Browsing: structured

Every BI team knows this bottleneck: a business user has a question that falls outside existing dashboards, so they file a ticket. An analyst writes the…

import subprocess import sys print(“📦 Installing system dependencies…”) subprocess.run([‘apt-get’, ‘update’, ‘-qq’], capture_output=True) subprocess.run([‘apt-get’, ‘install’, ‘-y’, ‘-qq’, ‘libnss3’, ‘libnspr4’, ‘libatk1.0-0’, ‘libatk-bridge2.0-0’, ‘libcups2’, ‘libdrm2’, ‘libxkbcommon0’, ‘libxcomposite1’, ‘libxdamage1’, ‘libxfixes3’,…

In this tutorial, we explore how to use Google’s LangExtract library to transform unstructured text into structured, machine-readable information. We begin by installing the required dependencies…

In this tutorial, we build a complete AgentScope workflow from the ground up and run everything in Colab. We start by wiring OpenAI through AgentScope and…

Most LLM agents work well for short tool-calling loops but start to break down when the task becomes multi-step, stateful, and artifact-heavy. LangChain’s Deep Agents is…

In recent times, many developments in the agent ecosystem have focused on enabling AI agents to interact with external tools and access domain-specific knowledge more effectively.…

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…

OpenAI has released Symphony, an open-source framework designed to manage autonomous AI coding agents through structured ‘implementation runs.’ The project provides…

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…

def executor_agent(step: Dict[str, Any], context: Dict[str, Any]) -> StepResult: step_id = int(step.get(“id”, 0)) title = step.get(“title”, f”Step {step_id}”) tool = step.get(“tool”, “llm”) ctx_compact = { “goal”:…