Every AI Agent Frameworks Explained in 8 Minutes!

This guide provides a quick overview of AI agent frameworks, focusing on LangChain and LangGraph for building agentic workflows. It covers the fundamentals of implementing AI applications with these frameworks, enabling developers to understand how to structure agents that can reason, plan, and execute tasks autonomously.

Key Points

• •LangChain is a framework for building language model applications with chains and memory management
• •LangGraph enables stateful, multi-step agentic workflows with graph-based execution
• •Agents use tools and reasoning loops to break down complex tasks into actionable steps
• •Memory and context management are critical for maintaining state across agent interactions
• •Tool integration allows agents to interact with external APIs, databases, and services
• •Agentic workflows follow a plan-execute-observe loop for autonomous decision-making
• •Both frameworks support streaming, async operations, and production-ready deployments
• •Agent frameworks abstract away complexity of prompt engineering and orchestration

Workflow Diagram

Concepts