AI · alternatives · 2026
24 alternatives to LLM agents to extract web data
Excited to more broadly share what we've been working on at @ReworkdAI ⚡️ This past year we've fully invested in building the next generation of web agents. They're already live in production today across a handful of companies serving as the simplest way to extract web data at scale. Come to us…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. LLM agents to extract web data launched in 2024; newer entries below may have overtaken it.
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com · its alternatives →
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Hey HN, We’ve been building browser agents for a while. In production, we kept converging on the same pattern: deterministic scripts for the happy path, agents only for edge cases. So we built Demonstrate Mode. The idea is simple: You perform your workflow once in a remote browser. Notte records the interactions and generates deterministic automation code. How it works: - Record clicks, inputs, navigations in a cloud browser - Compile them into deterministic code (no LLM at runtime) - Run and deploy on managed browser infrastructure Closest analog is Playwright codegen but: - Infrastructure…
2025 · notte.cc · its alternatives →
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Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com · its alternatives →
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Anakin▲148Every second thing I build needs to access the web, and I was frustrated with not finding something that just works all the time. With my team, we built this because I wanted to enable access to extremely difficult websites and apps for every AI agent. We have solved this cat-and-mouse problem with self-healing built in. It's one API to get data from hostile sites, and it can do easy sites too. You POST a URL and get back Markdown, JSON, HTML, a screenshot, or all of them. That same call handles anti-bot, proxy rotation, JS rendering, persistent authenticated sessions, and schema-based…
2021 · anakin.io · its alternatives →
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Gradient▲153Developer API for building private LLMs that you own
2023 · hyperagent.com · its alternatives →
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Trails▲81Hey all, Justin here. I previously built Phind, the AI search engine for developers. One of the biggest problems we had there was figuring out what went wrong with bad searches. We had tons of searches per day, but less than 1% of users gave any explicit feedback. So we were either manually digging through searches or making general system improvements and hoping they helped. This problem gets harder with agents. Traces are longer and more complex. It takes more effort to review them, so I'm building a tool that lets you analyze LLM outputs directly to help developers of LLM apps and agents…
Jan 2026 · trails-red.vercel.app · its alternatives →
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Sokosumi▲90I'm part of the team that build Sokosumi, a marketplace where you can hire and deploy AI agents for specific tasks (research, design, sentiment analysis, etc.). Instead of one big chatbot, they’re positioning it as a directory of specialized “freelancer” agents, with a blockchain-based backend for payments and identity. Developers can also list their own agents and monetize them. Curious what people here think about this model: Will specialization make agent marketplaces more useful than general-purpose LLM tools? How viable is blockchain as the backbone for an agent economy? What pitfalls…
Mar 2026 · sokosumi.com · its alternatives →
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Turn any webpage into structured data using LLMs. Contribute to mishushakov/llm-scraper development by creating an account on GitHub.
2024 · github.com · its alternatives →
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We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com · its alternatives →
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Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use (https://news.ycombinator.com/item?id=43173378), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these…
2025 · github.com · its alternatives →
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Enables small-to-large self-hosted ai models to use local source code when running tool-calling agentic workloads. We actively data mine 20,900+ (2+ TB) popular github repos using large and small ai models to create reuseable: json, markdown and parquet files for local-first tool-calling models. - d
May 2026 · github.com · its alternatives →
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Hey HN, as a former data analyst, I’ve been tooling around trying to get agents to do my old job. The result is this system that gets you maybe 80% of the way there. I think this is a good data point for what the current frontier models are capable of and where they are still lacking (in this case — hypothesis generation and general data intuition). Some initial learnings: - Generating web app-based reports goes much better if there are explicit templates/pre-defined components for the model to use. - Claude can “heal” broken charts if you give it access to chart images and run a…
Mar 2026 · rubenflamshepherd.com · its alternatives →
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We’ve just released Berri AI - a Python package https://github.com/ClerkieAI/berri_ai that makes it easy for developers to quickly deploy their LLM Agent from Google Colab to production (Web App and API Endpoint). Building LLM Apps can require working in online coding environments, like Colab, due to local environment limitations (e.g. running pytorch on older Macs). This can cause long dev cycles when deploying the app to production, as ported changes can only be tested once it is deployed after lengthy (>20min+) Docker builds. Berri lets you deploy directly from your…
2023 · github.com · its alternatives →
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I've spent weeks curating technical implementation details of how companies are actually deploying LLMs and Generative AI in production. The database now contains over 300 case studies with detailed technical summaries (230,000+ words) focusing exclusively on architectural decisions, deployment patterns, and real engineering challenges. Key features: * Each case study is technically focused - no marketing fluff * 150+ entries from technical conference talks and panels (saving you 100+ hours of video watching) * Sophisticated filtering by technical stack, RAG implementations, monitoring…
2024 · zenml.io · its alternatives →
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Hey HN! I'm excited to share Reagent, an open source Javascript framework to build UI rich AI agents using graph based workflow. Github: https://github.com/reagentai/reagent There are already amazing tools for building agents but most of them seem to be focused on building the AI workflow on the backend with markdown as final output. There is generative UI in AI SDK from vercel but it seems to work only with server components afaik. So, I created a framework for building AI agents with interactive UI components; you can render any component from LLM tool, update it's…
2024 · github.com · its alternatives →
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Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
2025 · github.com · its alternatives →
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Hi HN, Yesterday I live streamed myself for 6 hours building this from scratch. It’s an AI agent that uses Llama 2 (so far the 13b chat model) to generate a full react codebase from a single prompt. It had a “dev loop” that iterates on your feedback and resolves dependencies. In the end it kinda worked and I got excited so wanted to post here haha. Long story short a viewer on my discord suggested I build in of these and I just had to look into how these work (inspired by GPT Engineer and AutoGPT) I wanted to work on a more specific downstream task so I focused on web dev. Llama 2 isn’t the…
2023 · github.com · its alternatives →
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I've been working in the couple of months on an experiment, trying to make GPT-4 much more useful for web development / React, writing production code that is relevant to any repository without copy pasta from ChatGPT or having small snippets of auto-complete from Copilot that are not in your context. The agent is taking a user story text and generating and composing multiple react components to generate the relevant screens, based on atomic design principles, with Typescript, TailwindCSS and RadixUI. Is is still experimental but very interesting results, I would like to get your…
2023 · github.com · its alternatives →
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We just open sourced a first class our AI web app builder. Instead of using another hosted AI coding platform, you can fork this project and build your own AI app builder, fully customized and running under your own brand. It includes: Next.js + TypeScript AI chat with streaming Artifact generation File explorer Code editor Live preview Databases Sandboxes to be used by AI agents Versions Responsive production-ready UI And a lot more... The only required dependency is the Totalum API, which exposes the AI generation engine through a simple REST API. You can replace or extend the backend…
Jul 2026 · github.com · its alternatives →
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Hey HN! For that last 8 months I've been trying to make agents that can hack web applications to find vulnerabilities in them - An AI Security Tester. The system has 29 agents in total, a custom LLM Orchestration framework which works on the task-subtask architecture (old-school but works amazingly for my use case, and is pretty reliable) with custom agent calling mechanism. No Auo-Gen, Langchain and Crew AI - Everything custom built for pentesting. Each test runs in an isolated Kali linux environment (on AWS Fargate), where the agents have full access to the environment to undertake any…
2025 · its alternatives →
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No-code multi-agent framework to build LLM Agents, workflows and applications with your data - trypromptly/LLMStack
2023 · github.com · its alternatives →
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I built an open-source research agent. You ask a question, it searches the web via Tavily, synthesizes an answer with an LLM, and shows the sources it used. Answers stream in real-time. The interesting part is the backend. It's a single JS file (~100 lines) that handles web search, LLM streaming, and per-user conversation history. No vector database, no Redis, no separate storage service. It runs inside a cell — an isolated environment with a built-in database, search index, and filesystem. The cell handles persistence and streaming natively, so the agent code only has to deal with the…
Apr 2026 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →