better serialization for human feedback in flow with models defined a… (#5029)

* better serialization for human feedback in flow with models defined as dicts

* linted

* linted

* fix and adjust tests

lib/crewai/src/crewai/flow/async_feedback/types.py

@@ -60,7 +60,7 @@ class PendingFeedbackContext:
     emit: list[str] | None = None
     default_outcome: str | None = None
     metadata: dict[str, Any] = field(default_factory=dict)
-    llm: str | None = None
+    llm: dict[str, Any] | str | None = None
     requested_at: datetime = field(default_factory=datetime.now)
 
     def to_dict(self) -> dict[str, Any]:

lib/crewai/src/crewai/flow/flow.py

@@ -1316,25 +1316,25 @@ class Flow(Generic[T], metaclass=FlowMeta):
         emit = context.emit
         default_outcome = context.default_outcome
 
-        # Try to get the live LLM from the re-imported decorator instead of the
-        # serialized string. When a flow pauses for HITL and resumes (possibly in
-        # a different process), context.llm only contains a model string like
-        # 'gemini/gemini-3-flash-preview'. This loses credentials, project,
-        # location, safety_settings, and client_params. By looking up the method
-        # on the re-imported flow class, we can retrieve the fully-configured LLM
-        # that was passed to the @human_feedback decorator.
-        llm = context.llm  # fallback to serialized string
+        # Try to get the live LLM from the re-imported decorator first.
+        # This preserves the fully-configured object (credentials, safety_settings, etc.)
+        # for same-process resume. For cross-process resume, fall back to the
+        # serialized context.llm which is now a dict with full config (or a legacy string).
+        from crewai.flow.human_feedback import _deserialize_llm_from_context
+
+        llm = None
         method = self._methods.get(FlowMethodName(context.method_name))
         if method is not None:
             live_llm = getattr(method, "_hf_llm", None)
             if live_llm is not None:
                 from crewai.llms.base_llm import BaseLLM as BaseLLMClass
 
-                # Only use live LLM if it's a BaseLLM instance (not a string)
-                # String values offer no benefit over the serialized context.llm
                 if isinstance(live_llm, BaseLLMClass):
                     llm = live_llm
 
+        if llm is None:
+            llm = _deserialize_llm_from_context(context.llm)
+
         # Determine outcome
         collapsed_outcome: str | None = None
 

lib/crewai/src/crewai/flow/human_feedback.py

@@ -76,22 +76,48 @@ if TYPE_CHECKING:
 F = TypeVar("F", bound=Callable[..., Any])
 
 
-def _serialize_llm_for_context(llm: Any) -> str | None:
-    """Serialize a BaseLLM object to a model string with provider prefix.
+def _serialize_llm_for_context(llm: Any) -> dict[str, Any] | str | None:
+    """Serialize a BaseLLM object to a dict preserving full config.
 
-    When persisting the LLM for HITL resume, we need to store enough info
-    to reconstruct a working LLM on the resume worker. Just storing the bare
-    model name (e.g. "gemini-3-flash-preview") causes provider inference to
-    fail — it defaults to OpenAI. Including the provider prefix (e.g.
-    "gemini/gemini-3-flash-preview") allows LLM() to correctly route.
+    Delegates to ``llm.to_config_dict()`` when available (BaseLLM and
+    subclasses). Falls back to extracting the model string with provider
+    prefix for unknown LLM types.
     """
+    if hasattr(llm, "to_config_dict"):
+        return llm.to_config_dict()
+
+    # Fallback for non-BaseLLM objects: just extract model + provider prefix
     model = getattr(llm, "model", None)
     if not model:
         return None
     provider = getattr(llm, "provider", None)
-    if provider and "/" not in model:
-        return f"{provider}/{model}"
-    return model
+    return f"{provider}/{model}" if provider and "/" not in model else model
+
+
+def _deserialize_llm_from_context(
+    llm_data: dict[str, Any] | str | None,
+) -> BaseLLM | None:
+    """Reconstruct an LLM instance from serialized context data.
+
+    Handles both the new dict format (with full config) and the legacy
+    string format (model name only) for backward compatibility.
+
+    Returns a BaseLLM instance, or None if llm_data is None.
+    """
+    if llm_data is None:
+        return None
+
+    from crewai.llm import LLM
+
+    if isinstance(llm_data, str):
+        return LLM(model=llm_data)
+
+    if isinstance(llm_data, dict):
+        model = llm_data.pop("model", None)
+        if not model:
+            return None
+        return LLM(model=model, **llm_data)
+    return None
 
 
 @dataclass

lib/crewai/src/crewai/llms/base_llm.py

@@ -152,6 +152,28 @@ class BaseLLM(ABC):
             "cached_prompt_tokens": 0,
         }
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Serialize this LLM to a dict that can reconstruct it via ``LLM(**config)``.
+
+        Returns the core fields that BaseLLM owns. Provider subclasses should
+        override this (calling ``super().to_config_dict()``) to add their own
+        fields (e.g. ``project``, ``location``, ``safety_settings``).
+        """
+        model = self.model
+        provider = self.provider
+        model_str = f"{provider}/{model}" if provider and "/" not in model else model
+
+        config: dict[str, Any] = {"model": model_str}
+
+        if self.temperature is not None:
+            config["temperature"] = self.temperature
+        if self.base_url is not None:
+            config["base_url"] = self.base_url
+        if self.stop:
+            config["stop"] = self.stop
+
+        return config
+
     @property
     def provider(self) -> str:
         """Get the provider of the LLM."""

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -256,6 +256,19 @@ class AnthropicCompletion(BaseLLM):
         else:
             self.stop_sequences = []
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Anthropic-specific fields."""
+        config = super().to_config_dict()
+        if self.max_tokens != 4096:  # non-default
+            config["max_tokens"] = self.max_tokens
+        if self.max_retries != 2:  # non-default
+            config["max_retries"] = self.max_retries
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.timeout is not None:
+            config["timeout"] = self.timeout
+        return config
+
     def _get_client_params(self) -> dict[str, Any]:
         """Get client parameters."""
 

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -180,6 +180,27 @@ class AzureCompletion(BaseLLM):
             and "/openai/deployments/" in self.endpoint
         )
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Azure-specific fields."""
+        config = super().to_config_dict()
+        if self.endpoint:
+            config["endpoint"] = self.endpoint
+        if self.api_version and self.api_version != "2024-06-01":
+            config["api_version"] = self.api_version
+        if self.timeout is not None:
+            config["timeout"] = self.timeout
+        if self.max_retries != 2:
+            config["max_retries"] = self.max_retries
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.frequency_penalty is not None:
+            config["frequency_penalty"] = self.frequency_penalty
+        if self.presence_penalty is not None:
+            config["presence_penalty"] = self.presence_penalty
+        if self.max_tokens is not None:
+            config["max_tokens"] = self.max_tokens
+        return config
+
     @staticmethod
     def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
         """Validate and fix Azure endpoint URL format.

lib/crewai/src/crewai/llms/providers/bedrock/completion.py

@@ -346,6 +346,23 @@ class BedrockCompletion(BaseLLM):
         # Handle inference profiles for newer models
         self.model_id = model
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Bedrock-specific fields."""
+        config = super().to_config_dict()
+        # NOTE: AWS credentials (access_key, secret_key, session_token) are
+        # intentionally excluded — they must come from env on resume.
+        if self.region_name and self.region_name != "us-east-1":
+            config["region_name"] = self.region_name
+        if self.max_tokens is not None:
+            config["max_tokens"] = self.max_tokens
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.top_k is not None:
+            config["top_k"] = self.top_k
+        if self.guardrail_config:
+            config["guardrail_config"] = self.guardrail_config
+        return config
+
     @property
     def stop(self) -> list[str]:
         """Get stop sequences sent to the API."""
@@ -1880,7 +1897,9 @@ class BedrockCompletion(BaseLLM):
                 # Anthropic (Claude) models reject assistant-last messages when
                 # tools are in the request. Append a user message so the
                 # Converse API accepts the payload.
-                elif "anthropic" in self.model.lower() or "claude" in self.model.lower():
+                elif (
+                    "anthropic" in self.model.lower() or "claude" in self.model.lower()
+                ):
                     converse_messages.append(
                         {
                             "role": "user",

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -176,6 +176,28 @@ class GeminiCompletion(BaseLLM):
         else:
             self.stop_sequences = []
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with Gemini/Vertex-specific fields."""
+        config = super().to_config_dict()
+        if self.project:
+            config["project"] = self.project
+        if self.location and self.location != "us-central1":
+            config["location"] = self.location
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.top_k is not None:
+            config["top_k"] = self.top_k
+        if self.max_output_tokens is not None:
+            config["max_output_tokens"] = self.max_output_tokens
+        if self.safety_settings:
+            config["safety_settings"] = [
+                {"category": str(s.category), "threshold": str(s.threshold)}
+                if hasattr(s, "category") and hasattr(s, "threshold")
+                else s
+                for s in self.safety_settings
+            ]
+        return config
+
     def _initialize_client(self, use_vertexai: bool = False) -> genai.Client:
         """Initialize the Google Gen AI client with proper parameter handling.
 

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -329,6 +329,35 @@ class OpenAICompletion(BaseLLM):
         """
         self._last_reasoning_items = None
 
+    def to_config_dict(self) -> dict[str, Any]:
+        """Extend base config with OpenAI-specific fields."""
+        config = super().to_config_dict()
+        # Client-level params (from OpenAI SDK)
+        if self.organization:
+            config["organization"] = self.organization
+        if self.project:
+            config["project"] = self.project
+        if self.timeout is not None:
+            config["timeout"] = self.timeout
+        if self.max_retries != 2:
+            config["max_retries"] = self.max_retries
+        # Completion params
+        if self.top_p is not None:
+            config["top_p"] = self.top_p
+        if self.frequency_penalty is not None:
+            config["frequency_penalty"] = self.frequency_penalty
+        if self.presence_penalty is not None:
+            config["presence_penalty"] = self.presence_penalty
+        if self.max_tokens is not None:
+            config["max_tokens"] = self.max_tokens
+        if self.max_completion_tokens is not None:
+            config["max_completion_tokens"] = self.max_completion_tokens
+        if self.seed is not None:
+            config["seed"] = self.seed
+        if self.reasoning_effort is not None:
+            config["reasoning_effort"] = self.reasoning_effort
+        return config
+
     def _get_client_params(self) -> dict[str, Any]:
         """Get OpenAI client parameters."""
 

lib/crewai/tests/test_async_human_feedback.py

@@ -988,11 +988,9 @@ class TestLLMObjectPreservedInContext:
             db_path = os.path.join(tmpdir, "test_flows.db")
             persistence = SQLiteFlowPersistence(db_path)
 
-            # Create a mock BaseLLM object (not a string)
-            # Simulates LLM(model="gemini-2.0-flash", provider="gemini")
-            mock_llm_obj = MagicMock()
-            mock_llm_obj.model = "gemini-2.0-flash"
-            mock_llm_obj.provider = "gemini"
+            # Create a real LLM object (not a string)
+            from crewai.llm import LLM
+            mock_llm_obj = LLM(model="gemini-2.0-flash", provider="gemini")
 
             class PausingProvider:
                 def __init__(self, persistence: SQLiteFlowPersistence):
@@ -1041,32 +1039,37 @@ class TestLLMObjectPreservedInContext:
             result = flow1.kickoff()
             assert isinstance(result, HumanFeedbackPending)
 
-            # Verify the context stored the model STRING, not None
+            # Verify the context stored the model config dict, not None
             assert provider.captured_context is not None
-            assert provider.captured_context.llm == "gemini/gemini-2.0-flash"
+            assert isinstance(provider.captured_context.llm, dict)
+            assert provider.captured_context.llm["model"] == "gemini/gemini-2.0-flash"
 
             # Verify it survives persistence roundtrip
             flow_id = result.context.flow_id
             loaded = persistence.load_pending_feedback(flow_id)
             assert loaded is not None
             _, loaded_context = loaded
-            assert loaded_context.llm == "gemini/gemini-2.0-flash"
+            assert isinstance(loaded_context.llm, dict)
+            assert loaded_context.llm["model"] == "gemini/gemini-2.0-flash"
 
             # Phase 2: Resume with positive feedback - should use LLM to classify
             flow2 = TestFlow.from_pending(flow_id, persistence)
             assert flow2._pending_feedback_context is not None
-            assert flow2._pending_feedback_context.llm == "gemini/gemini-2.0-flash"
+            assert isinstance(flow2._pending_feedback_context.llm, dict)
+            assert flow2._pending_feedback_context.llm["model"] == "gemini/gemini-2.0-flash"
 
             # Mock _collapse_to_outcome to verify it gets called (not skipped)
             with patch.object(flow2, "_collapse_to_outcome", return_value="approved") as mock_collapse:
                 flow2.resume("this looks good, proceed!")
 
             # The key assertion: _collapse_to_outcome was called (not skipped due to llm=None)
-            mock_collapse.assert_called_once_with(
-                feedback="this looks good, proceed!",
-                outcomes=["needs_changes", "approved"],
-                llm="gemini/gemini-2.0-flash",
-            )
+            mock_collapse.assert_called_once()
+            call_kwargs = mock_collapse.call_args
+            assert call_kwargs.kwargs["feedback"] == "this looks good, proceed!"
+            assert call_kwargs.kwargs["outcomes"] == ["needs_changes", "approved"]
+            # LLM should be a live object (from _hf_llm) or reconstructed, not None
+            assert call_kwargs.kwargs["llm"] is not None
+            assert getattr(call_kwargs.kwargs["llm"], "model", None) == "gemini-2.0-flash"
             assert flow2.last_human_feedback.outcome == "approved"
             assert flow2.result_path == "approved"
 
@@ -1096,23 +1099,25 @@ class TestLLMObjectPreservedInContext:
     def test_provider_prefix_added_to_bare_model(self) -> None:
         """Test that provider prefix is added when model has no slash."""
         from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
 
-        mock_obj = MagicMock()
-        mock_obj.model = "gemini-3-flash-preview"
-        mock_obj.provider = "gemini"
-        assert _serialize_llm_for_context(mock_obj) == "gemini/gemini-3-flash-preview"
+        llm = LLM(model="gemini-2.0-flash", provider="gemini")
+        result = _serialize_llm_for_context(llm)
+        assert isinstance(result, dict)
+        assert result["model"] == "gemini/gemini-2.0-flash"
 
     def test_provider_prefix_not_doubled_when_already_present(self) -> None:
         """Test that provider prefix is not added when model already has a slash."""
         from crewai.flow.human_feedback import _serialize_llm_for_context
+        from crewai.llm import LLM
 
-        mock_obj = MagicMock()
-        mock_obj.model = "gemini/gemini-2.0-flash"
-        mock_obj.provider = "gemini"
-        assert _serialize_llm_for_context(mock_obj) == "gemini/gemini-2.0-flash"
+        llm = LLM(model="gemini/gemini-2.0-flash")
+        result = _serialize_llm_for_context(llm)
+        assert isinstance(result, dict)
+        assert result["model"] == "gemini/gemini-2.0-flash"
 
     def test_no_provider_attr_falls_back_to_bare_model(self) -> None:
-        """Test that bare model is used when no provider attribute exists."""
+        """Test that objects without to_config_dict fall back to model string."""
         from crewai.flow.human_feedback import _serialize_llm_for_context
 
         mock_obj = MagicMock(spec=[])
@@ -1402,9 +1407,11 @@ class TestLiveLLMPreservationOnResume:
             with patch.object(flow, "_collapse_to_outcome", side_effect=capture_llm):
                 flow.resume("looks good!")
 
-            # Should fall back to the serialized string
+            # Should fall back to deserialized LLM from context string
             assert len(captured_llm) == 1
-            assert captured_llm[0] == "gpt-4o-mini"
+            from crewai.llms.base_llm import BaseLLM as BaseLLMClass
+            assert isinstance(captured_llm[0], BaseLLMClass)
+            assert captured_llm[0].model == "gpt-4o-mini"
 
     @patch("crewai.flow.flow.crewai_event_bus.emit")
     def test_resume_async_uses_string_from_context_when_hf_llm_is_string(
@@ -1461,9 +1468,11 @@ class TestLiveLLMPreservationOnResume:
             with patch.object(flow, "_collapse_to_outcome", side_effect=capture_llm):
                 flow.resume("looks good!")
 
-            # Should use context.llm since _hf_llm is a string (not BaseLLM)
+            # _hf_llm is a string, so resume deserializes context.llm into an LLM instance
             assert len(captured_llm) == 1
-            assert captured_llm[0] == "gpt-4o-mini"
+            from crewai.llms.base_llm import BaseLLM as BaseLLMClass
+            assert isinstance(captured_llm[0], BaseLLMClass)
+            assert captured_llm[0].model == "gpt-4o-mini"
 
     def test_hf_llm_set_for_async_wrapper(self) -> None:
         """Test that _hf_llm is set on async wrapper functions."""

lib/crewai/tests/test_human_feedback_integration.py

@@ -772,3 +772,204 @@ class TestEdgeCases:
         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