
                flow,
                "_collapse_to_outcome",
                side_effect=["review", "approved"],
            ),
        ):
            result = flow.kickoff()

        assert execution_order == [
            "initial_func",
            "do_work",
            "review_work",   # first review -> rejected (review)
            "review_work",   # second review -> approved
            "approve_work",
        ]
        assert result == "published"
        assert len(flow.human_feedback_history) == 2
        assert flow.human_feedback_history[0].outcome == "review"
        assert flow.human_feedback_history[1].outcome == "approved"

    def test_hitl_self_loop_multiple_rejections(self):
        """Test that a HITL router can loop back multiple times before approving.

        Verifies the self-loop works for more than one rejection cycle.
        """
        execution_order: list[str] = []

        class MultiRejectFlow(Flow):
            @start()
            def generate(self):
                execution_order.append("generate")
                return "draft"

            @human_feedback(
                message="Review this content:",
                emit=["revise", "approved"],
                llm="gpt-4o-mini",
                default_outcome="approved",
            )
            @listen(or_("generate", "revise"))
            def review(self):
                execution_order.append("review")
                return "content v" + str(execution_order.count("review"))

            @listen("approved")
            def publish(self):
                execution_order.append("publish")
                return "published"

        flow = MultiRejectFlow()

        # Three rejections, then approval
        with (
            patch.object(
                flow,
                "_request_human_feedback",
                side_effect=["bad", "still bad", "not yet", "great"],
            ),
            patch.object(
                flow,
                "_collapse_to_outcome",
                side_effect=["revise", "revise", "revise", "approved"],
            ),
        ):
            result = flow.kickoff()

        assert execution_order == [
            "generate",
            "review",    # 1st review -> revise
            "review",    # 2nd review -> revise
            "review",    # 3rd review -> revise
            "review",    # 4th review -> approved
            "publish",
        ]
        assert result == "published"
        assert len(flow.human_feedback_history) == 4
        assert [r.outcome for r in flow.human_feedback_history] == [
            "revise", "revise", "revise", "approved"
        ]

    def test_hitl_self_loop_immediate_approval(self):
        """Test that a HITL self-loop flow works when approved on the first try.

        No looping occurs -- the flow should proceed straight through.
        """
        execution_order: list[str] = []

        class ImmediateApprovalFlow(Flow):
            @start()
            def generate(self):
                execution_order.append("generate")
                return "perfect draft"

            @human_feedback(
                message="Review:",
                emit=["revise", "approved"],
                llm="gpt-4o-mini",
            )
            @listen(or_("generate", "revise"))
            def review(self):
                execution_order.append("review")
                return "content"

            @listen("approved")
            def publish(self):
                execution_order.append("publish")
                return "published"

        flow = ImmediateApprovalFlow()

        with (
            patch.object(
                flow,
                "_request_human_feedback",
                return_value="perfect",
            ),
            patch.object(
                flow,
                "_collapse_to_outcome",
                return_value="approved",
            ),
        ):
            result = flow.kickoff()

        assert execution_order == ["generate", "review", "publish"]
        assert result == "published"
        assert len(flow.human_feedback_history) == 1
        assert flow.human_feedback_history[0].outcome == "approved"

    def test_router_and_non_router_listeners_for_same_outcome(self):
        """Test that both router and non-router listeners fire for the same outcome."""
        execution_order: list[str] = []

        class MixedListenerFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm="gpt-4o-mini",
            )
            def draft(self):
                execution_order.append("draft")
                return "draft"

            @listen("approved")
            @human_feedback(
                message="Final:",
                emit=["publish", "revise"],
                llm="gpt-4o-mini",
            )
            def router_listener(self, prev: HumanFeedbackResult):
                execution_order.append("router_listener")
                return "final"

            @listen("approved")
            def plain_listener(self, prev: HumanFeedbackResult):
                execution_order.append("plain_listener")
                return "logged"

            @listen("publish")
            def on_publish(self, prev: HumanFeedbackResult):
                execution_order.append("on_publish")
                return "published"

        flow = MixedListenerFlow()

        with (
            patch.object(
                flow,
                "_request_human_feedback",
                side_effect=["approve it", "publish it"],
            ),
            patch.object(
                flow,
                "_collapse_to_outcome",
                side_effect=["approved", "publish"],
            ),
        ):
            flow.kickoff()

        assert "draft" in execution_order
        assert "router_listener" in execution_order
        assert "plain_listener" in execution_order
        assert "on_publish" in execution_order


class TestStateManagement:
    """Tests for state management with human feedback."""

    @patch("builtins.input", return_value="approved")
    @patch("builtins.print")
    def test_feedback_available_in_listener(self, mock_print, mock_input):
        """Test that feedback is accessible in downstream listeners."""
        captured_feedback = []

        class StateFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm="gpt-4o-mini",
            )
            def review(self):
                return "Content to review"

            @listen("approved")
            def on_approved(self):
                # Access the feedback via property
                captured_feedback.append(self.last_human_feedback)
                return "done"

        flow = StateFlow()

        with (
            patch.object(flow, "_request_human_feedback", return_value="Great content!"),
            patch.object(flow, "_collapse_to_outcome", return_value="approved"),
        ):
            flow.kickoff()

        assert len(captured_feedback) == 1
        result = captured_feedback[0]
        assert isinstance(result, HumanFeedbackResult)
        assert result.output == "Content to review"
        assert result.feedback == "Great content!"
        assert result.outcome == "approved"

    @patch("builtins.input", return_value="")
    @patch("builtins.print")
    def test_history_preserved_across_steps(self, mock_print, mock_input):
        """Test that feedback history is preserved across flow execution."""

        class HistoryFlow(Flow):
            @start()
            @human_feedback(message="Step 1:")
            def step1(self):
                return "Step 1"

            @listen(step1)
            @human_feedback(message="Step 2:")
            def step2(self, result):
                return "Step 2"

            @listen(step2)
            def final(self, result):
                # Access history
                return len(self.human_feedback_history)

        flow = HistoryFlow()

        with patch.object(flow, "_request_human_feedback", return_value="feedback"):
            result = flow.kickoff()

        # Final method should see 2 feedback entries
        assert result == 2


class TestAsyncFlowIntegration:
    """Tests for async flow integration."""

    @pytest.mark.asyncio
    async def test_async_flow_with_human_feedback(self):
        """Test that @human_feedback works with async flows."""
        executed = []

        class AsyncFlow(Flow):
            @start()
            @human_feedback(message="Review:")
            async def async_review(self):
                executed.append("async_review")
                await asyncio.sleep(0.01)  # Simulate async work
                return "async content"

        flow = AsyncFlow()

        with patch.object(flow, "_request_human_feedback", return_value="feedback"):
            await flow.kickoff_async()

        assert "async_review" in executed
        assert flow.last_human_feedback is not None
        assert flow.last_human_feedback.output == "async content"


class TestWithStructuredState:
    """Tests for flows with structured (Pydantic) state."""

    @patch("builtins.input", return_value="approved")
    @patch("builtins.print")
    def test_with_pydantic_state(self, mock_print, mock_input):
        """Test human feedback with structured Pydantic state."""

        class ReviewState(FlowState):
            content: str = ""
            review_count: int = 0

        class StructuredFlow(Flow[ReviewState]):
            initial_state = ReviewState

            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm="gpt-4o-mini",
            )
            def review(self):
                self.state.content = "Generated content"
                self.state.review_count += 1
                return self.state.content

            @listen("approved")
            def on_approved(self):
                return f"Approved: {self.state.content}"

        flow = StructuredFlow()

        with (
            patch.object(flow, "_request_human_feedback", return_value="LGTM"),
            patch.object(flow, "_collapse_to_outcome", return_value="approved"),
        ):
            result = flow.kickoff()

        assert flow.state.review_count == 1
        assert flow.last_human_feedback is not None
        assert flow.last_human_feedback.feedback == "LGTM"


class TestMetadataPassthrough:
    """Tests for metadata passthrough functionality."""

    @patch("builtins.input", return_value="")
    @patch("builtins.print")
    def test_metadata_included_in_result(self, mock_print, mock_input):
        """Test that metadata is passed through to HumanFeedbackResult."""

        class MetadataFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                metadata={"channel": "slack", "priority": "high"},
            )
            def review(self):
                return "content"

        flow = MetadataFlow()

        with patch.object(flow, "_request_human_feedback", return_value="feedback"):
            flow.kickoff()

        result = flow.last_human_feedback
        assert result is not None
        assert result.metadata == {"channel": "slack", "priority": "high"}


class TestEventEmission:
    """Tests for event emission during human feedback."""

    @patch("builtins.input", return_value="test feedback")
    @patch("builtins.print")
    def test_events_emitted_on_feedback_request(self, mock_print, mock_input):
        """Test that events are emitted when feedback is requested."""
        from crewai.events.event_listener import event_listener

        class EventFlow(Flow):
            @start()
            @human_feedback(message="Review:")
            def review(self):
                return "content"

        flow = EventFlow()

        # We can't easily capture events in tests, but we can verify
        # the flow executes without errors
        with (
            patch.object(
                event_listener.formatter, "pause_live_updates", return_value=None
            ),
            patch.object(
                event_listener.formatter, "resume_live_updates", return_value=None
            ),
        ):
            flow.kickoff()

        assert flow.last_human_feedback is not None


class TestEdgeCases:
    """Tests for edge cases and error handling."""

    @patch("builtins.input", return_value="")
    @patch("builtins.print")
    def test_empty_feedback_first_outcome_fallback(self, mock_print, mock_input):
        """Test that empty feedback without default uses first outcome for routing, but returns method output."""

        class FallbackFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["first", "second", "third"],
                llm="gpt-4o-mini",
                # No default_outcome specified
            )
            def review(self):
                return "content"

        flow = FallbackFlow()

        with patch.object(flow, "_request_human_feedback", return_value=""):
            result = flow.kickoff()

        # Flow result is the method's return value, NOT the collapsed outcome
        assert result == "content"
        # But outcome is still set to first for routing purposes
        assert flow.last_human_feedback.outcome == "first"

    @patch("builtins.input", return_value="whitespace only   ")
    @patch("builtins.print")
    def test_whitespace_only_feedback_treated_as_empty(self, mock_print, mock_input):
        """Test that whitespace-only feedback is treated as empty for routing, but returns method output."""

        class WhitespaceFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approve", "reject"],
                llm="gpt-4o-mini",
                default_outcome="reject",
            )
            def review(self):
                return "content"

        flow = WhitespaceFlow()

        with patch.object(flow, "_request_human_feedback", return_value="   "):
            result = flow.kickoff()

        # Flow result is the method's return value, NOT the collapsed outcome
        assert result == "content"
        # But outcome is set to default because feedback is empty after strip
        assert flow.last_human_feedback.outcome == "reject"

    @patch("builtins.input", return_value="feedback")
    @patch("builtins.print")
    def test_feedback_result_without_routing(self, mock_print, mock_input):
        """Test that HumanFeedbackResult is returned when not routing."""

        class NoRoutingFlow(Flow):
            @start()
            @human_feedback(message="Review:")
            def review(self):
                return "content"

        flow = NoRoutingFlow()

        with patch.object(flow, "_request_human_feedback", return_value="feedback"):
            result = flow.kickoff()

        # Result should be HumanFeedbackResult when not routing
        assert isinstance(result, HumanFeedbackResult)
        assert result.output == "content"
        assert result.feedback == "feedback"
        assert result.outcome is None  # No routing, no outcome


class TestLLMConfigPreservation:
    """Tests that LLM config is preserved through @human_feedback serialization.

    PR #4970 introduced _hf_llm stashing so the live LLM object survives
    decorator wrapping for same-process resume. The serialization path
    (_serialize_llm_for_context / _deserialize_llm_from_context) preserves
    config for cross-process resume.
    """

    def test_hf_llm_stashed_on_wrapper_with_llm_instance(self):
        """Test that passing an LLM instance stashes it on the wrapper as _hf_llm."""
        from crewai.llm import LLM

        llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)

        class ConfigFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm=llm_instance,
            )
            def review(self):
                return "content"

        method = ConfigFlow.review
        assert hasattr(method, "_hf_llm"), "_hf_llm not found on wrapper"
        assert method._hf_llm is llm_instance, "_hf_llm is not the same object"

    def test_hf_llm_preserved_on_listen_method(self):
        """Test that _hf_llm is preserved when @human_feedback is on a @listen method."""
        from crewai.llm import LLM

        llm_instance = LLM(model="gpt-4o-mini", temperature=0.7)

        class ListenConfigFlow(Flow):
            @start()
            def generate(self):
                return "draft"

            @listen("generate")
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm=llm_instance,
            )
            def review(self):
                return "content"

        method = ListenConfigFlow.review
        assert hasattr(method, "_hf_llm")
        assert method._hf_llm is llm_instance

    def test_hf_llm_accessible_on_instance(self):
        """Test that _hf_llm survives Flow instantiation (bound method access)."""
        from crewai.llm import LLM

        llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)

        class InstanceFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm=llm_instance,
            )
            def review(self):
                return "content"

        flow = InstanceFlow()
        instance_method = flow.review
        assert hasattr(instance_method, "_hf_llm")
        assert instance_method._hf_llm is llm_instance

    def test_serialize_llm_preserves_config_fields(self):
        """Test that _serialize_llm_for_context captures temperature, base_url, etc."""
        from crewai.flow.human_feedback import _serialize_llm_for_context
        from crewai.llm import LLM

        llm = LLM(
            model="gpt-4o-mini",
            temperature=0.42,
            base_url="https://custom.example.com/v1",
        )

        serialized = _serialize_llm_for_context(llm)

        assert isinstance(serialized, dict), f"Expected dict, got {type(serialized)}"
        assert serialized["model"] == "openai/gpt-4o-mini"
        assert serialized["temperature"] == 0.42
        assert serialized["base_url"] == "https://custom.example.com/v1"

    def test_serialize_llm_excludes_api_key(self):
        """Test that api_key is NOT included in serialized output (security)."""
        from crewai.flow.human_feedback import _serialize_llm_for_context
        from crewai.llm import LLM

        llm = LLM(model="gpt-4o-mini")

        serialized = _serialize_llm_for_context(llm)
        assert isinstance(serialized, dict)
        assert "api_key" not in serialized

    def test_deserialize_round_trip_preserves_config(self):
        """Test that serialize → deserialize round-trip preserves all config."""
        from crewai.flow.human_feedback import (
            _deserialize_llm_from_context,
            _serialize_llm_for_context,
        )
        from crewai.llm import LLM

        original = LLM(
            model="gpt-4o-mini",
            temperature=0.42,
            base_url="https://custom.example.com/v1",
        )

        serialized = _serialize_llm_for_context(original)
        reconstructed = _deserialize_llm_from_context(serialized)

        assert reconstructed is not None
        assert reconstructed.model == original.model
        assert reconstructed.temperature == original.temperature
        assert reconstructed.base_url == original.base_url

    def test_deserialize_handles_legacy_string_format(self):
        """Test backward compat: plain string still reconstructs an LLM."""
        from crewai.flow.human_feedback import _deserialize_llm_from_context

        reconstructed = _deserialize_llm_from_context("openai/gpt-4o-mini")

        assert reconstructed is not None
        assert reconstructed.model == "gpt-4o-mini"

    def test_deserialize_returns_none_for_none(self):
        """Test that None input returns None."""
        from crewai.flow.human_feedback import _deserialize_llm_from_context

        assert _deserialize_llm_from_context(None) is None

    def test_serialize_llm_preserves_provider_specific_fields(self):
        """Test that provider-specific fields like project/location are serialized."""
        from crewai.flow.human_feedback import _serialize_llm_for_context
        from crewai.llm import LLM

        # Create a Gemini-style LLM with project and non-default location
        llm = LLM(
            model="gemini-2.0-flash",
            provider="gemini",
            project="my-project",
            location="europe-west1",
            temperature=0.3,
        )

        serialized = _serialize_llm_for_context(llm)

        assert isinstance(serialized, dict)
        assert serialized.get("project") == "my-project"
        assert serialized.get("location") == "europe-west1"
        assert serialized.get("temperature") == 0.3

    def test_config_preserved_through_full_flow_execution(self):
        """Test that the LLM with custom config is used during outcome collapsing."""
        from crewai.llm import LLM

        llm_instance = LLM(model="gpt-4o-mini", temperature=0.42)
        collapse_calls = []

        class FullFlow(Flow):
            @start()
            @human_feedback(
                message="Review:",
                emit=["approved", "rejected"],
                llm=llm_instance,
            )
            def review(self):
                return "content"

            @listen("approved")
            def on_approved(self):
                return "done"

        flow = FullFlow()

        original_collapse = flow._collapse_to_outcome

        def spy_collapse(feedback, outcomes, llm):
            collapse_calls.append(llm)
            return "approved"

        with (
            patch.object(flow, "_request_human_feedback", return_value="looks good"),
            patch.object(flow, "_collapse_to_outcome", side_effect=spy_collapse),
        ):
            flow.kickoff()

        assert len(collapse_calls) == 1
        # The LLM passed to _collapse_to_outcome should be the original instance
        assert collapse_calls[0] is llm_instance