spectus 0.2.5

AI-assisted web data extractor — paste a URL + plain-English instruction, get structured JSON or CSV. Resilient to DOM changes via semantic LLM extraction.

                          _
   ___ _ __   ___  ___ | |_ _   _ ___
  / __| '_ \ / _ \/ __|| __| | | / __|
  \__ \ |_) |  __/ (__ | |_| |_| \__ \
  |___/ .__/ \___|\___| \__|\__,_|___/
      |_|     AI-driven web extractor

spectus — paste a URL, describe what you want in plain English, get structured JSON or CSV. Resilient to DOM changes: when CSS selectors fail, falls back automatically to semantic LLM extraction over a facts bundle (structured data + visible text + anchors + label-value pairs). Same loop on any site; no per-site rules.

$ spectus extract https://news.ycombinator.com/ "Top stories: title, points, author, story_url" --output csv
title,points,author,story_url
Mercurial, 20 years and counting,70,ibobev,https://fosdem.org/...
...

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Install

pip install spectus
spectus install-browsers              # one-time Playwright Chromium download (~110 MB)
export OPENAI_API_KEY=sk-...          # Windows PowerShell:  $env:OPENAI_API_KEY="sk-..."

Requires Python 3.10+ (tested on 3.10 / 3.11 / 3.12 / 3.13). Linux / macOS / Windows.

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30-second tour

CLI

spectus extract https://example.com/products \
    "Each product: title, price, rating, link" --output json

Python (sync — works in Jupyter too)

from spectus import extract

result = extract(
    url="https://example.com/products",
    instruction="Each product: title, price, rating, link",
    openai_api_key="sk-...",          # optional; falls back to OPENAI_API_KEY env
)
print(result["records"])              # list[dict]
print(result["diagnostics"])          # strategy, quality_score, tokens, ...

Python (batched — reuses browser pool)

from spectus import SyncClient

with SyncClient.open(openai_api_key="sk-...") as client:
    r1 = client.extract(url1, "extract X, Y, Z")
    r2 = client.extract(url2, "another instruction")

Python (async — for FastAPI / aiohttp / asyncio code)

from spectus import Client

client = await Client.create(openai_api_key="sk-...")
result = await client.extract(url, instruction)
await client.close()

More patterns in EXAMPLES.md.

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Why spectus

• No selectors to maintain. You describe the data; the system finds it.
• Survives DOM changes. Semantic fallback reads page meaning, not CSS class names.
• Learns per domain. Successful extractions become templates → 3–5× faster on subsequent calls, planner LLM skipped.
• Built-in safety. SSRF gate, robots.txt cache, per-domain rate limit. No CAPTCHA solving, no auth bypass.
• Debug-friendly. Every job writes a full artifact bundle to disk: raw HTML, rendered HTML, screenshots, compact page representation, every LLM I/O, validation report.

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What you get back

extract() always returns a plain dict:

{
  "status": "success" | "partial_success" | "failed",
  "url": "...",
  "instruction": "...",
  "records": [ {...}, {...}, ... ],          # list of dicts; single dict for single-entity
  "diagnostics": {
    "strategy_used":    "semantic_extraction" | "repeated_dom_selector" | ...,
    "page_type":        "article" | "product_listing" | ...,
    "static_or_browser": "static" | "browser",
    "records_found":    int,
    "quality_score":    0.0 - 1.0,
    "field_coverage":   {field_name: 0.0-1.0},
    "missing_required": {field_name: count},
    "repair_attempts":  int,
    "template_used":    bool,
    "template_id":      uuid | null,
    "runtime_ms":       int,
    "llm_calls":        int,
    "llm_tokens_in":    int,
    "llm_tokens_out":   int,
    "warnings":         [str, ...]
  },
  "message": null | "repair hint when partial"
}

---

How it works (one paragraph)

Every request runs: URL normalize → SSRF + robots + rate-limit → parallel(intent-LLM, static-fetch + analyze) → template lookup → planner-LLM → executor → validator → repair loop (≤ 2 attempts) → resilience pass: semantic LLM extraction over a facts bundle, per-field merge with type-aware tie-breakers → save winning strategy as template → return JSON or CSV with diagnostics.

Seven extraction strategies, chosen automatically:

Strategy	When
structured_data	JSON-LD / OpenGraph / __NEXT_DATA__ / __NUXT__ present
repeated_dom_selector	Repeating containers (cards / rows / tiles) detected
single_dom_selector	Page-level data with clear DOM hooks
table_extraction	HTML tables with sensible headers
article_extraction	Long-form content (article, blog, encyclopedia)
visible_text_regex	Fallback regex over visible text
semantic_extraction	LLM reads facts bundle — no DOM dependency, survives DOM redesigns

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CLI reference

spectus extract URL "instruction"  [--browser auto|force|never] [--max-records N] [--output table|json|csv]
spectus templates                  [--status candidate|active|needs_review|deprecated] [--output table|json]
spectus migrate
spectus install-browsers
spectus version

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Configuration

Set via env var (or pass to Client.create(settings={...})).

Var	Default	Purpose
OPENAI_API_KEY	—	Required (or pass as openai_api_key= kwarg)
OPENAI_MODEL_INTENT	gpt-4o-mini	Intent parser model
OPENAI_MODEL_PLAN	gpt-4.1	Planner + semantic model
OPENAI_MODEL_REPAIR	gpt-4.1	Repair model
DB_URL	sqlite+aiosqlite:///./spectus.db	Swap to postgresql+asyncpg://... for Postgres
ARTIFACTS_DIR	./artifacts	Per-job debug bundles
BROWSER_POOL_SIZE	3	Playwright contexts
RATE_LIMIT_RPS	1.0	Per-domain token-bucket refill
ALLOW_PRIVATE_TARGETS	false	Set true only for local fixture testing
JOB_DEADLINE_SEC	180	Hard wall-time per request
LLM_INTENT_TIMEOUT_SEC	45	Intent parser timeout
LLM_PLANNER_TIMEOUT_SEC	60	Planner timeout
LLM_REPAIR_TIMEOUT_SEC	60	Repair timeout

GPT-5 / o-series support: pass OPENAI_MODEL_*=gpt-5-nano and bump timeouts. Client auto-uses max_completion_tokens + reasoning_effort=low for those models.

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Compliance + safety (built-in)

• SSRF: blocks private / loopback / link-local / reserved IPs before any fetch.
• Robots.txt: 1h-TTL cache, fail-open on 5xx.
• Per-domain rate-limit token bucket.
• Allowed selector attributes: text, href, src, srcset, alt, title, class, id, value, content, datetime, name, type, role, placeholder, download, rel, property, lang, data-*, aria-*. Anything else rejected at the Pydantic boundary.
• jQuery extensions (:has(), :is(), :visible, etc.) rejected. :contains('text') translated server-side.
• No CAPTCHA solve, no auth bypass, no anti-bot evasion. Out of scope by design.

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Links

• Examples: EXAMPLES.md — 12 recipes
• Source / issues: github.com/Mrrobi/spectus
• Contributing / dev loop: CONTRIBUTING.md

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License

MIT © 2026 Mrrobi