5-Minute Quick Start Guide

From Zero to AI Agent in Under 5 Minutes

Welcome to Entity Framework! This guide will get you from installation to a working AI agent in just 5 minutes. No configuration required, no complex setup - just pure AI power.

⏱️ Timeline: 5 Minutes Total

Minute 1: Installation and setup
Minute 2: Your first agent
Minute 3: Adding personality
Minute 4: Giving it superpowers with tools
Minute 5: Exploring what you’ve built

Minute 1: Installation & Setup

Install Entity Framework

# Quick install (30 seconds)
pip install entity-core

# Or use uv for speed
uv add entity-core

Set Up Your LLM (Optional)

Entity works with local or cloud LLMs:

# Option A: Local LLM (Recommended)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.2:3b
ollama serve

# Option B: Cloud API (Set environment variable)
export OPENAI_API_KEY="your-key-here"

Time check: 1 minute

Minute 2: Your First Agent

Create your first intelligent agent with just 3 lines of code:

`hello_agent.py`

import asyncio
from entity import Agent
from entity.defaults import load_defaults

async def main():
    # This single line sets up everything: LLM, memory, logging, storage
    resources = load_defaults()

    # Create your agent
    agent = Agent(resources=resources)

    # Start chatting!
    await agent.chat("")

if __name__ == "__main__":
    asyncio.run(main())

Run It!

python hello_agent.py

What just happened? You created a production-ready AI agent with:

🧠 Intelligent responses from local or cloud LLM
💾 Persistent memory that remembers conversations
📊 Automatic logging for monitoring and debugging
🔧 Zero configuration - it just works!

Time check: 2 minutes

🎭 Minute 3: Adding Personality

Let’s give your agent a specific role and personality using Entity’s configuration system:

`tutor_config.yaml`

# Python Tutor Agent Configuration
role: |
  You are an expert Python tutor with 10 years of teaching experience.

  Your personality:
  - Patient and encouraging with beginners
  - Enthusiastic about Python's elegance and power
  - Always provide practical, runnable examples
  - Explain concepts step-by-step with clear reasoning

  Your teaching style:
  - Start with simple explanations, then add complexity
  - Always include code examples with comments
  - Be encouraging and positive about learning

resources:
  llm:
    temperature: 0.3  # More consistent educational responses
    max_tokens: 2000  # Allow for detailed explanations

`tutor_agent.py`

import asyncio
from entity import Agent

async def main():
    # Load agent with custom personality
    agent = Agent.from_config("tutor_config.yaml")

    print("🐍 Python Tutor Agent loaded!")
    print("Ask me about Python concepts, and I'll teach you step-by-step!")

    await agent.chat("")

if __name__ == "__main__":
    asyncio.run(main())

Try It!

python tutor_agent.py
# Ask: "Explain Python decorators"

What’s different? Your agent now has:

🎭 Specific personality and expertise
🎯 Focused behavior as a Python tutor
⚙️ Configuration-driven customization (no code changes!)

Time check: 3 minutes

Minute 4: Superpowers with Tools

Let’s give your agent real-world capabilities with Entity’s built-in tools:

`superagent_config.yaml`

# Super-powered agent with tools
role: |
  You are a helpful AI assistant with access to powerful tools.

  Your approach:
  - Use web search for current information
  - Use calculator for mathematical operations
  - Use file operations when users mention files
  - Explain what tools you're using and why

# Enable built-in tools
tools:
  web_search:
    enabled: true
    max_results: 5

  calculator:
    enabled: true
    precision: 10

  file_operations:
    enabled: true
    read_only: false
    max_file_size_mb: 10

resources:
  llm:
    temperature: 0.1  # Lower for tool usage accuracy
    max_tokens: 3000

`super_agent.py`

import asyncio
from entity import Agent

async def main():
    agent = Agent.from_config("superagent_config.yaml")

    print("🦸‍♀️ Super Agent loaded with tools:")
    print("🌐 Web Search - Find current information")
    print("🧮 Calculator - Perform complex calculations")
    print("📁 File Operations - Read and write files")

    await agent.chat("")

if __name__ == "__main__":
    asyncio.run(main())

Test the Powers!

python super_agent.py
# Try: "Search for Python 3.12 features and calculate 15% of 200"
# Try: "Create a file called test.txt with a Python example"

Amazing! Your agent can now:

🌐 Search the web for current information
🧮 Perform calculations with perfect accuracy
📁 Work with files safely and intelligently
🔗 Chain tools together for complex tasks

Time check: 4 minutes

Minute 5: Exploring What You Built

Congratulations! In just 4 minutes, you’ve built increasingly sophisticated AI agents. Let’s explore what makes this special:

What You’ve Accomplished

🤖 Basic Agent: Zero-config intelligent assistant
🎭 Personality Agent: Custom role and behavior via YAML
🦸‍♀️ Super Agent: Multi-tool capabilities for real-world tasks

The Entity Advantage

Traditional AI Development:

# Hundreds of lines of boilerplate code:
# - LLM client setup and error handling
# - Memory management and persistence
# - Tool integration and safety
# - Configuration and environment handling
# - Logging and monitoring setup
# - User interface and interaction loops

Entity Framework:

# Just the essentials:
agent = Agent.from_config("config.yaml")
await agent.chat("")  # Everything else handled automatically

Key Entity Concepts You’ve Learned

✅ Zero Configuration: load_defaults() sets up everything automatically ✅ Configuration-Driven: YAML files control behavior without code changes ✅ Tool Integration: Built-in tools extend capabilities instantly ✅ Plugin Architecture: Modular, testable, reusable components ✅ Production Ready: Logging, monitoring, and safety built-in

Time check: 5 minutes

What’s Next?

You’ve mastered the basics! Here’s your learning path:

Continue Learning (10 minutes each)

Agent Personalities - Deep dive into customization
Tool Usage - Master the tool system
Memory Systems - Build agents that remember
Streaming Responses - Real-time interactions

Real-World Projects (30-60 minutes each)

Customer Service Bot - Business application
Research Assistant - Complex workflows
Code Reviewer - Developer tools

Production Development (2+ hours)

Multi-Agent Systems - Orchestrated collaboration
API Integration - FastAPI + Entity
Production Deployment - Scalable systems

Advanced Topics

Custom Plugin Development - Extend Entity framework
Performance Optimization - Scale to production
Contributing Guide - Join the community

Troubleshooting

Common Issues

“No LLM available” Error

# Install Ollama (recommended)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.2:3b
ollama serve

# Or set API key
export OPENAI_API_KEY="your-key"

“Permission denied” or Port Issues

# Check for port conflicts
lsof -i :11434  # Ollama port

# Restart Ollama if needed
pkill ollama
ollama serve

Import Errors

# Ensure Entity is installed correctly
pip install --upgrade entity-core

# Verify installation
python -c "import entity"
echo "Entity installed successfully!"

Get Help

📚 Documentation - Complete guides
🐛 GitHub Issues - Bug reports
💡 Discussions - Q&A and ideas

🎯 Success!

You’ve just experienced the Entity advantage:

⚡ 10x faster development - Minutes instead of hours
🔧 Zero boilerplate - Focus on your agent’s purpose, not infrastructure
🏗️ Plugin architecture - Modular, testable, maintainable
🚀 Production ready - Built-in safety, monitoring, and scaling

Ready to build the future of AI? Your Entity journey starts now! 🚀

What will you build next?

📚 Explore Examples • 🔧 Build Custom Tools • 🤝 Join Discussions

Entity Framework: Build better AI agents, faster. ✨