LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - How to Evaluate AI Agents using langgraph platform?

Code Repository: [https://github.com/homayounsrp/AgentEvaluation] Building an AI Research Agent with Automated Evaluation System | LLM Judge Project In this video, I walk you through my latest project - an intelligent research agent powered by LLMs that can automatically evaluate its own responses against specific criteria. This isn't just another AI chatbot - it's a complete system that demonstrates how to build reliable, self-evaluating AI agents. What You'll Learn: ✅ How to build a research agent using LangGraph and LangChain ✅ Implementing web search capabilities with Tavily API ✅ Creating automated evaluation systems for AI responses ✅ Using structured output parsing with Pydantic models ✅ Building end-to-end testing frameworks for AI agents Key Features: 🔧 Smart Research Agent: Uses GPT-4o-mini with web search tools to gather comprehensive information 📊 Automated Evaluation: Built-in judge system that grades responses against specific criteria 🧪 Testing Framework: Complete test suite for validating agent performance 📝 Structured Output: Clean, parseable responses with proper categorization Tech Stack: - LangGraph for agent orchestration - LangChain for LLM integration - Tavily for web search - Pydantic for data validation - OpenAI GPT-4o-mini Perfect for: - AI developers building research tools - Anyone interested in LLM evaluation methods - Developers learning about agent architectures - People building automated content generation systems Follow me for more AI/ML projects! #AI #LLM #LangChain #ResearchAgent #MachineLearning #Python #OpenAI #Automation