diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index aeea27cff675..b434fc648eb1 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -31,7 +31,7 @@
 from ..function_utils import get_function_schema, load_basemodels_if_needed, serialize_to_str
 from ..io.base import IOStream
 from ..oai.client import ModelClient, OpenAIWrapper
-from ..runtime_logging import log_event, log_new_agent, logging_enabled
+from ..runtime_logging import log_event, log_function_use, log_new_agent, logging_enabled
 from .agent import Agent, LLMAgent
 from .chat import ChatResult, a_initiate_chats, initiate_chats
 from .utils import consolidate_chat_info, gather_usage_summary
@@ -1357,9 +1357,7 @@ def _generate_oai_reply_from_client(self, llm_client, messages, cache) -> Union[
 
         # TODO: #1143 handle token limit exceeded error
         response = llm_client.create(
-            context=messages[-1].pop("context", None),
-            messages=all_messages,
-            cache=cache,
+            context=messages[-1].pop("context", None), messages=all_messages, cache=cache, agent=self
         )
         extracted_response = llm_client.extract_text_or_completion_object(response)[0]
 
@@ -2528,13 +2526,14 @@ def _wrap_function(self, func: F) -> F:
         @functools.wraps(func)
         def _wrapped_func(*args, **kwargs):
             retval = func(*args, **kwargs)
-
+            log_function_use(self, func, kwargs, retval)
             return serialize_to_str(retval)
 
         @load_basemodels_if_needed
         @functools.wraps(func)
         async def _a_wrapped_func(*args, **kwargs):
             retval = await func(*args, **kwargs)
+            log_function_use(self, func, kwargs, retval)
             return serialize_to_str(retval)
 
         wrapped_func = _a_wrapped_func if inspect.iscoroutinefunction(func) else _wrapped_func
diff --git a/autogen/logger/base_logger.py b/autogen/logger/base_logger.py
index 7c35f8a5091d..c5c236fa4ae2 100644
--- a/autogen/logger/base_logger.py
+++ b/autogen/logger/base_logger.py
@@ -3,7 +3,7 @@
 import sqlite3
 import uuid
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Any, Dict, List, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, TypeVar, Union
 
 from openai import AzureOpenAI, OpenAI
 from openai.types.chat import ChatCompletion
@@ -11,6 +11,7 @@
 if TYPE_CHECKING:
     from autogen import Agent, ConversableAgent, OpenAIWrapper
 
+F = TypeVar("F", bound=Callable[..., Any])
 ConfigItem = Dict[str, Union[str, List[str]]]
 LLMConfig = Dict[str, Union[None, float, int, ConfigItem, List[ConfigItem]]]
 
@@ -32,6 +33,7 @@ def log_chat_completion(
         invocation_id: uuid.UUID,
         client_id: int,
         wrapper_id: int,
+        source: Union[str, Agent],
         request: Dict[str, Union[float, str, List[Dict[str, str]]]],
         response: Union[str, ChatCompletion],
         is_cached: int,
@@ -49,9 +51,10 @@ def log_chat_completion(
             invocation_id (uuid):               A unique identifier for the invocation to the OpenAIWrapper.create method call
             client_id (int):                    A unique identifier for the underlying OpenAI client instance
             wrapper_id (int):                   A unique identifier for the OpenAIWrapper instance
-            request (dict):                     A dictionary representing the the request or call to the OpenAI client endpoint
+            source (str or Agent):              The source/creator of the event as a string name or an Agent instance
+            request (dict):                     A dictionary representing the request or call to the OpenAI client endpoint
             response (str or ChatCompletion):   The response from OpenAI
-            is_chached (int):                   1 if the response was a cache hit, 0 otherwise
+            is_cached (int):                    1 if the response was a cache hit, 0 otherwise
             cost(float):                        The cost for OpenAI response
             start_time (str):                   A string representing the moment the request was initiated
         """
@@ -104,6 +107,18 @@ def log_new_client(
         """
         ...
 
+    @abstractmethod
+    def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[str, Any], returns: Any) -> None:
+        """
+        Log the use of a registered function (could be a tool)
+
+        Args:
+            source (str or Agent):      The source/creator of the event as a string name or an Agent instance
+            function (F):               The function information
+            args (dict):                The function args to log
+            returns (any):              The return
+        """
+
     @abstractmethod
     def stop(self) -> None:
         """
diff --git a/autogen/logger/file_logger.py b/autogen/logger/file_logger.py
index fedcc134ab9e..15b2c457e420 100644
--- a/autogen/logger/file_logger.py
+++ b/autogen/logger/file_logger.py
@@ -5,7 +5,7 @@
 import os
 import threading
 import uuid
-from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, TypeVar, Union
 
 from openai import AzureOpenAI, OpenAI
 from openai.types.chat import ChatCompletion
@@ -21,9 +21,21 @@
 
 logger = logging.getLogger(__name__)
 
+F = TypeVar("F", bound=Callable[..., Any])
+
 __all__ = ("FileLogger",)
 
 
+def safe_serialize(obj: Any) -> str:
+    def default(o: Any) -> str:
+        if hasattr(o, "to_json"):
+            return str(o.to_json())
+        else:
+            return f"<<non-serializable: {type(o).__qualname__}>>"
+
+    return json.dumps(obj, default=default)
+
+
 class FileLogger(BaseLogger):
     def __init__(self, config: Dict[str, Any]):
         self.config = config
@@ -59,6 +71,7 @@ def log_chat_completion(
         invocation_id: uuid.UUID,
         client_id: int,
         wrapper_id: int,
+        source: Union[str, Agent],
         request: Dict[str, Union[float, str, List[Dict[str, str]]]],
         response: Union[str, ChatCompletion],
         is_cached: int,
@@ -69,6 +82,11 @@ def log_chat_completion(
         Log a chat completion.
         """
         thread_id = threading.get_ident()
+        source_name = None
+        if isinstance(source, str):
+            source_name = source
+        else:
+            source_name = source.name
         try:
             log_data = json.dumps(
                 {
@@ -82,6 +100,7 @@ def log_chat_completion(
                     "start_time": start_time,
                     "end_time": get_current_ts(),
                     "thread_id": thread_id,
+                    "source_name": source_name,
                 }
             )
 
@@ -204,6 +223,29 @@ def log_new_client(
         except Exception as e:
             self.logger.error(f"[file_logger] Failed to log event {e}")
 
+    def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[str, Any], returns: Any) -> None:
+        """
+        Log a registered function(can be a tool) use from an agent or a string source.
+        """
+        thread_id = threading.get_ident()
+
+        try:
+            log_data = json.dumps(
+                {
+                    "source_id": id(source),
+                    "source_name": str(source.name) if hasattr(source, "name") else source,
+                    "agent_module": source.__module__,
+                    "agent_class": source.__class__.__name__,
+                    "timestamp": get_current_ts(),
+                    "thread_id": thread_id,
+                    "input_args": safe_serialize(args),
+                    "returns": safe_serialize(returns),
+                }
+            )
+            self.logger.info(log_data)
+        except Exception as e:
+            self.logger.error(f"[file_logger] Failed to log event {e}")
+
     def get_connection(self) -> None:
         """Method is intentionally left blank because there is no specific connection needed for the FileLogger."""
         pass
diff --git a/autogen/logger/sqlite_logger.py b/autogen/logger/sqlite_logger.py
index 48d33082f86b..4ebea32cc199 100644
--- a/autogen/logger/sqlite_logger.py
+++ b/autogen/logger/sqlite_logger.py
@@ -6,7 +6,7 @@
 import sqlite3
 import threading
 import uuid
-from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, TypeVar, Union
 
 from openai import AzureOpenAI, OpenAI
 from openai.types.chat import ChatCompletion
@@ -25,6 +25,18 @@
 
 __all__ = ("SqliteLogger",)
 
+F = TypeVar("F", bound=Callable[..., Any])
+
+
+def safe_serialize(obj: Any) -> str:
+    def default(o: Any) -> str:
+        if hasattr(o, "to_json"):
+            return str(o.to_json())
+        else:
+            return f"<<non-serializable: {type(o).__qualname__}>>"
+
+    return json.dumps(obj, default=default)
+
 
 class SqliteLogger(BaseLogger):
     schema_version = 1
@@ -49,6 +61,7 @@ def start(self) -> str:
                     client_id INTEGER,
                     wrapper_id INTEGER,
                     session_id TEXT,
+                    source_name TEXT,
                     request TEXT,
                     response TEXT,
                     is_cached INEGER,
@@ -118,6 +131,18 @@ class TEXT,                                         -- type or class name of cli
             """
             self._run_query(query=query)
 
+            query = """
+                        CREATE TABLE IF NOT EXISTS function_calls (
+                            source_id INTEGER,
+                            source_name TEXT,
+                            function_name TEXT,
+                            args TEXT DEFAULT NULL,
+                            returns TEXT DEFAULT NULL,
+                            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+                        );
+                        """
+            self._run_query(query=query)
+
             current_verion = self._get_current_db_version()
             if current_verion is None:
                 self._run_query(
@@ -192,6 +217,7 @@ def log_chat_completion(
         invocation_id: uuid.UUID,
         client_id: int,
         wrapper_id: int,
+        source: Union[str, Agent],
         request: Dict[str, Union[float, str, List[Dict[str, str]]]],
         response: Union[str, ChatCompletion],
         is_cached: int,
@@ -208,10 +234,16 @@ def log_chat_completion(
         else:
             response_messages = json.dumps(to_dict(response), indent=4)
 
+        source_name = None
+        if isinstance(source, str):
+            source_name = source
+        else:
+            source_name = source.name
+
         query = """
             INSERT INTO chat_completions (
-                invocation_id, client_id, wrapper_id, session_id, request, response, is_cached, cost, start_time, end_time
-            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                invocation_id, client_id, wrapper_id, session_id, request, response, is_cached, cost, start_time, end_time, source_name
+            ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
         """
         args = (
             invocation_id,
@@ -224,6 +256,7 @@ def log_chat_completion(
             cost,
             start_time,
             end_time,
+            source_name,
         )
 
         self._run_query(query=query, args=args)
@@ -335,6 +368,24 @@ def log_new_wrapper(self, wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLM
         )
         self._run_query(query=query, args=args)
 
+    def log_function_use(self, source: Union[str, Agent], function: F, args: Dict[str, Any], returns: Any) -> None:
+
+        if self.con is None:
+            return
+
+        query = """
+        INSERT INTO function_calls (source_id, source_name, function_name, args, returns, timestamp) VALUES (?, ?, ?, ?, ?, ?)
+        """
+        query_args: Tuple[Any, ...] = (
+            id(source),
+            source.name if hasattr(source, "name") else source,
+            function.__name__,
+            safe_serialize(args),
+            safe_serialize(returns),
+            get_current_ts(),
+        )
+        self._run_query(query=query, args=query_args)
+
     def log_new_client(
         self, client: Union[AzureOpenAI, OpenAI, GeminiClient], wrapper: OpenAIWrapper, init_args: Dict[str, Any]
     ) -> None:
diff --git a/autogen/oai/client.py b/autogen/oai/client.py
index 3aaf01736957..4c1da7a39311 100644
--- a/autogen/oai/client.py
+++ b/autogen/oai/client.py
@@ -319,6 +319,7 @@ class OpenAIWrapper:
     """A wrapper class for openai client."""
 
     extra_kwargs = {
+        "agent",
         "cache",
         "cache_seed",
         "filter_func",
@@ -542,6 +543,7 @@ def create(self, **config: Any) -> ModelClient.ModelClientResponseProtocol:
                 Note that the cache argument overrides the legacy cache_seed argument: if this argument is provided,
                 then the cache_seed argument is ignored. If this argument is not provided or None,
                 then the cache_seed argument is used.
+            - agent (AbstractAgent | None): The object responsible for creating a completion if an agent.
             - (Legacy) cache_seed (int | None) for using the DiskCache. Default to 41.
                 An integer cache_seed is useful when implementing "controlled randomness" for the completion.
                 None for no caching.
@@ -589,6 +591,7 @@ def yes_or_no_filter(context, response):
             cache = extra_kwargs.get("cache")
             filter_func = extra_kwargs.get("filter_func")
             context = extra_kwargs.get("context")
+            agent = extra_kwargs.get("agent")
 
             total_usage = None
             actual_usage = None
@@ -626,6 +629,7 @@ def yes_or_no_filter(context, response):
                                 invocation_id=invocation_id,
                                 client_id=id(client),
                                 wrapper_id=id(self),
+                                agent=agent,
                                 request=params,
                                 response=response,
                                 is_cached=1,
@@ -658,6 +662,7 @@ def yes_or_no_filter(context, response):
                         invocation_id=invocation_id,
                         client_id=id(client),
                         wrapper_id=id(self),
+                        agent=agent,
                         request=params,
                         response=f"error_code:{error_code}, config {i} failed",
                         is_cached=0,
@@ -688,6 +693,7 @@ def yes_or_no_filter(context, response):
                         invocation_id=invocation_id,
                         client_id=id(client),
                         wrapper_id=id(self),
+                        agent=agent,
                         request=params,
                         response=response,
                         is_cached=0,
diff --git a/autogen/runtime_logging.py b/autogen/runtime_logging.py
index ffc741482e68..6ce207a2f330 100644
--- a/autogen/runtime_logging.py
+++ b/autogen/runtime_logging.py
@@ -3,7 +3,7 @@
 import logging
 import sqlite3
 import uuid
-from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Literal, Optional, TypeVar, Union
 
 from openai import AzureOpenAI, OpenAI
 from openai.types.chat import ChatCompletion
@@ -20,6 +20,8 @@
 autogen_logger = None
 is_logging = False
 
+F = TypeVar("F", bound=Callable[..., Any])
+
 
 def start(
     logger: Optional[BaseLogger] = None,
@@ -56,6 +58,7 @@ def log_chat_completion(
     invocation_id: uuid.UUID,
     client_id: int,
     wrapper_id: int,
+    agent: Union[str, Agent],
     request: Dict[str, Union[float, str, List[Dict[str, str]]]],
     response: Union[str, ChatCompletion],
     is_cached: int,
@@ -67,7 +70,7 @@ def log_chat_completion(
         return
 
     autogen_logger.log_chat_completion(
-        invocation_id, client_id, wrapper_id, request, response, is_cached, cost, start_time
+        invocation_id, client_id, wrapper_id, agent, request, response, is_cached, cost, start_time
     )
 
 
@@ -87,6 +90,14 @@ def log_event(source: Union[str, Agent], name: str, **kwargs: Dict[str, Any]) ->
     autogen_logger.log_event(source, name, **kwargs)
 
 
+def log_function_use(agent: Union[str, Agent], function: F, args: Dict[str, Any], returns: any):
+    if autogen_logger is None:
+        logger.error("[runtime logging] log_function_use: autogen logger is None")
+        return
+
+    autogen_logger.log_function_use(agent, function, args, returns)
+
+
 def log_new_wrapper(wrapper: OpenAIWrapper, init_args: Dict[str, Union[LLMConfig, List[LLMConfig]]]) -> None:
     if autogen_logger is None:
         logger.error("[runtime logging] log_new_wrapper: autogen logger is None")
diff --git a/notebook/agentchat_agentops.ipynb b/notebook/agentchat_agentops.ipynb
new file mode 100644
index 000000000000..293efa8e4bd6
--- /dev/null
+++ b/notebook/agentchat_agentops.ipynb
@@ -0,0 +1,533 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "abb8a01d85d8b146",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# AgentOps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a447802c88c8a240",
+   "metadata": {},
+   "source": [
+    "![logo](https://raw.githubusercontent.com/AgentOps-AI/agentops/35d5682866921a9e28d8ef66ae3c3b3d92d8fa6b/img/logo.png)\n",
+    "\n",
+    "[AgentOps](https://agentops.ai/?=autogen) provides session replays, metrics, and monitoring for AI agents.\n",
+    "\n",
+    "At a high level, AgentOps gives you the ability to monitor LLM calls, costs, latency, agent failures, multi-agent interactions, tool usage, session-wide statistics, and more. For more info, check out the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b354c068",
+   "metadata": {},
+   "source": [
+    "### Dashboard\n",
+    "![Agent Dashboard](https://github.com/AgentOps-AI/agentops/assets/14807319/158e082a-9a7d-49b7-9b41-51a49a1f7d3d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38182a5296dceb34",
+   "metadata": {},
+   "source": [
+    "## Adding AgentOps to an existing Autogen service.\n",
+    "To get started, you'll need to install the AgentOps package and set an API key.\n",
+    "\n",
+    "AgentOps automatically configures itself when it's initialized. This means your agents will be tracked and logged to your AgentOps account right away."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d9451f4",
+   "metadata": {},
+   "source": [
+    "````{=mdx}\n",
+    ":::info Requirements\n",
+    "Some extra dependencies are needed for this notebook, which can be installed via pip:\n",
+    "\n",
+    "```bash\n",
+    "pip install pyautogen agentops\n",
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n",
+    ":::\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6be9e11620b0e8d6",
+   "metadata": {},
+   "source": [
+    "### Set an API key\n",
+    "\n",
+    "By default, the AgentOps `init()` function will look for an environment variable named `AGENTOPS_API_KEY`. Alternatively, you can pass one in as an optional parameter.\n",
+    "\n",
+    "Create an account and API key at [AgentOps.ai](https://agentops.ai/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f31a28d20a13b377",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-31T22:48:27.679318Z",
+     "start_time": "2024-05-31T22:48:26.192071Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "🖇 AgentOps: \u001B[34m\u001B[34mSession Replay: https://app.agentops.ai/drilldown?session_id=8bfaeed1-fd51-4c68-b3ec-276b1a3ce8a4\u001B[0m\u001B[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": "UUID('8bfaeed1-fd51-4c68-b3ec-276b1a3ce8a4')"
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import agentops\n",
+    "\n",
+    "from autogen import ConversableAgent, UserProxyAgent, config_list_from_json\n",
+    "\n",
+    "agentops.init(api_key=\"7c94212b-b89d-47a6-a20c-23b2077d3226\")  # or agentops.init(api_key=\"...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4dd8f461ccd9cbef",
+   "metadata": {},
+   "source": [
+    "Autogen will now start automatically tracking\n",
+    "- LLM prompts and completions\n",
+    "- Token usage and costs\n",
+    "- Agent names and actions\n",
+    "- Correspondence between agents\n",
+    "- Tool usage\n",
+    "- Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "712315c520536eb8",
+   "metadata": {},
+   "source": [
+    "# Simple Chat Example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "66d68e66e9f4a677",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-31T22:48:32.813123Z",
+     "start_time": "2024-05-31T22:48:27.677564Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[33magent\u001B[0m (to user):\n",
+      "\n",
+      "How can I help you today?\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[33muser\u001B[0m (to agent):\n",
+      "\n",
+      "2+2\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33magent\u001B[0m (to user):\n",
+      "\n",
+      "2 + 2 equals 4.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "🖇 AgentOps: This run's cost $0.000960\n",
+      "🖇 AgentOps: \u001B[34m\u001B[34mSession Replay: https://app.agentops.ai/drilldown?session_id=8bfaeed1-fd51-4c68-b3ec-276b1a3ce8a4\u001B[0m\u001B[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import agentops\n",
+    "\n",
+    "# When initializing AgentOps, you can pass in optional tags to help filter sessions\n",
+    "agentops.init(tags=[\"simple-autogen-example\"])\n",
+    "\n",
+    "# Create the agent that uses the LLM.\n",
+    "config_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
+    "assistant = ConversableAgent(\"agent\", llm_config={\"config_list\": config_list})\n",
+    "\n",
+    "# Create the agent that represents the user in the conversation.\n",
+    "user_proxy = UserProxyAgent(\"user\", code_execution_config=False)\n",
+    "\n",
+    "# Let the assistant start the conversation.  It will end when the user types exit.\n",
+    "assistant.initiate_chat(user_proxy, message=\"How can I help you today?\")\n",
+    "\n",
+    "# Close your AgentOps session to indicate that it completed.\n",
+    "agentops.end_session(\"Success\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2217ed0f930cfcaa",
+   "metadata": {},
+   "source": [
+    "You can view data on this run at [app.agentops.ai](https://app.agentops.ai). \n",
+    "\n",
+    "The dashboard will display LLM events for each message sent by each agent, including those made by the human user."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "![Session_Overview](https://github.com/AgentOps-AI/agentops/assets/14807319/d7228019-1488-40d3-852f-a61e998658ad)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "cbd689b0f5617013"
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd78f1a816276cb7",
+   "metadata": {},
+   "source": [
+    "# Tool Example\n",
+    "AgentOps also tracks when Autogen agents use tools. You can find more information on this example in [tool-use.ipynb](https://github.com/microsoft/autogen/blob/main/website/docs/tutorial/tool-use.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3498aa6176c799ff",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-05-31T22:48:35.808674Z",
+     "start_time": "2024-05-31T22:48:32.813225Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "🖇 AgentOps: \u001B[34m\u001B[34mSession Replay: https://app.agentops.ai/drilldown?session_id=880c206b-751e-4c23-9313-8684537fc04d\u001B[0m\u001B[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "What is (1423 - 123) / 3 + (32 + 23) * 5?\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "\u001B[32m***** Suggested tool call (call_aINcGyo0Xkrh9g7buRuhyCz0): calculator *****\u001B[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "  \"a\": 1423,\n",
+      "  \"b\": 123,\n",
+      "  \"operator\": \"-\"\n",
+      "}\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[32m***** Response from calling tool (call_aINcGyo0Xkrh9g7buRuhyCz0) *****\u001B[0m\n",
+      "1300\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "\u001B[32m***** Suggested tool call (call_prJGf8V0QVT7cbD91e0Fcxpb): calculator *****\u001B[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "  \"a\": 1300,\n",
+      "  \"b\": 3,\n",
+      "  \"operator\": \"/\"\n",
+      "}\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[32m***** Response from calling tool (call_prJGf8V0QVT7cbD91e0Fcxpb) *****\u001B[0m\n",
+      "433\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/braelynboynton/Developer/agentops/autogen/autogen/agentchat/conversable_agent.py:2489: UserWarning: Function 'calculator' is being overridden.\n",
+      "  warnings.warn(f\"Function '{tool_sig['function']['name']}' is being overridden.\", UserWarning)\n",
+      "/Users/braelynboynton/Developer/agentops/autogen/autogen/agentchat/conversable_agent.py:2408: UserWarning: Function 'calculator' is being overridden.\n",
+      "  warnings.warn(f\"Function '{name}' is being overridden.\", UserWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "\u001B[32m***** Suggested tool call (call_CUIgHRsySLjayDKuUphI1TGm): calculator *****\u001B[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "  \"a\": 32,\n",
+      "  \"b\": 23,\n",
+      "  \"operator\": \"+\"\n",
+      "}\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[32m***** Response from calling tool (call_CUIgHRsySLjayDKuUphI1TGm) *****\u001B[0m\n",
+      "55\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "\u001B[32m***** Suggested tool call (call_L7pGtBLUf9V0MPL90BASyesr): calculator *****\u001B[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "  \"a\": 55,\n",
+      "  \"b\": 5,\n",
+      "  \"operator\": \"*\"\n",
+      "}\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[32m***** Response from calling tool (call_L7pGtBLUf9V0MPL90BASyesr) *****\u001B[0m\n",
+      "275\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "\u001B[32m***** Suggested tool call (call_Ygo6p4XfcxRjkYBflhG3UVv6): calculator *****\u001B[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "  \"a\": 433,\n",
+      "  \"b\": 275,\n",
+      "  \"operator\": \"+\"\n",
+      "}\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[32m***** Response from calling tool (call_Ygo6p4XfcxRjkYBflhG3UVv6) *****\u001B[0m\n",
+      "708\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "The result of the calculation is 708.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "🖇 AgentOps: This run's cost $0.001800\n",
+      "🖇 AgentOps: \u001B[34m\u001B[34mSession Replay: https://app.agentops.ai/drilldown?session_id=880c206b-751e-4c23-9313-8684537fc04d\u001B[0m\u001B[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "from typing import Annotated, Literal\n",
+    "\n",
+    "from autogen import ConversableAgent, config_list_from_json, register_function\n",
+    "\n",
+    "agentops.start_session(tags=[\"autogen-tool-example\"])\n",
+    "\n",
+    "Operator = Literal[\"+\", \"-\", \"*\", \"/\"]\n",
+    "\n",
+    "\n",
+    "def calculator(a: int, b: int, operator: Annotated[Operator, \"operator\"]) -> int:\n",
+    "    if operator == \"+\":\n",
+    "        return a + b\n",
+    "    elif operator == \"-\":\n",
+    "        return a - b\n",
+    "    elif operator == \"*\":\n",
+    "        return a * b\n",
+    "    elif operator == \"/\":\n",
+    "        return int(a / b)\n",
+    "    else:\n",
+    "        raise ValueError(\"Invalid operator\")\n",
+    "\n",
+    "\n",
+    "config_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
+    "\n",
+    "# Create the agent that uses the LLM.\n",
+    "assistant = ConversableAgent(\n",
+    "    name=\"Assistant\",\n",
+    "    system_message=\"You are a helpful AI assistant. \"\n",
+    "    \"You can help with simple calculations. \"\n",
+    "    \"Return 'TERMINATE' when the task is done.\",\n",
+    "    llm_config={\"config_list\": config_list},\n",
+    ")\n",
+    "\n",
+    "# The user proxy agent is used for interacting with the assistant agent\n",
+    "# and executes tool calls.\n",
+    "user_proxy = ConversableAgent(\n",
+    "    name=\"User\",\n",
+    "    llm_config=False,\n",
+    "    is_termination_msg=lambda msg: msg.get(\"content\") is not None and \"TERMINATE\" in msg[\"content\"],\n",
+    "    human_input_mode=\"NEVER\",\n",
+    ")\n",
+    "\n",
+    "assistant.register_for_llm(name=\"calculator\", description=\"A simple calculator\")(calculator)\n",
+    "user_proxy.register_for_execution(name=\"calculator\")(calculator)\n",
+    "\n",
+    "# Register the calculator function to the two agents.\n",
+    "register_function(\n",
+    "    calculator,\n",
+    "    caller=assistant,  # The assistant agent can suggest calls to the calculator.\n",
+    "    executor=user_proxy,  # The user proxy agent can execute the calculator calls.\n",
+    "    name=\"calculator\",  # By default, the function name is used as the tool name.\n",
+    "    description=\"A simple calculator\",  # A description of the tool.\n",
+    ")\n",
+    "\n",
+    "# Let the assistant start the conversation.  It will end when the user types exit.\n",
+    "user_proxy.initiate_chat(assistant, message=\"What is (1423 - 123) / 3 + (32 + 23) * 5?\")\n",
+    "\n",
+    "agentops.end_session(\"Success\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4edf8e70d17267",
+   "metadata": {},
+   "source": [
+    "You can see your run in action at [app.agentops.ai](https://app.agentops.ai). In this example, the AgentOps dashboard will show:\n",
+    "- Agents talking to each other\n",
+    "- Each use of the `calculator` tool\n",
+    "- Each call to OpenAI for LLM use"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "![Session Drilldown](https://github.com/AgentOps-AI/agentops/assets/14807319/561d59f3-c441-4066-914b-f6cfe32a598c)"
+   ],
+   "metadata": {
+    "collapsed": false
+   },
+   "id": "a922a52ab5fce31"
+  }
+ ],
+ "metadata": {
+  "front_matter": {
+   "description": "Use AgentOps to simplify the development process and monitor your agents in production.",
+   "tags": [
+    "monitoring",
+    "debugging"
+   ]
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebook/agentchat_auto_feedback_from_code_execution.ipynb b/notebook/agentchat_auto_feedback_from_code_execution.ipynb
index bf784889d61d..6ea6f662b93b 100644
--- a/notebook/agentchat_auto_feedback_from_code_execution.ipynb
+++ b/notebook/agentchat_auto_feedback_from_code_execution.ipynb
@@ -40,7 +40,7 @@
     "    filter_dict={\"tags\": [\"gpt-4\"]},  # comment out to get all\n",
     ")\n",
     "# When using a single openai endpoint, you can use the following:\n",
-    "# config_list = [{\"model\": \"gpt-4\", \"api_key\": os.getenv(\"OPENAI_API_KEY\")}]\n"
+    "# config_list = [{\"model\": \"gpt-4\", \"api_key\": os.getenv(\"OPENAI_API_KEY\")}]"
    ]
   },
   {
diff --git a/notebook/agentchat_logging.ipynb b/notebook/agentchat_logging.ipynb
index 7eb4138b4cc1..eb5a6e752e40 100644
--- a/notebook/agentchat_logging.ipynb
+++ b/notebook/agentchat_logging.ipynb
@@ -25,12 +25,12 @@
      "output_type": "stream",
      "text": [
       "Logging session ID: 6e08f3e0-392b-434e-8b69-4ab36c4fcf99\n",
-      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\u001B[33muser_proxy\u001B[0m (to assistant):\n",
       "\n",
       "What is the height of the Eiffel Tower? Only respond with the answer and terminate\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
+      "\u001B[33massistant\u001B[0m (to user_proxy):\n",
       "\n",
       "The height of the Eiffel Tower is approximately 330 meters.\n",
       "\n",
@@ -313,12 +313,12 @@
      "output_type": "stream",
      "text": [
       "Logging session ID: ed493ebf-d78e-49f0-b832-69557276d557\n",
-      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\u001B[33muser_proxy\u001B[0m (to assistant):\n",
       "\n",
       "What is the height of the Eiffel Tower? Only respond with the answer and terminate\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
+      "\u001B[33massistant\u001B[0m (to user_proxy):\n",
       "\n",
       "The height of the Eiffel Tower is 330 meters.\n",
       "TERMINATE\n",
@@ -328,7 +328,6 @@
     }
    ],
    "source": [
-    "\n",
     "import pandas as pd\n",
     "\n",
     "import autogen\n",
diff --git a/test/agentchat/test_agent_file_logging.py b/test/agentchat/test_agent_file_logging.py
index 9b930ba4ac91..1818b85fa9dc 100644
--- a/test/agentchat/test_agent_file_logging.py
+++ b/test/agentchat/test_agent_file_logging.py
@@ -3,6 +3,7 @@
 import sys
 import tempfile
 import uuid
+from typing import Any, Callable
 
 import pytest
 
@@ -19,6 +20,10 @@
 is_windows = sys.platform.startswith("win")
 
 
+def dummy_function(param1: str, param2: int) -> Any:
+    return param1 * param2
+
+
 @pytest.mark.skipif(is_windows, reason="Skipping file logging tests on Windows")
 @pytest.fixture
 def logger() -> FileLogger:
@@ -49,8 +54,19 @@ def test_log_chat_completion(logger: FileLogger):
     is_cached = 0
     cost = 0.5
     start_time = "2024-05-06 15:20:21.263231"
-
-    logger.log_chat_completion(invocation_id, client_id, wrapper_id, request, response, is_cached, cost, start_time)
+    agent = autogen.AssistantAgent(name="TestAgent", code_execution_config=False)
+
+    logger.log_chat_completion(
+        invocation_id=invocation_id,
+        client_id=client_id,
+        wrapper_id=wrapper_id,
+        request=request,
+        response=response,
+        is_cached=is_cached,
+        cost=cost,
+        start_time=start_time,
+        source=agent,
+    )
 
     with open(logger.log_file, "r") as f:
         lines = f.readlines()
@@ -63,6 +79,26 @@ def test_log_chat_completion(logger: FileLogger):
         assert log_data["is_cached"] == is_cached
         assert log_data["cost"] == cost
         assert log_data["start_time"] == start_time
+        assert log_data["source_name"] == "TestAgent"
+        assert isinstance(log_data["thread_id"], int)
+
+
+@pytest.mark.skipif(is_windows, reason="Skipping file logging tests on Windows")
+def test_log_function_use(logger: FileLogger):
+    source = autogen.AssistantAgent(name="TestAgent", code_execution_config=False)
+    func: Callable[[str, int], Any] = dummy_function
+    args = {"foo": "bar"}
+    returns = True
+
+    logger.log_function_use(source=source, function=func, args=args, returns=returns)
+
+    with open(logger.log_file, "r") as f:
+        lines = f.readlines()
+        assert len(lines) == 1
+        log_data = json.loads(lines[0])
+        assert log_data["source_name"] == "TestAgent"
+        assert log_data["input_args"] == json.dumps(args)
+        assert log_data["returns"] == json.dumps(returns)
         assert isinstance(log_data["thread_id"], int)
 
 
diff --git a/test/test_logging.py b/test/test_logging.py
index d824cc914008..c6f7a182c5c0 100644
--- a/test/test_logging.py
+++ b/test/test_logging.py
@@ -1,6 +1,7 @@
 import json
 import sqlite3
 import uuid
+from typing import Any, Callable
 from unittest.mock import Mock, patch
 
 import pytest
@@ -61,6 +62,11 @@
 """
 )
 
+
+def dummy_function(param1: str, param2: int) -> Any:
+    return param1 * param2
+
+
 ###############################################################
 
 
@@ -84,6 +90,7 @@ def get_sample_chat_completion(response):
         "is_cached": 0,
         "cost": 0.347,
         "start_time": get_current_ts(),
+        "agent": autogen.AssistantAgent(name="TestAgent", code_execution_config=False),
     }
 
 
@@ -103,7 +110,7 @@ def test_log_completion(response, expected_logged_response, db_connection):
 
     query = """
         SELECT invocation_id, client_id, wrapper_id, request, response, is_cached,
-            cost, start_time FROM chat_completions
+            cost, start_time, source_name FROM chat_completions
     """
 
     for row in cur.execute(query):
@@ -115,6 +122,28 @@ def test_log_completion(response, expected_logged_response, db_connection):
         assert row["is_cached"] == sample_completion["is_cached"]
         assert row["cost"] == sample_completion["cost"]
         assert row["start_time"] == sample_completion["start_time"]
+        assert row["source_name"] == "TestAgent"
+
+
+def test_log_function_use(db_connection):
+    cur = db_connection.cursor()
+
+    source = autogen.AssistantAgent(name="TestAgent", code_execution_config=False)
+    func: Callable[[str, int], Any] = dummy_function
+    args = {"foo": "bar"}
+    returns = True
+
+    autogen.runtime_logging.log_function_use(agent=source, function=func, args=args, returns=returns)
+
+    query = """
+        SELECT source_id, source_name, function_name, args, returns, timestamp
+        FROM function_calls
+    """
+
+    for row in cur.execute(query):
+        assert row["source_name"] == "TestAgent"
+        assert row["args"] == json.dumps(args)
+        assert row["returns"] == json.dumps(returns)
 
 
 def test_log_new_agent(db_connection):
diff --git a/website/docs/Examples.md b/website/docs/Examples.md
index 5062cc5deff3..2ec83d1e0f21 100644
--- a/website/docs/Examples.md
+++ b/website/docs/Examples.md
@@ -100,6 +100,9 @@ Links to notebook examples:
 - Automatically Build Multi-agent System with AgentBuilder - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb)
 - Automatically Build Multi-agent System from Agent Library - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb)
 
+### Observability
+- Track LLM calls, tool usage, actions and errors using AgentOps - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentops.ipynb)
+
 ## Enhanced Inferences
 
 ### Utilities
diff --git a/website/docs/ecosystem/agentops.md b/website/docs/ecosystem/agentops.md
new file mode 100644
index 000000000000..76995b6eb5e4
--- /dev/null
+++ b/website/docs/ecosystem/agentops.md
@@ -0,0 +1,83 @@
+# AgentOps 🖇️
+
+![logo](https://raw.githubusercontent.com/AgentOps-AI/agentops/35d5682866921a9e28d8ef66ae3c3b3d92d8fa6b/img/logo.png)
+
+[AgentOps](https://agentops.ai/?=autogen) provides session replays, metrics, and monitoring for agents.
+
+At a high level, AgentOps gives you the ability to monitor LLM calls, costs, latency, agent failures, multi-agent interactions, tool usage, session-wide statistics, and more. For more info, check out the [AgentOps Repo](https://github.com/AgentOps-AI/agentops).
+
+<details open>
+  <summary>Agent Dashboard</summary>
+  <a href="https://app.agentops.ai?ref=gh">
+   <img src="https://github.com/AgentOps-AI/agentops/assets/14807319/158e082a-9a7d-49b7-9b41-51a49a1f7d3d" style="width: 90%;" alt="Agent Dashboard"/>
+  </a>
+</details>
+
+<details>
+  <summary>Session Analytics</summary>
+  <a href="https://app.agentops.ai?ref=gh">
+    <img src="https://github.com/AgentOps-AI/agentops/assets/14807319/d7228019-1488-40d3-852f-a61e998658ad" style="width: 90%;" alt="Session Analytics"/>
+  </a>
+</details>
+
+<details>
+  <summary>Session Replays</summary>
+  <a href="https://app.agentops.ai?ref=gh">
+    <img src="https://github.com/AgentOps-AI/agentops/assets/14807319/561d59f3-c441-4066-914b-f6cfe32a598c" style="width: 90%;" alt="Session Replays"/>
+  </a>
+</details>
+
+
+## Installation
+
+AgentOps works seamlessly with applications built using Autogen.
+
+1. **Install AgentOps**
+```bash
+pip install agentops
+```
+
+2. **Create an API Key:**
+Create a user API key here: [Create API Key](https://app.agentops.ai/account)
+
+3. **Configure Your Environment:**
+Add your API key to your environment variables
+
+```
+AGENTOPS_API_KEY=<YOUR_AGENTOPS_API_KEY>
+```
+
+4. **Initialize AgentOps**
+
+To start tracking all available data on Autogen runs, simply add two lines of code before implementing Autogen.
+
+```python
+import agentops
+agentops.init() # Or: agentops.init(api_key="your-api-key-here")
+```
+
+After initializing AgentOps, Autogen will now start automatically tracking your agent runs.
+
+## Features
+
+- **LLM Costs**: Track spend with foundation model providers
+- **Replay Analytics**: Watch step-by-step agent execution graphs
+- **Recursive Thought Detection**: Identify when agents fall into infinite loops
+- **Custom Reporting:** Create custom analytics on agent performance
+- **Analytics Dashboard:** Monitor high level statistics about agents in development and production
+- **Public Model Testing**: Test your agents against benchmarks and leaderboards
+- **Custom Tests:** Run your agents against domain specific tests
+- **Time Travel Debugging**:  Save snapshots of session states to rewind and replay agent runs from chosen checkpoints.
+- **Compliance and Security**: Create audit logs and detect potential threats such as profanity and PII leaks
+- **Prompt Injection Detection**: Identify potential code injection and secret leaks
+
+## Autogen + AgentOps examples
+* [AgentChat with AgentOps Notebook](/docs/notebooks/agentchat_agentops)
+* [More AgentOps Examples](https://docs.agentops.ai/v1/quickstart)
+
+## Extra links
+
+- [🐦 Twitter](https://twitter.com/agentopsai/)
+- [📢 Discord](https://discord.gg/JHPt4C7r)
+- [🖇️ AgentOps Dashboard](https://app.agentops.ai/ref?=autogen)
+- [📙 Documentation](https://docs.agentops.ai/introduction)
diff --git a/website/docs/topics/llm-observability.md b/website/docs/topics/llm-observability.md
new file mode 100644
index 000000000000..6a95d185f979
--- /dev/null
+++ b/website/docs/topics/llm-observability.md
@@ -0,0 +1,42 @@
+# LLM Observability
+
+AutoGen supports advanced LLM observability and monitoring through built-in logging and partner providers.
+
+## What is LLM Observability
+AI agent observability is the ability to monitor, measure, and understand the internal states and behaviors of AI agent systems.
+Observability is crucial for ensuring transparency, reliability, and accountability in your agent systems.
+
+
+## Development
+
+### Agent Development in Terminal is Limited
+- Lose track of what your agents did in between executions
+- Parsing through terminal output searching for LLM completions
+- Printing “tool called”
+
+### Agent Development Dashboards Enable More
+- Visual dashboard so you can see what your agents did in human-readable format
+- LLM calls are magically recorded - prompt, completion, timestamps for each - with one line of code
+- Agents and their events (including tool calls) are recorded with one more line of code
+- Errors are magically associated to its causal event
+- Record any other events to your session with two more lines of code
+- Tons of other useful data if you’re developing with supported agent frameworks: SDK version
+
+## Compliance
+
+Observability and monitoring is critical to ensure AI agent systems adhere to laws and regulations in industries like finance and healthcare, preventing violations such as data breaches and privacy issues.
+
+- Insights into AI decision-making, allowing organizations to explain outcomes and build trust with stakeholders.
+- Helps detect anomalies and unintended behaviors early, mitigating operational, financial, and reputational risks.
+- Ensures compliance with data privacy regulations, preventing unauthorized access and misuse of sensitive information.
+- Quick identification and response to compliance violations, supporting incident analysis and prevention.
+
+## Available Observability Integrations
+
+### Logging
+- Autogen SQLite and File Logger - [Tutorial](/docs/notebooks/agentchat_logging)
+
+### Full-Service Partners
+Autogen is currently partnered with [AgentOps](https://agentops.ai) for seamless observability integration.
+
+[Learn how to install AgentOps](/docs/notebooks/agentchat_agentops)
diff --git a/website/docs/topics/llm_configuration.ipynb b/website/docs/topics/llm_configuration.ipynb
index 51abf1f46225..c0a1b7e74a98 100644
--- a/website/docs/topics/llm_configuration.ipynb
+++ b/website/docs/topics/llm_configuration.ipynb
@@ -279,6 +279,7 @@
     "    def __deepcopy__(self, memo):\n",
     "        return self\n",
     "\n",
+    "\n",
     "config_list = [\n",
     "    {\n",
     "        \"model\": \"my-gpt-4-deployment\",\n",
diff --git a/website/docs/tutorial/tool-use.ipynb b/website/docs/tutorial/tool-use.ipynb
index 12d0f5a8066c..b5e21559225c 100644
--- a/website/docs/tutorial/tool-use.ipynb
+++ b/website/docs/tutorial/tool-use.ipynb
@@ -39,8 +39,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:48.858694Z",
+     "start_time": "2024-04-25T01:49:48.854420Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from typing import Annotated, Literal\n",
@@ -91,8 +96,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:48.946697Z",
+     "start_time": "2024-04-25T01:49:48.857869Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.calculator(a: int, b: int, operator: Annotated[Literal['+', '-', '*', '/'], 'operator']) -> int>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -160,8 +181,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:48.953345Z",
+     "start_time": "2024-04-25T01:49:48.947026Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from autogen import register_function\n",
@@ -189,124 +215,128 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:57.947530Z",
+     "start_time": "2024-04-25T01:49:48.953943Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
       "What is (44232 + 13312 / (232 - 32)) * 5?\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_bACquf0OreI0VHh7rWiP6ZE7): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_4rElPoLggOYJmkUutbGaSTX1): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
-      "  \"a\": 13312,\n",
-      "  \"b\": 232 - 32,\n",
-      "  \"operator\": \"/\"\n",
+      "  \"a\": 232,\n",
+      "  \"b\": 32,\n",
+      "  \"operator\": \"-\"\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_bACquf0OreI0VHh7rWiP6ZE7\" *****\u001b[0m\n",
-      "Error: Expecting ',' delimiter: line 1 column 26 (char 25)\n",
-      " You argument should follow json format.\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_4rElPoLggOYJmkUutbGaSTX1) *****\u001B[0m\n",
+      "200\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_2c0H5gzX9SWsJ05x7nEOVbav): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_SGtr8tK9A4iOCJGdCqkKR2Ov): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
       "  \"a\": 13312,\n",
       "  \"b\": 200,\n",
       "  \"operator\": \"/\"\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_2c0H5gzX9SWsJ05x7nEOVbav\" *****\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_SGtr8tK9A4iOCJGdCqkKR2Ov) *****\u001B[0m\n",
       "66\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_ioceLhuKMpfU131E7TSQ8wCD): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_YsR95CM1Ice2GZ7ZoStYXI6M): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
       "  \"a\": 44232,\n",
       "  \"b\": 66,\n",
       "  \"operator\": \"+\"\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_ioceLhuKMpfU131E7TSQ8wCD\" *****\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_YsR95CM1Ice2GZ7ZoStYXI6M) *****\u001B[0m\n",
       "44298\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_0rhx9vrbigcbqLssKLh4sS7j): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_oqZn4rTjyvXYcmjAXkvVaJm1): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
       "  \"a\": 44298,\n",
       "  \"b\": 5,\n",
       "  \"operator\": \"*\"\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_0rhx9vrbigcbqLssKLh4sS7j\" *****\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_oqZn4rTjyvXYcmjAXkvVaJm1) *****\u001B[0m\n",
       "221490\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
-      "\n",
-      "The result of the calculation (44232 + 13312 / (232 - 32)) * 5 is 221490. \n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "TERMINATE\n",
+      "The result of the calculation is 221490. TERMINATE\n",
       "\n",
       "--------------------------------------------------------------------------------\n"
      ]
@@ -325,8 +355,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:57.956063Z",
+     "start_time": "2024-04-25T01:49:57.948882Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -334,7 +369,7 @@
        "221490"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,8 +404,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:57.956696Z",
+     "start_time": "2024-04-25T01:49:57.952767Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -387,7 +427,7 @@
        "    'required': ['a', 'b', 'operator']}}}]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,8 +457,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:57.965069Z",
+     "start_time": "2024-04-25T01:49:57.958274Z"
+    }
+   },
    "outputs": [],
    "source": [
     "from pydantic import BaseModel, Field\n",
@@ -459,8 +504,24 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:57.990811Z",
+     "start_time": "2024-04-25T01:49:57.962315Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.calculator(input: typing.Annotated[__main__.CalculatorInput, 'Input to the calculator.']) -> int>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "assistant.register_for_llm(name=\"calculator\", description=\"A calculator tool that accepts nested expression as input\")(\n",
     "    calculator\n",
@@ -477,8 +538,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:49:57.991342Z",
+     "start_time": "2024-04-25T01:49:57.972554Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -504,7 +570,7 @@
        "    'required': ['input']}}}]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -522,159 +588,192 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:50:17.808416Z",
+     "start_time": "2024-04-25T01:49:57.975143Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
       "What is (1423 - 123) / 3 + (32 + 23) * 5?\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_t9By3vewGRoSLWsvdTR7p8Zo): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_Uu4diKtxlTfkwXuY6MmJEb4E): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
-      "  \"input\": {\n",
-      "    \"a\": 1423,\n",
-      "    \"b\": 123,\n",
-      "    \"operator\": \"-\"\n",
-      "  }\n",
+      "    \"input\": {\n",
+      "        \"a\": (1423 - 123) / 3,\n",
+      "        \"b\": (32 + 23) * 5,\n",
+      "        \"operator\": \"+\"\n",
+      "    }\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_t9By3vewGRoSLWsvdTR7p8Zo\" *****\u001b[0m\n",
-      "1300\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_Uu4diKtxlTfkwXuY6MmJEb4E) *****\u001B[0m\n",
+      "Error: Expecting value: line 1 column 29 (char 28)\n",
+      " You argument should follow json format.\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "I apologize for the confusion, I seem to have made a mistake. Let me recalculate the expression properly.\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_rhecyhVCo0Y8HPL193xOUPE6): calculator *****\u001b[0m\n",
+      "First, we need to do the calculations within the brackets. So, calculating (1423 - 123), (32 + 23), and then performing remaining operations.\n",
+      "\u001B[32m***** Suggested tool call (call_mx3M3fNOwikFNoqSojDH1jIr): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
-      "  \"input\": {\n",
-      "    \"a\": 1300,\n",
-      "    \"b\": 3,\n",
-      "    \"operator\": \"/\"\n",
-      "  }\n",
+      "    \"input\": {\n",
+      "        \"a\": 1423,\n",
+      "        \"b\": 123,\n",
+      "        \"operator\": \"-\"\n",
+      "    }\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_rhecyhVCo0Y8HPL193xOUPE6\" *****\u001b[0m\n",
-      "433\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_mx3M3fNOwikFNoqSojDH1jIr) *****\u001B[0m\n",
+      "1300\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_zDpq9J5MYAsL7uS8cobOwa7S): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_hBAL2sYi6Y5ZtTHCNPCmxdN3): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
-      "  \"input\": {\n",
-      "    \"a\": 32,\n",
-      "    \"b\": 23,\n",
-      "    \"operator\": \"+\"\n",
-      "  }\n",
+      "    \"input\": {\n",
+      "        \"a\": 32,\n",
+      "        \"b\": 23,\n",
+      "        \"operator\": \"+\"\n",
+      "    }\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_zDpq9J5MYAsL7uS8cobOwa7S\" *****\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_hBAL2sYi6Y5ZtTHCNPCmxdN3) *****\u001B[0m\n",
       "55\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_mjDuVMojOIdaxmvDUIF4QtVi): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_wO3AP7EDeJvsVLCpvv5LohUa): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
-      "  \"input\": {\n",
-      "    \"a\": 55,\n",
-      "    \"b\": 5,\n",
-      "    \"operator\": \"*\"\n",
-      "  }\n",
+      "    \"input\": {\n",
+      "        \"a\": 1300,\n",
+      "        \"b\": 3,\n",
+      "        \"operator\": \"/\"\n",
+      "    }\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_mjDuVMojOIdaxmvDUIF4QtVi\" *****\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_wO3AP7EDeJvsVLCpvv5LohUa) *****\u001B[0m\n",
+      "433\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
+      "\n",
+      "\u001B[32m***** Suggested tool call (call_kQ2hDhqem8BHNlaHaE9ezvvQ): calculator *****\u001B[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "    \"input\": {\n",
+      "        \"a\": 55,\n",
+      "        \"b\": 5,\n",
+      "        \"operator\": \"*\"\n",
+      "    }\n",
+      "}\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
+      "\n",
+      "\u001B[32m***** Response from calling tool (call_kQ2hDhqem8BHNlaHaE9ezvvQ) *****\u001B[0m\n",
       "275\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
-      "\u001b[32m***** Suggested tool Call (call_hpirkAGKOewZstsDOxL2sYNW): calculator *****\u001b[0m\n",
+      "\u001B[32m***** Suggested tool call (call_1FLDUdvAZmjlSD7g5GFFJOpO): calculator *****\u001B[0m\n",
       "Arguments: \n",
       "{\n",
-      "  \"input\": {\n",
-      "    \"a\": 433,\n",
-      "    \"b\": 275,\n",
-      "    \"operator\": \"+\"\n",
-      "  }\n",
+      "    \"input\": {\n",
+      "        \"a\": 433,\n",
+      "        \"b\": 275,\n",
+      "        \"operator\": \"+\"\n",
+      "    }\n",
       "}\n",
-      "\u001b[32m***************************************************************************\u001b[0m\n",
+      "\u001B[32m***************************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[35m\n",
-      ">>>>>>>> EXECUTING FUNCTION calculator...\u001b[0m\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION calculator...\u001B[0m\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[33mUser\u001b[0m (to Assistant):\n",
+      "\u001B[33mUser\u001B[0m (to Assistant):\n",
       "\n",
-      "\u001b[32m***** Response from calling tool \"call_hpirkAGKOewZstsDOxL2sYNW\" *****\u001b[0m\n",
+      "\u001B[32m***** Response from calling tool (call_1FLDUdvAZmjlSD7g5GFFJOpO) *****\u001B[0m\n",
       "708\n",
-      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\u001B[32m**********************************************************************\u001B[0m\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "\u001b[31m\n",
-      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mAssistant\u001b[0m (to User):\n",
-      "\n",
-      "The result of the calculation is 708. \n",
+      "\u001B[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001B[0m\n",
+      "\u001B[33mAssistant\u001B[0m (to User):\n",
       "\n",
+      "The calculation result of the expression (1423 - 123) / 3 + (32 + 23) * 5 is 708. Let's proceed to the next task.\n",
       "TERMINATE\n",
       "\n",
       "--------------------------------------------------------------------------------\n"
@@ -694,8 +793,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-04-25T01:50:17.818095Z",
+     "start_time": "2024-04-25T01:50:17.808502Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -703,7 +807,7 @@
        "708"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }