Learning CrewAI - Multi-Agent AI System Projects

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Intelligent Multi-Agent Collaboration | Production-Ready Systems | Real-World Applications

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A comprehensive collection of enterprise-grade, multi-agent AI systems built with CrewAI. Each project demonstrates collaborative AI agents solving complex real-world problems through intelligent task orchestration and domain-specific expertise.

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Overview

graph LR
    A[Stock Researcher] --> E[Multi-Agent System]
    B[Cricket Analytics] --> E
    C[Budget Knowledge] --> E
    D[Research Writer] --> E
    E --> F[Intelligent Solutions]

This workspace contains multiple CrewAI projects that showcase advanced multi-agent AI capabilities:

• Stock Researcher: Financial analysis with S&P 500 benchmarking and investment recommendations
• Cricket Performance Analysis: Sports analytics with player performance metrics and visualizations
• Union Budget Knowledge: Government budget analysis and policy query system
• Research Writer Crew: Automated research and article generation

Quick Start

1. Environment Setup

# Navigate to workspace root
cd /Users/shinde/Desktop/Talbot/learning_crewai

# Activate shared virtual environment
source venv/bin/activate

# Create .env file with API keys
cp .env.example .env
# Edit .env and add your API keys:
# OPENAI_API_KEY=sk-...
# SERPER_API_KEY=...

2. Run a Project

# Stock Researcher
cd stock_researcher/src
python main.py "AAPL"

# Cricket Performance Analysis
cd cricketer_performance_analysis/src
python main.py "Virat Kohli" "2023"

# Union Budget Knowledge
cd union_budget_knowledge
PYTHONPATH=src python -c 'from union_budget_knowledge.main import kickoff; kickoff()'

# Research Writer Crew
cd research_writer_crew/src
python main.py

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Projects

1. Stock Researcher

Advanced financial analysis system with S&P 500 benchmarking

• Agents: 6 specialized financial analysts
• Capabilities:
  • Real-time stock data retrieval
  • Comparative S&P 500 analysis
  • Investment recommendations (BUY/HOLD/SELL)
  • Professional visualization and reporting

→ Full Documentation

Quick Run:

cd stock_researcher/src
python main.py "AAPL"  # Analyze Apple stock

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2. Cricket Performance Analysis

Sports analytics and player performance evaluation system

• Agents: 3 specialized sports analysts
• Capabilities:
  • Cricket player statistics aggregation
  • Performance metric calculations
  • Format-wise analysis (Test/ODI/T20)
  • Professional visualization and reporting

→ Full Documentation

Quick Run:

cd cricketer_performance_analysis/src
python main.py "Virat Kohli" "2023"

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3. Union Budget Knowledge

Government budget analysis and policy query system

• Agents: Multi-agent budget analysis system
• Capabilities:
  • PDF document processing and indexing
  • Natural language budget queries
  • Policy analysis and insights
  • Interactive Q&A interface

→ Full Documentation

Quick Run:

cd union_budget_knowledge
PYTHONPATH=src python -c 'from union_budget_knowledge.main import kickoff; kickoff()'

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4. Research Writer Crew

Automated research and article generation system

• Agents: Research and writing specialists
• Capabilities:
  • Topic research and data gathering
  • Article outline generation
  • Content writing and editing
  • SEO optimization

→ Full Documentation

Quick Run:

cd research_writer_crew/src
python main.py

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Shared Resources

Documentation

Location: docs/

Comprehensive learning resources and guides:

• Complete A-Z Guide to CrewAI - The ultimate 6,000+ line reference covering:
  • All CrewAI concepts from Agents to Zero-to-Production
  • Architecture patterns and best practices
  • Prompt engineering techniques
  • Production deployment strategies
  • Real-world code examples
  • Debugging and monitoring

Virtual Environment

Location: venv/

All projects use a shared Python virtual environment for efficient dependency management.

Setup:

# One-time setup (already done)
python3 -m venv venv
source venv/bin/activate
pip install crewai[tools] python-dotenv pypdf2 litellm

# Subsequent uses
source venv/bin/activate

Environment Configuration

Location: .env

Centralized API key management for all projects:

# Required for all projects
OPENAI_API_KEY=sk-your_openai_key
SERPER_API_KEY=your_serper_key

# Optional project-specific settings
LOG_LEVEL=INFO
DEBUG_MODE=false

Project Structure

learning_crewai/
├── .env                              # Shared environment variables
├── .gitignore                        # Git ignore rules
├── venv/                             # Shared virtual environment
│   ├── bin/
│   ├── lib/
│   └── ...
│
├── docs/                             # Documentation
│   └── crew_ai_context.md           # Complete A-Z Guide to CrewAI
│
├── stock_researcher/                 # Financial analysis project
│   ├── src/
│   │   ├── main.py
│   │   ├── crew.py
│   │   └── config/
│   │       ├── agents.yaml
│   │       └── tasks.yaml
│   └── README.md
│
├── cricketer_performance_analysis/   # Sports analytics project
│   ├── src/
│   │   ├── main.py
│   │   ├── crew_setup.py
│   │   └── config/
│   │       ├── agents.yaml
│   │       └── tasks.yaml
│   └── README.md
│
├── union_budget_knowledge/           # Budget analysis project
│   ├── src/
│   │   ├── main.py
│   │   ├── crews/
│   │   │   └── budget_response_crew/
│   │   │       ├── budget_crew.py
│   │   │       └── config/
│   │   │           ├── agents.yaml
│   │   │           └── tasks.yaml
│   │   └── tools/
│   ├── Union_Budget_Analysis-2023-24.pdf
│   └── README.md
│
├── research_writer_crew/             # Research & writing project
│   ├── src/
│   │   ├── main.py
│   │   ├── crew_setup.py
│   │   └── config/
│   │       ├── agents.yaml
│   │       └── tasks.yaml
│   └── README.md
│
└── README.md                         # This file

Prerequisites

System Requirements

• OS: macOS, Linux, or Windows
• Python: 3.10, 3.11, 3.12, or 3.13
• Memory: Minimum 4GB RAM (8GB recommended)
• Disk Space: 5GB for dependencies

API Keys Required

1. OpenAI API Key

   • Get at: https://platform.openai.com/api-keys
   • Cost: Pay-as-you-go (free tier available)
   • Models: GPT-4, GPT-3.5-turbo

2. Serper API Key (for web search projects)

   • Get at: https://serper.dev
   • Cost: Free tier (100 searches/month), paid plans from $5/month
   • Used by: Stock Researcher, Cricket Analysis

Setup Instructions

1. Initial Setup (One-time)

# Clone or navigate to the workspace
cd /Users/shinde/Desktop/Talbot/learning_crewai

# Create virtual environment (if not exists)
python3 -m venv venv

# Activate virtual environment
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt
# Or install manually:
pip install crewai[tools]>=0.86.0 python-dotenv pypdf2 litellm

# Create .env file
cp .env.example .env
# Edit .env with your API keys
nano .env

2. For Each Session

# Activate virtual environment
cd /Users/shinde/Desktop/Talbot/learning_crewai
source venv/bin/activate

# Navigate to desired project
cd <project_name>

# Run the project
python main.py [arguments]

3. Configuration

Update the .env file in the workspace root:

# File: /Users/shinde/Desktop/Talbot/learning_crewai/.env

# OpenAI Configuration (Required)
OPENAI_API_KEY=sk-your_key_here
OPENAI_MODEL_NAME=gpt-4  # or gpt-3.5-turbo

# Serper Configuration (Required for Stock & Cricket projects)
SERPER_API_KEY=your_serper_key_here

# Logging Configuration (Optional)
LOG_LEVEL=INFO  # DEBUG, INFO, WARNING, ERROR
DEBUG_MODE=false

# Project Settings (Optional)
STOCK_SYMBOL=AAPL
CRICKETER_NAME=Virat Kohli
YEAR=2023

Architecture Overview

graph TB
    subgraph Workspace["Learning CrewAI Workspace"]
        subgraph Resources["Shared Resources"]
            ENV[.env Configuration]
            VENV[Python Virtual Environment]
            DOCS[Documentation Hub]
        end
        
        subgraph Projects["Multi-Agent Projects"]
            STOCK[Stock Researcher<br/>Financial Analysis]
            CRICKET[Cricket Analytics<br/>Sports Intelligence]
            BUDGET[Budget Knowledge<br/>Policy Analysis]
            WRITER[Research Writer<br/>Content Creation]
        end
        
        subgraph Framework["CrewAI Framework"]
            AGENTS[Specialized Agents]
            TASKS[Orchestrated Tasks]
            TOOLS[Integrated Tools]
        end
        
        subgraph External["External Services"]
            OPENAI[OpenAI GPT-4]
            SERPER[Serper Search API]
            DATA[Data Sources]
        end
    end
    
    ENV --> Projects
    VENV --> Projects
    DOCS --> Projects
    
    STOCK --> Framework
    CRICKET --> Framework
    BUDGET --> Framework
    WRITER --> Framework
    
    Framework --> External
    
    AGENTS -.-> TASKS
    TASKS -.-> TOOLS
    
    style Workspace fill:#f9f9f9,stroke:#333,stroke-width:2px
    style Resources fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    style Projects fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style Framework fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    style External fill:#e8f5e9,stroke:#388e3c,stroke-width:2px

System Flow

sequenceDiagram
    participant User
    participant Project
    participant CrewAI
    participant Agents
    participant LLM
    participant Tools
    
    User->>Project: Execute Command
    Project->>CrewAI: Initialize Crew
    CrewAI->>Agents: Assign Tasks
    
    loop Task Execution
        Agents->>LLM: Process Information
        LLM-->>Agents: Generate Response
        Agents->>Tools: Use External Tools
        Tools-->>Agents: Return Data
    end
    
    Agents->>CrewAI: Complete Tasks
    CrewAI->>Project: Aggregate Results
    Project->>User: Deliver Output

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Features Across All Projects

Architecture	Configuration	Integration	Output
Multi-agent system	YAML-based	Real-time APIs	Profess