LLM Agents: From Concept to Code - LangChain & Scratch Implementation!

Description:
In this video, we discuss about LLM Agents (Large Language Model Agents) and their ability to revolutionize how we interact with AI. Starting with the core concepts, we’ll explore what LLM agents are, how they operate, and why they are so powerful for task automation and reasoning.

What’s Inside?
1.Introduction to LLM Agents
What are LLM agents?
How they combine reasoning with action-taking abilities.
Examples of tools LLM agents can use, like calculators, search engines, and more.

2️. LangChain: Simplifying LLM Agent Development
Hands-on coding with LangChain to build a multi-tool agent.
Tools like a calculator and a fallback mechanism in action.
Discussing the pros and cons of using LangChain’s abstractions.

3️. Building LLM Agents From Scratch
Coding a custom LLM agent without frameworks.
Using OpenAI's GPT models to enable intent detection and tool handling.
Adding layers like fallback tools and intent-specific tool calls.

4️. ive Demos
Query the agent to solve mathematical problems.
Handle ambiguous queries with a fallback tool.
See the difference between LangChain-powered agents and scratch-built agents.

Why Watch This Video?
By the end of this tutorial, you’ll not only understand the inner workings of LLM agents but also learn how to build them with and without frameworks. This is perfect for developers and AI enthusiasts looking to create intelligent, task-specific agents for real-world applications.

Timestamps:
0:00 - What are LLM Agents?
3:22 - Understanding LLM Agents with a simulated script
9:46 - Building an Agent with LangChain
18:12 - Coding an Agent from Scratch
24:00 - Final Thoughts & Next Steps


#llm #generativeai #artificialintelligence #machinelearning #largelanguagemodels #langchain

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