Build a FREE Web Search RAG Agent — 100% Open-Source

Описание: Build a powerful Web Search + RAG AI Agent using LangGraph and Ollama, completely FREE and 100% open-source.
In this video, I walk you through a single-file Python implementation of a production-grade Web QA agent that:

📦 Full Source Code & Materials
👉 GitHub: https://github.com/AIxorDie/ai-decoded

🔍 Searches the live web
📄 Fetches and cleans real web pages
✂️ Summarizes each source specifically for your question
🧠 Generates final answers using RAG-style evidence, not hallucinations

This agent runs locally, uses no paid APIs, and is built with modern agentic design patterns used in real systems.

🚀 What You’ll Learn
How LangGraph agents actually work (with a clear control loop)
How to design small, composable tools for agents
Why RAG-style retrieval beats parametric memory
How to avoid infinite tool loops in agent systems
How to stream, debug, and pretty-print agent execution

🧩 Agent Architecture (High Level)
Agent ↔ Tools loop (LangGraph)
Web search → page fetch → question-focused summarization
Final answer grounded only in retrieved evidence

🛠 Tech Stack
Python
LangGraph
LangChain
Ollama (local LLMs)
DuckDuckGo Search
BeautifulSoup

👥 Who This Is For
AI engineers & researchers
Developers building agentic AI systems
Anyone learning RAG, tool-calling, or LangGraph

People tired of black-box AI demos 😄
If you find this useful, like, share, and subscribe — more deep-dive agent tutorials coming soon 🚀