For both Python and TypeScript Some highlights: - New Docs - LangChain Agent: revamped and more flexible with middleware - LangGraph 1.0 - Standard content blocks: swap seamlessly between models LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones

Over the past weeks, I built and launched a sophisticated multi-agent retail operations platform powered by Google's Agent Development Kit (ADK) and Elastic's hybrid search—combining vector, semantic, and traditional search in real time. 👥 What makes it unique? - Multi-agent orchestration: Each specialized agent (Product Search, Review Analysis, Inventory, Shopping, Customer Support) works in harmony, managed by a central Coordinator Agent. - Conversational Intelligence: Handles natural language queries like “Show me blue summer dresses under $50 with good reviews in stock at my local store”—and delivers accurate, actionable results. - Cutting-edge tech: Google ADK, Gemini 2.0 Flash, Elastic kNN vector search, image-based product search (ImageBind embeddings), real-time dashboard UI, FastAPI backend, and more. - Real-world impact: Streamlines retail experience—instant product discovery, inventory insights, review analysis, FAQ support, and analytics. 🔗 Check out the codebase on GitHub:github.com/Yash-Kavaiya/AI-Accelerate-Retail-Agent-Teams 🔎 Full project details, technical deep dive & demo:devpost.com/software/ai-retail-agent-team-intelligent-retail-operations-platform Thanks to cutting-edge AI, this project brings together semantic/visual search, natural language capabilities, and enterprise-grade scalability for the next generation of retail operations! Would love to connect with retail tech enthusiasts, AI practitioners, and anyone interested in multi-agent systems for real-world applications. #ConversationalAI #GoogleADK #ElasticSearch #RetailTech #AI #MultiAgentSystems #GenAI #Gemini

You asked, and it's finally here—the single most requested tutorial from our community. In this step-by-step video, I’ll walk you through deploying a fully functional AI-powered goal planning application. By the end, you’ll have your own app where you can chat live with your deployed agent and watch its thought process in real-time. Here’s what you’ll learn: ✅ System Architecture Overview: Understand exactly how your NextJS app, Vercel, ADK, and Agent Engine integrate seamlessly. ✅ Deep Dive into the Code: Step-by-step walkthrough of the critical code segments so you know precisely how everything works and communicates. ✅ Easy Deployment: Separate, clear walkthroughs for deploying your ADK agent to Agent Engine and your NextJS app to Vercel—all included in the free source code giveaway. Plus, I’m including all the source code and both comprehensive deployment guides—completely free. If you’re serious about building and deploying real-world AI agentic applications, this tutorial is exactly what you’ve been waiting for. P.S. Here's a link to all the source code with deployment guides for ADK + NextJS. https://github.com/bhancockio/adk-fullstack-deploy-tutorial

Please help me give me tutorial how to make app education

VIEW RECORDING - 155 mins (No highlights) Meeting Purpose Weekly AI Developer Accelerator call to discuss projects, share updates, and provide mutual support among AI developers. Key Takeaways - New document extraction tool by Andrew Eng showcased, offering significant improvements in processing complex documents - Google ADK (AI Development Kit) highly recommended for most developers starting out due to ease of use and core functionality - Pricing strategies for AI projects discussed, emphasizing value-based pricing and clear milestone structures - Several participants shared progress on AI-powered tools and applications they are developing Topics Document Extraction Tool Demo - Bastian showcased a new document extraction tool by Andrew Eng - Tool can process complex documents including handwritten forms and medical reports - Uses agent-based approach to determine reading order and extract information - Significant improvement in processing time (from 2 minutes to 8 seconds per document) - Handles up to 50-page documents in the playground version