jacksonfan111/multiagents

MultiAgents Based on Andrew lecture

MultiAgents

MultiAgents Based on Andrew lecture

12.04.2024 - AI agentic workflows Friday, 12 April 2024 9:27 am Zero Shot Prompting

OR Agentic Workflow

Agentic with GPT 3.5 outperform 4 !!!!!

-----------------------------Design Patterns

1. Reflection (ask it to double checks)
2. Tool Use

emerging

3. Planning
4. Multi-agent Collaboration

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Coder Agent >> expert coder

Critic Agent >> expert code reviewer

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2. What tool we are going to use???

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New Stuff??

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3. Planning Algorithm

Multiple Agents and models??

4. Multi Agent Collaboration

*** CHAT DEV

A Gomoke Game

Content Operations Team

Software development team>>

GPT and Gemini debate together ?? >> how to achieve??

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Half a seconds

Or waiting for a minutes

Delegates tasks

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Sure! This image appears to show a hand-drawn diagram of a workflow involving multiple "agents" or systems that are collaborating on a task. Let's break down each part:

1. Agents: There are three agents listed - GPT-4, Claude 4, and Gemini 2.0. These could represent different artificial intelligence models or systems. Each one has a series of circles connected by arrows, which may represent steps or processes they undergo.
2. Reflection, Tool use, Multi-agent Collaboration: These phrases at the top suggest the diagram is outlining how these AI systems reflect, use tools, and collaborate with each other.
3. Data Loader, Planner, Iterator, Judges: Below the third agent, there are a series of roles or components through which data seems to flow. The "Data Loader" could be responsible for inputting data, the "Planner" for setting up the process, the "Iterator" for going through iterations or cycles of a process, and "Judges" might evaluate the outcome.
4. Prompt, Tasks, Phases, Roles: These words are connected to the Planner, indicating that the Planner considers these elements when organizing the workflow.

n the image, we're seeing a visual representation of a multi-agent collaboration environment that is related to software development. The letters A, B, C, D, and E each correspond to different roles within a software development team, which are circled on a pixelated office layout: • A (Designing): This role involves planning and designing the software. It's likely focused on the user experience (UX) and user interface (UI) design, determining how the software will look and function from the user's perspective. • B (Coding): This is the role of writing the actual code for the software. It involves implementing the design plans into a functioning program by writing and compiling code. • C (Documenting): This role focuses on creating documentation for the software. This can include user manuals, code comments, and other forms of written materials that explain how to use the software or how it was developed. • D (Testing): The tester's role is to check the software for bugs and ensure that it works as expected. This involves systematic attempts to break the software to find any weaknesses or errors. • E: It seems to be indicating a central point or role that might involve coordination, management, or integration, but since it’s not labeled in the picture, we can't specify exactly what it represents. It might stand for a project manager or lead developer who oversees the project, or it could represent a version control system where all parts of the project come together.

From https://chat.openai.com/c/588f98fd-d36e-4c62-9967-025e67496a78

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Prompt Create an illustrated flow diagram that represents a multi-agent software development process with five distinct roles. The flow should start with 'A: Designing', where the role is planning and UX/UI design, followed by 'B: Coding', which involves writing and compiling the code. Next is 'C: Documenting', where the role creates user manuals and code documentation. This is followed by 'D: Testing', which includes checking for bugs and testing software functionality. Finally, 'E' represents a central coordination or project management role, overseeing the entire process. The diagram should have arrows to show the flow from A to E, with each role connected in sequence, and should be easy to understand with a clean, professional appearance suitable for a business presentation.

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Okay, let's explore how we could implement this multi-agent software development workflow using LangChain.

LangChain is a framework for building applications with large language models (LLMs) like GPT-3. It provides a set of abstractions and tools to make it easier to build applications on top of LLMs.

Here's a high-level overview of how we could use LangChain to implement the workflow:

1. Agents: We can represent each of the five roles (A, B, C, D, E) as LangChain agents. Each agent would have its own set of capabilities, knowledge, and decision-making logic.
2. Workflows: We can use LangChain's workflow functionality to define the sequence of steps the agents will take to complete the software development process. This would involve connecting the agents in the order defined in the document (A -> B -> C -> D -> E).
3. Tools: The agents may need to use various tools to complete their tasks, such as a code editor, documentation generator, or testing framework. LangChain has a built-in tool abstraction that we can use to integrate these tools into the workflow.
4. Memory: LangChain provides a memory abstraction that allows agents to store and retrieve information as they progress through the workflow. This could be used to keep track of design decisions, code changes, test results, and other relevant data.
5. Prompt Engineering: Each agent may require custom prompts to effectively carry out its role. LangChain's prompt templates and prompt selectors can be used to create and manage these prompts.
6. Visualization: To create the "illustrated flow diagram" mentioned in the prompt, we could use a library like Graphviz or NetworkX to generate a visual representation of the workflow.

Okay, let's explore how we could implement this multi-agent software development workflow using LangChain.

LangChain is a framework for building applications with large language models (LLMs) like GPT-3. It provides a set of abstractions and tools to make it easier to build applications on top of LLMs.

Here's a high-level overview of how we could use LangChain to implement the workflow:

1. Agents: We can represent each of the five roles (A, B, C, D, E) as LangChain agents. Each agent would have its own set of capabilities, knowledge, and decision-making logic.

2. Workflows: We can use LangChain's workflow functionality to define the sequence of steps the agents will take to complete the software development process. This would involve connecting the agents in the order defined in the document (A -> B -> C -> D -> E).

3. Tools: The agents may need to use various tools to complete their tasks, such as a code editor, documentation generator, or testing framework. LangChain has a built-in tool abstraction that we can use to integrate these tools into the workflow.

4. Memory: LangChain provides a memory abstraction that allows agents to store and retrieve information as they progress through the workflow. This could be used to keep track of design decisions, code changes, test results, and other relevant data.

5. Prompt Engineering: Each agent may require custom prompts to effectively carry out its role. LangChain's prompt templates and prompt selectors can be used to create and manage these prompts.

6. Visualization: To create the "illustrated flow diagram" mentioned in the prompt, we could use a library like Graphviz or NetworkX to generate a visual representation of the workflow.

Here's a high-level pseudocode example of how this could be implemented using LangChain:

from langchain.agents import Agent
from langchain.workflows import Workflow
from langchain.tools import Tool
from langchain.memory import Memory
from langchain.prompts import PromptTemplate
import networkx as nx
import matplotlib.pyplot as plt
 
# Define the agents
agent_a = Agent(name="A", capabilities=["design", "ux", "ui"], prompt=PromptTemplate(...))
agent_b = Agent(name="B", capabilities=["coding", "compiling"], prompt=PromptTemplate(...))
agent_c = Agent(name="C", capabilities=["documentation"], prompt=PromptTemplate(...))
agent_d = Agent(name="D", capabilities=["testing", "bug_checking"], prompt=PromptTemplate(...))
agent_e = Agent(name="E", capabilities=["coordination", "management"], prompt=PromptTemplate(...))
 
# Define the tools
code_editor = Tool(name="Code Editor", description="...")
documentation_generator = Tool(name="Documentation Generator", description="...")
testing_framework = Tool(name="Testing Framework", description="...")
 
# Define the workflow
workflow = Workflow([agent_a, agent_b, agent_c, agent_d, agent_e])
workflow.add_step(agent_a, [code_editor, documentation_generator])
workflow.add_step(agent_b, [code_editor])
workflow.add_step(agent_c, [documentation_generator])
workflow.add_step(agent_d, [testing_framework])
workflow.add_step(agent_e, [])
 
# Execute the workflow
workflow.run()
 
# Visualize the workflow
graph = nx.DiGraph()
for agent in [agent_a, agent_b, agent_c, agent_d, agent_e]:
    graph.add_node(agent.name)
for i in range(len(workflow.steps) - 1):
    graph.add_edge(workflow.steps[i].agent.name, workflow.steps[i+1].agent.name)
pos = nx.spring_layout(graph)
plt.figure(figsize=(8, 6))
nx.draw(graph, pos, with_labels=True)
plt.savefig("workflow_diagram.png")

This is a simplified example, but it should give you an idea of how you could use LangChain to implement the multi-agent software development workflow described in the document. You may need to add more functionality, such as memory management, prompt engineering, and error handling, to create a production-ready system.

Let me know if you have any other questions!

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from openai import OpenAI client = OpenAI()

response = client.chat.completions.create( model="gpt-4-turbo-preview", messages=[ { "role": "system", "content": "# Define the agents\nagent_a = Agent(name="A", capabilities=["design", "ux", "ui"], prompt=PromptTemplate(...))\nagent_b = Agent(name="B", capabilities=["coding", "compiling"], prompt=PromptTemplate(...))\nagent_c = Agent(name="C", capabilities=["documentation"], prompt=PromptTemplate(...))\nagent_d = Agent(name="D", capabilities=["testing", "bug_checking"], prompt=PromptTemplate(...))\nagent_e = Agent(name="E", capabilities=["coordination", "management"], prompt=PromptTemplate(...))\n" }, { "role": "user", "content": "import pygame\nimport random\n\n# Initialize Pygame\npygame.init()\n\n# Set up the game window\nwidth, height = 400, 400\nwindow = pygame.display.set_mode((width, height))\npygame.display.set_caption("Pacman")\n\n# Define colors\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nYELLOW = (255, 255, 0)\nBLUE = (0, 0, 255)\n\n# Define game objects\npacman_pos = [200, 200]\npacman_speed = 5\nghost_pos = [random.randint(0, width - 20), random.randint(0, height - 20)]\nghost_speed = 3\npellet_pos = [random.randint(0, width - 10), random.randint(0, height - 10)]\nscore = 0\n\n# Game loop\nrunning = True\nclock = pygame.time.Clock()\n\nwhile running:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n\n # Move Pacman\n keys = pygame.key.get_pressed()\n if keys[pygame.K_LEFT] and pacman_pos[0] > 0:\n pacman_pos[0] -= pacman_speed\n if keys[pygame.K_RIGHT] and pacman_pos[0] < width - 20:\n pacman_pos[0] += pacman_speed\n if keys[pygame.K_UP] and pacman_pos[1] > 0:\n pacman_pos[1] -= pacman_speed\n if keys[pygame.K_DOWN] and pacman_pos[1] < height - 20:\n pacman_pos[1] += pacman_speed\n\n # Move ghost towards Pacman\n if ghost_pos[0] < pacman_pos[0]:\n ghost_pos[0] += ghost_speed\n if ghost_pos[0] > pacman_pos[0]:\n ghost_pos[0] -= ghost_speed\n if ghost_pos[1] < pacman_pos[1]:\n ghost_pos[1] += ghost_speed\n if ghost_pos[1] > pacman_pos[1]:\n ghost_pos[1] -= ghost_speed\n\n # Check collision with pellet\n if abs(pacman_pos[0] - pellet_pos[0]) < 10 and abs(pacman_pos[1] - pellet_pos[1]) < 10:\n score += 1\n pellet_pos = [random.randint(0, width - 10), random.randint(0, height - 10)]\n\n # Check collision with ghost\n if abs(pacman_pos[0] - ghost_pos[0]) < 10 and abs(pacman_pos[1] - ghost_pos[1]) < 10:\n running = False\n\n # Clear the window\n window.fill(BLACK)\n\n # Draw game objects\n pygame.draw.circle(window, YELLOW, pacman_pos, 10)\n pygame.draw.rect(window, BLUE, [ghost_pos[0], ghost_pos[1], 20, 20])\n pygame.draw.circle(window, WHITE, pellet_pos, 5)\n\n # Update the display\n pygame.display.update()\n clock.tick(60)\n\n# Quit the game\npygame.quit()" }, { "role": "assistant", "content": "As ssoftware dev team you are to QS this code" } ], temperature=0, max_tokens=256, top_p=1, frequency_penalty=0, presence_penalty=0 )

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Jackson Fan Data Analyst

p. | m. | e. [email protected] ravensdown.co.nz | Twitter | LinkedIn | Facebook | Smarter farming for a better New Zealand