3tears-scrape 0.19.1

AI-driven, self-healing web scraping: pluggable render backends + LLM-proposed extraction candidates validated and judged against real page content, never hand-rolled per-site parsers

3tears-scrape

AI-driven, self-healing web scraping. Onboarding a new site is a config addition (a ScrapeTarget row), never a hand-written parser: an LLM proposes extraction candidates, each is structurally validated against the real page, an LLM judge picks the winner by comparing candidate values against real page content, and the winning strategy is persisted as a recipe reused on every later fetch until it stops working.

pip install 3tears-scrape

What you get

• ScrapeDriver -- a pluggable, backend-agnostic render contract (RenderedPage, NavStep, NetworkCall). Eight implementations ship: NodriverSidecarDriver (real headless Chromium via an isolated HTTP sidecar -- see sidecar/), CamoufoxDriver (in-process stealth Firefox, no sidecar needed), DocumentDriver (PDF/DOCX/XLSX/CSV/TXT/MD/LaTeX via 3tears-agent-tools' parse_document), ApiDriver (stateless JSON API GET), NetworkCaptureDriver (authenticated in-session XHR capture for pages whose real data never reaches a statelessly-fetchable API), MultiDocumentDriver (a listing of links that each point at one whole document rather than a row, fetched via an injected inner driver and concatenated into one combined page, skipping documents already seen on a prior poll), ListingDetailDriver (a listing table whose rows each link to a per-record detail page, resolved row-scoped and merged back into one flat synthetic table -- detail value preferred, listing value as fallback, neither fabricated), and NodriverDownloadDriver (a document behind a real bot challenge a plain HTTP client can't pass, fetched through the sidecar's forced-download endpoint).
• Four extraction-strategy shapes (eval_loop.StrategyType) -- css selector candidates for real (or synthesized) HTML tables; regex candidates for text-block/prose pages with no table structure at all; per_document for independently-worded documents that share no template a single cached pattern could ever generalize across, which get a fresh extraction call each instead of a reused recipe; and multi_row_vision for a table whose own structure defeats text-based table extraction and needs the whole table read visually at once. All four run through the identical propose -> structurally-validate -> judge -> persist cycle; the judge only ever sees extracted values and real page content, never which mechanism produced them. Chosen explicitly per target, never auto-detected -- two superficially identical page shapes have needed opposite strategies.
• run_eval_loop / run_eval_loop_multi_row -- the self-healing cycle itself: reuse a target's existing ScrapeRecipe with zero LLM calls while it keeps validating; regenerate candidates once consecutive_validation_failures crosses a threshold, or immediately when a failure is classified as the page having genuinely changed (see challenge.py), since waiting two more polls for evidence already in hand is pure latency.
• ScrapeTarget / ScrapeRecipe / ScrapeExtraction / ScrapeTargetHealth -- the domain-agnostic persisted entities (three-tier L1/L2/L3-backed collections). ScrapeTargetHealth is per-target FETCH health, deliberately separate from the recipe: it exists for targets that never had a recipe at all, such as one walled off before it ever extracted successfully. The core never learns what a field means -- only that whatever a candidate extracts for a caller-supplied field name parses as its declared type.
• Pluggable target config -- TargetSource (StaticTargetSource / YamlTargetSource / CollectionTargetSource) and bootstrap_targets() for seeding a database-backed target collection from a git-tracked YAML file, never overwriting a row already present.
• ScrapeTool -- an ad-hoc, single-call MCP-exposed entry point: give it a URL and a field schema, get back real extracted data through the same eval loop, no target pre-configuration required.
• enrichment.py -- a secondary, separate LLM pass capturing free-form context a structured schema has no field for, kept distinct from validated structured data.
• parse_form() / build_form_post() -- deterministic, browser-free HTML form parsing and postback serialization: reduce a form to its POST target, its default "successful controls", and its submit controls, then serialize a replayable body. For the many server-rendered portals (the classic case being ASP.NET WebForms' __VIEWSTATE/__EVENTVALIDATION) that only return rows after a form POST, this replaces driving a browser with a plain HTTP round-trip. Pure and side-effect-free -- no network, no LLM, no hardcoded framework or field meaning.
• find_target_page() -- a bounded-turn research agent: give it a plain-language query ("Ohio WARN Act notices"), it searches and fetches candidate pages (WebSearch/WebFetch, capped turns), deterministically verifies the winner has real extractable structure (a table, a document link, a JSON API response -- never a browser fetch, so it never needs the sidecar running), and returns a PageFinderResult (url, a driver_backend guess, verified). Independently callable -- it never persists a ScrapeTarget or forces extraction to follow; a caller with a known URL never needs this at all.
• discover_candidates() / discover_row_candidates() -- "capture every variable on this page": the inverse of generate_candidates/generate_row_candidates, no caller-supplied field_schema required. An LLM proposes field names/types/selectors it finds on the page, each validated by the exact same validate_candidate/validate_row_candidate every schema-driven candidate already goes through -- a discovered field that doesn't structurally validate is dropped, never included. Returns a DiscoverySchemaResult whose field_schema/strategy are already shaped exactly as ScrapeTarget.field_schema/ScrapeRecipe.extraction_strategy expect. Never persists anything -- a caller decides what to do with the result.
• capture_request_shape() -- the "how" sibling to find_target_page()'s "what": drive a real browser session at a target and report back every XHR/fetch call it actually made, so an authenticated in-session API's real request shape is captured rather than guessed from remembered conventions. Deliberately does not pick "the one real call" out of the results -- a target's real data call is reliably neither the largest nor the first, so it returns everything and the caller decides.

Quickstart

from threetears.scrape.drivers.nodriver_sidecar import NodriverSidecarDriver
from threetears.scrape.eval_loop import run_eval_loop_multi_row
from threetears.scrape.collections import ScrapeRecipeCollection, ScrapeExtractionCollection

driver = NodriverSidecarDriver("http://localhost:8088")
page = await driver.render("https://example.gov/warn-notices")

extraction = await run_eval_loop_multi_row(
    "example_warn_notices", page.html, page.final_url,
    schema={"employer": str, "county": str, "affected_count": int},
    recipe_collection=ScrapeRecipeCollection(registry, config),
    extraction_collection=ScrapeExtractionCollection(registry, config),
    api_key=openrouter_api_key,
)
print(extraction.validation_status, extraction.structured_fields["records"])

The nodriver sidecar

NodriverSidecarDriver talks HTTP to a genuinely separate process/container -- this is the AGPL isolation boundary nodriver (AGPL-3.0) requires, since this package itself is MIT-licensed. Build and run it via docker buildx bake nodriver-sidecar from the repo root; the container definition, its pinned AGPL-3.0 licence, and its own contract tests all live in sidecar/. If you don't want the sidecar dependency at all, CamoufoxDriver is a fully in-process, MPL-2.0-safe alternative.

Architecture

Lifted from faidh's src/faidh/scrape/ into this package (docs/scrape-task-01-lift-core-package.md, 2026-07-15) as a directory move -- zero logic changes. faidh's WARN Act plugin is used throughout as the running example of a consumer; it remains a faidh-side module (faidh/src/faidh/intake/plugins/warn_act.py), not part of this package, and is the only place WARN-domain meaning exists.

Why this exists

WARN Act notices are published by ~24 US state labor departments in wildly inconsistent formats -- real HTML tables, PDF/DOCX/XLSX filings, prose text blocks, JSON APIs, and at least one state gated behind an authenticated search form with no stable URLs. The naive answer is "write a scraper per state." That's 24+ bespoke, brittle, un-composable parsers that all rot independently.

The actual answer: two orthogonal, config-selected axes -- how to fetch a page and how to extract from it -- cover every state as a combination of the two, with zero jurisdiction-specific code in the core.

1. Data flow: source → Postgres (faidh's WARN Act consumer, as an example)

flowchart TD
    A[ScrapeTarget config<br/>YAML seed row] --> B["ScrapeDriver.render(url, ...)"]
    B --> C[RenderedPage<br/>html, status, final_url, timing_ms, network_calls]
    C --> D{run_eval_loop /<br/>run_eval_loop_multi_row}
    D -->|reuse existing recipe| E[ScrapeRecipe]
    D -->|regenerate via LLM candidates + judge| E
    E --> F[ScrapeExtraction persisted<br/>structured_fields = records]
    F --> G["WarnActPlugin._produce()<br/>generic records → Tier-2 fields"]
    G --> H[ArbitrarySignalEntity.create<br/>signal_type=warn_act_notice]
    H --> I["PluginBase.collect()<br/>throttle-gate → _produce() → save_entity() → yield"]
    I --> J[(Postgres:<br/>faidh_arbitrary_signals)]
    I --> K["publish_arbitrary_signals()"]
    K --> L[NATS subject: arbitrary_signals]
    L --> M[Downstream: scoreboard, retrospect, ...]

    F -.-> N[(Postgres:<br/>scrape_targets / scrape_recipes /<br/>scrape_extractions / scrape_target_health -- provenance)]

Two Postgres-writing paths run in parallel, not sequentially:

• Provenance (scrape_targets, scrape_recipes, scrape_extractions, scrape_target_health) -- what was fetched, what strategy won, when, and whether the fetch itself is healthy. Domain-agnostic, lives in this package.
• Signal (faidh_arbitrary_signals) -- what faidh's forecasting actually reads. Faidh-specific, mapped by WarnActPlugin._produce(), stays in faidh.

2. The abstraction: ScrapeTarget is the entire adapter surface

Zero jurisdiction-specific Python exists anywhere in this package. In faidh's WARN Act consumer, every state is a ScrapeTarget row (faidh/src/faidh/intake/plugins/seeds/warn_act_targets.yaml), bootstrapped into the DB.

Field	Controls
url	what to fetch
driver_backend	which of the 8 ScrapeDriver implementations renders it
wait_for	CSS selector to settle on (async-loading pages)
nav_steps	ordered click/fill/wait_for/wait_ms/scroll_into_view/scroll_page/evaluate actions (search-form-gated, lazy-render, or in-page-JS-state targets)
timeout_seconds	per-target render budget
field_schema	field name → Python type, e.g. {"employer": str, "county": str}
extraction_strategy_type	"css", "regex", "per_document", or "multi_row_vision"
api_results_path / api_fragment_field	JSON traversal (api driver; also multi_document's JSON discovery mode)
link_selector	CSS selector matching the document links on a listing page (multi_document's HTML discovery mode)
multi_row	one record vs. many per page

The core (driver.py, extraction.py, eval_loop.py, collections.py) never branches on target identity -- only on ScrapeTarget fields and a caller-supplied field_schema. It doesn't know what employer means; it only enforces that whatever a candidate extracts for that field name parses as the declared type. Stated in extraction.py's own docstring: "this module never hardcodes what a field means."

3. Fetch × Extract = many combinations, not one scraper per site

flowchart LR
    subgraph Fetch["Axis 1 -- driver_backend (drivers/)"]
        D1[nodriver_sidecar<br/>headless Chromium, sidecar]
        D2[camoufox<br/>in-process stealth Firefox]
        D3[document<br/>PDF/DOCX/XLSX/CSV → parse_document]
        D4[api<br/>stateless JSON GET]
        D5[network_capture<br/>authenticated XHR capture]
        D6[multi_document<br/>link list → N whole documents<br/>combined into one page]
        D7[listing_detail<br/>listing rows + per-row detail pages<br/>merged row-scoped]
        D8[nodriver_download<br/>bot-challenged document<br/>via sidecar forced download]
    end
    subgraph Converge["Everything converges here"]
        R[RenderedPage.html<br/>real or synthetic -- identical downstream]
    end
    subgraph Extract["Axis 2 -- extraction_strategy_type"]
        S1[css<br/>real/synthesized table → LLM proposes selectors]
        S2[regex<br/>prose text block → LLM proposes named-group patterns]
        S3[per_document<br/>no shared template → fresh extraction<br/>per document, no cached recipe]
        S4[multi_row_vision<br/>table structure defeats text extraction<br/>→ vision read of the whole table]
    end
    D1 & D2 & D3 & D4 & D5 & D6 & D7 & D8 --> R
    R --> S1
    R --> S2
    R --> S3
    R --> S4
    S1 & S2 & S3 & S4 --> J[Identical propose → validate →<br/>judge → persist cycle, eval_loop.py]

In faidh's WARN Act consumer, every one of the ~24 onboarded states is one config row picking a combination from this matrix. A new driver/strategy is only written when a target needs a fetch mechanism or extraction shape that doesn't exist yet -- and each one that was written has a named real target behind it: the regex strategy (a prose listing with no table), ApiDriver (a JSON search endpoint), native CSV support, NetworkCaptureDriver (an authenticated Aura POST), MultiDocumentDriver plus per_document (one PDF letter per notice, no two worded alike), NodriverDownloadDriver (those PDFs behind a Cloudflare challenge), multi_row_vision (a born-digital PDF table that text extraction mis-split), and ListingDetailDriver (a listing whose records are genuinely split across two pages).

4. "The signal must carry the value; the model must never infer it" -- where the rule actually lives

Checkpoint	Mechanism	Proof (faidh's WARN Act consumer)
Structural validation, all-or-nothing per record	validate_candidate / validate_row_candidate (+ regex variants), extraction.py -- no coercion, no defaults, a record missing even one requested field is dropped entirely	Oklahoma: 217 real records exist, only 128 survived -- the other 89 genuinely lack a workforce-region value on the page
Schema omits what a page can't provide	field_schema per target -- never forces a value that isn't there	New York schema doesn't request affected_count/effective_date because its page has neither
Judge does semantic verification, not just structural plausibility	_judge_candidates / _judge_row_candidates, eval_loop.py -- sees real page HTML + candidate values, told structural validity is already checked, job is semantic correctness	Georgia: an earlier schema mistakenly asked for county; judge rejected the resulting hallucinated-mapping candidate rather than accepting it
Confirmed vs. best-guess survives to the signal	ScrapeExtraction.validation_status ∈ validated / needs_review / failed / blocked	WarnActPlugin._produce() logs a distinct warning for needs_review yields rather than treating them as confirmed
"We never received the page" is not "the page was wrong"	challenge.classify_failed_page, challenge.py -- asked only once extraction has already failed, and only about a page that differs from the last one that worked; a blocked verdict persists validation_status="blocked" with no records and leaves the recipe untouched	A bot wall used to increment the same failure counter a redesign does, so three polls later the recipe was discarded and an LLM round spent learning to extract data from a challenge page

Open tension, not fully resolved: a needs_review record still gets yielded as a real signal -- "surface for review, never silently drop" beats "silently withhold," by deliberate design. But nothing downstream currently filters on validation_status. The signal carries the distinction; no consumer currently reads it. A consumer treating every signal as equally trustworthy today would be wrong to. blocked widens that gap slightly: it is the one value that means no data was produced and none should have been, so a consumer that treats it as a failed extraction will over-count failures for a target that is merely walled.

5. Where new capabilities plug in

flowchart TD
    Core["This package<br/>driver.py / extraction.py / eval_loop.py / collections.py"]
    T02["find_target_page() -- shipped<br/>page_finder.py<br/>outputs a ScrapeTarget-shaped guess<br/>(URL + driver_backend, verified flag)<br/>docs/scrape-task-02-page-finder-agent.md"]
    T03["discover_candidates() / discover_row_candidates() -- shipped<br/>extraction.py<br/>schema OUT instead of schema IN<br/>docs/scrape-task-03-schema-discovery-mode.md"]
    T04["capture_request_shape() -- shipped<br/>request_shape_finder.py<br/>real captured request shapes<br/>for in-session XHR targets"]
    Core -.plain data in/out, no hidden state.-> T02
    Core -.plain data in/out, no hidden state.-> T03
    Core -.plain data in/out, no hidden state.-> T04
    T02 --> BT["bootstrap_targets()<br/>target_source.py"]
    T03 --> BT
    BT --> Core

All three front-end stages have shipped, and each landed without touching driver.py, eval_loop.py, or collections.py -- which is the point. They produce and consume the same plain data shapes (RenderedPage, FieldSchema, PageFinderResult, DiscoverySchemaResult) the core already uses, and none of them persists anything: a caller decides whether a discovered page or schema becomes a real ScrapeTarget.

Full designs live in docs/: scrape-lift-design.md (the overall shape and its decision log), scrape-task-01-lift-core-package.md (the lift itself and the AGPL isolation boundary), scrape-task-02-page-finder-agent.md, scrape-task-03-schema-discovery-mode.md, and scrape-task-04-multi-document-driver.md (the multi-document driver plus the sidecar's browser-forced-download mode).

6. Module map

packages/scrape/src/threetears/scrape/
├── driver.py                 ScrapeDriver ABC, RenderedPage, NavStep, NetworkCall
├── drivers/
│   ├── nodriver_sidecar.py   HTTP → headless Chromium sidecar (AGPL boundary)
│   ├── camoufox.py           in-process stealth Firefox (MPL-2.0-safe)
│   ├── document.py           parse_document() + document_text_to_html()
│   ├── api.py                stateless JSON GET, _resolve_path()
│   ├── network_capture.py    wraps another driver, _find_largest_record_list()
│   ├── multi_document.py     link list → N documents → one combined page, seen_urls dedup
│   ├── listing_detail.py     listing rows + per-row detail pages, merged into one table
│   └── nodriver_download.py  sidecar POST /v1/download → parse_document_bytes_to_html()
├── extraction.py             generate_candidates/generate_row_candidates (+ regex),
│                              validate_candidate/validate_row_candidate (+ regex),
│                              html_to_text(), strip_boilerplate()
├── eval_loop.py               run_eval_loop()/run_eval_loop_multi_row(),
│                              _judge_candidates()/_judge_row_candidates(),
│                              _persist_extraction(), _save_recipe()
├── challenge.py               PageVerdict, classify_failed_page() -- asks what a page that
│                              failed extraction actually is; no vendor markers
├── health.py                  ScrapeTargetHealth + collection, content_fingerprint(),
│                              record_validated_fetch(), record_classification()
├── collections.py             ScrapeTarget/ScrapeRecipe/ScrapeExtraction (BaseEntity)
│                              + BaseCollection subclasses (L1/L2/L3 via
│                              threetears.core.backends.protocol.DurableStore)
├── migrations.py               v001-v010, PACKAGE_NAME="3tears_scrape"
├── target_source.py            TargetSource ABC, YamlTargetSource, CollectionTargetSource,
│                              StaticTargetSource, bootstrap_targets()
├── page_finder.py              find_target_page() -- bounded-turn search/fetch research agent
├── request_shape_finder.py     capture_request_shape() -- real captured XHR/fetch shapes
├── forms.py                    parse_form()/build_form_post() -- browser-free postback replay
├── tool.py                     ScrapeTool -- ad-hoc single-call MCP entry point
├── enrichment.py                secondary free-form LLM notes pass (separate from structured_fields)
└── llm_retry.py                 bounded_retry_structured_call() -- shared by extraction.py + eval_loop.py

Example consumer (faidh repo, not part of this package):
faidh/src/faidh/intake/plugins/warn_act.py               WarnActPlugin -- the ONLY place WARN-domain meaning exists
faidh/src/faidh/intake/plugins/seeds/warn_act_targets.yaml   the ScrapeTarget config rows
faidh/src/faidh/intake/runner.py                          publish_observations(), publish_arbitrary_signals(), poll_scrape_targets()
faidh/src/faidh/intake/plugins/__init__.py                PluginBase.collect() -- persist-then-yield template method
faidh/src/faidh/intake/signals/arbitrary.py               ArbitrarySignalEntity/Collection -- the actual Postgres sink

Call chain, one live poll cycle (faidh's WARN Act consumer): runner.publish_observations() → WarnActPlugin.collect() (inherited PluginBase) → WarnActPlugin._produce() → resolves self._drivers[target.driver_backend] → driver.render(...) → run_eval_loop_multi_row(...) (or run_eval_loop if multi_row=False) → _run_reuse_cycle (re-runs the stored strategy; on a miss, classifies the page and routes) or _regenerate → _persist_extraction() writes scrape_extractions → back in _produce(), each record becomes an ArbitrarySignalEntity → collect()'s save_entity() writes faidh_arbitrary_signals → publish_arbitrary_signals() re-drives collect() and publishes each yielded entity to arbitrary_signals() on NATS.

License

MIT. See LICENSE. The bundled sidecar (sidecar/) wraps nodriver (AGPL-3.0) as a genuinely separate process, under its own sidecar/LICENSE.