diff --git a/surfsense_backend/app/proprietary/platforms/walmart/README.md b/surfsense_backend/app/proprietary/platforms/walmart/README.md
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+# Walmart Scraper
+
+Two verbs read Walmart's public, anonymous pages: `walmart.scrape` (products +
+search/category/browse listings, per-product billing) and `walmart.reviews`
+(deep paginated reviews, per-review billing).
+
+## Scope
+
+The scraper reads public pages available to anonymous visitors — no login, no
+account cookies. Data is extracted from the Next.js `__NEXT_DATA__` JSON blob
+embedded in each page (with an `__APP_DATA__` fallback), not from the rendered
+DOM, because Walmart obfuscates CSS classes and A/B-tests layout constantly.
+
+`walmart.scrape` returns a free sample of on-page reviews (rating distribution,
+aspects, top reviews) under `reviewsSample`. `walmart.reviews` fetches the full
+review history from the public `/reviews/product/{usItemId}` page, which
+robots.txt permits (unlike `/search`).
+
+## Architecture
+
+- `schemas.py` defines the stable input, product, review, and error models.
+- `url_resolver.py` classifies product (`/ip/`) vs listing (`/search`, `/cp/`,
+  `/browse/`) URLs and extracts the numeric `usItemId`.
+- `next_data.py` extracts and navigates the hidden Next.js JSON state.
+- `fetch.py` owns proxy-aware HTTP access (US-pinned), block detection, and
+  retries.
+- `parsers.py` contains pure, defensive JSON parsers.
+- `scraper.py` coordinates discovery, enrichment, pagination, concurrency,
+  limits, and in-stream error items.
+
+## Anti-bot
+
+Walmart runs Akamai (edge/TLS) + PerimeterX/HUMAN (behavioral JS). Requests go
+through US residential proxies with TLS-impersonated headers; blocked responses
+(body markers, `412`/`429`/`503`, or the `200`-OK CAPTCHA body) rotate to a
+fresh proxy exit.
+
+Known ceilings and upgrade paths (see `fetch.py` / `scraper.py` `ponytail:`
+notes): reviews page at 10/page; search capped at Walmart's 25-page limit;
+session warming (seed `_px3`/`_pxhd` on a sticky exit) is the next lever if
+block rates on the SSR pages climb, and the `/orchestra/*` GraphQL API is
+deliberately avoided (rotating persisted-query hashes make it brittle).
+
+## Verification
+
+Offline fixtures cover the parsers and both flows:
+
+    uv run pytest tests/unit/platforms/walmart/
+
+A manual live check (requires network + residential proxy):
+
+    uv run python scripts/e2e_walmart_scraper.py
diff --git a/surfsense_backend/app/proprietary/platforms/walmart/__init__.py b/surfsense_backend/app/proprietary/platforms/walmart/__init__.py
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+"""Platform-native Walmart scraper (products, listings, reviews)."""
+
+from .schemas import (
+    ErrorItem,
+    ProductItem,
+    ReviewItem,
+    WalmartReviewsInput,
+    WalmartScrapeInput,
+)
+from .scraper import (
+    iter_products,
+    iter_reviews,
+    scrape_products,
+    scrape_reviews,
+)
+
+__all__ = [
+    "ErrorItem",
+    "ProductItem",
+    "ReviewItem",
+    "WalmartReviewsInput",
+    "WalmartScrapeInput",
+    "iter_products",
+    "iter_reviews",
+    "scrape_products",
+    "scrape_reviews",
+]
diff --git a/surfsense_backend/app/proprietary/platforms/walmart/scraper.py b/surfsense_backend/app/proprietary/platforms/walmart/scraper.py
new file mode 100644
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+++ b/surfsense_backend/app/proprietary/platforms/walmart/scraper.py
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+"""Orchestrate public Walmart product discovery, enrichment, and reviews.
+
+Two streaming cores: :func:`iter_products` dispatches each start URL to a product
+or listing flow, and :func:`iter_reviews` paginates the public reviews page per
+item id. Network and parsing concerns stay isolated in their own modules so
+markup changes and retry policy can be tested independently.
+
+The failure model is in-stream error items (dicts with an ``error`` key), never
+exceptions — identical to the Amazon scraper.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncIterator
+from typing import Any
+from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
+
+from .fetch import fetch_page, gather_bounded
+from .next_data import extract_next_data
+from .parsers import parse_listing_page, parse_product, parse_reviews_page
+from .schemas import (
+    ErrorItem,
+    ProductItem,
+    ReviewItem,
+    WalmartReviewsInput,
+    WalmartScrapeInput,
+)
+from .url_resolver import ResolvedUrl, extract_item_id, resolve_url
+
+__all__ = ["iter_products", "iter_reviews", "scrape_products", "scrape_reviews"]
+
+_DETAIL_CONCURRENCY = 6
+_SEARCH_PAGE_LIMIT = 25  # Walmart caps result pagination at 25 pages.
+_REVIEWS_PAGE_LIMIT = 500  # 10 reviews/page → 5000-review safety ceiling.
+_DEFAULT_ITEMS_PER_START_URL = 40
+
+# Friendly sort → Walmart's ``sort`` query value on the reviews page.
+_REVIEW_SORT = {
+    "most-recent": "submission-desc",
+    "most-helpful": "helpful",
+    "rating-high": "rating-desc",
+    "rating-low": "rating-asc",
+}
+
+
+def _error(
+    code: str, description: str, *, input_url: str | None, url: str | None = None
+) -> dict[str, Any]:
+    return ErrorItem(
+        error=code,
+        errorDescription=description,
+        input=input_url,
+        url=url or input_url,
+    ).model_dump()
+
+
+def _page_url(url: str, page: int) -> str:
+    parsed = urlparse(url)
+    query = dict(parse_qsl(parsed.query, keep_blank_values=True))
+    query["page"] = str(page)
+    return urlunparse(parsed._replace(query=urlencode(query)))
+
+
+def _product_url(item_id: str) -> str:
+    return f"https://www.walmart.com/ip/{item_id}"
+
+
+def _reviews_url(item_id: str, page: int, sort: str) -> str:
+    query = urlencode(
+        {"page": page, "sort": sort, "entryPoint": "viewAllReviewsBottom"}
+    )
+    return f"https://www.walmart.com/reviews/product/{item_id}?{query}"
+
+
+# --------------------------------------------------------------------------- #
+# Product / listing flows.                                                    #
+# --------------------------------------------------------------------------- #
+
+
+async def _product_flow(
+    resolved: ResolvedUrl, input_model: WalmartScrapeInput
+) -> AsyncIterator[dict[str, Any]]:
+    """Fetch and parse one product detail page."""
+    url = _product_url(resolved.item_id) if resolved.item_id else resolved.url
+    response = await fetch_page(url, country=input_model.country)
+    if response is None:
+        yield _error(
+            "product_not_found",
+            "The product page could not be loaded after retrying proxy exits.",
+            input_url=resolved.url,
+        )
+        return
+    if response.status in {404, 410}:
+        yield _error(
+            "product_not_found",
+            "The product page was not found.",
+            input_url=resolved.url,
+        )
+        return
+
+    next_data = extract_next_data(response.html)
+    fields = (
+        parse_product(
+            next_data,
+            url=response.url,
+            include_reviews_sample=input_model.includeReviewsSample,
+        )
+        if next_data
+        else {}
+    )
+    if not fields.get("name"):
+        yield _error(
+            "product_not_found",
+            "The response did not contain a recognizable product.",
+            input_url=resolved.url,
+            url=response.url,
+        )
+        return
+    fields["input"] = resolved.url
+    yield ProductItem(**fields).to_output()
+
+
+async def _listing_flow(
+    resolved: ResolvedUrl, input_model: WalmartScrapeInput
+) -> AsyncIterator[dict[str, Any]]:
+    """Page through a search/category/browse listing and optionally enrich."""
+    cap = (
+        input_model.maxItemsPerStartUrl
+        if input_model.maxItemsPerStartUrl is not None
+        else _DEFAULT_ITEMS_PER_START_URL
+    )
+    max_pages = min(input_model.maxSearchPagesPerStartUrl, _SEARCH_PAGE_LIMIT)
+    seen: set[str] = set()
+    emitted = 0
+    for page in range(1, max_pages + 1):
+        response = await fetch_page(
+            _page_url(resolved.url, page), country=input_model.country
+        )
+        next_data = extract_next_data(response.html) if response else None
+        cards = parse_listing_page(next_data) if next_data else []
+        cards = [c for c in cards if c["usItemId"] not in seen]
+        for card in cards:
+            seen.add(card["usItemId"])
+        if not cards:
+            if page == 1:
+                yield _error(
+                    "no_results_found",
+                    "The listing page did not contain any products.",
+                    input_url=resolved.url,
+                )
+            return
+        cards = cards[: max(0, cap - emitted)]
+
+        if not input_model.includeDetails:
+            for card in cards:
+                card["input"] = resolved.url
+                yield ProductItem(**card).to_output()
+                emitted += 1
+        else:
+
+            async def load_card(card: dict[str, Any]) -> list[dict[str, Any]]:
+                product = resolve_url(card["url"]) if card.get("url") else None
+                if product is None or product.item_id is None:
+                    card["input"] = resolved.url
+                    return [ProductItem(**card).to_output()]
+                return [item async for item in _product_flow(product, input_model)]
+
+            for batch in await gather_bounded(
+                [lambda card=card: load_card(card) for card in cards],
+                concurrency=_DETAIL_CONCURRENCY,
+            ):
+                for item in batch:
+                    if "error" not in item and emitted >= cap:
+                        return
+                    yield item
+                    if "error" not in item:
+                        emitted += 1
+        if emitted >= cap:
+            return
+
+
+_FLOWS = {"product": _product_flow, "listing": _listing_flow}
+
+
+async def iter_products(
+    input_model: WalmartScrapeInput,
+) -> AsyncIterator[dict[str, Any]]:
+    """Yield product items for every start URL.
+
+    Each URL is classified and dispatched to its per-kind flow. An unrecognized
+    URL yields an ``invalid_url`` error item.
+    """
+    for url in input_model.startUrls:
+        resolved = resolve_url(url)
+        if resolved is None:
+            yield _error(
+                "invalid_url",
+                "Start URL was malformed or not a recognized Walmart URL.",
+                input_url=url,
+            )
+            continue
+        async for item in _FLOWS[resolved.kind](resolved, input_model):
+            yield item
+
+
+async def scrape_products(
+    input_model: WalmartScrapeInput, *, limit: int | None = None
+) -> list[dict[str, Any]]:
+    """Collect :func:`iter_products` into a list, honoring an optional ``limit``."""
+    from app.capabilities.core.progress import emit_progress
+
+    results: list[dict[str, Any]] = []
+    async for item in iter_products(input_model):
+        results.append(item)
+        emit_progress("scraping", current=len(results), total=limit, unit="product")
+        if limit is not None and len(results) >= limit:
+            break
+    return results
+
+
+# --------------------------------------------------------------------------- #
+# Reviews flow (deep pagination).                                             #
+# --------------------------------------------------------------------------- #
+
+
+async def _reviews_flow(
+    item_id: str, input_model: WalmartReviewsInput
+) -> AsyncIterator[dict[str, Any]]:
+    """Paginate the public reviews page until empty or ``maxReviews`` reached.
+
+    Walmart does not expose a reliable total up front, so the loop stops on the
+    first empty page rather than trusting a page count.
+    """
+    sort = _REVIEW_SORT[input_model.sort]
+    emitted = 0
+    for page in range(1, _REVIEWS_PAGE_LIMIT + 1):
+        response = await fetch_page(
+            _reviews_url(item_id, page, sort), country=input_model.country
+        )
+        next_data = extract_next_data(response.html) if response else None
+        reviews = parse_reviews_page(next_data) if next_data else []
+        if not reviews:
+            if page == 1 and emitted == 0:
+                yield _error(
+                    "reviews_not_found",
+                    "The product has no public reviews or could not be loaded.",
+                    input_url=item_id,
+                )
+            return
+        for raw in reviews:
+            raw["usItemId"] = item_id
+            raw["input"] = item_id
+            yield ReviewItem(**raw).to_output()
+            emitted += 1
+            if emitted >= input_model.maxReviews:
+                return
+
+
+async def iter_reviews(
+    input_model: WalmartReviewsInput,
+) -> AsyncIterator[dict[str, Any]]:
+    """Yield review items for every requested item id (or resolvable URL)."""
+    for raw_id in input_model.itemIds:
+        item_id = extract_item_id(raw_id)
+        if item_id is None:
+            yield _error(
+                "invalid_url",
+                "Could not extract a Walmart item id from the input.",
+                input_url=raw_id,
+            )
+            continue
+        async for item in _reviews_flow(item_id, input_model):
+            yield item
+
+
+async def scrape_reviews(
+    input_model: WalmartReviewsInput, *, limit: int | None = None
+) -> list[dict[str, Any]]:
+    """Collect :func:`iter_reviews` into a list, honoring an optional ``limit``."""
+    from app.capabilities.core.progress import emit_progress
+
+    results: list[dict[str, Any]] = []
+    async for item in iter_reviews(input_model):
+        results.append(item)
+        emit_progress("scraping", current=len(results), total=limit, unit="review")
+        if limit is not None and len(results) >= limit:
+            break
+    return results