diff --git a/libs/langchain/langchain/adapters/openai.py b/libs/langchain/langchain/adapters/openai.py
index 3c2429a2b17..d3bb51d709d 100644
--- a/libs/langchain/langchain/adapters/openai.py
+++ b/libs/langchain/langchain/adapters/openai.py
@@ -28,7 +28,7 @@ from langchain.schema.messages import (
 )
 
 
-async def aenumerate(
+async def _aenumerate(
     iterable: AsyncIterator[Any], start: int = 0
 ) -> AsyncIterator[tuple[int, Any]]:
     """Async version of enumerate."""
@@ -38,7 +38,7 @@ async def aenumerate(
         i += 1
 
 
-def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
+def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
     role = _dict["role"]
     if role == "user":
         return HumanMessage(content=_dict["content"])
@@ -59,7 +59,7 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
         return ChatMessage(content=_dict["content"], role=role)
 
 
-def convert_message_to_dict(message: BaseMessage) -> dict:
+def _convert_message_to_dict(message: BaseMessage) -> dict:
     message_dict: Dict[str, Any]
     if isinstance(message, ChatMessage):
         message_dict = {"role": message.role, "content": message.content}
@@ -87,7 +87,7 @@ def convert_message_to_dict(message: BaseMessage) -> dict:
     return message_dict
 
 
-def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMessage]:
+def _convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMessage]:
     """Convert dictionaries representing OpenAI messages to LangChain format.
 
     Args:
@@ -96,7 +96,7 @@ def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMess
     Returns:
         List of LangChain BaseMessage objects.
     """
-    return [convert_dict_to_message(m) for m in messages]
+    return [_convert_dict_to_message(m) for m in messages]
 
 
 def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
@@ -155,10 +155,10 @@ class ChatCompletion:
         models = importlib.import_module("langchain.chat_models")
         model_cls = getattr(models, provider)
         model_config = model_cls(**kwargs)
-        converted_messages = convert_openai_messages(messages)
+        converted_messages = _convert_openai_messages(messages)
         if not stream:
             result = model_config.invoke(converted_messages)
-            return {"choices": [{"message": convert_message_to_dict(result)}]}
+            return {"choices": [{"message": _convert_message_to_dict(result)}]}
         else:
             return (
                 _convert_message_chunk_to_delta(c, i)
@@ -198,14 +198,14 @@ class ChatCompletion:
         models = importlib.import_module("langchain.chat_models")
         model_cls = getattr(models, provider)
         model_config = model_cls(**kwargs)
-        converted_messages = convert_openai_messages(messages)
+        converted_messages = _convert_openai_messages(messages)
         if not stream:
             result = await model_config.ainvoke(converted_messages)
-            return {"choices": [{"message": convert_message_to_dict(result)}]}
+            return {"choices": [{"message": _convert_message_to_dict(result)}]}
         else:
             return (
                 _convert_message_chunk_to_delta(c, i)
-                async for i, c in aenumerate(model_config.astream(converted_messages))
+                async for i, c in _aenumerate(model_config.astream(converted_messages))
             )
 
 
@@ -214,12 +214,12 @@ def _has_assistant_message(session: ChatSession) -> bool:
     return any([isinstance(m, AIMessage) for m in session["messages"]])
 
 
-def convert_messages_for_finetuning(
+def _convert_messages_for_finetuning(
     sessions: Iterable[ChatSession],
 ) -> List[List[dict]]:
     """Convert messages to a list of lists of dictionaries for fine-tuning."""
     return [
-        [convert_message_to_dict(s) for s in session["messages"]]
+        [_convert_message_to_dict(s) for s in session["messages"]]
         for session in sessions
         if _has_assistant_message(session)
     ]
diff --git a/libs/langchain/langchain/callbacks/aim_callback.py b/libs/langchain/langchain/callbacks/aim_callback.py
index 9526f34f14a..8c329f9c079 100644
--- a/libs/langchain/langchain/callbacks/aim_callback.py
+++ b/libs/langchain/langchain/callbacks/aim_callback.py
@@ -5,7 +5,7 @@ from langchain.callbacks.base import BaseCallbackHandler
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 
-def import_aim() -> Any:
+def _import_aim() -> Any:
     """Import the aim python package and raise an error if it is not installed."""
     try:
         import aim
@@ -169,7 +169,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
         super().__init__()
 
-        aim = import_aim()
+        aim = _import_aim()
         self.repo = repo
         self.experiment_name = experiment_name
         self.system_tracking_interval = system_tracking_interval
@@ -184,7 +184,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self.action_records: list = []
 
     def setup(self, **kwargs: Any) -> None:
-        aim = import_aim()
+        aim = _import_aim()
 
         if not self._run:
             if self._run_hash:
@@ -210,7 +210,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
     ) -> None:
         """Run when LLM starts."""
-        aim = import_aim()
+        aim = _import_aim()
 
         self.step += 1
         self.llm_starts += 1
@@ -229,7 +229,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
         """Run when LLM ends running."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.llm_ends += 1
         self.ends += 1
@@ -264,7 +264,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
     ) -> None:
         """Run when chain starts running."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.chain_starts += 1
         self.starts += 1
@@ -280,7 +280,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None:
         """Run when chain ends running."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.chain_ends += 1
         self.ends += 1
@@ -303,7 +303,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
     ) -> None:
         """Run when tool starts running."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.tool_starts += 1
         self.starts += 1
@@ -315,7 +315,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def on_tool_end(self, output: str, **kwargs: Any) -> None:
         """Run when tool ends running."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.tool_ends += 1
         self.ends += 1
@@ -339,7 +339,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> None:
         """Run when agent ends running."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.agent_ends += 1
         self.ends += 1
@@ -356,7 +356,7 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
         """Run on agent action."""
-        aim = import_aim()
+        aim = _import_aim()
         self.step += 1
         self.tool_starts += 1
         self.starts += 1
diff --git a/libs/langchain/langchain/callbacks/arize_callback.py b/libs/langchain/langchain/callbacks/arize_callback.py
index a57de3a905d..d424f535047 100644
--- a/libs/langchain/langchain/callbacks/arize_callback.py
+++ b/libs/langchain/langchain/callbacks/arize_callback.py
@@ -2,7 +2,7 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional
 
 from langchain.callbacks.base import BaseCallbackHandler
-from langchain.callbacks.utils import import_pandas
+from langchain.callbacks.utils import _import_pandas
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 
@@ -60,7 +60,7 @@ class ArizeCallbackHandler(BaseCallbackHandler):
         pass
 
     def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
-        pd = import_pandas()
+        pd = _import_pandas()
         from arize.utils.types import (
             EmbeddingColumnNames,
             Environments,
diff --git a/libs/langchain/langchain/callbacks/clearml_callback.py b/libs/langchain/langchain/callbacks/clearml_callback.py
index daa495693bc..434806fc7c9 100644
--- a/libs/langchain/langchain/callbacks/clearml_callback.py
+++ b/libs/langchain/langchain/callbacks/clearml_callback.py
@@ -8,11 +8,11 @@ from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional, Sequence
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.utils import (
     BaseMetadataCallbackHandler,
+    _import_pandas,
+    _import_spacy,
+    _import_textstat,
     flatten_dict,
     hash_string,
-    import_pandas,
-    import_spacy,
-    import_textstat,
     load_json,
 )
 from langchain.schema import AgentAction, AgentFinish, LLMResult
@@ -21,7 +21,7 @@ if TYPE_CHECKING:
     import pandas as pd
 
 
-def import_clearml() -> Any:
+def _import_clearml() -> Any:
     """Import the clearml python package and raise an error if it is not installed."""
     try:
         import clearml  # noqa: F401
@@ -63,8 +63,8 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
     ) -> None:
         """Initialize callback handler."""
 
-        clearml = import_clearml()
-        spacy = import_spacy()
+        clearml = _import_clearml()
+        spacy = _import_spacy()
         super().__init__()
 
         self.task_type = task_type
@@ -329,8 +329,8 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
             (dict): A dictionary containing the complexity metrics.
         """
         resp = {}
-        textstat = import_textstat()
-        spacy = import_spacy()
+        textstat = _import_textstat()
+        spacy = _import_spacy()
         if self.complexity_metrics:
             text_complexity_metrics = {
                 "flesch_reading_ease": textstat.flesch_reading_ease(text),
@@ -399,7 +399,7 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def _create_session_analysis_df(self) -> Any:
         """Create a dataframe with all the information from the session."""
-        pd = import_pandas()
+        pd = _import_pandas()
         on_llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
 
         llm_input_prompts_df = ClearMLCallbackHandler._build_llm_df(
@@ -465,8 +465,8 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
             Returns:
                 None
         """
-        pd = import_pandas()
-        clearml = import_clearml()
+        pd = _import_pandas()
+        clearml = _import_clearml()
 
         # Log the action records
         self.logger.report_table(
diff --git a/libs/langchain/langchain/callbacks/comet_ml_callback.py b/libs/langchain/langchain/callbacks/comet_ml_callback.py
index 1e8aabb1c16..df92693ef5e 100644
--- a/libs/langchain/langchain/callbacks/comet_ml_callback.py
+++ b/libs/langchain/langchain/callbacks/comet_ml_callback.py
@@ -7,17 +7,17 @@ import langchain
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.utils import (
     BaseMetadataCallbackHandler,
+    _import_pandas,
+    _import_spacy,
+    _import_textstat,
     flatten_dict,
-    import_pandas,
-    import_spacy,
-    import_textstat,
 )
 from langchain.schema import AgentAction, AgentFinish, Generation, LLMResult
 
 LANGCHAIN_MODEL_NAME = "langchain-model"
 
 
-def import_comet_ml() -> Any:
+def _import_comet_ml() -> Any:
     """Import comet_ml and raise an error if it is not installed."""
     try:
         import comet_ml  # noqa: F401
@@ -33,7 +33,7 @@ def import_comet_ml() -> Any:
 def _get_experiment(
     workspace: Optional[str] = None, project_name: Optional[str] = None
 ) -> Any:
-    comet_ml = import_comet_ml()
+    comet_ml = _import_comet_ml()
 
     experiment = comet_ml.Experiment(  # type: ignore
         workspace=workspace,
@@ -44,7 +44,7 @@ def _get_experiment(
 
 
 def _fetch_text_complexity_metrics(text: str) -> dict:
-    textstat = import_textstat()
+    textstat = _import_textstat()
     text_complexity_metrics = {
         "flesch_reading_ease": textstat.flesch_reading_ease(text),
         "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
@@ -67,7 +67,7 @@ def _fetch_text_complexity_metrics(text: str) -> dict:
 
 
 def _summarize_metrics_for_generated_outputs(metrics: Sequence) -> dict:
-    pd = import_pandas()
+    pd = _import_pandas()
     metrics_df = pd.DataFrame(metrics)
     metrics_summary = metrics_df.describe()
 
@@ -107,7 +107,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
     ) -> None:
         """Initialize callback handler."""
 
-        self.comet_ml = import_comet_ml()
+        self.comet_ml = _import_comet_ml()
         super().__init__()
 
         self.task_type = task_type
@@ -140,7 +140,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self.action_records: list = []
         self.complexity_metrics = complexity_metrics
         if self.visualizations:
-            spacy = import_spacy()
+            spacy = _import_spacy()
             self.nlp = spacy.load("en_core_web_sm")
         else:
             self.nlp = None
@@ -535,7 +535,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         if not (self.visualizations and self.nlp):
             return
 
-        spacy = import_spacy()
+        spacy = _import_spacy()
 
         prompts = session_df["prompts"].tolist()
         outputs = session_df["text"].tolist()
@@ -603,7 +603,7 @@ class CometCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         self.temp_dir = tempfile.TemporaryDirectory()
 
     def _create_session_analysis_dataframe(self, langchain_asset: Any = None) -> dict:
-        pd = import_pandas()
+        pd = _import_pandas()
 
         llm_parameters = self._get_llm_parameters(langchain_asset)
         num_generations_per_prompt = llm_parameters.get("n", 1)
diff --git a/libs/langchain/langchain/callbacks/context_callback.py b/libs/langchain/langchain/callbacks/context_callback.py
index 6306763114e..c9548ce0688 100644
--- a/libs/langchain/langchain/callbacks/context_callback.py
+++ b/libs/langchain/langchain/callbacks/context_callback.py
@@ -10,7 +10,7 @@ from langchain.schema import (
 )
 
 
-def import_context() -> Any:
+def _import_context() -> Any:
     """Import the `getcontext` package."""
     try:
         import getcontext  # noqa: F401
@@ -98,7 +98,7 @@ class ContextCallbackHandler(BaseCallbackHandler):
             self.message_model,
             self.message_role_model,
             self.rating_model,
-        ) = import_context()
+        ) = _import_context()
 
         token = token or os.environ.get("CONTEXT_TOKEN") or ""
 
diff --git a/libs/langchain/langchain/callbacks/flyte_callback.py b/libs/langchain/langchain/callbacks/flyte_callback.py
index ad696b81393..e3eeb9ac1ff 100644
--- a/libs/langchain/langchain/callbacks/flyte_callback.py
+++ b/libs/langchain/langchain/callbacks/flyte_callback.py
@@ -8,10 +8,10 @@ from typing import TYPE_CHECKING, Any, Dict, List, Tuple
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.utils import (
     BaseMetadataCallbackHandler,
+    _import_pandas,
+    _import_spacy,
+    _import_textstat,
     flatten_dict,
-    import_pandas,
-    import_spacy,
-    import_textstat,
 )
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
@@ -22,7 +22,7 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-def import_flytekit() -> Tuple[flytekit, renderer]:
+def _import_flytekit() -> Tuple[flytekit, renderer]:
     """Import flytekit and flytekitplugins-deck-standard."""
     try:
         import flytekit  # noqa: F401
@@ -75,7 +75,7 @@ def analyze_text(
         resp.update(text_complexity_metrics)
 
     if nlp is not None:
-        spacy = import_spacy()
+        spacy = _import_spacy()
         doc = nlp(text)
         dep_out = spacy.displacy.render(  # type: ignore
             doc, style="dep", jupyter=False, page=True
@@ -97,12 +97,12 @@ class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def __init__(self) -> None:
         """Initialize callback handler."""
-        flytekit, renderer = import_flytekit()
-        self.pandas = import_pandas()
+        flytekit, renderer = _import_flytekit()
+        self.pandas = _import_pandas()
 
         self.textstat = None
         try:
-            self.textstat = import_textstat()
+            self.textstat = _import_textstat()
         except ImportError:
             logger.warning(
                 "Textstat library is not installed. \
@@ -112,7 +112,7 @@ class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
         spacy = None
         try:
-            spacy = import_spacy()
+            spacy = _import_spacy()
         except ImportError:
             logger.warning(
                 "Spacy library is not installed. \
diff --git a/libs/langchain/langchain/callbacks/infino_callback.py b/libs/langchain/langchain/callbacks/infino_callback.py
index 8d54f08037a..9ffb4ab119d 100644
--- a/libs/langchain/langchain/callbacks/infino_callback.py
+++ b/libs/langchain/langchain/callbacks/infino_callback.py
@@ -6,7 +6,7 @@ from langchain.schema import AgentAction, AgentFinish, LLMResult
 from langchain.schema.messages import BaseMessage
 
 
-def import_infino() -> Any:
+def _import_infino() -> Any:
     """Import the infino client."""
     try:
         from infinopy import InfinoClient
@@ -19,7 +19,7 @@ def import_infino() -> Any:
     return InfinoClient()
 
 
-def import_tiktoken() -> Any:
+def _import_tiktoken() -> Any:
     """Import tiktoken for counting tokens for OpenAI models."""
     try:
         import tiktoken
@@ -38,7 +38,7 @@ def get_num_tokens(string: str, openai_model_name: str) -> int:
     Official documentation: https://github.com/openai/openai-cookbook/blob/main
                             /examples/How_to_count_tokens_with_tiktoken.ipynb
     """
-    tiktoken = import_tiktoken()
+    tiktoken = _import_tiktoken()
 
     encoding = tiktoken.encoding_for_model(openai_model_name)
     num_tokens = len(encoding.encode(string))
@@ -55,7 +55,7 @@ class InfinoCallbackHandler(BaseCallbackHandler):
         verbose: bool = False,
     ) -> None:
         # Set Infino client
-        self.client = import_infino()
+        self.client = _import_infino()
         self.model_id = model_id
         self.model_version = model_version
         self.verbose = verbose
diff --git a/libs/langchain/langchain/callbacks/labelstudio_callback.py b/libs/langchain/langchain/callbacks/labelstudio_callback.py
index de3c669b00a..8bd96dfdf8c 100644
--- a/libs/langchain/langchain/callbacks/labelstudio_callback.py
+++ b/libs/langchain/langchain/callbacks/labelstudio_callback.py
@@ -23,7 +23,7 @@ class LabelStudioMode(Enum):
     CHAT = "chat"
 
 
-def get_default_label_configs(
+def _get_default_label_configs(
     mode: Union[str, LabelStudioMode]
 ) -> Tuple[str, LabelStudioMode]:
     """Get default Label Studio configs for the given mode.
@@ -173,7 +173,7 @@ class LabelStudioCallbackHandler(BaseCallbackHandler):
             self.project_config = project_config
             self.mode = None
         else:
-            self.project_config, self.mode = get_default_label_configs(mode)
+            self.project_config, self.mode = _get_default_label_configs(mode)
 
         self.project_id = project_id or os.getenv("LABEL_STUDIO_PROJECT_ID")
         if self.project_id is not None:
diff --git a/libs/langchain/langchain/callbacks/mlflow_callback.py b/libs/langchain/langchain/callbacks/mlflow_callback.py
index 85ffcfdb06d..207898feb7c 100644
--- a/libs/langchain/langchain/callbacks/mlflow_callback.py
+++ b/libs/langchain/langchain/callbacks/mlflow_callback.py
@@ -10,17 +10,17 @@ from typing import Any, Dict, List, Optional, Union
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.utils import (
     BaseMetadataCallbackHandler,
+    _import_pandas,
+    _import_spacy,
+    _import_textstat,
     flatten_dict,
     hash_string,
-    import_pandas,
-    import_spacy,
-    import_textstat,
 )
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 from langchain.utils import get_from_dict_or_env
 
 
-def import_mlflow() -> Any:
+def _import_mlflow() -> Any:
     """Import the mlflow python package and raise an error if it is not installed."""
     try:
         import mlflow
@@ -47,8 +47,8 @@ def analyze_text(
             files serialized to  HTML string.
     """
     resp: Dict[str, Any] = {}
-    textstat = import_textstat()
-    spacy = import_spacy()
+    textstat = _import_textstat()
+    spacy = _import_spacy()
     text_complexity_metrics = {
         "flesch_reading_ease": textstat.flesch_reading_ease(text),
         "flesch_kincaid_grade": textstat.flesch_kincaid_grade(text),
@@ -127,7 +127,7 @@ class MlflowLogger:
     """
 
     def __init__(self, **kwargs: Any):
-        self.mlflow = import_mlflow()
+        self.mlflow = _import_mlflow()
         if "DATABRICKS_RUNTIME_VERSION" in os.environ:
             self.mlflow.set_tracking_uri("databricks")
             self.mlf_expid = self.mlflow.tracking.fluent._get_experiment_id()
@@ -246,10 +246,10 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         tracking_uri: Optional[str] = None,
     ) -> None:
         """Initialize callback handler."""
-        import_pandas()
-        import_textstat()
-        import_mlflow()
-        spacy = import_spacy()
+        _import_pandas()
+        _import_textstat()
+        _import_mlflow()
+        spacy = _import_spacy()
         super().__init__()
 
         self.name = name
@@ -547,7 +547,7 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def _create_session_analysis_df(self) -> Any:
         """Create a dataframe with all the information from the session."""
-        pd = import_pandas()
+        pd = _import_pandas()
         on_llm_start_records_df = pd.DataFrame(self.records["on_llm_start_records"])
         on_llm_end_records_df = pd.DataFrame(self.records["on_llm_end_records"])
 
@@ -617,7 +617,7 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
         return session_analysis_df
 
     def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None:
-        pd = import_pandas()
+        pd = _import_pandas()
         self.mlflg.table("action_records", pd.DataFrame(self.records["action_records"]))
         session_analysis_df = self._create_session_analysis_df()
         chat_html = session_analysis_df.pop("chat_html")
diff --git a/libs/langchain/langchain/callbacks/utils.py b/libs/langchain/langchain/callbacks/utils.py
index b83bace6016..a04f014bb08 100644
--- a/libs/langchain/langchain/callbacks/utils.py
+++ b/libs/langchain/langchain/callbacks/utils.py
@@ -3,7 +3,7 @@ from pathlib import Path
 from typing import Any, Dict, Iterable, Tuple, Union
 
 
-def import_spacy() -> Any:
+def _import_spacy() -> Any:
     """Import the spacy python package and raise an error if it is not installed."""
     try:
         import spacy
@@ -15,7 +15,7 @@ def import_spacy() -> Any:
     return spacy
 
 
-def import_pandas() -> Any:
+def _import_pandas() -> Any:
     """Import the pandas python package and raise an error if it is not installed."""
     try:
         import pandas
@@ -27,7 +27,7 @@ def import_pandas() -> Any:
     return pandas
 
 
-def import_textstat() -> Any:
+def _import_textstat() -> Any:
     """Import the textstat python package and raise an error if it is not installed."""
     try:
         import textstat
diff --git a/libs/langchain/langchain/callbacks/wandb_callback.py b/libs/langchain/langchain/callbacks/wandb_callback.py
index 18102b34bdd..182ed08bc87 100644
--- a/libs/langchain/langchain/callbacks/wandb_callback.py
+++ b/libs/langchain/langchain/callbacks/wandb_callback.py
@@ -7,16 +7,16 @@ from typing import Any, Dict, List, Optional, Sequence, Union
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.utils import (
     BaseMetadataCallbackHandler,
+    _import_pandas,
+    _import_spacy,
+    _import_textstat,
     flatten_dict,
     hash_string,
-    import_pandas,
-    import_spacy,
-    import_textstat,
 )
 from langchain.schema import AgentAction, AgentFinish, LLMResult
 
 
-def import_wandb() -> Any:
+def _import_wandb() -> Any:
     """Import the wandb python package and raise an error if it is not installed."""
     try:
         import wandb  # noqa: F401
@@ -28,7 +28,7 @@ def import_wandb() -> Any:
     return wandb
 
 
-def load_json_to_dict(json_path: Union[str, Path]) -> dict:
+def _load_json_to_dict(json_path: Union[str, Path]) -> dict:
     """Load json file to a dictionary.
 
     Parameters:
@@ -42,7 +42,7 @@ def load_json_to_dict(json_path: Union[str, Path]) -> dict:
     return data
 
 
-def analyze_text(
+def _analyze_text(
     text: str,
     complexity_metrics: bool = True,
     visualize: bool = True,
@@ -63,9 +63,9 @@ def analyze_text(
             files serialized in a wandb.Html element.
     """
     resp = {}
-    textstat = import_textstat()
-    wandb = import_wandb()
-    spacy = import_spacy()
+    textstat = _import_textstat()
+    wandb = _import_wandb()
+    spacy = _import_spacy()
     if complexity_metrics:
         text_complexity_metrics = {
             "flesch_reading_ease": textstat.flesch_reading_ease(text),
@@ -120,7 +120,7 @@ def construct_html_from_prompt_and_generation(prompt: str, generation: str) -> A
 
     Returns:
         (wandb.Html): The html element."""
-    wandb = import_wandb()
+    wandb = _import_wandb()
     formatted_prompt = prompt.replace("\n", "<br>")
     formatted_generation = generation.replace("\n", "<br>")
 
@@ -173,10 +173,10 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
     ) -> None:
         """Initialize callback handler."""
 
-        wandb = import_wandb()
-        import_pandas()
-        import_textstat()
-        spacy = import_spacy()
+        wandb = _import_wandb()
+        _import_pandas()
+        _import_textstat()
+        spacy = _import_spacy()
         super().__init__()
 
         self.job_type = job_type
@@ -269,7 +269,7 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
                 generation_resp = deepcopy(resp)
                 generation_resp.update(flatten_dict(generation.dict()))
                 generation_resp.update(
-                    analyze_text(
+                    _analyze_text(
                         generation.text,
                         complexity_metrics=self.complexity_metrics,
                         visualize=self.visualize,
@@ -438,7 +438,7 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
 
     def _create_session_analysis_df(self) -> Any:
         """Create a dataframe with all the information from the session."""
-        pd = import_pandas()
+        pd = _import_pandas()
         on_llm_start_records_df = pd.DataFrame(self.on_llm_start_records)
         on_llm_end_records_df = pd.DataFrame(self.on_llm_end_records)
 
@@ -533,8 +533,8 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
             Returns:
                 None
         """
-        pd = import_pandas()
-        wandb = import_wandb()
+        pd = _import_pandas()
+        wandb = _import_wandb()
         action_records_table = wandb.Table(dataframe=pd.DataFrame(self.action_records))
         session_analysis_table = wandb.Table(
             dataframe=self._create_session_analysis_df()
@@ -554,11 +554,11 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler):
             try:
                 langchain_asset.save(langchain_asset_path)
                 model_artifact.add_file(str(langchain_asset_path))
-                model_artifact.metadata = load_json_to_dict(langchain_asset_path)
+                model_artifact.metadata = _load_json_to_dict(langchain_asset_path)
             except ValueError:
                 langchain_asset.save_agent(langchain_asset_path)
                 model_artifact.add_file(str(langchain_asset_path))
-                model_artifact.metadata = load_json_to_dict(langchain_asset_path)
+                model_artifact.metadata = _load_json_to_dict(langchain_asset_path)
             except NotImplementedError as e:
                 print("Could not save model.")
                 print(repr(e))
diff --git a/libs/langchain/langchain/callbacks/whylabs_callback.py b/libs/langchain/langchain/callbacks/whylabs_callback.py
index 0eef25d3609..103ba9afe1a 100644
--- a/libs/langchain/langchain/callbacks/whylabs_callback.py
+++ b/libs/langchain/langchain/callbacks/whylabs_callback.py
@@ -12,7 +12,7 @@ if TYPE_CHECKING:
 diagnostic_logger = logging.getLogger(__name__)
 
 
-def import_langkit(
+def _import_langkit(
     sentiment: bool = False,
     toxicity: bool = False,
     themes: bool = False,
@@ -159,7 +159,7 @@ class WhyLabsCallbackHandler(BaseCallbackHandler):
                 WhyLabs writer.
         """
         # langkit library will import necessary whylogs libraries
-        import_langkit(sentiment=sentiment, toxicity=toxicity, themes=themes)
+        _import_langkit(sentiment=sentiment, toxicity=toxicity, themes=themes)
 
         import whylogs as why
         from langkit.callback_handler import get_callback_instance
diff --git a/libs/langchain/langchain/chat_loaders/langsmith.py b/libs/langchain/langchain/chat_loaders/langsmith.py
index db9b85ea858..f05f01ffe12 100644
--- a/libs/langchain/langchain/chat_loaders/langsmith.py
+++ b/libs/langchain/langchain/chat_loaders/langsmith.py
@@ -149,7 +149,7 @@ class LangSmithDatasetChatLoader(BaseChatLoader):
         for data_point in data:
             yield ChatSession(
                 messages=[
-                    oai_adapter.convert_dict_to_message(m)
+                    oai_adapter._convert_dict_to_message(m)
                     for m in data_point.get("messages", [])
                 ],
                 functions=data_point.get("functions"),
diff --git a/libs/langchain/langchain/chat_models/anyscale.py b/libs/langchain/langchain/chat_models/anyscale.py
index 3a576820427..c05c299a217 100644
--- a/libs/langchain/langchain/chat_models/anyscale.py
+++ b/libs/langchain/langchain/chat_models/anyscale.py
@@ -8,7 +8,7 @@ from typing import TYPE_CHECKING, Dict, Optional, Set
 
 import requests
 
-from langchain.adapters.openai import convert_message_to_dict
+from langchain.adapters.openai import _convert_message_to_dict
 from langchain.chat_models.openai import (
     ChatOpenAI,
     _import_tiktoken,
@@ -178,7 +178,7 @@ class ChatAnyscale(ChatOpenAI):
         tokens_per_message = 3
         tokens_per_name = 1
         num_tokens = 0
-        messages_dict = [convert_message_to_dict(m) for m in messages]
+        messages_dict = [_convert_message_to_dict(m) for m in messages]
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
diff --git a/libs/langchain/langchain/chat_models/everlyai.py b/libs/langchain/langchain/chat_models/everlyai.py
index 0b5dbd85d06..ee60e2d5b27 100644
--- a/libs/langchain/langchain/chat_models/everlyai.py
+++ b/libs/langchain/langchain/chat_models/everlyai.py
@@ -5,7 +5,7 @@ import logging
 import sys
 from typing import TYPE_CHECKING, Dict, Optional, Set
 
-from langchain.adapters.openai import convert_message_to_dict
+from langchain.adapters.openai import _convert_message_to_dict
 from langchain.chat_models.openai import (
     ChatOpenAI,
     _import_tiktoken,
@@ -140,7 +140,7 @@ class ChatEverlyAI(ChatOpenAI):
         tokens_per_message = 3
         tokens_per_name = 1
         num_tokens = 0
-        messages_dict = [convert_message_to_dict(m) for m in messages]
+        messages_dict = [_convert_message_to_dict(m) for m in messages]
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
diff --git a/libs/langchain/langchain/chat_models/fireworks.py b/libs/langchain/langchain/chat_models/fireworks.py
index 841ee8a0d84..6fb1bacfe14 100644
--- a/libs/langchain/langchain/chat_models/fireworks.py
+++ b/libs/langchain/langchain/chat_models/fireworks.py
@@ -10,7 +10,7 @@ from typing import (
     Union,
 )
 
-from langchain.adapters.openai import convert_message_to_dict
+from langchain.adapters.openai import _convert_message_to_dict
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -168,7 +168,7 @@ class ChatFireworks(BaseChatModel):
     def _create_message_dicts(
         self, messages: List[BaseMessage]
     ) -> List[Dict[str, Any]]:
-        message_dicts = [convert_message_to_dict(m) for m in messages]
+        message_dicts = [_convert_message_to_dict(m) for m in messages]
         return message_dicts
 
     def _stream(
diff --git a/libs/langchain/langchain/chat_models/konko.py b/libs/langchain/langchain/chat_models/konko.py
index ab46a4c2d9a..daf0306e3f1 100644
--- a/libs/langchain/langchain/chat_models/konko.py
+++ b/libs/langchain/langchain/chat_models/konko.py
@@ -17,7 +17,7 @@ from typing import (
 
 import requests
 
-from langchain.adapters.openai import convert_dict_to_message, convert_message_to_dict
+from langchain.adapters.openai import _convert_dict_to_message, _convert_message_to_dict
 from langchain.callbacks.manager import (
     CallbackManagerForLLMRun,
 )
@@ -246,13 +246,13 @@ class ChatKonko(ChatOpenAI):
             if "stop" in params:
                 raise ValueError("`stop` found in both the input and default params.")
             params["stop"] = stop
-        message_dicts = [convert_message_to_dict(m) for m in messages]
+        message_dicts = [_convert_message_to_dict(m) for m in messages]
         return message_dicts, params
 
     def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
         generations = []
         for res in response["choices"]:
-            message = convert_dict_to_message(res["message"])
+            message = _convert_dict_to_message(res["message"])
             gen = ChatGeneration(
                 message=message,
                 generation_info=dict(finish_reason=res.get("finish_reason")),
diff --git a/libs/langchain/langchain/chat_models/openai.py b/libs/langchain/langchain/chat_models/openai.py
index a09dfd3e9cb..549a458f2f9 100644
--- a/libs/langchain/langchain/chat_models/openai.py
+++ b/libs/langchain/langchain/chat_models/openai.py
@@ -18,7 +18,7 @@ from typing import (
     Union,
 )
 
-from langchain.adapters.openai import convert_dict_to_message, convert_message_to_dict
+from langchain.adapters.openai import _convert_dict_to_message, _convert_message_to_dict
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
     CallbackManagerForLLMRun,
@@ -370,13 +370,13 @@ class ChatOpenAI(BaseChatModel):
             if "stop" in params:
                 raise ValueError("`stop` found in both the input and default params.")
             params["stop"] = stop
-        message_dicts = [convert_message_to_dict(m) for m in messages]
+        message_dicts = [_convert_message_to_dict(m) for m in messages]
         return message_dicts, params
 
     def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
         generations = []
         for res in response["choices"]:
-            message = convert_dict_to_message(res["message"])
+            message = _convert_dict_to_message(res["message"])
             gen = ChatGeneration(
                 message=message,
                 generation_info=dict(finish_reason=res.get("finish_reason")),
@@ -528,7 +528,7 @@ class ChatOpenAI(BaseChatModel):
                 "information on how messages are converted to tokens."
             )
         num_tokens = 0
-        messages_dict = [convert_message_to_dict(m) for m in messages]
+        messages_dict = [_convert_message_to_dict(m) for m in messages]
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_openai.py b/libs/langchain/tests/unit_tests/chat_models/test_openai.py
index c2337247240..f261f344130 100644
--- a/libs/langchain/tests/unit_tests/chat_models/test_openai.py
+++ b/libs/langchain/tests/unit_tests/chat_models/test_openai.py
@@ -5,7 +5,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from langchain.adapters.openai import convert_dict_to_message
+from langchain.adapters.openai import _convert_dict_to_message
 from langchain.chat_models.openai import ChatOpenAI
 from langchain.schema.messages import (
     AIMessage,
@@ -26,7 +26,7 @@ def test_openai_model_param() -> None:
 def test_function_message_dict_to_function_message() -> None:
     content = json.dumps({"result": "Example #1"})
     name = "test_function"
-    result = convert_dict_to_message(
+    result = _convert_dict_to_message(
         {
             "role": "function",
             "name": name,
@@ -40,21 +40,21 @@ def test_function_message_dict_to_function_message() -> None:
 
 def test__convert_dict_to_message_human() -> None:
     message = {"role": "user", "content": "foo"}
-    result = convert_dict_to_message(message)
+    result = _convert_dict_to_message(message)
     expected_output = HumanMessage(content="foo")
     assert result == expected_output
 
 
 def test__convert_dict_to_message_ai() -> None:
     message = {"role": "assistant", "content": "foo"}
-    result = convert_dict_to_message(message)
+    result = _convert_dict_to_message(message)
     expected_output = AIMessage(content="foo")
     assert result == expected_output
 
 
 def test__convert_dict_to_message_system() -> None:
     message = {"role": "system", "content": "foo"}
-    result = convert_dict_to_message(message)
+    result = _convert_dict_to_message(message)
     expected_output = SystemMessage(content="foo")
     assert result == expected_output