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microsoft / autogen

A programming framework for agentic AI 🤖
https://microsoft.github.io/autogen/
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Infinite Loops #108

Closed adriangalilea closed 1 year ago

adriangalilea commented 1 year ago

Hello,

https://github.com/JayZeeDesign/microsoft-autogen-experiments/blob/main/content_agent.py

I got a few instances of infinite loops running the above with gpt4.

Don't have the logs sorry.

LittleLittleCloud commented 1 year ago

Thanks, am looking into this

adriangalilea commented 1 year ago 
...
Termination.

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------

>>>>>>>> USING AUTO REPLY...
admin (to chat_manager):

--------------------------------------------------------------------------------
[autogen.oai.completion: 10-04 22:44:34] {237} INFO - retrying in 10 seconds...
Traceback (most recent call last):
  File "/Users/adrian/Developer/microsoft-autogen-experiments/venv/lib/python3.11/site-packages/autogen/oai/completion.py", line 209, in _get_response
    response = openai_completion.create(request_timeout=request_timeout, **config)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/adrian/Developer/microsoft-autogen-experiments/venv/lib/python3.11/site-packages/openai/api_resources/chat_completion.py", line 25, in create
    return super().create(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/adrian/Developer/microsoft-autogen-experiments/venv/lib/python3.11/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 155, in create
    response, _, api_key = requestor.request(
                           ^^^^^^^^^^^^^^^^^^
  File "/Users/adrian/Developer/microsoft-autogen-experiments/venv/lib/python3.11/site-packages/openai/api_requestor.py", line 299, in request
    resp, got_stream = self._interpret_response(result, stream)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/adrian/Developer/microsoft-autogen-experiments/venv/lib/python3.11/site-packages/openai/api_requestor.py", line 710, in _interpret_response
    self._interpret_response_line(
  File "/Users/adrian/Developer/microsoft-autogen-experiments/venv/lib/python3.11/site-packages/openai/api_requestor.py", line 775, in _interpret_response_line
    raise self.handle_error_response(
openai.error.RateLimitError: Rate limit reached for default-gpt-4 in organization [REDACTED] on tokens per min. Limit: 40000 / min. Please try again in 1ms. Contact us through our help center at help.openai.com if you continue to have issues.
LittleLittleCloud commented 1 year ago

@adriangalilea Just a reminder that you are exposing an API key from your repo, please make your repo private or remove sensitive information ASAP

adriangalilea commented 1 year ago

I think you are refering to this: https://github.com/JayZeeDesign/microsoft-autogen-experiments/issues/1

It's the original user which I notified and disabled the API keys,

Thank you tho.

adriangalilea commented 1 year ago

Btw identified another instance of this:

(Note: The blog content has been generated based on the given prompts and has been tweaked to provide a concise step-by-step guide. TERMINATE)

--------------------------------------------------------------------------------
admin (to chat_manager):

--------------------------------------------------------------------------------
admin (to chat_manager):

I think the problem is this:

user_proxy = autogen.UserProxyAgent(
        name="User_proxy",
        code_execution_config={"last_n_messages": 2, "work_dir": "coding"},
        is_termination_msg=lambda x: x.get("content", "") and x.get(
            "content", "").rstrip().endswith("TERMINATE"),
        human_input_mode="NEVER",
        function_map={
            "search": search,
            "scrape": scrape,
        }
    )

The endswith part.

LittleLittleCloud commented 1 year ago

Yes you are right. the is_termination_msg asking for an exact match of TERMINATE but admin replies (...TERMINATE) or Terminate.

@sonichi We would need a more robust method to terminate the chat. How about making it a terminate function for admin to call via function_call

adriangalilea commented 1 year ago

Yes you are right. the is_termination_msg asking for an exact match of TERMINATE but admin replies (...TERMINATE) or Terminate.

@sonichi We would need a more robust method to terminate the chat. How about making it a terminate function for admin to call via function_call

Do you happen to have any example at hand?

LittleLittleCloud commented 1 year ago

@adriangalilea haven't yet. I can create a demo example in #102 to give it a try though.

In the meantime, after a closer look, it seems that the empty message is because admin doesn't have a llm_config, so it uses its default_reply which is an empty string.

adriangalilea commented 1 year ago

@LittleLittleCloud Hello,

I've been trying to search for documentation of the llm_config and I didn't find much helpful information.

What would you say it's a good default_reply to avoid these kind of errors?

Also, by a terminate function you mean a function that simply outputs TERMINATE?

Thank you so much!

LittleLittleCloud commented 1 year ago

Yep, you can set default_auto_reply to TERMINATE and is_termination_msg to lambda x: x["content"] == "TERMINATE" to avoid an infinite loop.

Here's the complete code, you might want to reply JulyChat to GPT-3.5-turbo or other model suppose you are using openai models.

file: content_agent.py
import os
from autogen import config_list_from_json
import autogen

import requests
from bs4 import BeautifulSoup
import json

from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain import PromptTemplate
import openai
from dotenv import load_dotenv

# Get API key
load_dotenv()
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST.json")
browserless_api_key = os.getenv("BROWSERLESS_API_KEY")
X_API_KEY = os.getenv("X_API_KEY")

# Define research function

def search(query):
    url = "https://google.serper.dev/search"

    payload = json.dumps({
        "q": query
    })
    headers = {
        'X-API-KEY': X_API_KEY,
        'Content-Type': 'application/json'
    }

    response = requests.request("POST", url, headers=headers, data=payload)

    return response.json()

def scrape(url: str):
    # scrape website, and also will summarize the content based on objective if the content is too large
    # objective is the original objective & task that user give to the agent, url is the url of the website to be scraped

    print("Scraping website...")
    # Define the headers for the request
    headers = {
        'Cache-Control': 'no-cache',
        'Content-Type': 'application/json',
    }

    # Define the data to be sent in the request
    data = {
        "url": url
    }

    # Convert Python object to JSON string
    data_json = json.dumps(data)

    # Send the POST request
    response = requests.post(
        f"https://chrome.browserless.io/content?token={browserless_api_key}", headers=headers, data=data_json)

    # Check the response status code
    if response.status_code == 200:
        soup = BeautifulSoup(response.content, "html.parser")
        text = soup.get_text()
        print("CONTENTTTTTT:", text)
        if len(text) > 8000:
            output = summary(text)
            return output
        else:
            return text
    else:
        print(f"HTTP request failed with status code {response.status_code}")
        return f"HTTP request failed with status code {response.status_code}"

def summary(content):
    llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-16k-0613")
    text_splitter = RecursiveCharacterTextSplitter(
        separators=["\n\n", "\n"], chunk_size=10000, chunk_overlap=500)
    docs = text_splitter.create_documents([content])
    map_prompt = """
    Write a detailed summary of the following text for a research purpose:
    "{text}"
    SUMMARY:
    """
    map_prompt_template = PromptTemplate(
        template=map_prompt, input_variables=["text"])

    summary_chain = load_summarize_chain(
        llm=llm,
        chain_type='map_reduce',
        map_prompt=map_prompt_template,
        combine_prompt=map_prompt_template,
        verbose=True
    )

    output = summary_chain.run(input_documents=docs,)

    return output

def research(query):
    llm_config_researcher = {
        "model": "JulyChat",
        "functions": [
            {
                "name": "search",
                "description": "google search for relevant information",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "Google search query",
                        }
                    },
                    "required": ["query"],
                },
            },
            {
                "name": "scrape",
                "description": "Scraping website content based on url",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "url": {
                            "type": "string",
                            "description": "Website url to scrape",
                        }
                    },
                    "required": ["url"],
                },
            },
        ],
        "config_list": config_list}

    researcher = autogen.AssistantAgent(
        name="researcher",
        system_message="Research about a given query, collect as many information as possible, and generate detailed research results with loads of technique details with all reference links attached; Add TERMINATE to the end of the research report;",
        llm_config=llm_config_researcher,
    )

    user_proxy = autogen.UserProxyAgent(
        name="User_proxy",
        code_execution_config={"last_n_messages": 2, "work_dir": "coding"},
        is_termination_msg=lambda x: x["content"] == "TERMINATE",
        default_auto_reply="TERMINATE",
        human_input_mode="NEVER",
        function_map={
            "search": search,
            "scrape": scrape,
        }
    )

    user_proxy.initiate_chat(researcher, message=query)

    # set the receiver to be researcher, and get a summary of the research report
    user_proxy.stop_reply_at_receive(researcher)
    user_proxy.send(
        "Give me the research report that just generated again, return ONLY the report & reference links", researcher)

    # return the last message the expert received
    return user_proxy.last_message()["content"]

# Define write content function
def write_content(research_material, topic):
    gpt_35_config = {
        "model": "JulyChat",
        "config_list": config_list,
    }
    editor = autogen.AssistantAgent(
        name="editor",
        system_message="You are a senior editor of an AI blogger, you will define the structure of a short blog post based on material provided by the researcher, and give it to the writer to write the blog post",
        llm_config=gpt_35_config,
    )

    writer = autogen.AssistantAgent(
        name="writer",
        system_message="You are a professional AI blogger who is writing a blog post about AI, you will write a short blog post based on the structured provided by the editor, and feedback from reviewer; After 2 rounds of content iteration, add TERMINATE to the end of the message",
        llm_config=gpt_35_config,
    )

    reviewer = autogen.AssistantAgent(
        name="reviewer",
        system_message="You are a world class hash tech blog content critic, you will review & critic the written blog and provide feedback to writer.After 2 rounds of content iteration, add TERMINATE to the end of the message",
        llm_config=gpt_35_config,
    )

    user_proxy = autogen.UserProxyAgent(
        name="admin",
        system_message="A human admin. Interact with editor to discuss the structure. Actual writing needs to be approved by this admin.",
        code_execution_config=False,
        is_termination_msg=lambda x: x["content"] == "TERMINATE", # this ends the conversation
        human_input_mode="NEVER", # along with default_auto_reply="TERMINATE", this will make the agent to reply "TERMINATE" automatically
        default_auto_reply="TERMINATE",
    )

    groupchat = autogen.GroupChat(
        agents=[user_proxy, editor, writer, reviewer],
        messages=[],
        max_round=20)
    manager = autogen.GroupChatManager(groupchat=groupchat)

    # phase 1
    # collaborate with editor, reviewer and writer over the structure of the blog and the content
    user_proxy.initiate_chat(
        manager, message=f"Write a blog about {topic}, here are the material: {research_material}.")

    # phase 2
    # ask writer to write the blog based on the structure
    user_proxy.send("writer, now complete the blog based on feedback you already received. Let me know once you complete", manager)

    # return the last message from writer
    writer_messages = filter(lambda x: x["name"] == "writer", groupchat.messages)
    last_writer_message = list(writer_messages)[-1]
    return last_writer_message['content']

# Define content assistant agent
llm_config_content_assistant = {
    "model": "JulyChat",
    "functions": [
        {
            "name": "research",
            "description": "research about a given topic, return the research material including reference links",
            "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": "The topic to be researched about",
                        }
                    },
                "required": ["query"],
            },
        },
        {
            "name": "write_content",
            "description": "Write content based on the given research material & topic",
            "parameters": {
                    "type": "object",
                    "properties": {
                        "research_material": {
                            "type": "string",
                            "description": "research material of a given topic, including reference links when available",
                        },
                        "topic": {
                            "type": "string",
                            "description": "The topic of the content",
                        }
                    },
                "required": ["research_material", "topic"],
            },
        },
    ],
    "config_list": config_list}

writing_assistant = autogen.AssistantAgent(
    name="writing_assistant",
    system_message="You are a writing assistant, you can use research function to collect latest information about a given topic, and then use write_content function to write a very well written content; Reply TERMINATE when your task is done",
    llm_config=llm_config_content_assistant,
)

user_proxy = autogen.UserProxyAgent(
    name="User_proxy",
    human_input_mode="NEVER",
    default_auto_reply="TERMINATE",
    is_termination_msg=lambda x: x["content"] == "TERMINATE",
    function_map={
        "write_content": write_content,
        "research": research,
    }
)

user_proxy.initiate_chat(
    writing_assistant, message="write a blog about autogen multi AI agent framework")

An example output

chat history User_proxy (to writing_assistant): write a blog about autogen multi AI agent framework -------------------------------------------------------------------------------- writing_assistant (to User_proxy): ***** Suggested function Call: research ***** Arguments: { "query": "autogen multi AI agent framework" } ********************************************* -------------------------------------------------------------------------------- >>>>>>>> EXECUTING FUNCTION research... User_proxy (to researcher): autogen multi AI agent framework -------------------------------------------------------------------------------- researcher (to User_proxy): Research Report: Autogen Multi AI Agent Framework Introduction: An Autogen Multi AI Agent Framework is a software framework that enables the creation and deployment of multiple AI agents for various tasks and applications. It provides a structured environment for building, managing, and coordinating autonomous agents to perform complex tasks collaboratively or individually. This research report aims to provide detailed information about the Autogen Multi AI Agent Framework. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework is designed to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. It offers a set of tools, libraries, and APIs to facilitate the development of autonomous agents that can communicate, collaborate, and cooperate with each other. 2. Key Features and Capabilities: - Agent Creation and Configuration: The framework allows developers to define and configure AI agents with specific capabilities and behaviors. It provides a flexible and extensible architecture to accommodate various agent types, such as rule-based agents, machine learning agents, or deep learning agents. - Communication and Collaboration: The framework enables agents to communicate with each other using predefined protocols or custom communication channels. This facilitates coordination and collaboration among agents, allowing them to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: The framework supports agent coordination and planning mechanisms to ensure efficient utilization of resources and optimal task allocation. It provides algorithms and techniques for decision-making, task assignment, and coordination among multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes machine learning and reinforcement learning capabilities to enable agents to learn from data and improve their performance over time. It facilitates the integration of various learning algorithms and provides tools for data preprocessing, feature extraction, and model training. - Scalability and Extensibility: The framework is designed to be scalable and extensible, allowing the addition of new agents, functionalities, and modules. It supports the integration of external libraries, APIs, and services, enabling the creation of diverse and comprehensive AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework typically consists of the following components: - Agent Manager: Manages the creation, configuration, and lifecycle of agents. It provides an interface for adding, removing, and monitoring agents in the system. - Communication Middleware: Handles communication and message passing between agents. It ensures reliable and secure data transfer among agents using various communication protocols or custom channels. - Decision-Making Module: Implements decision-making algorithms to guide agent behavior and task allocation. It can be rule-based, heuristic-based, or machine learning-based, depending on the specific requirements. - Learning Module: Facilitates agent learning and adaptation. It includes data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques to improve agent performance. - Task Scheduler: Manages task assignment and coordination among the agents. It optimizes resource allocation, prioritizes tasks, and monitors task progress. 4. Potential Applications: The Autogen Multi AI Agent Framework has a wide range of applications across various domains, including but not limited to: - Robotics: Autonomous robots can be developed using the framework to perform tasks collaboratively, such as warehouse automation, swarm robotics, and hazardous environment exploration. - Smart Manufacturing: The framework can be applied in manufacturing environments to coordinate and optimize the production process, resource allocation, and maintenance tasks. - Traffic Management: AI agents can be used to monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework can enable intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans. - Financial Services: Agents can be developed to handle financial transactions, fraud detection, risk management, and portfolio optimization in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework provides a comprehensive and flexible solution for building and managing multiple AI agents in various applications. It simplifies the development process by providing reusable components and tools for agent creation, communication, coordination, and learning. The framework's scalability and extensibility allow it to adapt to diverse use cases and accommodate emerging technologies. With its rich set of features and capabilities, the Autogen Multi AI Agent Framework is a valuable resource for developers aiming to build intelligent and collaborative AI systems. TERMINATE -------------------------------------------------------------------------------- User_proxy (to researcher): TERMINATE -------------------------------------------------------------------------------- User_proxy (to researcher): Give me the research report that just generated again, return ONLY the report & reference links -------------------------------------------------------------------------------- researcher (to User_proxy): Research Report: Autogen Multi AI Agent Framework Introduction: An Autogen Multi AI Agent Framework is a software framework that enables the creation and deployment of multiple AI agents for various tasks and applications. It provides a structured environment for building, managing, and coordinating autonomous agents to perform complex tasks collaboratively or individually. This research report aims to provide detailed information about the Autogen Multi AI Agent Framework. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework is designed to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. It offers a set of tools, libraries, and APIs to facilitate the development of autonomous agents that can communicate, collaborate, and cooperate with each other. 2. Key Features and Capabilities: - Agent Creation and Configuration: The framework allows developers to define and configure AI agents with specific capabilities and behaviors. It provides a flexible and extensible architecture to accommodate various agent types, such as rule-based agents, machine learning agents, or deep learning agents. - Communication and Collaboration: The framework enables agents to communicate with each other using predefined protocols or custom communication channels. This facilitates coordination and collaboration among agents, allowing them to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: The framework supports agent coordination and planning mechanisms to ensure efficient utilization of resources and optimal task allocation. It provides algorithms and techniques for decision-making, task assignment, and coordination among multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes machine learning and reinforcement learning capabilities to enable agents to learn from data and improve their performance over time. It facilitates the integration of various learning algorithms and provides tools for data preprocessing, feature extraction, and model training. - Scalability and Extensibility: The framework is designed to be scalable and extensible, allowing the addition of new agents, functionalities, and modules. It supports the integration of external libraries, APIs, and services, enabling the creation of diverse and comprehensive AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework typically consists of the following components: - Agent Manager: Manages the creation, configuration, and lifecycle of agents. It provides an interface for adding, removing, and monitoring agents in the system. - Communication Middleware: Handles communication and message passing between agents. It ensures reliable and secure data transfer among agents using various communication protocols or custom channels. - Decision-Making Module: Implements decision-making algorithms to guide agent behavior and task allocation. It can be rule-based, heuristic-based, or machine learning-based, depending on the specific requirements. - Learning Module: Facilitates agent learning and adaptation. It includes data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques to improve agent performance. - Task Scheduler: Manages task assignment and coordination among the agents. It optimizes resource allocation, prioritizes tasks, and monitors task progress. 4. Potential Applications: The Autogen Multi AI Agent Framework has a wide range of applications across various domains, including but not limited to: - Robotics: Autonomous robots can be developed using the framework to perform tasks collaboratively, such as warehouse automation, swarm robotics, and hazardous environment exploration. - Smart Manufacturing: The framework can be applied in manufacturing environments to coordinate and optimize the production process, resource allocation, and maintenance tasks. - Traffic Management: AI agents can be used to monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework can enable intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans. - Financial Services: Agents can be developed to handle financial transactions, fraud detection, risk management, and portfolio optimization in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework provides a comprehensive and flexible solution for building and managing multiple AI agents in various applications. It simplifies the development process by providing reusable components and tools for agent creation, communication, coordination, and learning. The framework's scalability and extensibility allow it to adapt to diverse use cases and accommodate emerging technologies. With its rich set of features and capabilities, the Autogen Multi AI Agent Framework is a valuable resource for developers aiming to build intelligent and collaborative AI systems. References: 1. Autogen Multi AI Agent Framework Documentation. [Link](https://autogen.com/framework/documentation) 2. "A Framework for Multi-Agent Systems" by Michael Wooldridge. [PDF](https://link.springer.com/content/pdf/bfm%3A978-0-387-21446-5%2F1.pdf) 3. "Multi-agent Systems: An Introduction to Distributed Artificial Intelligence" by Jacopo Vizzari. [Book](https://www.amazon.com/Multi-agent-Systems-Introduction-Distributed-Intelligence/dp/3540882136) TERMINATE -------------------------------------------------------------------------------- User_proxy (to writing_assistant): ***** Response from calling function "research" ***** Research Report: Autogen Multi AI Agent Framework Introduction: An Autogen Multi AI Agent Framework is a software framework that enables the creation and deployment of multiple AI agents for various tasks and applications. It provides a structured environment for building, managing, and coordinating autonomous agents to perform complex tasks collaboratively or individually. This research report aims to provide detailed information about the Autogen Multi AI Agent Framework. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework is designed to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. It offers a set of tools, libraries, and APIs to facilitate the development of autonomous agents that can communicate, collaborate, and cooperate with each other. 2. Key Features and Capabilities: - Agent Creation and Configuration: The framework allows developers to define and configure AI agents with specific capabilities and behaviors. It provides a flexible and extensible architecture to accommodate various agent types, such as rule-based agents, machine learning agents, or deep learning agents. - Communication and Collaboration: The framework enables agents to communicate with each other using predefined protocols or custom communication channels. This facilitates coordination and collaboration among agents, allowing them to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: The framework supports agent coordination and planning mechanisms to ensure efficient utilization of resources and optimal task allocation. It provides algorithms and techniques for decision-making, task assignment, and coordination among multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes machine learning and reinforcement learning capabilities to enable agents to learn from data and improve their performance over time. It facilitates the integration of various learning algorithms and provides tools for data preprocessing, feature extraction, and model training. - Scalability and Extensibility: The framework is designed to be scalable and extensible, allowing the addition of new agents, functionalities, and modules. It supports the integration of external libraries, APIs, and services, enabling the creation of diverse and comprehensive AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework typically consists of the following components: - Agent Manager: Manages the creation, configuration, and lifecycle of agents. It provides an interface for adding, removing, and monitoring agents in the system. - Communication Middleware: Handles communication and message passing between agents. It ensures reliable and secure data transfer among agents using various communication protocols or custom channels. - Decision-Making Module: Implements decision-making algorithms to guide agent behavior and task allocation. It can be rule-based, heuristic-based, or machine learning-based, depending on the specific requirements. - Learning Module: Facilitates agent learning and adaptation. It includes data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques to improve agent performance. - Task Scheduler: Manages task assignment and coordination among the agents. It optimizes resource allocation, prioritizes tasks, and monitors task progress. 4. Potential Applications: The Autogen Multi AI Agent Framework has a wide range of applications across various domains, including but not limited to: - Robotics: Autonomous robots can be developed using the framework to perform tasks collaboratively, such as warehouse automation, swarm robotics, and hazardous environment exploration. - Smart Manufacturing: The framework can be applied in manufacturing environments to coordinate and optimize the production process, resource allocation, and maintenance tasks. - Traffic Management: AI agents can be used to monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework can enable intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans. - Financial Services: Agents can be developed to handle financial transactions, fraud detection, risk management, and portfolio optimization in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework provides a comprehensive and flexible solution for building and managing multiple AI agents in various applications. It simplifies the development process by providing reusable components and tools for agent creation, communication, coordination, and learning. The framework's scalability and extensibility allow it to adapt to diverse use cases and accommodate emerging technologies. With its rich set of features and capabilities, the Autogen Multi AI Agent Framework is a valuable resource for developers aiming to build intelligent and collaborative AI systems. References: 1. Autogen Multi AI Agent Framework Documentation. [Link](https://autogen.com/framework/documentation) 2. "A Framework for Multi-Agent Systems" by Michael Wooldridge. [PDF](https://link.springer.com/content/pdf/bfm%3A978-0-387-21446-5%2F1.pdf) 3. "Multi-agent Systems: An Introduction to Distributed Artificial Intelligence" by Jacopo Vizzari. [Book](https://www.amazon.com/Multi-agent-Systems-Introduction-Distributed-Intelligence/dp/3540882136) TERMINATE ***************************************************** -------------------------------------------------------------------------------- writing_assistant (to User_proxy): ***** Suggested function Call: write_content ***** Arguments: { "research_material": "Research Report: Autogen Multi AI Agent Framework\n\nIntroduction:\nAn Autogen Multi AI Agent Framework is a software framework that enables the creation and deployment of multiple AI agents for various tasks and applications. It provides a structured environment for building, managing, and coordinating autonomous agents to perform complex tasks collaboratively or individually. This research report aims to provide detailed information about the Autogen Multi AI Agent Framework.\n\n1. Autogen Multi AI Agent Framework Overview:\nThe Autogen Multi AI Agent Framework is designed to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. It offers a set of tools, libraries, and APIs to facilitate the development of autonomous agents that can communicate, collaborate, and cooperate with each other.\n\n2. Key Features and Capabilities:\n- Agent Creation and Configuration: The framework allows developers to define and configure AI agents with specific capabilities and behaviors. It provides a flexible and extensible architecture to accommodate various agent types, such as rule-based agents, machine learning agents, or deep learning agents.\n\n- Communication and Collaboration: The framework enables agents to communicate with each other using predefined protocols or custom communication channels. This facilitates coordination and collaboration among agents, allowing them to exchange data, share knowledge, and work together on complex tasks.\n\n- Agent Coordination and Planning: The framework supports agent coordination and planning mechanisms to ensure efficient utilization of resources and optimal task allocation. It provides algorithms and techniques for decision-making, task assignment, and coordination among multiple agents.\n\n- Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes machine learning and reinforcement learning capabilities to enable agents to learn from data and improve their performance over time. It facilitates the integration of various learning algorithms and provides tools for data preprocessing, feature extraction, and model training.\n\n- Scalability and Extensibility: The framework is designed to be scalable and extensible, allowing the addition of new agents, functionalities, and modules. It supports the integration of external libraries, APIs, and services, enabling the creation of diverse and comprehensive AI systems.\n\n3. Architecture and Components:\nThe Autogen Multi AI Agent Framework typically consists of the following components:\n\n- Agent Manager: Manages the creation, configuration, and lifecycle of agents. It provides an interface for adding, removing, and monitoring agents in the system.\n\n- Communication Middleware: Handles communication and message passing between agents. It ensures reliable and secure data transfer among agents using various communication protocols or custom channels.\n\n- Decision-Making Module: Implements decision-making algorithms to guide agent behavior and task allocation. It can be rule-based, heuristic-based, or machine learning-based, depending on the specific requirements.\n\n- Learning Module: Facilitates agent learning and adaptation. It includes data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques to improve agent performance.\n\n- Task Scheduler: Manages task assignment and coordination among the agents. It optimizes resource allocation, prioritizes tasks, and monitors task progress.\n\n4. Potential Applications:\nThe Autogen Multi AI Agent Framework has a wide range of applications across various domains, including but not limited to:\n\n- Robotics: Autonomous robots can be developed using the framework to perform tasks collaboratively, such as warehouse automation, swarm robotics, and hazardous environment exploration.\n\n- Smart Manufacturing: The framework can be applied in manufacturing environments to coordinate and optimize the production process, resource allocation, and maintenance tasks.\n\n- Traffic Management: AI agents can be used to monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas.\n\n- Healthcare: The framework can enable intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans.\n\n- Financial Services: Agents can be developed to handle financial transactions, fraud detection, risk management, and portfolio optimization in the banking and finance industry.\n\nConclusion:\nThe Autogen Multi AI Agent Framework provides a comprehensive and flexible solution for building and managing multiple AI agents in various applications. It simplifies the development process by providing reusable components and tools for agent creation, communication, coordination, and learning. The framework's scalability and extensibility allow it to adapt to diverse use cases and accommodate emerging technologies. With its rich set of features and capabilities, the Autogen Multi AI Agent Framework is a valuable resource for developers aiming to build intelligent and collaborative AI systems.\n\nReferences:\n1. Autogen Multi AI Agent Framework Documentation. [Link](https://autogen.com/framework/documentation)\n2. \"A Framework for Multi-Agent Systems\" by Michael Wooldridge. [PDF](https://link.springer.com/content/pdf/bfm%3A978-0-387-21446-5%2F1.pdf)\n3. \"Multi-agent Systems: An Introduction to Distributed Artificial Intelligence\" by Jacopo Vizzari. [Book](https://www.amazon.com/Multi-agent-Systems-Introduction-Distributed-Intelligence/dp/3540882136)", "topic": "Autogen Multi AI Agent Framework" } ************************************************** -------------------------------------------------------------------------------- >>>>>>>> EXECUTING FUNCTION write_content... admin (to chat_manager): Write a blog about Autogen Multi AI Agent Framework, here are the material: Research Report: Autogen Multi AI Agent Framework Introduction: An Autogen Multi AI Agent Framework is a software framework that enables the creation and deployment of multiple AI agents for various tasks and applications. It provides a structured environment for building, managing, and coordinating autonomous agents to perform complex tasks collaboratively or individually. This research report aims to provide detailed information about the Autogen Multi AI Agent Framework. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework is designed to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. It offers a set of tools, libraries, and APIs to facilitate the development of autonomous agents that can communicate, collaborate, and cooperate with each other. 2. Key Features and Capabilities: - Agent Creation and Configuration: The framework allows developers to define and configure AI agents with specific capabilities and behaviors. It provides a flexible and extensible architecture to accommodate various agent types, such as rule-based agents, machine learning agents, or deep learning agents. - Communication and Collaboration: The framework enables agents to communicate with each other using predefined protocols or custom communication channels. This facilitates coordination and collaboration among agents, allowing them to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: The framework supports agent coordination and planning mechanisms to ensure efficient utilization of resources and optimal task allocation. It provides algorithms and techniques for decision-making, task assignment, and coordination among multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes machine learning and reinforcement learning capabilities to enable agents to learn from data and improve their performance over time. It facilitates the integration of various learning algorithms and provides tools for data preprocessing, feature extraction, and model training. - Scalability and Extensibility: The framework is designed to be scalable and extensible, allowing the addition of new agents, functionalities, and modules. It supports the integration of external libraries, APIs, and services, enabling the creation of diverse and comprehensive AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework typically consists of the following components: - Agent Manager: Manages the creation, configuration, and lifecycle of agents. It provides an interface for adding, removing, and monitoring agents in the system. - Communication Middleware: Handles communication and message passing between agents. It ensures reliable and secure data transfer among agents using various communication protocols or custom channels. - Decision-Making Module: Implements decision-making algorithms to guide agent behavior and task allocation. It can be rule-based, heuristic-based, or machine learning-based, depending on the specific requirements. - Learning Module: Facilitates agent learning and adaptation. It includes data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques to improve agent performance. - Task Scheduler: Manages task assignment and coordination among the agents. It optimizes resource allocation, prioritizes tasks, and monitors task progress. 4. Potential Applications: The Autogen Multi AI Agent Framework has a wide range of applications across various domains, including but not limited to: - Robotics: Autonomous robots can be developed using the framework to perform tasks collaboratively, such as warehouse automation, swarm robotics, and hazardous environment exploration. - Smart Manufacturing: The framework can be applied in manufacturing environments to coordinate and optimize the production process, resource allocation, and maintenance tasks. - Traffic Management: AI agents can be used to monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework can enable intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans. - Financial Services: Agents can be developed to handle financial transactions, fraud detection, risk management, and portfolio optimization in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework provides a comprehensive and flexible solution for building and managing multiple AI agents in various applications. It simplifies the development process by providing reusable components and tools for agent creation, communication, coordination, and learning. The framework's scalability and extensibility allow it to adapt to diverse use cases and accommodate emerging technologies. With its rich set of features and capabilities, the Autogen Multi AI Agent Framework is a valuable resource for developers aiming to build intelligent and collaborative AI systems. References: 1. Autogen Multi AI Agent Framework Documentation. [Link](https://autogen.com/framework/documentation) 2. "A Framework for Multi-Agent Systems" by Michael Wooldridge. [PDF](https://link.springer.com/content/pdf/bfm%3A978-0-387-21446-5%2F1.pdf) 3. "Multi-agent Systems: An Introduction to Distributed Artificial Intelligence" by Jacopo Vizzari. [Book](https://www.amazon.com/Multi-agent-Systems-Introduction-Distributed-Intelligence/dp/3540882136). -------------------------------------------------------------------------------- editor (to chat_manager): Title: Building Intelligent Systems with Autogen Multi AI Agent Framework Introduction: In today's technological landscape, the field of artificial intelligence (AI) has witnessed significant advancements. One such development is the Autogen Multi AI Agent Framework, which provides a structured environment for creating and deploying multiple AI agents. This research report aims to explore the various features, capabilities, and potential applications of the Autogen Multi AI Agent Framework. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework is designed to simplify the development process of AI-based systems. It offers a reusable structure that enables the creation and integration of multiple AI agents. The framework provides developers with essential tools, libraries, and APIs, streamlining the process of building autonomous agents that can collaborate and communicate effectively. 2. Key Features and Capabilities: Within the Autogen Multi AI Agent Framework, developers can take advantage of several key features and capabilities. These include: - Agent Creation and Configuration: The framework facilitates the definition and configuration of AI agents with specific capabilities and behaviors. Its architecture is flexible and extensible, accommodating diverse agent types such as rule-based agents, machine learning agents, or deep learning agents. - Communication and Collaboration: The framework enables seamless communication among agents through predefined protocols or custom communication channels. This fosters collaboration, allowing agents to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: To ensure efficient resource utilization and optimal task allocation, the framework supports agent coordination and planning mechanisms. It incorporates decision-making algorithms, task assignment, and coordination techniques for multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes machine learning and reinforcement learning capabilities. Agents can learn from data, improve their performance over time, and adapt to changing circumstances. The framework provides tools for data preprocessing, feature extraction, and model training. - Scalability and Extensibility: Designed with scalability and extensibility in mind, the framework allows for the addition of new agents, functionalities, and modules. Developers can integrate external libraries, APIs, and services, enabling the creation of comprehensive and diverse AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework consists of several essential components: - Agent Manager: Responsible for managing the creation, configuration, and lifecycle of agents. It provides a user-friendly interface for adding, removing, and monitoring agents in the system. - Communication Middleware: Handles communication and message passing between agents. It ensures reliable and secure data transfer, employing various communication protocols or custom channels. - Decision-Making Module: Implements decision-making algorithms to guide agent behavior and task allocation. This module can be rule-based, heuristic-based, or machine learning-based, depending on specific requirements. - Learning Module: Facilitates agent learning and adaptation. It includes data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques to enhance agent performance. - Task Scheduler: Manages task assignment and coordination among agents. It optimizes resource allocation, prioritizes tasks, and monitors task progress. 4. Potential Applications: The Autogen Multi AI Agent Framework finds applications in diverse domains, including: - Robotics: The framework enables the development of autonomous robots for tasks such as warehouse automation, swarm robotics, and exploration of hazardous environments. - Smart Manufacturing: In manufacturing environments, the framework can be utilized to coordinate and optimize the production process, resource allocation, and maintenance tasks. - Traffic Management: AI agents developed using the framework can monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework facilitates intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans. - Financial Services: Agents can be developed within the framework to handle financial transactions, fraud detection, risk management, and portfolio optimization in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework serves as a comprehensive and flexible solution for building and managing multiple AI agents across various applications. It empowers developers by providing reusable components and tools for agent creation, communication, coordination, and learning. With its scalability and extensibility, the Autogen Multi AI Agent Framework can adapt to diverse use cases and accommodate emerging technologies. By leveraging its rich set of features and capabilities, developers can create intelligent and collaborative AI systems. References: 1. Autogen Multi AI Agent Framework Documentation. [Link] 2. "A Framework for Multi-Agent Systems" by Michael Wooldridge. [PDF] 3. "Multi-agent Systems: An Introduction to Distributed Artificial Intelligence" by Jacopo Vizzari. [Book] -------------------------------------------------------------------------------- writer (to chat_manager): TERMINATE -------------------------------------------------------------------------------- admin (to chat_manager): writer, now complete the blog based on feedback you already received. Let me know once you complete -------------------------------------------------------------------------------- writer (to chat_manager): Title: Building Intelligent Systems with Autogen Multi AI Agent Framework Introduction: In today's technological landscape, the field of artificial intelligence (AI) has witnessed significant advancements. One such development that stands out is the Autogen Multi AI Agent Framework. This powerful software framework provides a structured environment for creating and deploying multiple AI agents, offering a range of features and capabilities. In this blog post, we will explore the Autogen Multi AI Agent Framework in detail, highlighting its key components, functionalities, and potential applications. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework aims to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. With this framework, developers can take advantage of a comprehensive set of tools, libraries, and APIs that streamline the creation of autonomous agents capable of both collaboration and individual tasks. 2. Key Features and Capabilities: The Autogen Multi AI Agent Framework boasts several essential features and capabilities: - Agent Creation and Configuration: The framework offers a flexible and extensible architecture that allows developers to define and configure AI agents with specific capabilities and behaviors. Whether it's rule-based agents, machine learning agents, or deep learning agents, the framework accommodates various agent types. - Communication and Collaboration: Seamless communication among agents is enabled through predefined protocols or custom communication channels. This fosters collaboration, allowing agents to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: To ensure efficient resource utilization and optimal task allocation, the framework supports robust agent coordination and planning mechanisms. Decision-making algorithms, task assignment, and coordination techniques are incorporated to facilitate effective collaboration among multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes built-in machine learning and reinforcement learning capabilities, empowering agents to learn from data and improve their performance over time. Developers can leverage data preprocessing, feature extraction, and model training tools to enhance agent learning and adaptation. - Scalability and Extensibility: Designed with scalability and extensibility in mind, the framework allows developers to seamlessly add new agents, functionalities, and modules. External libraries, APIs, and services can be easily integrated, enabling the creation of comprehensive and diverse AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework comprises several essential components: - Agent Manager: Responsible for managing the creation, configuration, and lifecycle of agents, the Agent Manager provides a user-friendly interface for developers to add, remove, and monitor agents within the system. - Communication Middleware: This component handles communication and message passing between agents, ensuring reliable and secure data transfer using various communication protocols or custom channels. - Decision-Making Module: The Decision-Making Module implements powerful algorithms that guide agent behavior and task allocation. The module can be rule-based, heuristic-based, or machine learning-based, depending on the specific AI system requirements. - Learning Module: To enable agent learning and adaptation, the Learning Module offers functionalities such as data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques. These tools contribute to improved agent performance and flexibility. - Task Scheduler: The Task Scheduler plays a vital role in managing task assignment and coordination among agents within the framework. It optimizes resource allocation, prioritizes tasks, and monitors task progress, ensuring efficient utilization of available resources. 4. Potential Applications: The Autogen Multi AI Agent Framework finds applications across various domains, including but not limited to: - Robotics: The framework can be leveraged to develop autonomous robots capable of collaborative tasks, such as warehouse automation, swarm robotics, and exploration of hazardous environments. - Smart Manufacturing: In manufacturing environments, the framework can optimize the production process, resource allocation, and maintenance tasks, leading to increased efficiency and productivity. - Traffic Management: Leveraging the Autogen Multi AI Agent Framework, AI agents can effectively monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework enables the creation of intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans, leading to improved patient outcomes. - Financial Services: AI agents built within the framework can handle financial transactions, perform fraud detection, manage risk, and optimize portfolios in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework stands as a comprehensive and flexible solution for building and managing multiple AI agents, offering various features and capabilities. It simplifies the development process, allowing developers to focus on agent creation, communication, coordination, and learning. With its scalability and extensibility, the framework can adapt to different use cases and accommodate emerging technologies. By leveraging the Autogen Multi AI Agent Framework's rich set of features and capabilities, developers can build intelligent and collaborative AI systems to address complex challenges across diverse domains. References: 1. Autogen Multi AI Agent Framework Documentation. [Link] 2. Wooldridge, Michael. "A Framework for Multi-Agent Systems". [PDF] 3. Vizzari, Jacopo. "Multi-agent Systems: An Introduction to Distributed Artificial Intelligence". [Book] TERMINATE -------------------------------------------------------------------------------- reviewer (to chat_manager): Great! The blog has been completed. It covers all the necessary aspects of the Autogen Multi AI Agent Framework, including its overview, key features and capabilities, architecture and components, potential applications, and references. The content is well-structured and provides relevant information to the readers. TERMINATE -------------------------------------------------------------------------------- [autogen.oai.completion: 10-05 13:30:58] {237} INFO - retrying in 10 seconds... Traceback (most recent call last): File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\autogen\oai\completion.py", line 209, in _get_response response = openai_completion.create(request_timeout=request_timeout, **config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.RateLimitError: Rate limit reached for 10KTPM-200RPM in organization org-8ZH5xsxnXWaMLk0Bopo2Sg5r on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues. [autogen.oai.completion: 10-05 13:31:08] {237} INFO - retrying in 10 seconds... Traceback (most recent call last): File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\autogen\oai\completion.py", line 209, in _get_response response = openai_completion.create(request_timeout=request_timeout, **config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.RateLimitError: Rate limit reached for 10KTPM-200RPM in organization org-8ZH5xsxnXWaMLk0Bopo2Sg5r on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues. admin (to chat_manager): TERMINATE -------------------------------------------------------------------------------- [autogen.oai.completion: 10-05 13:31:19] {237} INFO - retrying in 10 seconds... Traceback (most recent call last): File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\autogen\oai\completion.py", line 209, in _get_response response = openai_completion.create(request_timeout=request_timeout, **config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.RateLimitError: Rate limit reached for 10KTPM-200RPM in organization org-8ZH5xsxnXWaMLk0Bopo2Sg5r on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues. [autogen.oai.completion: 10-05 13:31:30] {237} INFO - retrying in 10 seconds... Traceback (most recent call last): File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\autogen\oai\completion.py", line 209, in _get_response response = openai_completion.create(request_timeout=request_timeout, **config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.RateLimitError: Rate limit reached for 10KTPM-200RPM in organization org-8ZH5xsxnXWaMLk0Bopo2Sg5r on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues. [autogen.oai.completion: 10-05 13:31:40] {237} INFO - retrying in 10 seconds... Traceback (most recent call last): File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\autogen\oai\completion.py", line 209, in _get_response response = openai_completion.create(request_timeout=request_timeout, **config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_resources\abstract\engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "C:\Users\xiaoyuz\source\repos\microsoft-autogen-experiments\venv\Lib\site-packages\openai\api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.RateLimitError: Rate limit reached for 10KTPM-200RPM in organization org-8ZH5xsxnXWaMLk0Bopo2Sg5r on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues. User_proxy (to writing_assistant): ***** Response from calling function "write_content" ***** Title: Building Intelligent Systems with Autogen Multi AI Agent Framework Introduction: In today's technological landscape, the field of artificial intelligence (AI) has witnessed significant advancements. One such development that stands out is the Autogen Multi AI Agent Framework. This powerful software framework provides a structured environment for creating and deploying multiple AI agents, offering a range of features and capabilities. In this blog post, we will explore the Autogen Multi AI Agent Framework in detail, highlighting its key components, functionalities, and potential applications. 1. Autogen Multi AI Agent Framework Overview: The Autogen Multi AI Agent Framework aims to simplify the development process of AI-based systems by providing a reusable structure for creating and integrating multiple AI agents. With this framework, developers can take advantage of a comprehensive set of tools, libraries, and APIs that streamline the creation of autonomous agents capable of both collaboration and individual tasks. 2. Key Features and Capabilities: The Autogen Multi AI Agent Framework boasts several essential features and capabilities: - Agent Creation and Configuration: The framework offers a flexible and extensible architecture that allows developers to define and configure AI agents with specific capabilities and behaviors. Whether it's rule-based agents, machine learning agents, or deep learning agents, the framework accommodates various agent types. - Communication and Collaboration: Seamless communication among agents is enabled through predefined protocols or custom communication channels. This fosters collaboration, allowing agents to exchange data, share knowledge, and work together on complex tasks. - Agent Coordination and Planning: To ensure efficient resource utilization and optimal task allocation, the framework supports robust agent coordination and planning mechanisms. Decision-making algorithms, task assignment, and coordination techniques are incorporated to facilitate effective collaboration among multiple agents. - Agent Learning and Adaptation: The Autogen Multi AI Agent Framework includes built-in machine learning and reinforcement learning capabilities, empowering agents to learn from data and improve their performance over time. Developers can leverage data preprocessing, feature extraction, and model training tools to enhance agent learning and adaptation. - Scalability and Extensibility: Designed with scalability and extensibility in mind, the framework allows developers to seamlessly add new agents, functionalities, and modules. External libraries, APIs, and services can be easily integrated, enabling the creation of comprehensive and diverse AI systems. 3. Architecture and Components: The Autogen Multi AI Agent Framework comprises several essential components: - Agent Manager: Responsible for managing the creation, configuration, and lifecycle of agents, the Agent Manager provides a user-friendly interface for developers to add, remove, and monitor agents within the system. - Communication Middleware: This component handles communication and message passing between agents, ensuring reliable and secure data transfer using various communication protocols or custom channels. - Decision-Making Module: The Decision-Making Module implements powerful algorithms that guide agent behavior and task allocation. The module can be rule-based, heuristic-based, or machine learning-based, depending on the specific AI system requirements. - Learning Module: To enable agent learning and adaptation, the Learning Module offers functionalities such as data preprocessing, feature extraction, machine learning algorithms, and reinforcement learning techniques. These tools contribute to improved agent performance and flexibility. - Task Scheduler: The Task Scheduler plays a vital role in managing task assignment and coordination among agents within the framework. It optimizes resource allocation, prioritizes tasks, and monitors task progress, ensuring efficient utilization of available resources. 4. Potential Applications: The Autogen Multi AI Agent Framework finds applications across various domains, including but not limited to: - Robotics: The framework can be leveraged to develop autonomous robots capable of collaborative tasks, such as warehouse automation, swarm robotics, and exploration of hazardous environments. - Smart Manufacturing: In manufacturing environments, the framework can optimize the production process, resource allocation, and maintenance tasks, leading to increased efficiency and productivity. - Traffic Management: Leveraging the Autogen Multi AI Agent Framework, AI agents can effectively monitor and control traffic flow, optimize transportation routes, and manage congestion in urban areas. - Healthcare: The framework enables the creation of intelligent healthcare systems by coordinating medical devices, monitoring patient health, and assisting in diagnoses and treatment plans, leading to improved patient outcomes. - Financial Services: AI agents built within the framework can handle financial transactions, perform fraud detection, manage risk, and optimize portfolios in the banking and finance industry. Conclusion: The Autogen Multi AI Agent Framework stands as a comprehensive and flexible solution for building and managing multiple AI agents, offering various features and capabilities. It simplifies the development process, allowing developers to focus on agent creation, communication, coordination, and learning. With its scalability and extensibility, the framework can adapt to different use cases and accommodate emerging technologies. By leveraging the Autogen Multi AI Agent Framework's rich set of features and capabilities, developers can build intelligent and collaborative AI systems to address complex challenges across diverse domains. References: 1. Autogen Multi AI Agent Framework Documentation. [Link] 2. Wooldridge, Michael. "A Framework for Multi-Agent Systems". [PDF] 3. Vizzari, Jacopo. "Multi-agent Systems: An Introduction to Distributed Artificial Intelligence". [Book] TERMINATE ********************************************************** -------------------------------------------------------------------------------- writing_assistant (to User_proxy): Thank you for using my assistance! If you have any more questions, feel free to ask. -------------------------------------------------------------------------------- User_proxy (to writing_assistant): TERMINATE
afourney commented 1 year ago

FWIW, we've been using:

is_termination_msg = lambda x: True if "TERMINATE" in x.get("content") else False,

This helps a little with the exact match, or ends with "TERMINATE" problem.

Finding a more robust means of detecting termination is definitely on our to-do list!

adriangalilea commented 1 year ago

With that approach there's the slight chance of it stopping if for whatever reason TERMINATE appears.

I think the function approach might be less error prone at the cost of some context but not sure how to build it, waiting for that example.

LittleLittleCloud commented 1 year ago

Not in this case. The group chat will be terminated only when user_proxy replies ‘TERMINATE’. Considering that user_proxy in this example is not backed by LLM and will always return the default message, which is TERMINATE. This makes the exit check logic here quite robust

things will be different when user_proxy is powered by an LLM, where a function call to finish a group chat would be a preferred way to go. Let me come up with an example on that scenario

adriangalilea commented 1 year ago

@LittleLittleCloud oh wow I missed your previous message, will be testing it out, thank you so much.

EDIT: All working flawless now, no infinite loops detected, thank you again.

Are you guys present over discord?

LittleLittleCloud commented 1 year ago

@adriangalilea Yes I'm on discord. I'm the one who respond to you and ask if you could create this issue on github

LittleLittleCloud commented 1 year ago

If this issue get resolved I am going to close it. Feel free to reopen it if you have further questions