Gemini

What I Built Basic RAG retrieves docs and generates an answer. I wanted something smarter — a system that picks the right strategy and retries with heavier techniques when the first answer isn’t good enough. This project uses three agents: Router Agent — reads the question and checks answer quality Basic Generator Agent — fast retrieval for simple questions Advanced Generator Agent — uses heavier techniques for hard questions The system tries the fast path first. If the answer is weak, it automatically switches to advanced methods like query decomposition, HyDE, and multi-query retrieval. ...

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: ...