LangGraph: guides & tutorials

Production “agents” are mostly orchestration: LLM calls, tools, memory/state, retries, and guardrails. Three ecosystems lead in 2026—LangGraph, AutoGen, and CrewAI—each with different ergonomics for web data workloads.

An AI research agent automates the full loop: given a research question, it searches the web via Apify, fetches and reads pages, extracts key findings, and synthesizes a structured report. This guide walks you through building one with LangGraph and the Apify Python client.

LangGraph is LangChain's graph-based framework for building stateful, multi-step AI agents. Unlike simple chains, LangGraph lets you define nodes (functions), edges (transitions), conditional branching, loops, and human-in-the-loop checkpoints. It's the go-to choice for production agents that need persistence, interrupts, and complex control flow. Use Apify for web data in LangGraph.

People picture AI agents that dream up a scraping plan, pick tools, shrug off blocks, and fix themselves when markup shifts. In production, that rarely holds: models are strong orchestrators and weak primary scrapers. What actually works is narrower—an LLM routing work through known tools—so we stick to a concrete stack: LLM → Apify Actors as tools → optional vector store → downstream analysis. Below are three patterns (static tool use, dynamic scraper generation, RAG-augmented scraping), how that maps to LangGraph-style graphs, how to recover when runs go empty, and the trade-offs teams feel in real autonomous data collection systems.