In this tutorial, we guide you through the development of an advanced Graph Agent framework, powered by the Google Gemini API. Our goal is to build intelligent, multi-step agents that execute tasks through a well-defined graph structure of interconnected nodes. Each node represents a specific function, ranging from taking input, performing logical processing, making decisions,…
The importance of memory in AI agents cannot be overstated. As artificial intelligence matures from simple statistical models to autonomous agents, the ability to remember, learn, and adapt becomes a foundational capability. Memory distinguishes basic reactive bots from truly interactive, context-aware digital entities capable of supporting nuanced, humanlike interactions and decision-making. Why Is Memory Vital in AI…
In this tutorial, we build a GPU‑capable local LLM stack that unifies Ollama and LangChain. We install the required libraries, launch the Ollama server, pull a model, and wrap it in a custom LangChain LLM, allowing us to control temperature, token limits, and context. We add a Retrieval-Augmented Generation layer that ingests PDFs or text,…
In this tutorial, we are walking through a hands-on fusion of symbolic logic and generative AI. We set up PySwip to embed a Prolog knowledge base, wrap its predicates as LangChain tools, and then wire everything into a ReAct-style agent. Along the way, we are crafting family-relationship rules, mathematical predicates like factorial, and list utilities,…
GitHub has introduced Spark, a groundbreaking addition to its suite of developer tools, aimed at revolutionizing the way full-stack intelligent applications are built and deployed. With Spark, available in public preview for Copilot Pro+ subscribers, developers can go from idea to a fully deployed app in minutes—all using natural language prompts and without the usual…
Model Context Protocol (MCP) servers have fast become a backbone for scalable, secure, and agentic application integrations, especially as organizations seek to expose their services to AI-driven workflows while keeping developer experience, performance, and security intact. Here are seven data-driven best practices for building, testing, and packaging robust MCP servers. 1. Intentional Tool Budget Management Define…
Introduction Qwen has unveiled Qwen3-Coder-480B-A35B-Instruct, their most powerful open agentic code model released to date. With a distinctive Mixture-of-Experts (MoE) architecture and comprehensive agentic coding capabilities, Qwen3-Coder not only sets a new standard for open-source coding models but also redefines what’s possible for large-scale, autonomous developer assistance. Model Architecture and Specifications Key Features Model Size: 480 billion…
In this tutorial, we begin by setting up a compact yet capable AI agent that runs smoothly, leveraging Hugging Face transformers. We integrate dialog generation, question‑answering, sentiment analysis, web search stubs, weather look‑ups, and a safe calculator into a single Python class. As we progress, we install only the essential libraries, load lightweight models that respect…
Building effective AI agents means more than just picking a powerful language model. As the Manus project discovered, how you design and manage the “context” – the information the AI processes to make decisions – is paramount. This “context engineering” directly impacts an agent’s speed, cost, reliability, and intelligence. Initially, the choice was clear: leverage…
WrenAI is an open-source Generative Business Intelligence (GenBI) agent developed by Canner, designed to enable seamless, natural-language interaction with structured data. It targets both technical and non-technical teams, providing the tools to query, analyze, and visualize data without writing SQL. All capabilities and integrations are verified against the official documentation and latest releases. Key Capabilities…