YouTube Summarizer: What I Built with LangGraph and Gemini
What I Built I wanted to paste a YouTube URL and get a summary — or ask questions about the video — without watching the whole thing. This project uses LangGraph for the workflow and Gemini for the actual summarizing and Q&A. Key Features of Agentic Workflows Modularity — each step can be built and tested on its own State — data flows from step to step in a structured way Reusability — the same steps can be reused in other projects Autonomy — each step can decide what to do based on the input it receives Popular Tools for Agentic Workflows Tool Description Language Best For LangGraph Graph-based AI workflow framework with state management. Python Custom step-by-step pipelines CrewAI Agent orchestration tool inspired by human teams and role delegation. Python Role-based agent collaboration Autogen Microsoft’s multi-agent framework for goal-oriented dialogue and tasks. Python Conversational multi-agent systems LangChain General framework for chaining LLMs with tools, memory, and logic. Python Broader LLM apps beyond just agents AgentOps Infra layer for deploying and monitoring agentic systems. Platform Scaling, testing, and managing agents Why I Chose LangGraph For this project, I chose LangGraph because: ...