Auto Added by WPeMatico
In this tutorial, we build a complete cognitive blueprint and runtime agent framework. We define structured blueprints for identity, goals, planning, memory, validation, and tool access, and use them to create agents that not only respond but also plan, execute, validate, and systematically improve their outputs. Along the tutorial, we show how the same runtime […]
In this tutorial, we explore tqdm in depth and demonstrate how we build powerful, real-time progress tracking into modern Python workflows. We begin with nested progress bars and manual progress control, then move into practical scenarios such as streaming downloads, pandas data processing, parallel execution, structured logging, and asynchronous tasks. Throughout this tutorial, we focus
In this tutorial, we implement a production-grade, large-scale graph analytics pipeline in NetworKit, focusing on speed, memory efficiency, and version-safe APIs in NetworKit 11.2.1. We generate a large-scale free network, extract the largest connected component, and then compute structural backbone signals via k-core decomposition and centrality ranking. We also detect communities with PLM and quantify
In this tutorial, we explore how we use Daft as a high-performance, Python-native data engine to build an end-to-end analytical pipeline. We start by loading a real-world MNIST dataset, then progressively transform it using UDFs, feature engineering, aggregations, joins, and lazy execution. Also, we demonstrate how to seamlessly combine structured data processing, numerical computation, and
In this tutorial, we build an advanced Tree-of-Thoughts (ToT) multi-branch reasoning agent from scratch. Instead of relying on linear chain-of-thought reasoning, we design a system that generates multiple reasoning branches, scores each branch using a heuristic evaluation function, prunes weak candidates, and continues expanding only the strongest paths. We combine an instruction-tuned transformer model with
In this tutorial, we build an EverMem-style persistent agent OS. We combine short-term conversational context (STM) with long-term vector memory using FAISS so the agent can recall relevant past information before generating each response. Alongside semantic memory, we also store structured records in SQLite to persist metadata like timestamps, importance scores, and memory signals (preference,
In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory. We generate a realistic, large-scale dataset, engineer rich behavioral and city-level features using lazy expressions and approximate statistics, and aggregate insights at scale. We then integrate Vaex with scikit-learn
In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract
In this tutorial, we build an advanced multi-agent communication system using a structured message bus architecture powered by LangGraph and Pydantic. We define a strict ACP-style message schema that allows agents to communicate via a shared state rather than calling each other directly, enabling modularity, traceability, and production-grade orchestration. We implement three specialized agents, a
In this tutorial, we build a complete, production-grade ML experimentation and deployment workflow using MLflow. We start by launching a dedicated MLflow Tracking Server with a structured backend and artifact store, enabling us to track experiments in a scalable, reproducible manner. We then train multiple machine learning models using a nested hyperparameter sweep while automatically
