🤖 Building AI Agents with Phidata: Complete Step-by-Step Tutorial
Master the creation of powerful AI agents using Phidata - an open-source framework for building AI applications and automation. This comprehensive tutorial walks through creating intelligent agents, adding tools, implementing memory systems, and building a user interface.
Cookbook: https://dub.sh/mervin_cb_ag101
GitHub: https://dub.sh/mervin_git_ag101
📋 Complete Tutorial Breakdown:
Environment Setup
• Installing required packages: phidata, openai, duckduckgo-search ..
• Setting up OpenAI API key
• Configuring local development environment
• Initial package verification
Package Implementation
• Importing essential libraries
• Setting up agent frameworks
• Configuring search tools
• Implementing financial tools
• Setting up storage systems
AI Agent Creation
Web Search Agent:
• Agent ID configuration
• Role: Web information searcher
• Model: GPT-4 integration
• Tool: DuckDuckGo search implementation
• Memory storage setup
• Source tracking implementation
Financial Analyst Agent:
• Agent ID and role definition
• Yahoo Finance tool integration
• Data table display configuration
• Local storage implementation
• Market data processing setup
Agent Team Configuration
• Combining web and financial agents
• Setting up inter-agent communication
• Implementing shared memory systems
• Configuring team workflows
• Establishing task distribution
User Interface Development
• Setting up the playground
• Implementing agent selection
• Creating task input systems
• Configuring result displays
• Setting up real-time updates
🔧 Technical Specifications:
Environment Requirements:
Python 3.8+
OpenAI API access
Local storage capability
Internet connection for web tools
Agent Capabilities:
Web searching and data extraction
Financial data analysis
Real-time market data processing
Multi-agent task coordination
Memory persistence
Source verification
Storage Features:
Local data storage
Private information handling
Persistent memory systems
Secure API integration
UI Features:
Agent selection interface
Task input system
Real-time progress tracking
Results display
Multiple agent coordination
💡 Use Cases:
Automated market research
Stock price analysis
Company information gathering
Combined financial and news analysis
Automated report generation
Real-time market monitoring
⚙️ Implementation Example:
Task: "Search about Nvidia and Tesla, also find stock prices"
Process Flow:
Web agent searches for latest company information
Financial agent pulls current stock data
Agents collaborate to create comprehensive report
Results display in formatted tables and text
🔍 Key Features:
Independent AI systems working together
Complex task automation
Real-time web searching
Financial data analysis
Local data storage
User-friendly interface
Scalable architecture
Memory persistence
Tool integration
Source verification
⚡ Performance Notes:
Faster than manual research
Real-time data processing
Accurate financial calculations
Reliable source tracking
Efficient task distribution
Quick response times
🛡️ Security Features:
Local data storage
Private information handling
Secure API integration
Protected memory systems
Advanced Capabilities:
Team-based agent operations
Memory management
Knowledge preservation
Tool coordination
Reasoning systems
API integration
Database compatibility
Vector database support
#AIAgents #Phidata #ArtificialIntelligence #Python #AIAutomation #FinancialAnalysis #WebScraping #AITutorial #PythonProgramming #DataAnalysis #MachineLearning #TechTutorial #OpenSource #AITools #SoftwareDevelopment #programmingtutorial
0:00 - Introduction
0:35 - Overview of AI Agents
2:04 - Step 1: Environment Setup
2:34 - Step 2: Package Importing
3:05 - Step 3: Creating AI Agents with Tools
4:34 - Step 4: Creating User Interface
5:11 - Running the Code & Authentication
5:45 - Testing Web Agent & Agent Team
6:51 - Conclusion
