LangGraph: Building Stateful Multi-Agent Workflows with LangChain

Fri, 01 May 2026 00:00:00 +0000

The first generation of LLM agents followed a simple, predictable loop – the ReAct pattern of Thought, Action, Observation. But real-world applications require more sophisticated orchestration: multiple agents working together, conditional branching, human oversight, persistent state across complex workflows, and the ability to loop back for refinement. LangGraph provides the graph-based architecture that makes these patterns possible.

LangGraph extends LangChain’s agent capabilities from linear chains to directed graphs, where each node is a computational step and edges define the control flow. This deceptively simple generalization – from chains to graphs – enables an enormous range of previously impractical agent architectures.

ScrapeGraphAI: LLM-Powered Web Scraping with Graph Logic

Fri, 01 May 2026 00:00:00 +0000

Traditional web scraping is fragile. A scraper built around CSS selectors and XPath expressions breaks the moment the target website updates its HTML structure. Maintaining scrapers at scale becomes a constant game of catching up with layout changes, restructuring selectors, and re-testing pipelines. ScrapeGraphAI takes a fundamentally different approach: instead of hard-coding extraction rules, it uses LLMs to understand page content semantically and extract the data you actually want.

The core idea is that an LLM – given a page’s rendered content and a description of what to extract – can identify the relevant information without knowing the page’s CSS structure. This makes ScrapeGraphAI scrapers resilient to layout changes. A website redesign that would break a traditional scraper barely registers: the LLM simply reads the new layout and finds the same information.

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LangGraph: Building Stateful Multi-Agent Workflows with LangChain

ScrapeGraphAI: LLM-Powered Web Scraping with Graph Logic