Multi-AI-Agent-Systems

☰ Table of Contents

• Goal
• Key concepts in the projects
  • General AI agentic flow
  • Principles for defining a good agent
  • crewAI agentic process
  • crewAI set up
  • crewAI tool packages
• References
• Research papers

🎯 Goal

Build a crew of AI agents to execute their own specific tasks as a whole

Quick access to projects (jupyter notebook)

• Customize a research writing

• Customize resume

• Customize an outreach campaign

• Automate customer support

• Automate event planning

• Automate financial analysis

💡 Key concepts in the projects

General AI agentic flow

• Breakdown a task into smaller ones for multiple agents to execute
• A crew of agents can optimize existing prompting by one central agent
• Task from human > Agents with tools and processes > Response by LLMs

Principles for defining a good agent

• Role playing
  • Each agent has a specific title and context that focus on specific task
• Focus
  • Each agent focuses to execute its task only
• Tool
  • Each agent has a set of specific tools
  • Property 1 - Versatile
    • Multi-function, can manage all types of inputs and respond with strong outputs
  • Property 2 - Fault-tolerant
    • Continue to execute and send error messages back to agent for correction
  • Property 3 - Caching
    • Store the same previous task requests, avoid hitting API rate limit, and save time to execute
    • e.g. Cross caching layer
• Collaboration
  • All agents can talk to each other to collaborate, delegate tasks, and execute the main task as a whole
• Guardrail
  • Default in crewAI framework
  • Prevent AI hallucination
  • Ensure agents achieve their tasks
• Memory
  • Type 1 - Short term memory
    • During crew execution
  • Type 2 - Long term memory
    • After crew execution, for self-improvement and reuse
  • Type 3 - Entity memory
    • During crew execution
    • Divided by categories, e.g. person name, org name, etc

crewAI agentic process

• Step 1: Defining tools
  • Agent level tools
    • Agent-specific and can be used when an agent execute its tasks
  • Task level tools
    • Task-specific and can only be used when that task is executed
• Step 2: Defining agents
  • Role
    • A role that the agent plays
  • Goal
    • A goal that the agent should achieve
  • Agent level tools (depend on use cases)
    • A set of tools that can help the agent to work
  • Backstory
    • Detailed description of the background of agent and its role playing
  • Delegation (depend on use cases)
    • Decide if the agent can delegate tasks to other agents or not
• Step 3: Defining tasks
  • Description
    • A description of the task
  • Expected_output
    • Expected output that the agent should achieve in task
  • Task level tools (depend on use cases)
    • A set of tools that can help the task being executed
    • Can be assigned to multiple agents
  • Agent
    • Agent that is assigned with the task
• Step 4: Defining crew
  • Agent list
    • Order agents by list
    • Ordering is not applicable if the process is in hierarchical
  • Task list
    • Order tasks by list
    • Ordering is not applicable if the process is in hierarchical
  • Process type
    • Decide which process to run a crew of agents
    • In parallel
      • All agents execute their own tasks at the same time, tasks are not dependently executed
    • In series/ sequential
      • Each agent execute its own task one by one before passing to next agent
    • In hierarchical
      • Crew manager agent can delegate tasks to different agents to execute
      • Remark: hierarchical + series/ parallel can happen at the same time
  • Memory (depend on use cases)
    • Decide if storing the memory of the agentic process in terms of short term, long term, entity memory
• Step 5: Run crew
  • Define input
    • Provide an input that meets the variables defined in the Agent and Task
  • Kickoff crew
    • Run the crew with input
    • Output can be different even the agentic process is run each time with the same input

crewAI set up

• crewAI installation: Installing crewAI
• Install crewAI
  • !pip install crewai==0.28.8 crewai_tools==0.1.6 langchain_community==0.0.29
• Control warning
  • import warnings warnings.filterwarnings('ignore')
• Import crewAI libraries
  • from crewai import Agent, Task, Crew, Process
• Import LLM for building agents, e.g. Openai, Llama
  • import os from utils import get_openai_api_key openai_api_key = get_openai_api_key() os.environ["OPENAI_MODEL_NAME"] = 'gpt-3.5-turbo'

crewAI tool packages

• crewAI tool package: crewAI tool
• Defined tools
  • Tools provided by crewAI
• Custom tool
  • A tool that is defined with name and description by user

📚 References

Key LLMs and APIs for running agentic process and other tasks

• OpenAI: OpenAI API
• Llama: Llama API
• Ollama: Ollama framework
• Mistral AI: Mistral AI API
• Cohere: Cohere API
• Hugging Face: Hugging Face models
• Serper: Google Search API

• Remark: Some regions are restricted in using particular models. Please check the model availability in related providers.

🔎 Research papers

• Communicative_Agents_for_Software_Development.pdf

🛠️ This project is supported by DeepLearning.AI and crewAI.