Large language models turn HTML and docs into JSON, summaries, and answers. Model picks, prompting, and Apify hooks for extraction beyond brittle parsers.
OpenClaw and Hermes Agent are both self-hosted AI assistants you run on your own hardware — but they solve different problems. OpenClaw is a gateway that routes messages to an LLM and executes skills on demand. Hermes Agent is an autonomous agent with persistent memory and a learning loop that improves on repeated workflows. This post compares the two directly so you can choose the right tool for your setup.
Many RAG (Retrieval-Augmented Generation) projects fail in production not because the technology doesn't work, but because teams skip the hard parts: chunking strategy, embedding model selection, retrieval quality measurement, and stale data management. Validate each step on your own corpus — field names, SDK versions, and Actor outputs change over time.
The pipeline: crawl websites → chunk intelligently → embed → store in vector DB → retrieve with reranking → generate answers with citations.
AI-powered web scrapers use LLMs to replace fragile CSS selectors with natural language extraction prompts. Instead of writing $('.product .price').text(), you instruct the model: "Extract all products with name, price, and availability status."
The result is scrapers that work across multiple page layouts — but come with higher cost and latency than traditional extraction.
Freshness note: Pricing and features verified March 2026.
Both Firecrawl and Jina Reader convert URLs to clean Markdown for LLMs — but their scope, pricing, and capabilities differ significantly.
TL;DR: Jina Reader is free and instant for single-URL lookups. Firecrawl is the production choice for full-site crawls, structured extraction, and RAG pipelines.
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.
Ollama runs open-source LLMs locally. Open WebUI provides a ChatGPT-like interface that connects to Ollama (or OpenAI/Anthropic). Together they give you a fully private chat experience — no data leaves your server. This guide covers installation, model choices by hardware, and production setup on CPU or GPU VPS.
TL;DR: Use Claude for long documents and tool use, GPT-4o for general coding and the best ecosystem, Groq for cost-sensitive real-time apps with open models. All three support OpenAI-compatible clients; migration is straightforward.
Choosing an LLM API in 2026 means balancing quality, context length, speed, and cost. This guide compares OpenAI, Anthropic, and Groq so you can pick the right provider for web scraping pipelines, content summarization, coding agents, and production AI.
OpenClaw is a multi-platform AI assistant — web UI, API, Telegram, Discord, Slack, and more — all fronting a single LLM backend. Connect it to Ollama and your local model becomes usable everywhere: from your phone via Telegram, from code via API, from the web dashboard. No data leaves your machine. This guide covers setup, config, use cases, and connecting multiple backends (Ollama + Anthropic + OpenAI) on a Liquid Web VPS.
Google's Gemma 3 is an open-weight model family optimized for efficiency and quality. Run it locally with Ollama in minutes — no API keys, no cloud dependency. This guide covers installation, model sizes (2B, 7B, 27B), hardware requirements, and how Gemma 3 compares to LLaMA 3.2 and Phi-3. (Issue #396)
LLMs did not replace CSS overnight, but they did change where teams spend time: instead of nursing a selector file per theme, you can often send cleaned HTML (or markdown) to a model and ask for JSON against a schema—handy when layouts drift or you touch many domains. Large language models reason about semantics, so fields like price, rating, and stock can come back without hand-built paths—at the cost of latency, tokens, and occasional guesses. The sections that follow spell out four common roles for LLMs in scraping (selector help, structured extraction, classification, validation), where that breaks down, and when traditional selectors or pre-built Actors still win. Try Firecrawl extract · Apify Store
