LangChain Agents Introduction — Free AI Agents Tutorial
Learn LangChain Agents Introduction in AI Agents with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn LangChain Agents Introduction in AI Agents with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
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LangChain Agents Introduction in AI Agents
LangChain is a framework for building agent applications using LLMs. It handles: agent logic (reasoning loops), tool management, memory, and integration with external APIs. Agents decide which tools to use at each step.
LangChain agents work like: LLM thinks about problem → decides which tool to use → tool executes → result feeds back to LLM → repeat. This loop continues until goal is achieved.
Common LangChain agents: ReActAgent (Reasoning + Acting), ZeroShotAgent (no prior examples), CustomAgent (user-defined logic). In education: ReActAgent could teach by trying approaches, reflecting, and adapting.
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