Master AI Agents: Code Along Tutorial (LangChain & Crew AI)

The session bridges the gap between theoretical understanding and practical implementation, showing how AI agents can autonomously perform complex tasks through tool use and multi-agent collaboration.

Workshop Recording:

Check out the session recording on YouTube - Master AI Agents: Code Along Tutorial

Deep Dive into AI Agents

Why AI Agents Matter

Traditional LLMs like ChatGPT, while powerful, operate as passive instruction-following systems. This means users must guide them step-by-step through tasks, limiting their practical utility in real-world applications. AI agents overcome these limitations by introducing autonomous decision-making and tool usage capabilities.

Key limitations of traditional LLMs that agents address:

• Passive instruction-based operation
• Inability to self-critique and adjust
• No connection to external tools or data sources
• Limited to knowledge cutoff dates
• Lack of autonomous operation

Understanding AI Agent Architecture

AI agents fundamentally transform how we interact with AI systems by introducing:

1. Goal-Based Operation: Instead of responding to individual instructions, agents work toward achieving broader objectives
2. Autonomous Planning: Agents can break down complex tasks into manageable steps
3. Tool Integration: Ability to use external tools like web searches, calculators, and databases
4. Self-Criticism: Continuous evaluation and adjustment of approaches
5. Multi-Agent Collaboration: Different specialized agents working together to solve complex problems

Practical Demonstrations

1. Internet Search Agent (LangChain Implementation)

This demonstration showed how to create an agent that can access real-time information through web searches, effectively overcoming the knowledge cutoff limitation of traditional LLMs.

# Basic setup for Internet Search Agent
!pip install langchain
!pip install googlesearch-python

from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent
from langchain.utilities import GoogleSearchAPIWrapper

The agent demonstrated:

• Real-time information retrieval about cricket team captains
• Current stock price checking
• Intelligent decision-making about when to use web search vs. existing knowledge
• Proper attribution of information sources

Full code snippet: Google Colab Notebook

2. Multi-Agent Collaboration System (Crew AI)

One of the workshop's highlights was the creation of a collaborative system where multiple AI agents work together to create a functioning application.

# Setup for Multi-Agent System
!pip install crew-ai
!pip install googlesearch-python

from crew_ai import Agent, Task, Crew

The system featured:

• Product Manager Agent: Responsible for defining requirements and specifications
• Developer Agent: Handles code implementation based on requirements
• Inter-Agent Communication: Natural language communication between agents to clarify requirements and discuss implementation details

Full code snippet: Google Colab Notebook

The agents successfully collaborated to create a working Ping Pong game, demonstrating how complex tasks can be broken down and handled by specialized agents.

3. Google's Gemini Advanced Deep Research Agent

As a bonus, participants got a glimpse of Google's powerful deep research capabilities:

• Ability to analyze multiple sources simultaneously
• Comprehensive research synthesis
• Structured output generation
• Real-world demonstration using EV market analysis

Resources and Community

Join our community of 12,000+ AI enthusiasts and learn to build powerful AI applications! Whether you're a beginner or an experienced developer, this tutorial will help you understand and implement AI agents in your projects.

• Website: www.buildfastwithai.com
• LinkedIn: linkedin.com/company/build-fast-with-ai/
• Instagram: instagram.com/buildfastwithai/
• Twitter: x.com/satvikps
• Telegram: t.me/BuildFastWithAI

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