LangGraph patterns, Text-to-SQL chatbots, vision-language breakthroughs, and smarter memory for long-running systems.
5 min readJust now
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Good morning, AI enthusiasts,
This week, we explore how AI systems are becoming more structured, contextual, and multimodal. We examine how vision-language models like GPT-4o and Qwen 2.5 VL are redefining what it means for AI to “see” and “understand,” with use cases that span manufacturing, healthcare, and on-device deployment.
From there, we turn to the blueprints of intelligent systems. You’ll find a practical guide to building agentic workflows with LangGraph, from routing and reflection to multi-agent collaboration; a full walkthrough for creating a Text-to-SQL chatbot that bridges natural language with databases, and a deep dive int…
LangGraph patterns, Text-to-SQL chatbots, vision-language breakthroughs, and smarter memory for long-running systems.
5 min readJust now
–
Good morning, AI enthusiasts,
This week, we explore how AI systems are becoming more structured, contextual, and multimodal. We examine how vision-language models like GPT-4o and Qwen 2.5 VL are redefining what it means for AI to “see” and “understand,” with use cases that span manufacturing, healthcare, and on-device deployment.
From there, we turn to the blueprints of intelligent systems. You’ll find a practical guide to building agentic workflows with LangGraph, from routing and reflection to multi-agent collaboration; a full walkthrough for creating a Text-to-SQL chatbot that bridges natural language with databases, and a deep dive into how efficient memory architectures — from hierarchical systems to selective forgetting — keep agents grounded and scalable.
Let’s get into it.
— Louis-François Bouchard, Towards AI Co-founder & Head of Community
Learn AI Together Community Section!
Featured Community post from the Discord
Eschnou has released a new open-source project, Patchsmith, that focuses on AI-augmented static code analysis. It wraps a classic code analyzer (CodeQL) with an agent layer (Claude SDK) to write custom queries based on the code and a prompt, triage SARIF output, group issues, extract the most important ones, investigate issues for risk, false positives, etc., and prepare pull requests with fixes. Check it out on GitHub and support a fellow community member. If you have any feedback, share it in the thread!
AI poll of the week!
The room leans toward China, with a solid minority still backing the USA. Probably, people are reacting to the pace of recent China releases and coordination, weighed against the US edge in frontier labs, chips, and the research-to-product pipeline. Name two concrete signals you’ll watch in 2025 that could flip your view, e.g., domestic compute supply, eval wins tied to shipped products, enterprise adoption, or policy shifts, and the threshold that would count as decisive. Let’s talk in the thread!
Collaboration Opportunities
The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!
1. Mr.jack45 is building an AI-powered app that brings innovation to the EV ecosystem and is looking for people who want to team up and build something impactful. If you’re into AI, app development, design, or business growth, connect with him in the thread!
2. Grit_george is looking for a staff-level prompt engineer, someone who deeply understands how to structure and orchestrate large-model behavior across complex, dynamic tasks. If this sounds relevant, reach out to them in the thread!
3. Just_chilling7 is building a portability layer to make it easy to migrate prompts, embeddings, and fine-tunes across models like OpenAI, Claude, Gemini, and Llama, and is looking for a technical ML expert. If this sounds like you, contact them in the thread!
Meme of the week!
Meme shared by bin4ry_d3struct0r
TAI Curated Section
Article of the week
The Future is Here: Multimodal & Vision-Language Models Transforming AI By Abhinaya Pinreddy
AI is advancing beyond specialized models for text or images, moving toward integrated systems that process both simultaneously. This piece examines multimodal AI, with a focus on vision-language models (VLMs). It covers their architecture, training processes, and applications in industries like healthcare and manufacturing. It also provides an overview of current models, including GPT-4o and the open-source Qwen 2.5 VL. It addresses implementation challenges, such as computational cost and hallucinations, while also examining future developments, including agentic capabilities and on-device deployment, to offer a balanced perspective on the technology’s current state.
Our must-read articles
1. Mastering Agentic Design Patterns with LangGraph: A Complete Guide to Building Intelligent AI Systems By Mahendra Medapati
To move AI agents from demos to production, structured engineering is essential. This guide provides a practical framework for building reliable systems using LangGraph. It details seven agentic design patterns, including routing, reflection, and multi-agent collaboration, providing code examples and diagrams for each. The piece demonstrates how to create adaptive workflows for complex tasks, drawing on real-world applications from companies such as Uber and Replit.
2. Building an AI-Powered Text-to-SQL Chatbot: Your Data’s New Best Friend By Abhinaya Pinreddy
This article describes the development of an AI-powered Text-to-SQL chatbot, designed to make database querying accessible to non-technical users. It explains how Retrieval-Augmented Generation (RAG) is used to provide an AI with specific database schema context, enabling it to generate accurate SQL queries from natural language questions while avoiding common errors. It outlines the complete architecture, from the SQLite database and vector store to the LangChain orchestrator and Streamlit user interface. The process includes schema indexing, query generation, safety validation to prevent unauthorized operations, and execution to return results through a simple chat interface.
3. How to Build Effective Agentic Systems with LangGraph By Eivind Kjosbakken
This article explores the construction of agentic AI systems utilizing LangGraph by organizing them as graphs of nodes and edges. It demonstrates this approach by creating a document management workflow where a router first classifies user intent — such as adding, deleting, or searching — before triggering the correct function. The piece highlights how such frameworks simplify complex processes, such as state management and tool use. It also provides a balanced perspective on LangGraph, covering its benefits, such as being open-source and easy to set up, alongside its drawbacks, including the need for some boilerplate code.
4. How to Design Efficient Memory Architectures for Agentic AI Systems By Suchitra Malimbada
To ensure AI agents can handle long conversations and maintain factual accuracy, a strategic approach to memory design is necessary. This piece moves beyond basic vector storage, which can lead to context errors and high costs at scale. It introduces several advanced architectures, including hierarchical memory systems like MemGPT, for efficiently managing extended dialogues. For applications requiring verifiable reasoning, it details the use of knowledge graphs. It also covers selective forgetting mechanisms to prune dated information, optimizing performance and reducing storage costs for long-running agents.
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