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Merge pull request #126 from QZGao/main

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Integration with AutoGen workflow
This commit is contained in:
Charles Packer
2023-10-27 12:33:34 -07:00
committed by GitHub
parent 0f27c1a57a 5227b477f4
commit 3cab8c19a1
3 changed files with 163 additions and 16 deletions
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66
memgpt/autogen/examples/agent_autoreply.py Normal file
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@@ -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.",
)

36
memgpt/autogen/examples/agent_groupchat.py
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@@ -11,7 +11,7 @@ Begin by doing:
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 @@ config_list = [
},
]
# 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 @@ if not USE_MEMGPT:
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 @@ manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=llm_config)
# 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.",
)

77
memgpt/autogen/memgpt_agent.py
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@@ -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
@@ -69,6 +124,7 @@ 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
@@ -80,6 +136,7 @@ class MemGPTAgent(ConversableAgent):
agent: AgentAsync,
skip_verify=False,
concat_other_agent_messages=False,
is_termination_msg: Optional[Callable[[Dict], bool]] = None,
):
super().__init__(name)
self.agent = agent
@@ -89,6 +146,10 @@ class MemGPTAgent(ConversableAgent):
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']}"
@@ -158,6 +219,10 @@ class MemGPTAgent(ConversableAgent):
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)