Browsing: structured

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”:…

This post is cowritten with Remi Louf, CEO and technical founder of Dottxt. Structured output in AI applications refers to AI-generated responses conforming to formats that…

In this tutorial, we build a production-style Route Optimizer Agent for a logistics dispatch center using the latest LangChain agent APIs. We design a tool-driven workflow…