We extract musical instrument listings, historical transaction prices, seller intelligence, and condition metadata from Reverb. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Active Listings objects from reverb.com. All fields typed and schema-versioned.
"listing_id": "8472910", "title": "Fender Stratocaster 1979 Antigua", "brand": "Fender", "condition": "Very Good", "price": 2800.0, "currency": "USD", "accepts_offers": true
| # | listing_id | title | brand | model | year | finish |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Price Guide Data objects from reverb.com. All fields typed and schema-versioned.
"transaction_id": "tx_99281", "brand": "Moog", "model": "Sub 37", "condition": "Excellent", "sold_price": 1150.0, "date_sold": "2023-11-04", "days_on_market": 14
| # | transaction_id | listing_id | brand | model | year | condition |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Seller Shop Data objects from reverb.com. All fields typed and schema-versioned.
"shop_id": "sh_4482", "shop_name": "Chicago Music Exchange", "total_sales": 145020, "rating_pct": 99.8, "review_count": 48291, "preferred_seller": true, "quick_shipper": true
| # | shop_id | shop_name | location | joined_date | total_sales | rating_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Shipping Location objects from reverb.com. All fields typed and schema-versioned.
"listing_id": "8472910", "shop_location_city": "London", "ships_to": "Worldwide", "shipping_rate_domestic": 0.0, "shipping_rate_international": 150.0, "local_pickup_available": true
| # | listing_id | shop_location_city | shop_location_country | ships_to | shipping_rate_domestic | shipping_rate_international |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from reverb.com. All fields typed and schema-versioned.
"keyword": "analog synthesizer", "position": 3, "listing_id": "992831", "is_bumped": true, "price": 850.0, "discount_pct": 10, "scraped_at": "2023-11-05T10:00:00Z"
| # | keyword | category | page_num | position | listing_id | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Reverb scraper handles every layer of the platform: storefront listings, historical Price Guide data, seller intelligence, and condition metadata, with Cloudflare circumvention built in.
Capture brand, model, year, finish, condition rating, and detailed descriptions for every gear listing.
Extract historical transaction data from Reverb Price Guide to map depreciation curves and market value.
Track asking price, accepted offer status, shipping costs, and price drops over time.
Monitor shop inventory levels, total sales volumes, review counts, and Preferred Seller status.
Identify promoted listings and track ad spend visibility across specific categories and search terms.
Normalise Reverb condition grades like Mint, Excellent, and Non-Functioning for structured analysis.
Extract shipping zones, local pickup availability, and currency-converted pricing across international shops.
Parse unstructured descriptions to extract serial numbers, modification history, and original part verification.
Run daily pipelines that only emit new listings, sold items, or price changes to minimise storage overhead.
Brief in. Clean data out.
Provide category URLs, keyword sets, or shop IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and GraphQL parsing for reverb.com.
Schema validation, null-rate checks, price-outlier detection, and sample payloads before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Reverb relies on Cloudflare and complex GraphQL endpoints. Here is how we stay resilient.
Reverb uses Cloudflare to block automated traffic. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass rate limits.
Reverb relies heavily on internal GraphQL endpoints for listings and search. We intercept and parse these JSON payloads directly, resulting in cleaner data and faster extraction than DOM parsing.
Historical sales data in the Price Guide is deeply paginated. We handle cursor-based GraphQL pagination and session state to extract complete, multi-year transaction histories.
Shipping costs vary by IP location. We route requests through region-specific proxies to capture accurate domestic and international shipping rates for every listing.
Reverb listings change status quickly. Our high-frequency crawlers detect when an item transitions from active to sold, or when an offer is accepted, capturing the final state.
Insurers and appraisers use historical Reverb Price Guide data to calculate replacement values for vintage instruments.
Dealers monitor underpriced listings and local pickup opportunities for immediate purchase and resale.
Large music retailers track competitor shop inventory, pricing strategies, and sales velocity.
Audio brands audit Reverb to detect unauthorised dealers selling new B-stock or violating Minimum Advertised Price policies.
Hedge funds and market analysts track secondary market prices for high-end audio gear as alternative asset indicators.
Marketplaces use structured Reverb transaction data to train automated pricing recommendation engines.
"Reverb holds the definitive historical ledger of musical instrument transactions, but extracting structured pricing curves requires navigating deep GraphQL pagination and strict rate limits."
Building a reliable Reverb scraper means handling Cloudflare protections, reverse-engineering undocumented GraphQL APIs, and normalising highly unstructured vintage gear descriptions. DataFlirt manages the proxy rotation, session handling, and schema validation so your data science team can focus on pricing models, not broken selectors.
Everything supported by our reverb.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies across regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About reverb.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Reverb is generally permissible under applicable law. DataFlirt targets only public, non-authenticated listings, pricing, and shop data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to bypass Cloudflare and strict rate limits.
Yes. We extract historical transaction data from the Reverb Price Guide, mapping sold prices, condition ratings, and transaction dates for specific models.
Yes. We use region-specific proxies to capture accurate domestic and international shipping rates based on the buyer location.
Real-time streaming pipelines achieve sub-60-minute latency for new listings. Full category refreshes at daily cadence complete within a 4-8 hour window.
Our smallest packages start at a defined category or brand list with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off Price Guide export or a continuous inventory feed across thousands of shops, we scope, build, and operate the pipeline. Tell us what you need.
