Integration with AutoGen workflow

memgpt/autogen/examples/agent_autoreply.py

@@ -0,0 +1,66 @@
+"""Example of how to add MemGPT into an AutoGen groupchat
+
+Based on the official AutoGen example here: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb
+
+Begin by doing:
+  pip install "pyautogen[teachable]"
+  pip install pymemgpt
+  or
+  pip install -e . (inside the MemGPT home directory)
+"""
+
+import os
+import autogen
+from memgpt.autogen.memgpt_agent import create_memgpt_autogen_agent_from_config
+
+config_list = [
+    {
+        "model": "gpt-4",
+        "api_key": os.getenv("OPENAI_API_KEY"),
+    },
+]
+
+# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo
+# (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
+# If USE_MEMGPT is True, then we swap out the "coder" agent with a MemGPT agent
+USE_MEMGPT = True
+
+llm_config = {"config_list": config_list, "seed": 42}
+
+# The user agent
+user_proxy = autogen.UserProxyAgent(
+    name="User_proxy",
+    system_message="A human admin.",
+    code_execution_config={"last_n_messages": 2, "work_dir": "groupchat"},
+    human_input_mode="TERMINATE",  # needed?
+    default_auto_reply="You are going to figure all out by your own. "
+                       "Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation.",
+)
+
+if not USE_MEMGPT:
+    # In the AutoGen example, we create an AssistantAgent to play the role of the coder
+    coder = autogen.AssistantAgent(
+        name="Coder",
+        llm_config=llm_config,
+        system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
+                       f"(which I make sure to tell everyone I work with).",
+        human_input_mode="TERMINATE",
+    )
+
+else:
+    # In our example, we swap this AutoGen agent with a MemGPT agent
+    # This MemGPT agent will have all the benefits of MemGPT, ie persistent memory, etc.
+    coder = create_memgpt_autogen_agent_from_config(
+        "MemGPT_coder",
+        llm_config=llm_config,
+        system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
+                       f"(which I make sure to tell everyone I work with).",
+        human_input_mode="TERMINATE",
+    )
+
+# Begin the group chat with a message from the user
+user_proxy.initiate_chat(
+    coder,
+    message="I want to design an app to make me one million dollars in one month. "
+            "Tell me all the details, then try out every steps.",
+)

memgpt/autogen/examples/agent_groupchat.py

@@ -11,7 +11,7 @@
 
 import os
 import autogen
-from memgpt.autogen.memgpt_agent import create_autogen_memgpt_agent
+from memgpt.autogen.memgpt_agent import create_autogen_memgpt_agent, create_memgpt_autogen_agent_from_config
 
 config_list = [
     {
@@ -20,10 +20,13 @@
     },
 ]
 
-# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
+# If USE_MEMGPT is False, then this example will be the same as the official AutoGen repo
+# (https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
 # If USE_MEMGPT is True, then we swap out the "coder" agent with a MemGPT agent
 USE_MEMGPT = True
 
+USE_AUTOGEN_WORKFLOW = False
+
 llm_config = {"config_list": config_list, "seed": 42}
 
 # The user agent
@@ -51,13 +54,25 @@
 else:
     # In our example, we swap this AutoGen agent with a MemGPT agent
     # This MemGPT agent will have all the benefits of MemGPT, ie persistent memory, etc.
-    coder = create_autogen_memgpt_agent(
-        "MemGPT_coder",
-        persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber (which I make sure to tell everyone I work with).",
-        user_description=f"You are participating in a group chat with a user ({user_proxy.name}) and a product manager ({pm.name}).",
-        # extra options
-        # interface_kwargs={"debug": True},
-    )
+    if not USE_AUTOGEN_WORKFLOW:
+        coder = create_autogen_memgpt_agent(
+            "MemGPT_coder",
+            persona_description="I am a 10x engineer, trained in Python. I was the first engineer at Uber "
+                                "(which I make sure to tell everyone I work with).",
+            user_description=f"You are participating in a group chat with a user ({user_proxy.name}) "
+                             f"and a product manager ({pm.name}).",
+            # extra options
+            # interface_kwargs={"debug": True},
+        )
+    else:
+        coder = create_memgpt_autogen_agent_from_config(
+            "MemGPT_coder",
+            llm_config=llm_config,
+            system_message=f"I am a 10x engineer, trained in Python. I was the first engineer at Uber "
+                           f"(which I make sure to tell everyone I work with).\n"
+                           f"You are participating in a group chat with a user ({user_proxy.name}) "
+                           f"and a product manager ({pm.name}).",
+        )
 
 # Initialize the group chat between the user and two LLM agents (PM and coder)
 groupchat = autogen.GroupChat(agents=[user_proxy, pm, coder], messages=[], max_round=12)
@@ -66,5 +81,6 @@
 # Begin the group chat with a message from the user
 user_proxy.initiate_chat(
     manager,
-    message="I want to design an app to make me one million dollars in one month. Yes, your heard that right.",
+    message="I want to design an app to make me one million dollars in one month. "
+            "Yes, your heard that right.",
 )

memgpt/autogen/memgpt_agent.py

@@ -1,4 +1,4 @@
-from autogen.agentchat import ConversableAgent, Agent
+from autogen.agentchat import Agent, ConversableAgent, UserProxyAgent, GroupChat, GroupChatManager
 from ..agent import AgentAsync
 
 import asyncio
@@ -18,16 +18,70 @@ def create_memgpt_autogen_agent_from_config(
     system_message: Optional[str] = "You are a helpful AI Assistant.",
     is_termination_msg: Optional[Callable[[Dict], bool]] = None,
     max_consecutive_auto_reply: Optional[int] = None,
-    human_input_mode: Optional[str] = "TERMINATE",
+    human_input_mode: Optional[str] = "ALWAYS",
     function_map: Optional[Dict[str, Callable]] = None,
     code_execution_config: Optional[Union[Dict, bool]] = None,
     llm_config: Optional[Union[Dict, bool]] = None,
     default_auto_reply: Optional[Union[str, Dict, None]] = "",
 ):
-    """
-    TODO support AutoGen config workflow in a clean way with constructors
-    """
-    raise NotImplementedError
+    """Construct AutoGen config workflow in a clean way."""
+
+    model = constants.DEFAULT_MEMGPT_MODEL if llm_config is None else llm_config["config_list"][0]["model"]
+    persona_desc = personas.DEFAULT if system_message == "" else system_message
+    if human_input_mode == "ALWAYS":
+        user_desc = humans.DEFAULT
+    elif human_input_mode == "TERMINATE":
+        user_desc = "Work by yourself, the user won't reply until you output `TERMINATE` to end the conversation."
+    else:
+        user_desc = "Work by yourself, the user won't reply. Elaborate as much as possible."
+
+    if function_map is not None or code_execution_config is not None:
+        raise NotImplementedError
+
+    autogen_memgpt_agent = create_autogen_memgpt_agent(
+        name,
+        preset=presets.DEFAULT,
+        model=model,
+        persona_description=persona_desc,
+        user_description=user_desc,
+        is_termination_msg=is_termination_msg,
+    )
+
+    if human_input_mode != "ALWAYS":
+        coop_agent1 = create_autogen_memgpt_agent(
+            name,
+            preset=presets.DEFAULT,
+            model=model,
+            persona_description=persona_desc,
+            user_description=user_desc,
+            is_termination_msg=is_termination_msg,
+        )
+        if default_auto_reply != "":
+            coop_agent2 = UserProxyAgent(
+                name,
+                human_input_mode="NEVER",
+                default_auto_reply=default_auto_reply,
+            )
+        else:
+            coop_agent2 = create_autogen_memgpt_agent(
+                name,
+                preset=presets.DEFAULT,
+                model=model,
+                persona_description=persona_desc,
+                user_description=user_desc,
+                is_termination_msg=is_termination_msg,
+            )
+
+        groupchat = GroupChat(
+            agents=[autogen_memgpt_agent, coop_agent1, coop_agent2],
+            messages=[],
+            max_round=12 if max_consecutive_auto_reply is None else max_consecutive_auto_reply
+        )
+        manager = GroupChatManager(name=name, groupchat=groupchat, llm_config=llm_config)
+        return manager
+
+    else:
+        return autogen_memgpt_agent
 
 
 def create_autogen_memgpt_agent(
@@ -40,6 +94,7 @@ def create_autogen_memgpt_agent(
     interface_kwargs={},
     persistence_manager=None,
     persistence_manager_kwargs={},
+    is_termination_msg: Optional[Callable[[Dict], bool]] = None,
 ):
     """
     See AutoGenInterface.__init__ for available options you can pass into
@@ -73,6 +128,7 @@ def create_autogen_memgpt_agent(
     autogen_memgpt_agent = MemGPTAgent(
         name=autogen_name,
         agent=memgpt_agent,
+        is_termination_msg=is_termination_msg,
     )
     return autogen_memgpt_agent
 
@@ -84,6 +140,7 @@ def __init__(
         agent: AgentAsync,
         skip_verify=False,
         concat_other_agent_messages=False,
+        is_termination_msg: Optional[Callable[[Dict], bool]] = None,
     ):
         super().__init__(name)
         self.agent = agent
@@ -95,6 +152,10 @@ def __init__(
         self.register_reply([Agent, None], MemGPTAgent._generate_reply_for_user_message)
         self.messages_processed_up_to_idx = 0
 
+        self._is_termination_msg = (
+            is_termination_msg if is_termination_msg is not None else (lambda x: x == "TERMINATE")
+        )
+
     def format_other_agent_message(self, msg):
         if "name" in msg:
             user_message = f"{msg['name']}: {msg['content']}"
@@ -144,8 +205,10 @@ async def _a_generate_reply_for_user_message(
                 # Extend the MemGPT message list with multiple 'user' messages, then push the last one with agent.step()
                 self.agent.messages.extend(new_messages[:-1])
                 user_message = new_messages[-1]
-        else:
+        elif len(new_messages) == 1:
             user_message = new_messages[0]
+        else:
+            return True, self._default_auto_reply
 
         # Package the user message
         user_message = system.package_user_message(user_message)
@@ -172,6 +235,10 @@ async def _a_generate_reply_for_user_message(
             else:
                 break
 
+        # Stop the conversation
+        if self._is_termination_msg(new_messages[-1]['content']):
+            return True, None
+
         # Pass back to AutoGen the pretty-printed calls MemGPT made to the interface
         pretty_ret = MemGPTAgent.pretty_concat(self.agent.interface.message_list)
         self.messages_processed_up_to_idx += len(new_messages)