🧍🏻‍♂️🕴🏻🕵🏻 Multi AI Agent Systems with crewAI 🦸🏻‍♂️

This project aims to automate business workflows by utilizing multi-agent AI systems. By harnessing the capabilities of autonomous AI agents, this framework ensures efficient and effective execution of complex, multi-step tasks.

Goal:

Designing effective AI agents and organizing a team of these agents to perform intricate, multi-step tasks seamlessly. Our focus is on creating a collaborative ecosystem where AI agents can communicate and cooperate to achieve common objectives.

Why AI Agents are Superior to LLMs

LLMs:

• Provide human feedback iteratively to refine responses.

• Primarily function as standalone models requiring external input.

AI Agents:

• When LLMs operate autonomously, they transform into AI agents.

• AI agents independently ask and answer questions.

• Capable of continuous learning and adaptation based on task requirements and environmental changes.

Advantages of Multi-Agent Systems:

1. Scalability: Easily scale tasks by adding more AI agents.

2. Redundancy: Multiple agents can cover for each other, ensuring task completion.

3. Specialization: Different agents can specialize in various aspects of a task, enhancing overall performance.

4. Collaboration: AI agents can work together, sharing knowledge and strategies to tackle complex problems.

LLMs + Cognitive Capabilities = Intelligent AI Agents

Source: Deeplearning.AI

By integrating cognitive abilities into LLMs, we create AI agents that can think, learn, and act autonomously, leading to more efficient and intelligent business workflows.

Goal:

Creating capable AI agents and organizing them into collaborative teams to undertake challenging, step-by-step operations.

Why AI Agents better than LLMs?

• LLMs provide human feedback iteratively to refine responses.

• LLMs primarily function as standalone models requiring external input.

• AI agents when LLMs operate autonomously, they transform into AI agents.

• AI agents independently ask and answer questions.

• AI agents capable of continuous learning and adaptation based on task requirements and environmental changes.

crewAI Framework

• Crew: The top-level organization that manages AI agent teams, oversees workflows, ensures collaboration, and delivers outcomes.

• CrewAI: A framework that empowers AI agents to be autonomous, adaptive, and collaborative.

Why Multi AI Agents rather single agents?

• Multiple AI agents can cover for each other, ensuring task completion.

• Different AI agents can specialize in various aspects of a task, enhancing overall performance.

• AI agents can work together, sharing knowledge and strategies to tackle complex problems.

Source: Deeplearning.AI

Applications of multi-agent systems.

• Tailor resumes and interview prep for job applications
• Research, write and edit technical articles
• Automate customer support inquiries
• Conduct customer outreach campaigns
• Plan and execute events
• Perform financial analysis

What is Agentic Automation?

New way to write software. Provide fizzy inputs, apply fuzzy tranformations and get fuzzy outputs.

Reason why people love chatGPT: Probablistic nature of the model.

Source: Deeplearning.AI

How Agentic Automation improves regular automation?

• It is a more human-like interaction, as it is based on natural language.

Regular Automation (Regular Data Collection and Analysis)

• Capture information about the company
• Use classification to generate scores for company
• Prioritise for sales

Source: Deeplearning.AI

Agentic Automation (Data Collection and Analysis using crew)

• AI agent research about company (via Google, internal database)
• AI agent compares companies (new ones, old ones)
• AI agent scores companies (based on parameters)
• AI agent provides intelligent questions to ask based on scores
• AI agent provides results to sales team

Source: Deeplearning.AI

Key Components of AI Agent

• Role: Assign specialized role to agents
• Memory: Provide agents with short-term, long-term and entity memory
• Tools: Assign pre-built and custom tools to each agent (eg. for web search)
• Focus: Break down task, goals and tools and assign multiple AI agents for better performance
• Guardrails: Effectively handle errors, hallucinations and infinite loops.
• Cooperation: Perform tasks in series, in parallel and hierarchical fashion

Source: Deeplearning.AI

Role Playing:

More specific role = Better response. Gives clear idea about agent's function in the crew.

Example: You are a financial analyst v/s you are FINRA approved financial analyst.

from crewai import Agent

agent = Agent(
  role='Data Analyst',
  goal='Extract actionable insights',
  backstory="""You're a data analyst at a large company.
  You're responsible for analyzing data and providing insights
  to the business."""
)

Focus:

Assinging too many tasks, tools, context to a single agent, cause losing essential information and hallucinate.

Therefore, break down task, goals and tools and assign to multiple AI agents for better performance.

research_ai_task = Task(
    description='Find and summarize the latest AI news',
    expected_output='A bullet list summary of the top 5 most important AI news',
    agent=research_agent,
    tools=[search_tool]
)

research_ops_task = Task(
    description='Find and summarize the latest AI Ops news',
    expected_output='A bullet list summary of the top 5 most important AI Ops news',
    agent=research_agent,
    tools=[search_tool]
)

write_blog_task = Task(
    description="Write a full blog post about the importance of AI and its latest news",
    expected_output='Full blog post that is 4 paragraphs long',
    agent=writer_agent,
    context=[research_ai_task, research_ops_task]
)

Tools:

Assign tools to AI Agents and Tasks for improving execution and performance.

from crewai import Agent

researcher = Agent(
    role='Market Research Analyst',
    goal='Provide up-to-date market analysis of the AI industry',
    backstory='An expert analyst with a keen eye for market trends.',
    tools=[search_tool, web_rag_tool]
)

Note: Tasks specific tools override an agent's default tools.

task = Task(
  description='Find and summarize the latest AI news',
  expected_output='A bullet list summary of the top 5 most important AI news',
  agent=research_agent,
  tools=[search_tool]
)

Collaboration:

AI agents can work together, sharing knowledge and strategies to tackle complex problems.

Sequential Collaboration Ideal for projects requiring tasks to be completed in a specific order.

report_crew = Crew(
  agents=[researcher, analyst, writer],
  tasks=[research_task, analysis_task, writing_task], # tasks executed in the order of listing, with output of one task serving as context for the next
  process=Process.sequential
)

Hierarchical Collaboration:

• CrewAI automatically creates a manager agent, requiring the specification of a manager language model (manager_llm) for the manager agent.
• THe manager allocates tasks among crew members based on their roles, tools and capabilities.
• The manager evaluates outcomes to ensure they meet the required standards.
• Set process attribute to Process.hierarchical for Crew object
• set manager_llm for Crew Object. Mandatory for hierarchical process

from crewai import Crew
from crewai.process import Process
from langchain_openai import ChatOpenAI

# Example: Creating a crew with a hierarchical process
# Ensure to provide a manager_llm
crew = Crew(
    agents=my_agents,
    tasks=my_tasks,
    process=Process.hierarchical,
    manager_llm=ChatOpenAI(model="gpt-4")
)

Parallel Collaboration:

• Tasks can now be executed asynchronously, allowing for parallel processing and efficiency improvements

list_ideas = Task(
    description="List of 5 interesting ideas to explore for an article about AI.",
    expected_output="Bullet point list of 5 ideas for an article.",
    agent=researcher,
    async_execution=True # Will be executed asynchronously
)

list_important_history = Task(
    description="Research the history of AI and give me the 5 most important events.",
    expected_output="Bullet point list of 5 important events.",
    agent=researcher,
    async_execution=True # Will be executed asynchronously
)

write_article = Task(
    description="Write an article about AI, its history, and interesting ideas.",
    expected_output="A 4 paragraph article about AI.",
    agent=writer,
    context=[list_ideas, list_important_history] # Will wait for the output of the two tasks to be completed
)

Guardrails:

Implemented at Framework level to prevrnt hallucinations, errors and infintite loops.

Memory:

CrewAI provides short-term memory, long-term memory, entity memory, and newly identified contextual memory to help AI agents to remember, reason, and learn from past interactions.

Advantages of Memory:

• More contexual awareness, leading to more coherent and relevant responses
• Experience Accumulation, learning from past actions to improve future decision-making and problem-solving.
• Entity Understanding, agents can recognize and remember key entities, enhancing understanding.

Source: deeplearning.ai

Enable memory by setting memory=True in the Crew objects arguments.

from crewai import Crew, Agent, Task, Process

# Assemble your crew with memory capabilities
my_crew = Crew(
    agents=[...],
    tasks=[...],
    process=Process.sequential,
    memory=True,
    verbose=True
)

Source: deeplearning.ai

Mental Framework for Agent creations

Think of yourself as a Manager Answer 3 questions:

1. What are my goals?
2. What are the Process?
3. What kind of people I would like to hire, to get the work done?

This will help to create agents (roles, goals, backstory)

Source: deeplearning.ai

What makes a great Tool?

• Versatile: Hndle Fuzzy inputs and provide strongly typed outputs
• Caching Mechanism: Reuse previous results. Caching layer prevent unnecessary requests, stay within rate limits, speed up execution time
• Error Handling: Gracefully handle erors & exceptions. How ? Sending error message to agent and ask agent to retry

NOTE: CrewAI supports both crewAI Toolkit and LangChain Tools

Mental Framework for Task creations

Think of yourself as a Manager Ask what kind of process and tasks I expect individuals on my team to do.

Task requires min. 3 things:

1. Description
2. Expected_output
3. Agent that will perform the task

This will help to create tasks (description, expected_output, agent)

Source: deeplearning.ai

Multi-agent Collaboration

• Problem with Sequential Collaboration Initial context fades away as tasks flows from agent to agent.

Source: deeplearning.ai

Advantages with Hierarchical Collaboration

• Manager always remeber initial goal
• Automatically delegates tasks
• Asks agents for further improvement, if required.