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AgentOps_integration #79

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114 changes: 46 additions & 68 deletions ae/core/agents/browser_nav_agent.py
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Original file line number Diff line number Diff line change
@@ -1,26 +1,28 @@
from datetime import datetime
from string import Template
import os

import autogen # type: ignore
import agentops

from ae.core.memory.static_ltm import get_user_ltm
from ae.core.prompts import LLM_PROMPTS
from ae.core.skills.click_using_selector import click as click_element

# from ae.core.skills.enter_text_and_click import enter_text_and_click
from ae.core.skills.enter_text_using_selector import bulk_enter_text
from ae.core.skills.enter_text_using_selector import entertext
from ae.core.skills.get_dom_with_content_type import get_dom_with_content_type
from ae.core.skills.get_url import geturl
from ae.core.skills.open_url import openurl
from ae.core.skills.pdf_text_extractor import extract_text_from_pdf

#from ae.core.skills.pdf_text_extractor import extract_text_from_pdf
from ae.core.skills.press_key_combination import press_key_combination

# Initialize AgentOps
agentops.init(os.getenv("AGENTOPS_API_KEY"))

@agentops.track_agent(name='BrowserNavAgent')
class BrowserNavAgent:
def __init__(self, config_list, browser_nav_executor: autogen.UserProxyAgent): # type: ignore
@agentops.record_function('init_browser_nav_agent')
def __init__(self, config_list, browser_nav_executor: autogen.UserProxyAgent):
"""
Initialize the BrowserNavAgent and store the AssistantAgent instance
as an instance attribute for external access.
Expand All @@ -33,7 +35,7 @@ def __init__(self, config_list, browser_nav_executor: autogen.UserProxyAgent): #
user_ltm = self.__get_ltm()
system_message = LLM_PROMPTS["BROWSER_AGENT_PROMPT"]
system_message = system_message + "\n" + f"Today's date is {datetime.now().strftime('%d %B %Y')}"
if user_ltm: #add the user LTM to the system prompt if it exists
if user_ltm:
user_ltm = "\n" + user_ltm
system_message = Template(system_message).substitute(basic_user_information=user_ltm)

Expand All @@ -50,75 +52,51 @@ def __init__(self, config_list, browser_nav_executor: autogen.UserProxyAgent): #
)
self.__register_skills()


@agentops.record_function('get_ltm')
def __get_ltm(self):
"""
Get the the long term memory of the user.
Get the long term memory of the user.
returns: str | None - The user LTM or None if not found.
"""
return get_user_ltm()


@agentops.record_function('register_skills')
def __register_skills(self):
"""
Register all the skills that the agent can perform.
"""

# Register openurl skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["OPEN_URL_PROMPT"])(openurl)
# Register openurl skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(openurl)

# Register enter_text_and_click skill for LLM by assistant agent
# self.agent.register_for_llm(description=LLM_PROMPTS["ENTER_TEXT_AND_CLICK_PROMPT"])(enter_text_and_click)
# Register enter_text_and_click skill for execution by user_proxy_agent
# self.browser_nav_executor.register_for_execution()(enter_text_and_click)

# Register get_dom_with_content_type skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["GET_DOM_WITH_CONTENT_TYPE_PROMPT"])(get_dom_with_content_type)
# Register get_dom_with_content_type skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(get_dom_with_content_type)

# Register click_element skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["CLICK_PROMPT"])(click_element)
# Register click_element skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(click_element)

# Register geturl skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["GET_URL_PROMPT"])(geturl)
# Register geturl skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(geturl)

# Register bulk_enter_text skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["BULK_ENTER_TEXT_PROMPT"])(bulk_enter_text)
# Register bulk_enter_text skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(bulk_enter_text)

# Register entertext skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["ENTER_TEXT_PROMPT"])(entertext)
# Register entertext skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(entertext)

# Register entertext skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["PRESS_KEY_COMBINATION_PROMPT"])(press_key_combination)
# Register entertext skill for execution by user_proxy_agent
self.browser_nav_executor.register_for_execution()(press_key_combination)

self.agent.register_for_llm(description=LLM_PROMPTS["EXTRACT_TEXT_FROM_PDF_PROMPT"])(extract_text_from_pdf)
self.browser_nav_executor.register_for_execution()(extract_text_from_pdf)

'''
# Register reply function for printing messages
self.browser_nav_executor.register_reply( # type: ignore
[autogen.Agent, None],
reply_func=print_message_from_user_proxy,
config={"callback": None},
)
self.agent.register_reply( # type: ignore
[autogen.Agent, None],
reply_func=print_message_from_browser_agent,
config={"callback": None},
)
'''
# print(f">>> Function map: {self.browser_nav_executor.function_map}") # type: ignore
# print(">>> Registered skills for BrowserNavAgent and BrowserNavExecutorAgent")
skills = [
(openurl, LLM_PROMPTS["OPEN_URL_PROMPT"]),
(get_dom_with_content_type, LLM_PROMPTS["GET_DOM_WITH_CONTENT_TYPE_PROMPT"]),
(click_element, LLM_PROMPTS["CLICK_PROMPT"]),
(geturl, LLM_PROMPTS["GET_URL_PROMPT"]),
(bulk_enter_text, LLM_PROMPTS["BULK_ENTER_TEXT_PROMPT"]),
(entertext, LLM_PROMPTS["ENTER_TEXT_PROMPT"]),
(press_key_combination, LLM_PROMPTS["PRESS_KEY_COMBINATION_PROMPT"]),
(extract_text_from_pdf, LLM_PROMPTS["EXTRACT_TEXT_FROM_PDF_PROMPT"]),
]

for skill, prompt in skills:
self.__register_skill(skill, prompt)

@agentops.record_function('register_skill')
def __register_skill(self, skill, prompt):
"""
Register a single skill for both the agent and the executor.
"""
self.agent.register_for_llm(description=prompt)(skill)
self.browser_nav_executor.register_for_execution()(skill)

# Example usage
if __name__ == "__main__":
# This is just a placeholder. You'd typically create this with actual config and executor.
config_list = [{}]
browser_nav_executor = autogen.UserProxyAgent(name="executor")

browser_nav_agent = BrowserNavAgent(config_list, browser_nav_executor)

# Simulate some actions
browser_nav_agent.agent.generate_reply("Open https://www.example.com")

# End the AgentOps session
agentops.end_session('Success')
57 changes: 45 additions & 12 deletions ae/core/agents/high_level_planner_agent.py
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Original file line number Diff line number Diff line change
@@ -1,17 +1,23 @@
from datetime import datetime
from string import Template
import os

import autogen # type: ignore
from autogen import ConversableAgent # type: ignore
import agentops

from ae.core.memory.static_ltm import get_user_ltm
from ae.core.post_process_responses import final_reply_callback_planner_agent as print_message_as_planner # type: ignore
from ae.core.prompts import LLM_PROMPTS
from ae.core.skills.get_user_input import get_user_input

# Initialize AgentOps
agentops.init(os.getenv("AGENTOPS_API_KEY"))

@agentops.track_agent(name='PlannerAgent')
class PlannerAgent:
def __init__(self, config_list, user_proxy_agent:ConversableAgent): # type: ignore
@agentops.record_function('init_planner_agent')
def __init__(self, config_list, user_proxy_agent: ConversableAgent):
"""
Initialize the PlannerAgent and store the AssistantAgent instance
as an instance attribute for external access.
Expand All @@ -20,14 +26,14 @@ def __init__(self, config_list, user_proxy_agent:ConversableAgent): # type: igno
- config_list: A list of configuration parameters required for AssistantAgent.
- user_proxy_agent: An instance of the UserProxyAgent class.
"""

user_ltm = self.__get_ltm()
system_message = LLM_PROMPTS["PLANNER_AGENT_PROMPT"]

if user_ltm: #add the user LTM to the system prompt if it exists
if user_ltm: # add the user LTM to the system prompt if it exists
user_ltm = "\n" + user_ltm
system_message = Template(system_message).substitute(basic_user_information=user_ltm)
system_message = system_message + "\n" + f"Today's date is {datetime.now().strftime('%d %B %Y')}"

self.agent = autogen.AssistantAgent(
name="planner_agent",
system_message=system_message,
Expand All @@ -36,26 +42,53 @@ def __init__(self, config_list, user_proxy_agent:ConversableAgent): # type: igno
"cache_seed": None,
"temperature": 0.0,
"top_p": 0.001,
"seed":12345
"seed": 12345
},
)

self.__register_skills(user_proxy_agent)
self.__register_reply()

@agentops.record_function('get_ltm')
def __get_ltm(self):
"""
Get the long term memory of the user.
returns: str | None - The user LTM or None if not found.
"""
return get_user_ltm()

@agentops.record_function('register_skills')
def __register_skills(self, user_proxy_agent: ConversableAgent):
"""
Register all the skills that the agent can perform.
"""
# Register get_user_input skill for LLM by assistant agent
self.agent.register_for_llm(description=LLM_PROMPTS["GET_USER_INPUT_PROMPT"])(get_user_input)
# Register get_user_input skill for execution by user_proxy_agent
user_proxy_agent.register_for_execution()(get_user_input)

self.agent.register_reply( # type: ignore
@agentops.record_function('register_reply')
def __register_reply(self):
"""
Register the reply function for the agent.
"""
self.agent.register_reply( # type: ignore
[autogen.AssistantAgent, None],
reply_func=print_message_as_planner,
config={"callback": None},
ignore_async_in_sync_chat=True
)

def __get_ltm(self):
"""
Get the the long term memory of the user.
returns: str | None - The user LTM or None if not found.
"""
return get_user_ltm()

# Example usage
if __name__ == "__main__":
# This is just a placeholder. You'd typically create this with actual config and user_proxy_agent.
config_list = [{}]
user_proxy_agent = ConversableAgent(name="user_proxy")

planner_agent = PlannerAgent(config_list, user_proxy_agent)

# Simulate some actions
planner_agent.agent.generate_reply("Plan a task")

# End the AgentOps session
agentops.end_session('Success')
33 changes: 25 additions & 8 deletions ae/core/autogen_wrapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,8 +10,8 @@
import autogen # type: ignore
import nest_asyncio # type: ignore
import openai
import agentops # Add this import

#from autogen import Cache
from dotenv import load_dotenv

from ae.config import SOURCE_LOG_FOLDER_PATH
Expand Down Expand Up @@ -48,7 +48,11 @@ def __init__(self, max_chat_round: int = 1000):
self.config_list: list[dict[str, str]] | None = None
self.chat_logs_dir: str = SOURCE_LOG_FOLDER_PATH

# Initialize AgentOps
agentops.init(os.getenv("AGENTOPS_API_KEY"))

@classmethod
@agentops.record_function('create_autogen_wrapper')
async def create(cls, agents_needed: list[str] | None = None, max_chat_round: int = 1000):
"""
Create an instance of AutogenWrapper.
Expand Down Expand Up @@ -162,7 +166,6 @@ def reflection_message(recipient, messages, sender, config): # type: ignore

return self


def get_chat_logs_dir(self) -> str|None:
"""
Get the directory for saving chat logs.
Expand All @@ -183,14 +186,14 @@ def set_chat_logs_dir(self, chat_logs_dir: str):
"""
self.chat_logs_dir = chat_logs_dir


@agentops.record_function('save_chat_log')
def __save_chat_log(self, chat_log: list[dict[str, Any]]):
chat_logs_file = os.path.join(self.get_chat_logs_dir() or "", f"nested_chat_log_{str(time_ns())}.json")
# Save the chat log to a file
with open(chat_logs_file, "w") as file:
json.dump(chat_log, file, indent=4)


@agentops.record_function('initialize_agents')
async def __initialize_agents(self, agents_needed: list[str]):
"""
Instantiate all agents with their appropriate prompts/skills.
Expand Down Expand Up @@ -223,7 +226,7 @@ async def __initialize_agents(self, agents_needed: list[str]):
raise ValueError(f"Unknown agent type: {agent_needed}")
return agents_map


@agentops.record_function('create_user_delegate_agent')
async def __create_user_delegate_agent(self) -> autogen.ConversableAgent:
"""
Create a ConversableAgent instance.
Expand Down Expand Up @@ -267,6 +270,7 @@ def is_planner_termination_message(x: dict[str, str])->bool: # type: ignore
)
return task_delegate_agent

@agentops.record_function('create_browser_nav_executor_agent')
def __create_browser_nav_executor_agent(self):
"""
Create a UserProxyAgent instance for executing browser control.
Expand Down Expand Up @@ -297,6 +301,7 @@ def is_browser_executor_termination_message(x: dict[str, str])->bool: # type: ig
print(">>> Created browser_nav_executor_agent:", browser_nav_executor_agent)
return browser_nav_executor_agent

@agentops.record_function('create_browser_nav_agent')
def __create_browser_nav_agent(self, user_proxy_agent: UserProxyAgent_SequentialFunctionExecution) -> autogen.ConversableAgent:
"""
Create a BrowserNavAgent instance.
Expand All @@ -312,6 +317,7 @@ def __create_browser_nav_agent(self, user_proxy_agent: UserProxyAgent_Sequential
#print(">>> browser agent tools:", json.dumps(browser_nav_agent.agent.llm_config.get("tools"), indent=2))
return browser_nav_agent.agent

@agentops.record_function('create_planner_agent')
def __create_planner_agent(self, assistant_agent: autogen.ConversableAgent):
"""
Create a Planner Agent instance. This is mainly used for exploration at this point
Expand All @@ -323,6 +329,7 @@ def __create_planner_agent(self, assistant_agent: autogen.ConversableAgent):
planner_agent = PlannerAgent(self.config_list, assistant_agent) # type: ignore
return planner_agent.agent

@agentops.record_function('process_command')
async def process_command(self, command: str, current_url: str | None = None) -> autogen.ChatResult | None:
"""
Process a command by sending it to one or more agents.
Expand All @@ -341,15 +348,13 @@ async def process_command(self, command: str, current_url: str | None = None) ->

prompt = Template(LLM_PROMPTS["COMMAND_EXECUTION_PROMPT"]).substitute(command=command, current_url_prompt_segment=current_url_prompt_segment)
logger.info(f"Prompt for command: {prompt}")
#with Cache.disk() as cache:
try:
if self.agents_map is None:
raise ValueError("Agents map is not initialized.")

result=await self.agents_map["user"].a_initiate_chat( # type: ignore
result = await self.agents_map["user"].a_initiate_chat( # type: ignore
self.agents_map["planner_agent"], # self.manager # type: ignore
max_turns=self.number_of_rounds,
#clear_history=True,
message=prompt,
silent=False,
cache=None,
Expand All @@ -362,4 +367,16 @@ async def process_command(self, command: str, current_url: str | None = None) ->
except openai.BadRequestError as bre:
logger.error(f"Unable to process command: \"{command}\". {bre}")
traceback.print_exc()
finally:
agentops.end_session('Success') # Or 'Fail' based on the outcome

# Additional methods or code that might be part of the AutogenWrapper class can be added here

# Example usage of the AutogenWrapper class
async def main():
wrapper = await AutogenWrapper.create()
result = await wrapper.process_command("Your command here")
print(result)

if __name__ == "__main__":
asyncio.run(main())
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