Browsing: ProductionGrade

In this tutorial, we implement an advanced agentic AI system using the CAMEL framework, orchestrating multiple specialized agents to collaboratively solve a complex task. We design…

consumer = huey.create_consumer( workers=4, worker_type=WORKER_THREAD, periodic=True, initial_delay=0.1, backoff=1.15, max_delay=2.0, scheduler_interval=1, check_worker_health=True, health_check_interval=10, flush_locks=False, ) consumer_thread = threading.Thread(target=consumer.run, daemon=True) consumer_thread.start() print(“Consumer started (threaded).”) print(“\nEnqueue basics…”) r1 =…

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…

In this tutorial, we build an advanced Griptape-based customer support automation system that combines deterministic tooling with agentic reasoning to process real-world support tickets end-to-end. We…

In this tutorial, we walk through an advanced, end-to-end exploration of Polyfactory, focusing on how we can generate rich, realistic mock data directly from Python type…

In this tutorial, we build an ultra-advanced agentic AI workflow that behaves like a production-grade research and reasoning system rather than a single prompt call. We…

Schemas, and Composable DataFrame ContractsIn this tutorial, we demonstrate how to build robust, production-grade data validation pipelines using Pandera with typed DataFrame models. We start by…

@tool def sql_investigate(query: str) -> dict: try: df = con.execute(query).df() head = df.head(30) return { “rows”: int(len(df)), “columns”: list(df.columns), “preview”: head.to_dict(orient=”records”) } except Exception as e:…

In this tutorial, we build a production-grade tabular machine learning pipeline using AutoGluon, taking a real-world mixed-type dataset from raw ingestion through to deployment-ready artifacts. We…

In this tutorial, we build an end-to-end, production-style agentic workflow using GraphBit that demonstrates how graph-structured execution, tool calling, and optional LLM-driven agents can coexist in…