Add AgentOps monitoring functionality and example

README.md

@@ -144,6 +144,27 @@ response = autogen.Completion.create(context=test_instance, **config)
 
 Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples#tune-gpt-models) for this feature. -->
 
+## Monitoring
+Basic monitoring is demonstrated in `test/twoagent-monitored.py`
+
+Monitoring can be implemented using the AgentOps library.
+
+1. `pip install agentops`
+2. Create an account at https://agentops.ai and generate an API key
+3. `export AGENTOPS_API_KEY=<your_key>`
+4. When creating a `ConversableAgent` or `AssistantAgent`, first create an Agent Ops client instance
+    ```python
+   from agentops import Client
+   ao_client = Client(api_key=os.environ.get('AGENTOPS_API_KEY'),
+                   tags=['describe your session here'])
+   ```
+5. Pass this client instance into the constructor arguments of your Agent
+   ```python
+   assistant = AssistantAgent("assistant", llm_config={"config_list": config_list}, ao_client=ao_client)
+   ```
+6. Run your agent(s) and view the results of your session on the [AgentOps Dashboard](https://app.agentops.ai/dashboard)
+
+
 ## Documentation
 
 You can find detailed documentation about AutoGen [here](https://microsoft.github.io/autogen/).

autogen/agentchat/assistant_agent.py

@@ -1,4 +1,5 @@
 from typing import Callable, Dict, Literal, Optional, Union
+from agentops import Client as AOClient
 
 from .conversable_agent import ConversableAgent
 
@@ -39,6 +40,7 @@ def __init__(
         human_input_mode: Optional[str] = "NEVER",
         code_execution_config: Optional[Union[Dict, Literal[False]]] = False,
         description: Optional[str] = None,
+        ao_client: AOClient = None,
         **kwargs,
     ):
         """
@@ -67,6 +69,7 @@ def __init__(
             code_execution_config=code_execution_config,
             llm_config=llm_config,
             description=description,
+            ao_client=ao_client,
             **kwargs,
         )
 

autogen/agentchat/conversable_agent.py

@@ -10,6 +10,7 @@
 from autogen.code_utils import DEFAULT_MODEL, UNKNOWN, content_str, execute_code, extract_code, infer_lang
 
 from .agent import Agent
+from agentops import Client as AOClient
 
 try:
     from termcolor import colored
@@ -56,6 +57,7 @@ def __init__(
         llm_config: Optional[Union[Dict, Literal[False]]] = None,
         default_auto_reply: Optional[Union[str, Dict, None]] = "",
         description: Optional[str] = None,
+        ao_client: AOClient = None
     ):
         """
         Args:
@@ -99,6 +101,7 @@ def __init__(
             default_auto_reply (str or dict or None): default auto reply when no code execution or llm-based reply is generated.
             description (str): a short description of the agent. This description is used by other agents
                 (e.g. the GroupChatManager) to decide when to call upon this agent. (Default: system_message)
+            ao_client (AgentOps Client Instance): an instance of and AgentOps client. All agents should share one instance. (Default: None)
         """
         super().__init__(name)
         # a dictionary of conversations, default value is list
@@ -140,6 +143,7 @@ def __init__(
         self.register_reply([Agent, None], ConversableAgent.a_generate_function_call_reply)
         self.register_reply([Agent, None], ConversableAgent.check_termination_and_human_reply)
         self.register_reply([Agent, None], ConversableAgent.a_check_termination_and_human_reply)
+        self.ao_client = ao_client
 
     def register_reply(
         self,
@@ -349,6 +353,8 @@ def send(
         """
         # When the agent composes and sends the message, the role of the message is "assistant"
         # unless it's "function".
+        if self.ao_client:
+            self.ao_client.record_action("send_message_to_another_agent", tags=['conversable_agent', str(self.name())])
         valid = self._append_oai_message(message, "assistant", recipient)
         if valid:
             recipient.receive(message, self, request_reply, silent)
@@ -481,6 +487,8 @@ def receive(
         Raises:
             ValueError: if the message can't be converted into a valid ChatCompletion message.
         """
+        if self.ao_client:
+            self.ao_client.record_action("received_message_from_another_agent", tags=['conversable_agent', str(self.name())])
         self._process_received_message(message, sender, silent)
         if request_reply is False or request_reply is None and self.reply_at_receive[sender] is False:
             return

setup.py

@@ -20,6 +20,7 @@
     "flaml",
     "python-dotenv",
     "tiktoken",
+    "agentops"
 ]
 
 setuptools.setup(

test/oai/test_utils.py

@@ -17,6 +17,7 @@
     "OPENAI_API_KEY": "sk-********************",
     "HUGGING_FACE_API_KEY": "**************************",
     "ANOTHER_API_KEY": "1234567890234567890",
+    "AGENTOPS_API_KEY": "********-****-****-****-************"
 }
 
 # Example model to API key mappings

test/twoagent-monitored.py

@@ -0,0 +1,16 @@
+import os
+from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
+from agentops import Client
+
+ao_client = Client(api_key=os.environ.get('AGENTOPS_API_KEY'),
+                   tags=['autogen', 'Autogen Example'])
+
+# Load LLM inference endpoints from an env variable or a file
+# See https://microsoft.github.io/autogen/docs/FAQ#set-your-api-endpoints
+# and OAI_CONFIG_LIST_sample
+config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
+
+# pass in the AgentOps client instance for agent monitoring and visibility
+assistant = AssistantAgent("assistant", llm_config={"config_list": config_list}, ao_client=ao_client)
+user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding"})
+user_proxy.initiate_chat(assistant, message="Plot a chart of NVDA and TESLA stock price change YTD.")