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For a list of domains, the most practical Wappalyzer-style Python workflow is to send normalized URLs to a hosted technology lookup API, then save each response with its status and timestamp. Wappalyzer’s documented API supports batches of up to 10 URLs for standard lookups; recursive live scans are asynchronous. BuiltWith also documents technology lookup and bulk API options. A local detector may suit custom, bounded jobs, but the sources here do not establish a maintained Python package as a drop-in replacement.
Choose the lookup route that fits the job
“Tech stack” results are detections based on signals visible to the service, not a guaranteed inventory of every component behind a website. Treat them as leads, and manually validate findings before using them for high-stakes decisions. The available provider documentation describes features and formats, not comparative accuracy, detection recall, or coverage guarantees.
| Route | Best fit | What to compare |
|---|---|---|
| Wappalyzer Technology Lookup API | Python or data workflows that need hosted website lookups | Cached versus live results, recursive depth, batch rules, callback handling, credit use, and plan eligibility |
| BuiltWith Domain/Bulk API | Hosted technology data or bulk/file-oriented workflows | Available output formats, domain-volume fit, current pricing, freshness, and data coverage |
| Self-managed Python detection | Local control or customization for a bounded list | Fingerprint source and update cadence, JavaScript rendering needs, maintenance, access policies, and validation quality |
| Browser extension spot checks | Manual verification of a few sites | Convenience and reproducibility; not a substitute for a bulk Python workflow |
Wappalyzer lists extensions for Chrome, Firefox, Edge, and Safari that reveal technologies on the site being visited. They can help with manual spot checks, but do not process a domain list as a batch job. Wappalyzer’s lookup page describes its lookup offering, while BuiltWith’s API overview describes its technology data services.
What Wappalyzer’s lookup API allows
Wappalyzer documents the Technology Lookup API as an HTTPS, JSON-returning service authenticated with an API key in the x-api-key request header. Its API overview includes a Python example tab; confirm the current request syntax in the lookup API reference before implementing a client. The docs state that API access requires a Business plan.
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| Mode or limit | Documented behavior |
|---|---|
| Standard lookup | 1 credit per URL; batches of 1–10 URLs per request; up to 10 requests per second |
Batching with recursive=false |
Multiple URLs are not supported; shallow scans are single-URL operations |
| Cached lookup | Described by Wappalyzer as faster and more complete than live analysis |
live=true |
Requests real-time analysis |
live=true and recursive=true |
5 credits per URL; asynchronous crawl; callback URL required for the documented recursive live workflow; crawl may take up to 15 minutes |
Shallow scan with recursive=false |
Documented request timeout is 30 seconds; use this when an immediate result is needed without a callback |
These are product limits and credit terms documented by Wappalyzer, not independent performance measurements. Plans, limits, and billing can change, so confirm the current reference and account terms before running a large job. The initial response to a recursive request may indicate that crawling is underway before technology results are ready; a client should not assume that all detections arrive in the first response.
Build a reliable Python batch pipeline
The work is more than issuing HTTP requests. Normalize inputs, respect the selected endpoint’s batch rules, protect credentials, and preserve the difference between a successful empty result and a failed request. The following is an implementation outline, not a tested code sample; use the provider’s current API reference for exact parameters and response fields.
- Normalize and validate input. Read one domain or URL per record, trim whitespace, and ensure each value is a usable URL in the form the provider accepts. Keep the original input alongside the normalized value so you can trace a result back to its source.
- Choose scan behavior before batching. For Wappalyzer, standard lookup requests can contain 1–10 URLs. Do not send multiple URLs with
recursive=false; that mode is a single-URL shallow scan. Decide whether cached results meet the freshness requirement or whether live analysis is necessary. - Keep API credentials out of code and version control. Send the Wappalyzer key in the documented
x-api-keyheader, and load secrets from an environment variable or a secret manager rather than embedding them in a script or notebook. - Control request rate and concurrency. Wappalyzer documents a limit of 10 requests per second. Keep the client within that provider limit, use bounded concurrency, and add retry/backoff for transient errors. The documentation does not establish a safe higher concurrency setting or retry schedule, so do not infer one.
- Handle asynchronous scans explicitly. For recursive live scans, provide a callback endpoint that can receive later results, or implement a deliberate later-retry workflow. Do not treat an initial “crawl in progress” response as a completed detection report.
- Parse outcomes per URL. Record the provider response or callback against the corresponding input. Distinguish detected technologies, a successful response with no detections, invalid input, authentication or quota errors, and transient request failures.
- Save structured output and provenance. Store the normalized URL, detection list, scan mode, status, timestamp, and provider. Retain enough request metadata to explain later whether a result was cached, live, shallow, or recursive.
A practical output format is one row per URL with a separate serialized detection field, or a normalized table with one row per URL-and-technology pair. Either way, preserve request status independently from the technology list: an empty list should not silently mean that a lookup failed.
Compare providers before processing a large list
BuiltWith’s official API materials describe technology lookups, bulk API access, and XML, JSON, CSV, and XLSX formats. Those options may suit workflows built around files or existing data systems; verify the current endpoint and supported workflow in its API documentation.
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Do not assume that similar labels mean equivalent service. Before committing a large domain list, compare the provider’s current pricing at your volume, freshness options, batch or file limits, response format, and how it represents missing or delayed results. The available documentation does not establish like-for-like pricing or detection accuracy for Wappalyzer and BuiltWith.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When a local detector makes sense
Running detection locally can provide control over fingerprints and processing, but it shifts maintenance and validation to you. A detector needs current rules for interpreting site signals; it may also need to render JavaScript-heavy pages to see evidence unavailable in a simple HTTP response. Check how fingerprints are maintained, what access and rate policies apply to target sites, and how you will validate detections.
The sources reviewed here do not establish a currently maintained Python library that can be recommended as a drop-in Wappalyzer replacement. For a broad managed technographic lookup, a documented hosted API is the better-supported route in this guide; choose a local implementation only when its customization or control benefits justify the upkeep.
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