Give your AI agents the whole web, for a fraction of the tokens.
- Browserbase for browser sessions ·
$20/mo - Firecrawl for scraping and search ·
$19/mo - TinyFish for web agents ·
$0.016 per step - Apify for ready-made scrapers ·
$19/mo - Headroom for shrinking context ·
one more library to run
CrawlGenie lets AI agents use the whole web for a fraction of the tokens: a browser, search, document reading, ready-made agents, monitoring and memory behind one key. It hands your model only what answers, and every call comes with a count of the tokens you saved.
Your AI uses websites like you do, with 95% fewer tokens.
Download this month’s invoices from our supplier portalYour own agent asks CrawlGenie to
download this month’s invoices from the supplier portal
After every click, your AI re-reads the whole page.
It reads a short list of what it can click, then only what changed.
measured3 real browser tasks with 18 page views: 2,096,615 tokens as HTML, 107,850 with CrawlGenie.
Answers from the live web, with sources, without reading every page in full.
How much does the Browserbase Developer plan cost per month?Your own agent asks CrawlGenie to
find what the Browserbase Developer plan costs, with sources
Your AI reads the three pages it found from top to bottom.
It gets the sentences that answer, each with its source.
measured3 questions, 3 pages each: 13,573 tokens in full, 1,854 returned. Oct 16, 2026.
A 152-page report, down to the passages you asked about.
What does Berkshire Hathaway’s 2023 annual report say about buybacks?Your own agent asks CrawlGenie to
find what the 2023 annual report says about buybacks
Your AI reads all 152 pages to find one answer.
It reads the passages that answer, with their page numbers.
measuredThe whole report is 140,355 tokens; the passages that answer are 1,277.
Ready-made agents hand your AI a table, not three web pages.
Collect the plans and prices from these three pricing pagesYour own agent asks CrawlGenie to
collect plans and prices from three pricing pages
Your AI reads three pricing pages in full, every time.
A ready-made agent reads the pages as code and returns just the table.
measured3 pricing pages: 9,071 tokens in full, 1,229 for the table. Oct 16, 2026.
Know when a competitor changes a page, without your AI re-reading it.
Tell me when one of these pricing pages changesYour own agent asks CrawlGenie to
watch three pricing pages and report what changed
Your AI re-reads every page, every time, to spot a change.
you hear only when one moves
CrawlGenie checks the pages and hands over only what changed.
measured3 pricing pages: 9,071 tokens to re-read, 12 for the change summary. Oct 16, 2026.
Everything your agents read, ready to ask again for a fraction of the tokens.
From the pages we saved, how many credits does Firecrawl Hobby include?Your own agent asks CrawlGenie to
answer from saved pages: credits in Firecrawl Hobby
Your AI opens and reads the pages again.
It answers from what you already saved, with the source.
measured3 saved pages: 9,071 tokens in full, 523 for the passages that answer. Oct 16, 2026.
Costs are what your AI pays its model to read, at $2 per million input tokens (Claude Sonnet 5.5 and GPT-6.1 Sol), per 1,000 runs. Every figure is measured on live sites with the models’ own tokenizer.
One platform instead of five tools, and the token bill that comes with them.
Teams stitch together a browser service, a scraping API, an agent runner, a scraper marketplace and a compression layer, then still pay their model to read most of what those tools return. CrawlGenie does the whole job behind one key, and shows the tokens it saved on every call.
See the feature-by-feature comparison yes partial no
| Capability | TinyFish | Firecrawl | Browserbase | Apify | Headroom | CrawlGenie |
|---|---|---|---|---|---|---|
| Token savings on every call | no | no | no | no | compresses results after they arrive | yes, per call and per team |
| Only the passages that answer a question | clean page content | model-assisted formats | clean page content | no | works on what other tools fetched | yes, deterministic, with the token savings |
| Goal in, structured JSON out, in batches | 100 per batch | agent endpoint | via Stagehand | via actors | no | 200 per batch, your model key |
| Ready-made agents with no model key | needs a model | needs a model | no | paid per actor | no | 61 included |
| Documents: PDF with OCR, Office, Google Docs, YouTube captions | web pages only | PDF and Office | no | per actor | no | all of them |
| Monitoring with trends and weekly reports | page and topic monitors | alerts | no | schedules | no | any agent, trends, 7-day reports |
| Memory: ask what was already read | no | hosted indexes | no | datasets | no | your own collections |
| Approvals before pay, send or delete | no | no | no | no | no | yes, enforced server-side |
| Works inside ChatGPT and Claude | plugins, MCP | plugin, MCP | MCP | MCP | MCP server, local proxy | connector, savings per connector |
| Signed agent identity (Web Bot Auth) | not published | not published | yes | not published | no | yes, your own key |
| Private deployment for enterprises | VPC | partial | enterprise plans | no | self-host, Enterprise VPC | yes, enterprise plan |
| Shrink content you already have (logs, JSON, tool output) | no | no | no | no | yes, reversible | Compress, 85–89% measured |
| How you pay | per agent step, per browser minute, prepaid wallet | credits per page | per browser-hour, plus proxies | per actor run, plus platform usage | open source free; Enterprise on request | flat plans, no per-page credits, no token markup |
Facts from each company's public pages and docs, 13–14 October 2026. Prices in USD. Tell us if something changed: hello@crawlgenie.com.
Where does the token bill come from, and what changes?
Hover or tap a row to see the before and after. Numbers are measured on live sites with the real tokenizer.
Tools paste the whole page into the chat. A long article costs 73,964 tokens to find two sentences.
Every step re-reads the page as HTML or a full tree; retries after a bot wall burn more.
Dropped into a chat, the whole file is parsed and billed, every time you ask.
People and agents re-read the same job boards, price lists and filings daily.
Measured, not promised
The same tokenizer the models use, on live sites, for every customer. Your own numbers are on the Token savings page, per API, MCP, dashboard and connector.
Source: CrawlGenie benchmarks on live sites, October 2026, counted with the o200k tokenizer that current OpenAI models use.
What is in CrawlGenie, in plain words
Use one product or all six behind the same key, the same ChatGPT or Claude connector and the same dashboard. Every call comes with a count of the tokens it read and the tokens it avoided.
Across all six: a token savings on every call (tokens kept and avoided, per team and per tool), connectors for ChatGPT, Claude, Cursor and Codex, and a signed identity websites can verify.
Access the web behind the login.
Your agent signs in with your own logins, including one-time codes, does the work on the live site, and brings back a result your software can use. It records every step, and anything that pays, sends or deletes waits for a person.
Verify it’s you
Enter the 6-digit code from your authenticator app.
{{totp:acme}} · the seed never leaves the server| Invoice | Date | Amount | Status | |
|---|---|---|---|---|
| INV-2291 | Oct 3 | $4,120.00 | Paid | Download PDF |
| INV-2297 | Oct 9 | $980.00 | Due | Download PDF |
| INV-2304 | Oct 14 | $2,375.00 | Due | Download PDF |
How do I start? Three steps, no code.
Most people are done in ten minutes. Developers get an API and SDKs on top.
Get your own trial address, add it to Claude or ChatGPT as a connector and ask. Every answer can tell you the tokens it saved. No account, no card.
Create a workspace
Free to start. Your pages, documents and results stay in your workspace; teams that need it get a private deployment.
Connect what you already use
In ChatGPT or Claude, add CrawlGenie as a connector in Settings. In Cursor, Codex or Claude Code, paste one line. For your own agents, use the API or SDK.
Ask, in your own words
"Read this PDF and tell me the refund policy." "Watch Greenhouse for new PM roles." "Compare these three pricing pages." The answer comes back with the tokens it saved.
Which problems does it solve, by role?
Teams running LLM apps
Every web or document call your product makes goes through one layer that returns passages, not pages. Savings are metered per surface so finance sees the line move.
Cut the bill →Agent builders
MCP, OpenAI/Anthropic/Gemini tool schemas, TypeScript and Python SDKs, a skill file the model reads first, token budgets per call.
Integrations →Sales and marketing
Lead enrichment from a domain list, competitor pricing watches, brand mentions anywhere, SEO + AIO reports on every page you ship.
Try the free report →Recruiting
Greenhouse, Lever, Ashby and job boards as watches: one CSV of new roles each morning, zero model tokens.
Recruiting use cases →Finance, legal, research
SEC filings, Federal Register, court opinions, arXiv and PubMed; 200-page PDFs answered from the right pages; PII redacted before a model sees it.
Analyst use cases →Operations and support
Supplier portals with logins become routines; approvals gate anything that pays; the help-centre crawl answers tickets from passages.
Routines and approvals →What does it cost?
Free to start. Flat monthly plans with no per-page credits, no browser-hour meter and no markup on your model's tokens. Private deployment for enterprises.
Questions people ask
What is a browser for AI agents?
A service that runs real browsers and returns what a model can act on: compact ranked snapshots instead of HTML, the passages that answer a question instead of whole pages, rows instead of browsing. CrawlGenie adds documents, agents and memory on the same key; the model you use is your choice.
Do I still need Browserbase, Firecrawl, TinyFish or Apify alongside CrawlGenie?
No. CrawlGenie covers what those four sell separately: browser sessions with approvals (Browserbase), scrape, crawl and search as passages (Firecrawl), goal agents in batches (TinyFish) and 61 ready-made agents at zero model tokens (Apify), plus monitoring and memory. One key, one flat plan, and the token savings on every call that none of them offer.
How much does it cut LLM token costs?
On live sites, question-focused reading sends 2–10% of a long page to the model (96% saved across 8 long pages); whole browsing tasks measured 19× cheaper than raw HTML; scheduled agents cost zero model tokens. Your own numbers are on the Token savings page, per surface, counted with the real tokenizer.
Can we run it on our own infrastructure?
Yes, under the enterprise plan: the same product deployed privately in your cloud or on-premises, with SSO, audit exports and support. Most teams start on the hosted plans.
Does it bypass CAPTCHAs or bot protection?
No. It detects challenges and stops so no tokens are wasted on retries, lets a person take over, and can sign its requests with your own key (Web Bot Auth) so supporting sites can recognise your agent. Sites that need a residential proxy are marked honestly in the extractor library.
How are the token numbers measured?
With the same tokenizer the models use, on live sites, per observation and per whole task. The method is described on the Token savings page and the dashboard shows your own numbers per surface: API, MCP, dashboard, built-in agents and each ChatGPT or Claude connector.
Which models and frameworks work with it?
Any. ChatGPT and claude.ai through an OAuth connector; Claude Code, Cursor, Codex and Gemini CLI through MCP; OpenAI, Anthropic and Gemini SDKs through tool schemas; LangChain, Vercel AI SDK, n8n and Zapier through REST. Agents use your model key or a workspace key; there is no model gateway and no markup on tokens.
Can it read PDFs, scanned documents, Google Docs and YouTube?
Yes: PDF text layers and OCR for scans, DOCX, XLSX, PPTX, ODT, RTF, images, Google Docs, Sheets and Slides share links, and YouTube caption tracks. Audio and video files are not transcribed because there is no speech model on the server.
Spend tokens on answers, not on pages.
Create a workspace in a minute. Connect ChatGPT, Claude, Cursor or your own agents. Every answer comes with the token savings.