AI Agents with Conditional Workflow in LangGraph | Beginner Friendly Guide #AI #LangGraph #AIAgents

Description

In this video, we will understand Conditional Workflow in LangGraph and how it helps in building dynamic AI agents. When a user asks a question to an LLM, the question is first stored in a shared state. This shared state allows every step in the LangGraph workflow to access the same information. Next, the LLM analyzes the question and a decision point (conditional workflow) determines the next step in the graph. Based on the condition, the workflow can take different paths: 1. Call an external search step to gather additional information before generating the answer 2. Or directly generate the final response and return it to the user This conditional logic works similar to an if-else statement, but instead of applying to a single function, it controls the entire graph workflow. Using conditional workflows, we can build context-aware and intelligent AI agents that dynamically decide what action to take next. If you are learning LangGraph, AI Agents, or LLM workflows, this concept is extremely important. GitHub Code Reference: https://github.com/toimrank/summarizedai/blob/develop/langgraph/condition.py If you enjoy learning about AI, RAG, LangGraph, and AI Agents, make sure to like the video and subscribe to the channel. #AI #LangGraph #AIAgents #LLM #GenerativeAI #MachineLearning #ArtificialIntelligence #RAG #Python #AIWorkflow #AIEngineering #SummarizedAI