What Are Tools for AI and How Do You Choose the Right Ones?
Tools for AI are ready-to-run programs or services that either help you build AI applications or give AI agents the ability to act on real-world data. On Apify, for example, the marketplace lists 73,229 tools ("Actors") for tasks like scraping TikTok, Google Maps, Instagram, and e-commerce sites, or crawling website content to feed LLMs and RAG pipelines. Choose based on your data source, the output format you need, and whether the tool connects to your agent or workflow via API. The rest of this article explains the categories, selection criteria, and a concrete way to wire a scraper into an AI pipeline.
Two kinds of "tools for AI"
The phrase covers two different things, and mixing them up leads to the wrong purchase:
- Tools that build AI — frameworks, vector databases, model hosting, and prompt tooling. You use these to create or train a model.
- Tools that AI uses — data collectors and automations that an agent or LLM calls to fetch fresh information, monitor a site, or trigger an action. Apify's marketplace sits mostly in this second category: "ready-to-run tools for AI" that supply real-time web data.
If your goal is to give a chatbot current prices, reviews, or social metrics, you want the second kind.
Main categories you'll find
| Category | What it does | Example from the marketplace |
|---|---|---|
| Web scraping | Pull structured data from a specific site | TikTok Scraper (videos, hashtags, users), Instagram Scraper (posts, reels, comments) |
| Data extraction / crawling | Crawl whole sites and clean the text | Website Content Crawler (Markdown output for LLMs, vector DBs, RAG) |
| Business & lead data | Collect contact and location info | Google Maps Scraper (reviews, emails, opening hours, prices) |
| E-commerce monitoring | Track prices and compare retailers | E-commerce Scraping Tool |
| Social monitoring | Track posts and engagement | Facebook Posts Scraper |
Each listing shows a run count and a user rating — useful signals for how battle-tested a tool is. TikTok Scraper, for instance, shows 291K runs and 4.8 (371); Google Maps Scraper shows 616K runs and 4.7 (1,817).
How ready-to-run tools connect to AI agents
The pattern is consistent across these tools:
- Input — you supply URLs or search queries (e.g., a TikTok profile URL, a Google Maps search, or a list of site pages).
- Action — the tool runs, either on demand, on a schedule, or via API.
- Output — you get structured data (JSON, exported datasets) or cleaned text (Markdown for the Website Content Crawler).
- Integration — you feed that output into your app, agent, or pipeline. The Website Content Crawler explicitly integrates with LangChain, LlamaIndex, and the wider LLM ecosystem.
So the tool does the collection and cleaning; your agent does the reasoning. You don't need to build a scraper to give an agent web access — you connect one.
Criteria for choosing a tool
- Data source match — does it cover the exact site or platform you need? A TikTok scraper won't help with retail pricing.
- Output format — do you need structured fields (emails, prices, metrics) or clean prose for an LLM? The Website Content Crawler outputs Markdown specifically for RAG; the social scrapers output content, metrics, and metadata.
- Integration support — check for API access, scheduling, and named integrations (LangChain, LlamaIndex) if you're building an agent.
- Maturity — run counts and ratings indicate reliability. A tool with hundreds of thousands of runs has been tested at scale.
- Pricing — Apify publishes a pricing page; check it against your expected run volume rather than assuming any tier is free.
Concrete example: feeding a RAG pipeline
Say you want a chatbot that answers questions about your competitor's website.
- Run the Website Content Crawler with the competitor's URLs as input.
- It crawls the pages, cleans the HTML, and returns text in Markdown, optionally downloading files.
- Load that Markdown into your vector database via the LangChain or LlamaIndex integration.
- Your LLM retrieves relevant chunks and answers user questions with current site content.
The same pattern works for social or business data: scrape with the matching tool, export or call via API, then index or pass the results to your agent.
Common pitfalls
- Blocked scrapers — sites change their defenses; a tool that worked last month may need updates. Prefer actively maintained tools with high run counts.
- Rate limits — high-volume runs can hit platform limits. Use scheduling and monitoring rather than one giant burst.
- Data cleaning — raw scraped data often needs normalization before it's useful to an LLM. Tools that output Markdown or structured fields reduce this work, but verify the schema.
- Assuming free access — pricing and login requirements vary; check the pricing page and each tool's listing instead of assuming a free tier.
Bottom line
Pick tools for AI by matching the data source, the output format your pipeline needs, and the integration path to your agent. Start with a high-run, well-rated tool in the right category, test it on a small input, then wire it into your workflow via API or a named integration like LangChain.