If you run scrapers on Apify, you know the routine: copy a batch of dataset rows, paste them into a chat window, ask an AI to make sense of them, repeat. HotBot’s native Apify connector removes that step. This apify ai integration lets HotBot work with your Apify data directly, with no exports or clipboard juggling. Sign in as a member, connect Apify from inside HotBot, and the assistant reads your Actors, runs, and datasets. From there you can review code, write queries, triage incidents, and draft docs without leaving the conversation.
Below is the setup, plus six workflows with prompts you can copy straight into your own chat.
What You’ll Need
- A HotBot membership (connectors are members-only, covered below)
- An Apify account with at least one Actor run or dataset. The Apify free plan is $0/month with $5 of platform credit and no credit card at signup, so you can spin one up in ten minutes if you don’t have one
- A browser. No CLI, no local MCP setup required for the HotBot side
- Optional: a repo or PR you want reviewed, if code review is your goal
Estimated time: 10–15 minutes to connect and run your first workflow. Difficulty level: Beginner. If you can run an Actor, you can do this.
Step 1: Sign in and connect Apify inside HotBot
HotBot connectors are a members-only feature. Signed-in members connect supported apps inside HotBot so the assistant works with that app’s data directly. Head to the HotBot login page and sign in, or subscribe if you’re not a member yet. Pricing is a free tier, $7.95/week or $39.95/quarter, laid out on the pricing page.
Once you’re signed in, connect Apify from inside HotBot. I won’t walk you through fake menu screenshots, because the interface is the interface; you’ll find the connector option once you’re in. Authorize it against your Apify account and that’s the setup.
From there, HotBot can reference your Actors, runs, and dataset results in the conversation. No more pasting JSON blobs.
Step 2: Review code and pull requests
This is where a lot of us live day to day. Once Apify’s connected, you can point HotBot at Actor source or a PR and ask for a real review, not just “looks good” but actual reasoning about edge cases.
Example prompt:
“Review the input-handling logic in my latest Actor run. Flag any unvalidated inputs, missing retries on failed requests, and places where a bad input returns a wall of errors instead of zero rows.”
That last part matters. As the Apify beginner docs point out, a well-behaved Actor returns zero rows on bad input rather than exploding; the Google Maps Scraper is beloved partly for exactly this. A good review catches when yours doesn’t.
Which model? For quick “did I miss anything obvious” passes, HotBot Chat is fast and fine. For an architecture-level review across multiple files, use HotBot Chat Pro. Its 1M-token context means you can feed it a whole Actor without chopping it into pieces, and that’s where the big context window earns its keep. Community threads like this Ask HN on AI code review show teams still juggling multiple tools for this, so one assistant that already has your data cuts down the shuffle.
Step 3: Write and explain dataset queries
Apify datasets are append-only structured storage, and you can export them as JSON, CSV, Excel, XML, or RSS. Often, though, you just want to ask a question of the data and get an answer back.
There’s an official Apify dataset query engine Actor that converts natural-language queries to SQL before running them against a dataset. The HotBot connector lets you do the conversational version of that right in chat.
Example prompt:
“In my last run’s dataset, how many rows are missing a phone number? Group the results by city and show me the top five cities by missing-data count.”
The reverse works too: hand HotBot a gnarly SQL query someone else wrote and ask, “Explain what this does and where it’d break on an empty dataset.”
Which model? For everyday “count this, group that” questions, HotBot Chat handles it. For multi-step query logic where the model needs to reason through joins and edge cases before it answers, step up to HotBot Chat Plus. The step-by-step thinking is what query-building wants.
Step 4: Triage incidents and errors
A scheduled Actor fails overnight. You wake up to a run log and a vague sense of dread. With Apify connected, HotBot can read that log and help you triage, which beats scrolling through timestamps at 8am.
Example prompt:
“My scheduled Actor failed on last night’s run. Read the log, tell me the most likely root cause, and rank the top three things to check first. Was it a memory issue, a network block, or bad input?”
Memory is a common culprit. Apify lets you set memory in MB (powers of 2, minimum 128), and under-provisioned runs fail in ways that look mysterious until someone points at the cause. A good triage pass catches that fast.
Which model? Incident triage is reasoning-heavy. You want the model connecting symptoms to causes, not pattern-matching. Use HotBot Chat Pro for the gnarly ones. For a quick “is this a known error string” check, HotBot Chat is plenty.
Step 5: Draft docs and changelogs
Nobody wants to write the README. HotBot can draft one from your actual Actor, its inputs, its outputs, its behavior, instead of leaving you staring at a blank file.
Example prompt:
“Draft a README for this Actor. Cover the input schema field by field, the output format (it exports CSV/JSON/Excel), and a quick-start with one search term and one location, place limit 10. Then generate a changelog entry for the pay-per-event pricing change.”
The dataset query engine repo is a useful model here; its changelog tracks exactly that kind of pricing-model change (Actor start $0.05, query completed $0.01). Clear docs make that stuff legible to your users.
Which model? Drafting docs is fast, everyday work, so HotBot Chat is the right call. If you want tone matched to existing docs or a longer structured guide, a third-party model on HotBot (you’ve got access to 800+ models from every major provider on one subscription) might nail the voice better. Try a couple and keep the one you like.
Step 6: Feed live scraped data into your reasoning
Apify’s real strength is live web data. Its Store has thousands of ready-made Actors, and results can flow straight into AI workflows. With the connector, HotBot can reason over freshly scraped datasets without you building a pipeline first.
Example prompt:
“Pull the results from my latest competitor-pricing scrape and summarize the three biggest price changes since last week. Flag anything that looks like a scraping error versus a real change.”
That “error versus real change” distinction is the whole game, and it’s exactly the kind of judgment call a strong reasoning model makes well. Use HotBot Chat Pro for the analysis; its vision support also helps if your data includes screenshots or charts.
Without the connector (free tier)
No membership yet? You can still get most of the value the manual way. Export your Apify dataset as JSON or CSV, paste it into HotBot, and prompt against it. Same for run logs: copy the log, paste it, ask for triage.
It’s clunkier. You’re doing the copy-paste ritual from the top of this article. But the model quality is identical; the connector just removes the friction. Try the free tier this way, and if you find yourself pasting datasets more than twice a week, the connector pays for itself in saved clicks.
Common Issues
- “HotBot can’t see my data.” Make sure you’re signed in as a member and Apify is actually connected from inside HotBot. The connector is the bridge; no connection, no data.
- Big datasets feel truncated. Even with a large context window, don’t dump a 500K-row dataset raw. Query it or summarize first, then dig in. Apify’s docs recommend breaking complex tasks into smaller pieces, and the same logic applies here.
- Sensitive scraped data. Only connect and share what you’re comfortable with the assistant processing. If a dataset contains personal data, treat it accordingly. When in doubt, work with a redacted sample.
- Wrong-feeling answers on queries. For anything involving multi-step logic, bump up to Chat Plus or Pro. Fast models are great until the reasoning gets deep.
What’s Next
Once you’ve got one workflow humming, chain them. Review the Actor code, draft its docs, then set up a triage prompt you reuse every time a run fails. That’s the real payoff: the connector turns HotBot into a persistent teammate that already knows your Apify setup.
If you’re still on the fence, start on the free tier with the paste method, then sign in and connect Apify properly once you feel the friction. And if you want to experiment with different models for different jobs, browse the full model lineup. One subscription, the whole toolbox.
FAQ
Do I need to be a member to connect Apify?
Yes. HotBot connectors are a members-only feature; you connect supported apps from inside HotBot once you’re signed in. Free-tier users can still paste Apify exports into the chat manually, but the native connector requires membership.
Is HotBot an official Apify product?
No. HotBot is an independent AI chat service and is not affiliated with or endorsed by Apify. Third-party names like Apify identify the apps and data sources HotBot can work with, nothing more.
Which HotBot model should I use for Apify work?
Use HotBot Chat for fast everyday tasks (quick queries, doc drafts), HotBot Chat Plus for step-by-step reasoning like multi-part query logic, and HotBot Chat Pro for deep work like architecture-level code review and incident triage. Its 1M-token context and vision help with large Actors and visual data.
Is HotBot a good ChatGPT alternative for developers?
If you want one subscription that spans 800+ models plus connectors to your dev tools, it’s worth a look. Unlike single-model tools, HotBot lets you match the model to the task: a fast one for docs, a deep reasoner for triage.
How much does HotBot cost?
There’s a free tier, and paid plans are $7.95/week or $39.95/quarter. The free tier is enough to test the paste-in workflow; connectors require a paid membership.
Can HotBot run Apify Actors for me?
The connector lets HotBot work with your Apify data (Actors, runs, and datasets) directly in the conversation. For programmatic Actor execution and full pipeline control, Apify’s own REST API and MCP server are the right tools; HotBot is where you reason about the results.
