Firecrawl does one thing well: it turns messy web pages into clean, LLM-ready markdown. That markdown still has to land somewhere useful, though, and that’s the gap the HotBot Firecrawl integration fills. Once you’re a signed-in member, you connect Firecrawl inside HotBot and the assistant works with your Firecrawl data directly — no copy-paste, no context juggling. This guide covers the setup and five workflows built on top of it: reviewing code and PRs, writing and explaining queries, triaging incidents, and drafting docs. If you’ve been looking for a Firecrawl ChatGPT alternative that handles all of this in one place, this is for you.
One disclosure before we start: HotBot is an independent AI chat service. It’s not an official Firecrawl product, and it’s not affiliated with or endorsed by Firecrawl. Third-party names here just identify the tools and models you can use.
What You’ll Need
- A HotBot account — you must be a member to use connectors (sign in or subscribe here)
- A Firecrawl account with data you want to work with (Firecrawl offers a free tier with 1,000 credits, so testing is cheap)
- Some code, queries, logs, or docs you actually care about — real work beats toy examples
- Basic familiarity with your own stack (this guide won’t explain what a PR is)
Estimated time: 15–20 minutes for setup and your first workflow Difficulty level: Beginner to intermediate. If you can use a chat app, you can do this.
Step 1: Sign In and Become a Member
Connectors are members-only. The free tier lets you chat and paste content in manually (covered later), but connecting an app like Firecrawl so the assistant reads your data directly requires a subscription.
Go to the HotBot login page and sign in, or subscribe if you’re new. Pricing is simple: a free tier, or paid at $7.95/week or $39.95/quarter. One subscription covers 800+ models plus HotBot’s own engines. The pricing page has the current details.
If you’re already paying separate bills for a chat app and a couple of model APIs, consolidating them into one subscription usually comes out cheaper. Do the math on your own setup before deciding.
Step 2: Connect Firecrawl Inside HotBot
Once you’re signed in as a member, connect Firecrawl from inside HotBot. The connector lives in the HotBot interface, and you link your Firecrawl account there so the assistant can work with that data directly.
The mental model that matters: after connecting, you’re not pasting raw markdown into every prompt. The assistant reaches into your Firecrawl data as part of the conversation. That’s what removes the friction that breaks copy-paste workflows on anything longer than a page.
Firecrawl’s clean markdown output does a lot of work here. According to Espressio’s Firecrawl guide, markdown output uses 93% fewer LLM input tokens than raw HTML, which means the assistant spends its context budget on reasoning instead of parsing div soup.
Step 3: Review Code and Pull Requests
This is where the integration earns its place for most developers. Suppose you’ve used Firecrawl to pull in a library’s docs, a competitor’s API reference, or your own crawled internal wiki. Now you want the assistant to review a PR against that context.
Try a prompt like this:
Using the Firecrawl-scraped docs for the Stripe Python SDK,
review this PR for correct usage of the checkout session API.
Flag any deprecated methods or parameters I've missed.
For fast, everyday reviews — style nits, obvious bugs, “does this look right?” — HotBot Chat is the workhorse. It’s quick and cheap on effort. When the PR touches something harder, like a tricky refactor, concurrency, or a migration, step up to HotBot Chat Plus for step-by-step reasoning that walks the logic.
A deep architectural review across many files is a HotBot Chat Pro job. The 1M-token context and vision support let you throw a lot at it, diagrams and screenshots included, without it losing the thread. Start a session at HotBot Chat.
Step 4: Write and Explain Queries
This one comes up constantly. You’ve scraped a database’s documentation with Firecrawl — a specific version of PostgreSQL, say, or some vendor’s proprietary query language — and now you need queries that match that exact version’s syntax.
Ask something like:
Based on the Firecrawl docs I connected, write a query that
aggregates orders by region for the last 90 days. Then explain
each clause so I understand what it's doing.
For writing and explaining straightforward queries, HotBot Chat handles it. When you’re optimizing something slow, reasoning about query plans, or untangling a five-table join with subqueries, HotBot Chat Plus earns its keep with visible step-by-step thinking.
There’s also the model-choice angle. A specific third-party model sometimes fits a particular SQL dialect better than the defaults. Because HotBot isn’t locked to one model, you can switch to whichever provider handles your dialect best from the model list and keep the same connected Firecrawl data.
Step 5: Triage Incidents and Draft Docs
Two workflows in one section, because both turn raw context into something clear under pressure.
Incident triage. When something’s on fire, you don’t have time to read a vendor’s entire status page or changelog. Use Firecrawl to pull that page in, then ask HotBot to make sense of it:
Here's the vendor status page and error log I connected via Firecrawl.
Summarize the likely cause, list the affected services, and give me
three things to check first.
For live triage, HotBot Chat wins on speed. For a proper post-incident root-cause analysis where you’re correlating logs, timelines, and code changes, HotBot Chat Pro‘s deep reasoning and large context are the right tool. This mirrors the Firecrawl-plus-model pattern Apify documented: the model decides what it needs, Firecrawl fetches, the model synthesizes with source attribution.
Docs and changelogs. Scrape your merged PRs or release notes, then:
Turn these merged PRs (connected via Firecrawl) into a user-facing
changelog. Group by feature, fixes, and breaking changes. Keep it
skimmable.
Drafting docs is a HotBot Chat or Chat Plus task depending on how much polish you need. Firecrawl’s own developer index, per its SKILL.md, can surface issues, merged PRs, and READMEs as markdown, which makes good raw material for changelog generation.
Without the Connector: The Free-Tier Approach
Not a member yet? You can still get a lot done. Run your Firecrawl scrape, grab the clean markdown output, and paste it straight into a HotBot chat with your prompt. Firecrawl returns LLM-ready markdown by design, so it drops in cleanly.
The tradeoff is friction. You’re copy-pasting, you’re bounded by what fits in one message, and you re-paste every time you start fresh. That’s fine for a one-off and tedious as a daily habit. Removing that friction is the whole reason the connector exists, but the free tier is a legitimate way to try the workflows before you commit.
Limits and Privacy Notes
A few caveats worth keeping in mind:
- Firecrawl sends data through its servers. As DigitalApplied notes, if data must stay fully local, a managed API isn’t the right fit. Know what you’re scraping and where it goes.
- Credits are finite. Firecrawl’s crawl endpoint costs roughly 1 credit per page, with extras for JSON extraction and PDF parsing. Crawl broadly and you’ll burn through a free tier fast.
- Context isn’t infinite. Even at 1M tokens on Chat Pro, be deliberate about what you connect. Relevant beats large.
- Don’t paste secrets into any chat you wouldn’t want logged. Basic hygiene, but worth repeating.
Common Issues
The assistant isn’t using my Firecrawl data. Confirm you’re signed in as a member and the connector is actually linked — the free tier doesn’t include connectors. If you’re on free, use the paste method from the free-tier section above.
My scraped content looks garbled. That’s usually a Firecrawl-side scrape issue, not a HotBot one. Re-scrape the page and confirm you’re getting clean markdown before bringing it into HotBot.
Responses feel shallow on complex problems. You’re probably on a fast tier for a deep-reasoning task. Bump up to Chat Plus or Chat Pro, and match the model to the job.
I hit a Firecrawl rate limit. Space out your crawls and respect target-site limits. Hammering sites is the most common scraping mistake, and it’ll get you throttled.
What’s Next
Once Firecrawl and HotBot are talking, try chaining the workflows. Scrape a vendor’s API docs, review your integration PR against them, generate the changelog, and draft the incident-response runbook — all in connected sessions without leaving HotBot.
From there, experiment with model switching. Run the same prompt through HotBot Chat, then a third-party model from the model catalog, and see which one fits your stack. One subscription across 800+ models means you stop guessing and start comparing.
Ready to set it up? Sign in or subscribe and connect Firecrawl from inside HotBot.
FAQ
Do I need to be a member to connect Firecrawl?
Yes. Connectors are members-only — you must be signed in with a subscription to link Firecrawl so HotBot works with your data directly. The free tier still lets you paste Firecrawl’s markdown output into a chat manually.
Is HotBot an official Firecrawl product?
No. HotBot is an independent AI chat service and is not affiliated with or endorsed by Firecrawl. Third-party names like Firecrawl simply identify the tools and models you can use within HotBot.
Which HotBot model should I use for code review?
Use HotBot Chat for fast everyday reviews, HotBot Chat Plus for step-by-step reasoning on tricky changes, and HotBot Chat Pro for deep architectural reviews that need its 1M-token context and vision. Match the tier to the complexity.
What does it cost?
HotBot has a free tier, or you can subscribe for $7.95/week or $39.95/quarter — one subscription covering 800+ models plus HotBot’s own engines. Check the pricing page for details.
Can I use third-party models with my connected Firecrawl data?
Yes. Connect Firecrawl once, then switch between HotBot’s own engines and 800+ third-party models to find the best fit for SQL, code, or docs without re-connecting anything.
