productiongrade

@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: return {“error”: str(e)} @tool …

In this tutorial, we build a production-grade tabular machine learning pipeline using autogluonTaking mixed types of real-world datasets from raw ingestion to deployment-ready artifacts. We train high-quality stacked and bagged …

In this tutorial, we build an end-to-end, production-style agentive workflow using graphbit It demonstrates how graph-structured execution, tool calling, and alternative LLM-driven agents can coexist in the same system. We …