diff --git a/langchain/chat_models/azure_openai.py b/langchain/chat_models/azure_openai.py
index ed79be35..4d4792e8 100644
--- a/langchain/chat_models/azure_openai.py
+++ b/langchain/chat_models/azure_openai.py
@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import logging
-from typing import Any, Dict
+from typing import Any, Dict, Mapping
 
 from pydantic import root_validator
 
@@ -110,3 +110,12 @@ class AzureChatOpenAI(ChatOpenAI):
             **super()._default_params,
             "engine": self.deployment_name,
         }
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {**self._default_params}
+
+    @property
+    def _llm_type(self) -> str:
+        return "azure-openai-chat"
diff --git a/langchain/chat_models/base.py b/langchain/chat_models/base.py
index 3a8bfb1a..14b8e486 100644
--- a/langchain/chat_models/base.py
+++ b/langchain/chat_models/base.py
@@ -2,7 +2,7 @@ import asyncio
 import inspect
 import warnings
 from abc import ABC, abstractmethod
-from typing import Dict, List, Optional
+from typing import Any, Dict, List, Mapping, Optional
 
 from pydantic import Extra, Field, root_validator
 
@@ -65,11 +65,14 @@ class BaseChatModel(BaseLanguageModel, ABC):
     ) -> LLMResult:
         """Top Level call"""
 
+        params = self.dict()
+        params["stop"] = stop
+
         callback_manager = CallbackManager.configure(
             callbacks, self.callbacks, self.verbose
         )
         run_manager = callback_manager.on_chat_model_start(
-            {"name": self.__class__.__name__}, messages
+            {"name": self.__class__.__name__}, messages, invocation_params=params
         )
 
         new_arg_supported = inspect.signature(self._generate).parameters.get(
@@ -98,12 +101,14 @@ class BaseChatModel(BaseLanguageModel, ABC):
         callbacks: Callbacks = None,
     ) -> LLMResult:
         """Top Level call"""
+        params = self.dict()
+        params["stop"] = stop
 
         callback_manager = AsyncCallbackManager.configure(
             callbacks, self.callbacks, self.verbose
         )
         run_manager = await callback_manager.on_chat_model_start(
-            {"name": self.__class__.__name__}, messages
+            {"name": self.__class__.__name__}, messages, invocation_params=params
         )
 
         new_arg_supported = inspect.signature(self._agenerate).parameters.get(
@@ -181,6 +186,22 @@ class BaseChatModel(BaseLanguageModel, ABC):
         result = self([HumanMessage(content=message)], stop=stop)
         return result.content
 
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {}
+
+    @property
+    @abstractmethod
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+
+    def dict(self, **kwargs: Any) -> Dict:
+        """Return a dictionary of the LLM."""
+        starter_dict = dict(self._identifying_params)
+        starter_dict["_type"] = self._llm_type
+        return starter_dict
+
 
 class SimpleChatModel(BaseChatModel):
     def _generate(
diff --git a/langchain/chat_models/google_palm.py b/langchain/chat_models/google_palm.py
index 4431918e..0e9a2a15 100644
--- a/langchain/chat_models/google_palm.py
+++ b/langchain/chat_models/google_palm.py
@@ -1,7 +1,7 @@
 """Wrapper around Google's PaLM Chat API."""
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Mapping, Optional
 
 from pydantic import BaseModel, root_validator
 
@@ -256,3 +256,18 @@ class ChatGooglePalm(BaseChatModel, BaseModel):
         )
 
         return _response_to_result(response, stop)
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        """Get the identifying parameters."""
+        return {
+            "model_name": self.model_name,
+            "temperature": self.temperature,
+            "top_p": self.top_p,
+            "top_k": self.top_k,
+            "n": self.n,
+        }
+
+    @property
+    def _llm_type(self) -> str:
+        return "google-palm-chat"
diff --git a/langchain/chat_models/openai.py b/langchain/chat_models/openai.py
index a4d2ac87..cf60fdc4 100644
--- a/langchain/chat_models/openai.py
+++ b/langchain/chat_models/openai.py
@@ -347,6 +347,11 @@ class ChatOpenAI(BaseChatModel):
         """Get the identifying parameters."""
         return {**{"model_name": self.model_name}, **self._default_params}
 
+    @property
+    def _llm_type(self) -> str:
+        """Return type of chat model."""
+        return "openai-chat"
+
     def get_num_tokens(self, text: str) -> int:
         """Calculate num tokens with tiktoken package."""
         # tiktoken NOT supported for Python 3.7 or below
diff --git a/langchain/chat_models/promptlayer_openai.py b/langchain/chat_models/promptlayer_openai.py
index 6f9b9a08..59695994 100644
--- a/langchain/chat_models/promptlayer_openai.py
+++ b/langchain/chat_models/promptlayer_openai.py
@@ -1,6 +1,6 @@
 """PromptLayer wrapper."""
 import datetime
-from typing import List, Optional
+from typing import Any, List, Mapping, Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -109,3 +109,15 @@ class PromptLayerChatOpenAI(ChatOpenAI):
                     generation.generation_info = {}
                 generation.generation_info["pl_request_id"] = pl_request_id
         return generated_responses
+
+    @property
+    def _llm_type(self) -> str:
+        return "promptlayer-openai-chat"
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {
+            **super()._identifying_params,
+            "pl_tags": self.pl_tags,
+            "return_pl_id": self.return_pl_id,
+        }
diff --git a/tests/unit_tests/llms/fake_chat_model.py b/tests/unit_tests/llms/fake_chat_model.py
index 1f0a8e28..c8705d1c 100644
--- a/tests/unit_tests/llms/fake_chat_model.py
+++ b/tests/unit_tests/llms/fake_chat_model.py
@@ -1,5 +1,5 @@
 """Fake Chat Model wrapper for testing purposes."""
-from typing import List, Optional
+from typing import Any, List, Mapping, Optional
 
 from langchain.callbacks.manager import (
     AsyncCallbackManagerForLLMRun,
@@ -30,3 +30,11 @@ class FakeChatModel(SimpleChatModel):
         message = AIMessage(content=output_str)
         generation = ChatGeneration(message=message)
         return ChatResult(generations=[generation])
+
+    @property
+    def _llm_type(self) -> str:
+        return "fake-chat-model"
+
+    @property
+    def _identifying_params(self) -> Mapping[str, Any]:
+        return {"key": "fake"}