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docs: apply fleet-template (16-artifact scaffold)

Browse source 
Adds missing standard artifacts:
- README.md (if missing)
- AGENTS.md (AI agent contract)
- PLAN.md (current sprint)
- STATUS.md (where we are)
- DEVELOPMENT.md (dev workflow)
- DEPLOYMENT.md (deploy procedure)
- TESTING.md (test strategy)
- DECISIONS.md (ADR index + templates)
- .github/CODEOWNERS
- .github/workflows/ci.yml

Preserves all existing artifacts.

Refs: RugMunchMedia/fleet-template
This commit is contained in:
Crypto Rug Munch 2026-07-02 02:07:13 +07:00
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.git
.gitignore
__pycache__
.mypy_cache
.ruff_cache
.pytest_cache
.venv
.env
tests/
*.md
Dockerfile
docker-compose.yml
Makefile
test.sh

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# ── Pry Configuration ──
# Copy this to .env and adjust values.
# All PRY_* vars are auto-loaded by PrySettings.
# ── Core ──
PRY_HOST=0.0.0.0
PRY_PORT=8002
PRY_URL=http://localhost:8002
# ── LLM / AI ──
# Ollama endpoint (used for summarization, categorization, extraction)
PRY_OLLAMA_URL=http://100.100.18.18:11434
# OpenRouter API key (optional — used for vision model queries)
# PRY_OPENROUTER_API_KEY=sk-or-v1-...
# ── Web Scraping ──
# FlareSolverr endpoint for Cloudflare bypass
PRY_FLARESOLVERR_URL=http://flaresolverr:8191/v1
# ── Webhook ──
# Secret used to sign job completion webhooks
# PRY_WEBHOOK_SECRET=pry-webhook-secret
# ── Authentication ──
# API key for endpoint authentication (empty = auth disabled)
# Set a strong random key to protect your Pry instance
# PRY_API_KEY=
# ── Proxy / Tor ──
# PRY_PROXY_URL=http://proxy:8080
# PRY_PROXY_TYPE=http
# PRY_PROXY_USERNAME=user
# PRY_PROXY_PASSWORD=pass
# PRY_TOR_ENABLED=false
# PRY_TOR_SOCKS5_HOST=tor
# PRY_TOR_SOCKS5_PORT=9050
# ── Rotation / Retry ──
# PRY_IP_ROTATION=
# PRY_MAX_RETRIES=3
# PRY_MIN_QUALITY=50
# PRY_RATE_LIMIT_RPM=120
# ── Redis ──
# PRY_REDIS_URL=redis://localhost:6379/0

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# Default owners
* @crmuncher
# Standards + fleet (slower review)
/AGENTS.md @crmuncher
/ARCHITECTURE.md @crmuncher
/README.md @crmuncher
/STATUS.md @crmuncher
/PLAN.md @crmuncher
/ROADMAP.md @crmuncher
/DECISIONS.md @crmuncher
# Sensitive paths
/.secrets/ @crmuncher
/backend/chain_vault/ @crmuncher

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name: CI
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- run: pip install ruff
- run: ruff check .
- run: ruff format --check .
typecheck:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- run: pip install mypy httpx trafilatura lxml markdownify pydantic pyyaml pandas
- run: mypy --python-version 3.12 --strict --ignore-missing-imports --exclude 'build/|dist/|.git/|__pycache__/' *.py
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- run: pip install pytest pytest-asyncio pytest-cov httpx trafilatura lxml markdownify pydantic pyyaml pandas
- run: pytest tests/ -v --cov=. --cov-report=term-missing
security:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.12"
- run: pip install bandit
- run: bandit -r . -x tests/,.venv,__pycache__

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# ═══════════════════════════════════════════════════════════
# PryScraper — .gitignore (RMI universal + Pry-specific)
# ═══════════════════════════════════════════════════════════
# ── SECRETS (zero tolerance) ────────────────────────────────
.env
.env.*
!.env.example
!.env.template
*.pem
*.key
*.p12
*.pfx
id_rsa
id_ed25519
*.session
credentials.json
service-account.json
wallet.json
vault.json
.secrets/
.rmi/wallets/
# ── Python ──────────────────────────────────────────────────
__pycache__/
*.pyc
*.pyo
.venv/
venv/
*.egg-info/
dist/
build/
.mypy_cache/
.ruff_cache/
.pytest_cache/
htmlcov/
.coverage
.coverage.*
htmlcov/
.coverage
# ── Pry-specific cache ──────────────────────────────────────
.warmed_cookies/
.proxy_cache/
.browser_profiles/
.screenshots/
scraped_data/
# ── Node (extension) ────────────────────────────────────────
node_modules/
.npm/
.pnpm-store/
# ── Docker ──────────────────────────────────────────────────
docker-compose.override.yml
# ── Data (too large for git, use HF S3) ─────────────────────
data/
*.dump
*.rdb
*.dfs
*.tar.gz
!requirements/*.tar.gz
# ── IDE ─────────────────────────────────────────────────────
.vscode/
.idea/
*.swp
*.swo
*~
.DS_Store
# ── OS ──────────────────────────────────────────────────────
Thumbs.db
desktop.ini
# ── Logs ────────────────────────────────────────────────────
*.log
logs/
# ── Cache ───────────────────────────────────────────────────
.cache/
tmp/
*.tmp

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repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.11.0
hooks:
- id: ruff
args: [--fix]
- id: ruff-format
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.15.0
hooks:
- id: mypy
args: [--python-version, "3.12", --strict, --ignore-missing-imports]
additional_dependencies:
- types-setuptools
- repo: https://github.com/gitleaks/gitleaks
rev: v8.24.0
hooks:
- id: gitleaks
- repo: https://github.com/PyCQA/bandit
rev: 1.8.3
hooks:
- id: bandit
args: [-r, -x, "tests/,.venv,__pycache__"]
stages: [pre-commit]
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-json
- id: check-merge-conflict

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\.dockerignore$
\.env\.example$
SECRET=

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# AGENTS.md — PryScraper
> AI agent contract. Read this before touching anything in this repo.
## Status
canonical · owner=crmuncher · last_updated=2026-07-02
## What This Repo Is
Multi-source crypto intelligence crawler (munchcrawl). Scraper, extractor, parser, automator, job queue. CLI + MCP server + SDK.
## Type
`backend` · language=Python 3.12 + FastAPI
## Where It Lives
- Source: `https://git.rugmunch.io/RugMunchMedia/pryscraper`
- Deployed: Docker on Talos (/srv/pry/)
- Port: 8005
## Built With
- Standards: [git.rugmunch.io/RugMunchMedia/standards](https://git.rugmunch.io/RugMunchMedia/standards)
- Toolchain: [TOOLCHAIN.md](https://git.rugmunch.io/RugMunchMedia/standards/raw/branch/main/TOOLCHAIN.md)
- Conventions: [CONVENTIONS.md](https://git.rugmunch.io/RugMunchMedia/standards/raw/branch/main/CONVENTIONS.md)
## Components
- `/srv/pry/*.py`: 83 Python modules (api.py, extractor.py, parser.py, etc.)
- `browser-extension/`: Chrome/Firefox extension
## Dependencies
- postgresql (job storage)
- redis (cache)
- flaresolverr (Cloudflare bypass, on Hydra)
- playwright (browser automation)
## 🚨 CRITICAL RULES
1. **No nested repos.** Don't commit other complete project trees inside this one.
2. **No secrets.** Never commit `.secrets/`, `.env`, `*.pem`, `*.key`, `*.crt`. Use gopass.
3. **No data blobs.** Don't commit `*.zip`, `*.parquet`, model weights, sqlite files.
4. **No broken files.** Shell heredoc accidents must be caught before commit.
5. **No duplicate scaffolds.** If a `backend/` or `frontend/` subdir exists at root, it's either THE app or bloat.
6. **Update STATUS.md before committing.** Builders must know where we are.
7. **Read DECISIONS.md before architectural changes.** Don't repeat rejected designs.
## Commands
```bash
make install # install deps
make dev # dev server
make test # tests
make lint # ruff + format check
make typecheck # mypy strict
make security # bandit + gitleaks
make ci # full pipeline
make commit # conventional commit
make clean # remove build artifacts
```
## Architecture
See [ARCHITECTURE.md](ARCHITECTURE.md). Update it when components change.
## Plan
[PLAN.md](PLAN.md) shows current sprint. Update weekly.
## Status
[STATUS.md](STATUS.md) shows where we are RIGHT NOW. Update before each commit.
## Owners
- Primary: crmuncher
- Backup: TBD
## Related Repos
See [standards/PRODUCTS.md](https://git.rugmunch.io/RugMunchMedia/standards/raw/branch/main/PRODUCTS.md).

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---
title: Pry Architecture & Design
status: canonical
owner: Rug Munch Media LLC Engineering
last_updated: 2026-06-30
audience:
- engineers
- agents
note: Replaces Firecrawl, Crawl4AI, and Browserless in a single self-hosted application.
---
# Pry Architecture
## Overview
Pry (formerly MunchCrawl) v3.0.0 is a self-hosted web scraping + browser automation API.
It replaces Firecrawl, Crawl4AI, and Browserless in a single application. No API keys needed.
**Stack**: Python 3.11+, FastAPI, Playwright, FlareSolverr, Redis, httpx
**Entry**: `cli.py:main()` → CLI or `api.py:app` → FastAPI
**Deployment**: Docker Compose (recommended) or bare metal
## Scraping Pipeline (10-tier fallback)
```
Client → Pry API → Ultimate Scraper → 10-tier fallback chain
```
| Tier | Method | Purpose |
|------|--------|---------|
| 1 | Direct HTTP | Rotating UAs, browser-like headers |
| 2 | cloudscraper | Python-native Cloudflare JS challenge |
| 3 | FlareSolverr | Headless Chrome Cloudflare/WAF bypass |
| 4 | undetected-chromedriver | Modified Chrome, no automation flags |
| 5 | Playwright | Full browser with human behavior (mouse, scroll, timing) |
| 6 | Googlebot UA | Search engine crawl mimic |
| 7 | Tor proxy | Anonymous routing via SOCKS5 |
| 8 | Archive.org / Wayback Machine | Cached version fallback |
| 9 | Google Cache | Cached snapshot |
| 10 | Textise dot iitty | Text-only version |
## Data Flow
```
┌──────────────┐ ┌──────────────┐ ┌──────────────────┐
│ CLI/SDK │───→│ Pry API │───→│ Ultimate Scraper │
│ (httpx) │ │ (FastAPI) │ │ (10-tier chain) │
└──────────────┘ └──────┬───────┘ └────────┬─────────┘
│ │
┌────┴────┐ ┌─────┴──────┐
│ Cache │ │ Playwright │
│ Redis │ │ FlareSolv. │
│ Rate │ │ cloudscraper│
│ Limit │ │ Tor/SOCKS5 │
└─────────┘ └────────────┘
│ │
┌────┴────┐ ┌─────┴──────┐
│ MCP │ │ Parse/ │
│ Server │ │ Extract │
└─────────┘ └────────────┘
```
## Database & Storage
All data stored under `~/.pry/`:
| Directory | Purpose |
|-----------|---------|
| `~/.pry/quality/` | Quality check history |
| `~/.pry/reviews/` | Human review queue |
| `~/.pry/intel/` | Competitive intelligence snapshots |
| `~/.pry/costing/` | Usage tracking |
| `~/.pry/freshness/` | Content fingerprints |
| `~/.pry/structure/` | Page structure monitor history |
| `~/.pry/seo/` | SEO snapshot history |
| `~/.pry/monitors/` | Scheduled monitors |
| `~/.pry/vault/` | Encrypted credentials |
| `~/.pry/accounts/` | Registered account pool |
| `~/.pry/reports/` | Generated client reports |
| `~/.pry/training/` | AI training datasets |
| `~/.pry/pipelines/` | Saved pipeline definitions |
| `~/.pry/gdpr/` | Consent records, deletion requests |
| `~/.pry/agency/` | Agency/client management |
## Module Map
| Module | Lines | Type | Purpose |
|--------|-------|------|---------|
| `api.py` | 3,849 | API | FastAPI application — 115 endpoints across 46 tag groups |
| `ultimate_scraper.py` | 343 | Scraper | 10-tier fallback scraper (direct → FlareSolverr → Playwright → Tor → cache) |
| `scraper.py` | 561 | Scraper | Core scraper engine, anti-detection, content extraction |
| `pipeline.py` | 160 | Pipeline | Hook point definitions for data processing pipelines |
| `pipelines.py` | 499 | Pipeline | Step types registry, pipeline validation and execution |
| `extraction.py` | 315 | Extraction | Chunking strategies for LLM extraction |
| `extractor.py` | 136 | Extraction | Structured data extraction from web content |
| `parser.py` | 121 | Parsing | Document parsing (PDF, DOCX, CSV, JSON, images) |
| `adaptive.py` | 175 | Detection | Anti-block adaptive scraper — rotates strategies |
| `browser_pool.py` | 95 | Browser | Playwright manager and browser pre-warming |
| `stealth_engine.py` | 176 | Browser | Stealth initialization scripts for browser contexts |
| `automator.py` | 319 | Browser | Persistent browser session with cookie management |
| `sessions.py` | 105 | Browser | Session save/restore to filesystem |
| `captcha_solver.py` | 238 | Auth | 6+ CAPTCHA solver providers with auto-fallback |
| `auth_connector.py` | 328 | Auth | Credential vault (encrypted), SSO login script generation |
| `signup_automator.py` | 177 | Auth | Identity generation, email/SMS verification automation |
| `account_manager.py` | 111 | Auth | Account pool management, session persistence, proxy scoring |
| `shadow_dom.py` | 109 | Parsing | Shadow DOM content extraction |
| `lazy_load.py` | 96 | Parsing | Lazy load and infinite scroll handling |
| `freshness.py` | 227 | Monitoring | Content hash computation for change detection |
| `monitor.py` | 301 | Monitoring | Scheduled content monitors with cron-based scheduling |
| `seo_monitor.py` | 262 | SEO | SEO analysis — title, meta, headings, keywords, readability |
| `structure_monitor.py` | 234 | Monitoring | Page structure change monitoring |
| `alerter.py` | 189 | Alerts | Multi-channel alerting (webhook, Slack, email, SMS) |
| `quality.py` | 397 | Quality | Content quality metrics — completeness, accuracy, freshness |
| `review.py` | 258 | Review | Human-in-the-loop review queue with approval/rejection |
| `reconciliation.py` | 370 | Data | Schema reconciliation across verticals (e-commerce, jobs, etc.) |
| `enrichment.py` | 206 | Data | Tech stack detection, metadata enrichment |
| `training_data.py` | 362 | Data | AI training dataset generation, PII stripping, license classification |
| `reports.py` | 266 | Reports | Client report generation |
| `costing.py` | 266 | Costing | Per-operation cost tracking and analytics |
| `email_scraper.py` | 364 | Email | Email data extraction (Gmail, Outlook, raw email) |
| `commerce_sync.py` | 191 | Commerce | WooCommerce/Shopify product sync |
| `crm_sync.py` | 359 | CRM | Salesforce/HubSpot/Zoho CRM sync |
| `destinations.py` | 361 | Export | Data export — webhooks, Slack, S3, GCS, SFTP |
| `intelligence.py` | 287 | Intel | Competitive intelligence — snapshots, alerts, diff tracking |
| `compliance.py` | 443 | Compliance | GDPR/CCPA compliance — sensitive data detection, consent |
| `gdpr.py` | 316 | GDPR | Consent management, data retention, deletion requests |
| `agency.py` | 248 | Agency | White-label agency profiles, client management, quotas |
| `jobqueue.py` | 119 | Jobs | Async job queue with status tracking |
| `network.py` | 145 | Network | Proxy rotation, Tor routing, network utilities |
| `markdown_gen.py` | 181 | Export | Markdown generation from scraped content |
| `template_engine.py` | 138 | Templates | Pre-built scraper template registry (110 templates) |
| `advanced.py` | 249 | Features | Premium features — diff tracking, vision AI, batch processing |
| `pryextras.py` | 172 | Extras | WebSocket streaming, real-time job progress |
| `mcp_server.py` | 127 | MCP | MCP tool discovery + execution (Claude/Hermes/Cursor) |
| `mconfig.py` | 129 | Config | Configuration manager — env vars, config file, API overrides |
| `settings.py` | 55 | Config | Pydantic settings with environment variable loading |
| `errors.py` | 49 | Core | Base exception hierarchy |
| `client.py` | 43 | Core | Shared httpx client singleton |
| `cache.py` | 80 | Core | LRU cache key generation with TTL |
| `ratelimit.py` | 84 | Core | Token bucket rate limiter (configurable RPM) |
| `pryfile.py` | 119 | Config | Pryfile (pry.yml) job definition parser and executor |
| `pry_sdk.py` | 130 | SDK | Async + sync Python SDK for API consumption |
| `cli.py` | 339 | CLI | Click-based CLI with autocomplete support |
| `ai_plugin.py` | 55 | AI | OpenAPI spec generation for AI agent integration |
| `api.py` | 3,849 | API | FastAPI application — 115 endpoints across 46 tag groups |
## API Endpoint Groups
| Tag | Endpoints | Purpose |
|-----|-----------|---------|
| Health | 3 | `/health`, `/live`, `/ready` |
| Stats | 1 | `/v0/stats` |
| Costing | 4 | Dashboard, usage, record, costs |
| Freshness | 3 | Dashboard, check, details |
| Scraping | 8 | Scrape, detect-block, crawl, map, PDF, lazy capture, link scrape, sitemap |
| Parsing | 3 | Parse, PDF, shadow-dom |
| Automation | 2 | Automate, screenshot |
| Vision | 2 | Analyze, describe |
| Sessions | 5 | Create, close, list, save, restore |
| Batch | 2 | Batch scrape, batch-file |
| Analysis | 6 | Compare, watch, summarize, diff, categorize, suggest |
| Extraction | 8 | Links, SEO, schema, emails, extract, extract/llm, fields, extraction run |
| Export | 1 | Export |
| Email | 3 | Scrape, gmail, outlook |
| Alerts | 2 | Send, channels |
| Jobs | 2 | Status, list |
| MCP | 2 | Tools, call |
| Config | 3 | Get, update, tor |
| Recorder | 4 | Start, step, export, clear |
| Transform | 2 | Transform, pipe |
| Execute | 1 | Run |
| Dashboard | 1 | Dashboard |
| Circuit Breaker | 2 | Status, reset |
| Pipeline | 3 | Hook, list hooks, run |
| Share | 2 | Create, view |
| Compliance | 2 | Check, docs |
| GDPR | 8 | Consent, status, revoke, deletion request/execute, retention, export, audit, policy |
| Training | 4 | Clean, dataset, list datasets, dataset detail |
| Monitoring | 5 | Create, run, list, delete, check |
| Structure | 3 | Check, history, archive |
| Intelligence | 4 | Snapshot, list, diff, report |
| Auth | 6 | Credentials store/list/delete, SSO, CAPTCHA, session health |
| Review | 6 | Submit, approve, reject, list, detail, queue |
| Quality | 3 | Check, history, score |
| Enrichment | 2 | Analyze, history |
| Reconciliation | 2 | Run, history |
| Commerce | 2 | Sync, platforms |
| CRM | 2 | Sync, platforms |
| Pipelines | 7 | Validate, run, save, list, get, delete, schema |
| Agency | 7 | Create, get, branding, client create, client list, analytics, quota |
| SEO | 3 | Analyze, track, keywords |
| Reports | 3 | Generate, list, get |
| Templates | 3 | List, get, generate |
| Integrations | 3 | Zappier, Make, webhooks |
| AI | 2 | GPT manifest, MCP config |
| System | 1 | System info |
## Security Model
- **Vault**: Encrypted credential storage at `~/.pry/vault/`
- **GDPR**: Full consent management, retention policies, deletion workflow
- **Rate limiting**: Token bucket per-IP (default 120 RPM)
- **Circuit breaker**: Per-domain backoff on failures
- **No API keys required**: Self-hosted, fully private
## External Dependencies
| Dependency | Purpose |
|------------|---------|
| FlareSolverr | Cloudflare/WAF challenge bypass (Docker sidecar) |
| Redis | Optional cache backend |
| Playwright | Full browser automation (Chromium) |
| Tor | Anonymous routing (optional) |
| OpenRouter | Vision AI models (optional) |
## Project Files
| File | Lines | Purpose |
|------|-------|---------|
| Python source files | 101 files | Core application |
| Test files | 43 files | pytest test suite |
| JSON templates | 110 files | Pre-built scraper templates |
| HTML templates | 2 files | Coverage report, browser popup |
| Stealth scripts | 6 JS files | Browser anti-detection |
| Browser extension | 5 files | Chrome extension |
| Shopify app | 3 files | Commerce integration |
| WP plugin | 1 file | WordPress integration |
| Config files | 8 files | pyproject.toml, Dockerfile, Makefile, etc. |

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---
title: Pry — Brutal Honest Audit & Action Plan
status: canonical
owner: Rug Munch Media LLC Engineering
last_updated: 2026-06-30
audited_by: Claude (Sonnet 4.5)
---
# Pry — Brutal Honest Audit & Action Plan
> A no-BS assessment of the Pry project as built over the past few days.
> What works, what's theater, what's missing, and what to do about it.
---
## TL;DR — The Honest State
| Claim | Reality |
|-------|---------|
| "110 working scraper templates" | 110 valid JSON files. 4-5 work on landing pages. The rest need specific article/product URLs + sometimes Playwright. |
| "6 CAPTCHA providers" | 4 have actual API implementations. 2 are stubs (deathbycaptcha, nextcaptcha). None work without paid API keys. |
| "10-tier anti-bot fallback" | 8 tiers work (direct → cloudscraper → FlareSolverr → undetected-chromedriver → Playwright → Googlebot → Archive.org → Google Cache). **Tor is documented but NOT implemented.** |
| "Multi-tenant agency platform" | JSON files in `~/.pry/agency/`. No real isolation, no per-tenant auth, no billing. |
| "GDPR compliance" | File-based consent records and deletion log. No real DPO, no right-to-access flow, no DPIA. |
| "264 tests passing" | True. But many tests are trivial (e.g., checking a string is `in` a result). Real integration tests: only a few. |
| "AI-powered extraction" | LLM calls are wired in but not actually called in tests. Extraction is regex/heuristic. |
| "Built to compete with Firecrawl" | Different product. Pry = self-hosted free. Firecrawl = hosted SaaS. They serve different markets. |
---
## What's Actually Real (Verified by Testing)
### ✅ Works — Tested on Talos with live HTTP requests
| Feature | Test Result |
|---------|-------------|
| API server starts and responds | ✅ All endpoints registered |
| 10-tier scraper fallback | ✅ All 6 tested sites return content (BBC, NYT, Forbes, Amazon, etc.) |
| FlareSolverr integration | ✅ Connected to Hydra, bypasses Cloudflare |
| Playwright with stealth | ✅ Works against 6+ tested sites |
| cloudscraper (Python Cloudflare bypass) | ✅ Returns content for basic Cloudflare sites |
| Archive.org fallback | ✅ Implemented (CDX API + Wayback Machine fetch) |
| Template execution (4 sites) | ✅ Wikipedia 4/5, GitHub 5/8, BBC 1/7, NYT 4/6 |
| Health check returns "ok" | ✅ When FlareSolverr is up |
| All 264 tests pass | ✅ ruff + mypy clean on new files |
| Profile generation | ✅ Generates realistic identities |
| CAPTCHA solver imports | ✅ Code exists, requires API keys to actually solve |
| Account pool CRUD | ✅ JSON file persistence works |
| Stealth script generation | ✅ Generates WebGL/Canvas/Audio/typing scripts |
### ⚠️ Partially Works — Implemented but not robust
| Feature | Issue |
|---------|-------|
| 110 templates | Most use generic CSS selectors like `[class*='title']` that work on many sites but don't extract *all* fields |
| cloudscraper | Works for simple Cloudflare, fails on JavaScript challenges |
| undetected-chromedriver | Imported but the method is **synchronous** while the rest of the system is async — won't be called in practice |
| FlareSolverr | Works for Cloudflare challenges, not DataDome/Akamai |
| Quality check (anomaly detection) | Basic z-score only. No seasonality, no multi-dimensional, no ML |
| Compliance scoring | 352 regex patterns. No actual legal NLP, no real ToS analysis |
| Entity reconciliation | Field mapping by name aliases. No ML, no semantic understanding |
| SEO monitor | Regex-only. Doesn't actually understand SEO impact |
| CRM reverse ETL | HTTP calls only. No auth flow, no field mapping engine, no per-CRM schema |
### ❌ Doesn't Work — Theater / Stubs
| Feature | Reality |
|---------|---------|
| Tor proxy tier | Documented but **no `_tier_tor` method exists**. Code claims it, code doesn't have it. |
| DeathByCaptcha solver | Referenced in priority list, no implementation |
| NextCaptcha solver | Referenced in priority list, no implementation |
| `capsolver` direct call | Module references but no `_capsolver` method (name is wrong) |
| AI training data export | PII redaction is regex, not semantic. License classifier is regex. No real provenance tracking. |
| Auto-generated reports | Static HTML templates with placeholder data. Not data-driven. |
| Anomaly detection | Single-field z-score. Doesn't handle seasonality, multivariate anomalies, or change-point detection. |
| Auto-reports (PDF) | Only HTML templates. No PDF generation, no scheduling, no email delivery. |
| Multi-tenant billing | No billing code at all. No usage metering per client. |
| Email inbox scraping | Gmail/Graph OAuth not actually wired. Just regex on body text. |
| AI-powered compliance | No LLM calls. Just regex pattern matching. |
| AI-powered SEO | No LLM calls. Just regex field extraction. |
| Documented "100+ templates" | 110 files exist but ~30% work end-to-end |
---
## Code Quality Issues
### Silent Error Swallowing
```
35 'except: pass' patterns
46 broad except clauses
0 TODOs/FIXMEs (suspicious — either perfect code or no one admits problems)
```
Examples:
```python
# api.py has bare except in error handlers
try:
...
except: # catches everything including KeyboardInterrupt
return None
# Many files have:
except Exception as e:
logger.warning(...)
# returns default, hides what actually broke
```
### API Key Handling
- `api.py` has hardcoded references to "secrets" in endpoints
- No secrets vault (HashiCorp Vault, AWS Secrets Manager, etc.)
- API keys passed in request bodies (not secure)
- No key rotation support
### Testing Reality
- 264 tests pass
- But: test for `assert "pry" in result["profiles"]` — tests the string exists, not the data extraction works
- No load tests, no security tests, no performance benchmarks
- No integration tests (all tests are unit-level)
### Documentation Gaps
- `ARCHITECTURE.md` exists but doesn't explain *why* design decisions were made
- `FEATURES.md` is a feature catalog, not a usage guide
- `USAGE.md` is 200 lines, should be 2000+ for a production tool
- `ROADMAP.md` lists "remaining" items without prioritization or effort estimates
- No API versioning strategy
- No security model document
- No deployment guide
- No operational runbook
---
## Architecture Review
### What's Good
- Clear module separation: scraper / extraction / templates / integrations
- Templates are external JSON files (easy to update without code changes)
- Settings via Pydantic (typed, env-var-backed)
- 10-tier fallback pattern is genuinely good design
- Unified PryScraper delegates to UltimateScraper (single entry point)
- Health check actually checks dependencies
### What's Wrong
**1. State Management**
```
All state in JSON files in ~/.pry/
├── quality/
├── reviews/
├── intel/
├── costing/
├── freshness/
├── structure/
├── seo/
├── monitors/
├── vault/
├── accounts/
├── reports/
├── training/
├── pipelines/
├── gdpr/
└── agency/
```
**Problem:** No concurrency control. Two requests modifying the same file = corruption. No transactions. No query language. Can't scale horizontally.
**2. Error Handling**
- 35 silent `except: pass` patterns
- No structured error types
- No error context propagation
- No retry/backoff logic
- No dead letter queue
**3. The "Plugin/Template" System is Half-Baked**
- Templates are external JSON (good)
- But: no version control, no A/B testing, no per-template rate limits, no template marketplace
- Templates validated by schema only, not by actual data quality
**4. The "Real-Time" Claims Aren't Real-Time**
- "Real-time monitoring" = poll every N hours via cron
- "Real-time change detection" = compare snapshots
- "Real-time alerts" = webhook after the fact
- True real-time would need: WebSockets + change data capture + event streaming
**5. Anti-Detection is Best-Effort**
- Works for: simple Cloudflare, basic bot detection
- Fails for: DataDome, PerimeterX, advanced fingerprinting
- No residential proxy pool
- No mobile user-agent simulation
- No human-in-the-loop fallback
---
## What Pry Actually Is (Honest Version)
**Pry is a self-hosted web scraping API with:**
- 110 pre-configured site templates (JSON)
- 10-tier anti-bot fallback system
- Basic quality / compliance / cost / SEO / monitoring features
- Template engine for structured extraction
- Integrations: Slack, Discord, Teams, Telegram, SMS, Email, Sheets, Airtable, Shopify, WooCommerce, Salesforce, HubSpot, Pipedrive, Close, WordPress
- Browser extension for one-click scrape
- WordPress plugin
- Shopify app scaffold
**What Pry is NOT:**
- A drop-in Firecrawl replacement (Firecrawl has hosted infrastructure, AI features, team management)
- A guaranteed 100% scrape success rate (no tool can guarantee this)
- Production-ready (no auth, no scaling, no observability)
- An AI product (almost no LLM integration in actual code)
**Comparable to:** Crawl4AI (open source) + Scrapy (framework) + Browserless (self-hosted) combined.
---
## Priority Action Plan
### 🔴 Critical (Must Fix Before Production)
| # | Issue | Effort | Impact |
|---|-------|--------|--------|
| 1 | **Add authentication** — JWT or API key with per-user rate limits | 3 days | Can't deploy without this |
| 2 | **Replace JSON storage with database** — PostgreSQL or SQLite for single-node | 5 days | Data corruption risk today |
| 3 | **Fix silent error swallowing** — Replace `except: pass` with `except SpecificError as e: logger.exception(...)` | 1 day | Hides all bugs |
| 4 | **Add real Tor proxy tier** — Implement `_tier_tor` using `aiohttp_socks` + `stem` | 2 days | One of the 10 claimed tiers is missing |
| 5 | **Wire up LLM extraction** — Actually call Ollama/OpenRouter for the AI features | 3 days | Most "AI" features are regex |
| 6 | **Implement missing CAPTCHA providers** — deathbycaptcha, nextcaptcha, and fix capsolver name | 1 day | 2 of 6 claimed providers are stubs |
| 7 | **Add concurrency safety** — File locks or move to SQLite | 1 day | Race conditions today |
### 🟡 Important (Should Fix This Quarter)
| # | Issue | Effort | Impact |
|---|-------|--------|--------|
| 8 | **Template URL auto-suggestion** — For each template, pre-generate working example URLs | 3 days | Currently templates need specific URLs |
| 9 | **LLM-powered extraction fallback** — If CSS selectors fail, use LLM to extract | 5 days | Templates become resilient |
| 10 | **Add observability** — Prometheus metrics, structured logging, OpenTelemetry | 5 days | Can't operate what you can't observe |
| 11 | **Per-API-key rate limiting and quotas** | 2 days | Required for SaaS model |
| 12 | **Real template testing on real sites** — CI runs templates against sandbox sites, measures success rate | 3 days | Currently 30-40% success rate is unknown |
| 13 | **Add OpenAPI SDK generation** — Generate Python/JS/Go SDKs from OpenAPI spec | 1 day | Current SDK is hand-maintained |
| 14 | **Secrets management** — HashiCorp Vault or similar | 3 days | API keys in env vars are not safe |
| 15 | **Backup/restore** — `pry backup` and `pry restore` CLI commands | 2 days | No way to backup today |
### 🟢 Nice to Have
| # | Issue | Effort |
|---|-------|--------|
| 16 | Add Redis for shared state (multi-worker) | 3 days |
| 17 | Horizontal scaling with K8s manifests | 5 days |
| 18 | Real PDF generation for reports | 2 days |
| 19 | Email scheduling for digests | 3 days |
| 20 | Mobile app (React Native) | 10 days |
| 21 | Public template marketplace | 10 days |
| 22 | Community version with forum | 20 days |
| 23 | AI agent for automatic template generation | 10 days |
| 24 | Real-time WebSocket streaming | 5 days |
| 25 | GraphQL API alongside REST | 5 days |
---
## Code Quality Improvements (Small Effort, High Value)
### Immediate Fixes (1-2 hours each)
```python
# BAD: Silent error swallowing
try:
data = scrape(url)
except:
return None
# GOOD: Specific exception, context, re-raise or handle
try:
data = scrape(url)
except ConnectionError as e:
logger.warning("scrape_connection_failed", extra={"url": url, "error": str(e)})
return None
except ValueError as e:
logger.error("scrape_invalid_response", extra={"url": url, "error": str(e)})
raise
```
### Consistency Rules
1. **All errors should be logged with context** (URL, params, etc.)
2. **All async functions should have explicit return types**
3. **All public endpoints should have Pydantic request/response models**
4. **All file I/O should use the shared client or be wrapped in error handling**
5. **All template execution should return a standard result format**
### Testing Rules
1. **Every endpoint should have an integration test** (not just unit)
2. **Every template should have a real-site test** (smoke test)
3. **Every external service call should have a mock test**
4. **Every error path should have a test**
---
## Market Position (Honest)
**Pry's real advantage:**
- **Free and self-hosted** — no per-request pricing like Firecrawl
- **Template library** — 110 pre-configured sites is more than Firecrawl's public templates
- **Open source** — can be modified, self-hosted, audited
- **No vendor lock-in** — data stays on your machine
**Pry's real disadvantage:**
- **No hosted option** — must deploy and maintain yourself
- **No team features** — no UI, no collaboration, no sharing
- **No LLM extraction** — most "AI" claims are regex
- **30-40% template success rate** — not production-ready
- **No SLA** — if it breaks, you fix it
**Realistic target market:**
- Developers who want a self-hosted Firecrawl alternative
- Teams doing competitive intelligence who need pre-built templates
- Privacy-conscious companies who can't use hosted SaaS
- Cost-sensitive startups that can't afford Firecrawl pricing
**Not viable for:**
- Non-technical teams (no UI)
- Enterprise (no compliance certifications, no SLA, no SSO)
- Production at scale (JSON files don't scale)
---
## What To Do Tomorrow
If I had one day to make Pry significantly better:
1. **Add authentication** (4 hours) — JWT-based, per-user rate limits
2. **Wire up LLM extraction** (3 hours) — Actually call Ollama for the AI features that claim to use it
3. **Fix silent errors** (1 hour) — Find all 35 `except: pass` and replace with proper handling
4. **Add real Tor tier** (2 hours) — Implement the missing 7th tier
5. **Test all 110 templates against real URLs** (4 hours) — Measure actual success rate, fix broken selectors
6. **Add a simple dashboard** (2 hours) — Web UI showing scrape history, costs, errors
After that day, Pry would be: tested, authenticated, LLM-powered, properly error-handled, and we'd know which templates actually work.
---
## Final Verdict
**Pry is a solid prototype, not a product.**
It's better than starting from scratch. It has more features than any open-source competitor. But it needs significant work to be production-ready:
- **Architecture:** Good shape, needs DB and auth
- **Code quality:** Mostly good, needs error handling fixes
- **Features:** Comprehensive, many are skeletal
- **Testing:** Decent coverage, missing integration tests
- **Documentation:** Present, thin on operational details
- **Production readiness:** Not ready. 4-6 weeks of focused work to be shippable.
The good news: the hard parts (scraping engine, template system, anti-bot fallback) are done and work. The remaining work is infrastructure (auth, DB, scaling) and quality (error handling, real implementations of stub features).
**Build on what's there. Don't rewrite.**

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# DECISIONS.md — PryScraper
> Architecture Decision Records (ADRs). Read before architectural changes.
## What is an ADR?
A short document capturing an important architectural decision:
- Context (what's the problem?)
- Decision (what did we choose?)
- Consequences (what does this imply?)
- Alternatives (what else did we consider?)
## How to Write a New ADR
1. Copy `docs/adr/0000-template.md` to `docs/adr/NNNN-short-title.md`
2. Fill in: Status, Context, Decision, Consequences, Alternatives
3. Add link below in "Index"
4. Commit as part of the change that triggered the ADR
## Index
<!-- Add new ADRs here, oldest first -->
| Number | Title | Status | Date |
|--------|-------|--------|------|
| [0001](docs/adr/0001-initial-architecture.md) | Initial architecture | Accepted | 2026-07-02 |
## Fleet-wide ADRs (in standards repo)
- [ADR-0001: Tailscale mesh as the only perimeter](https://git.rugmunch.io/RugMunchMedia/standards/raw/branch/main/MCP-ARCHITECTURE.md)
- [ADR-0012: 21-day sprint, not 10-week](https://git.rugmunch.io/RugMunchMedia/standards/raw/branch/main/FLEET-PLAN.md)

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# DEPLOYMENT.md — PryScraper
> How to deploy. Build, push, restart, rollback.
## Architecture
- Container: Docker
- Image registry: local (Talos)
- Orchestration: docker-compose on Talos
- Reverse proxy: nginx on Talos
- Auto-deploy: forgejo webhook → Talos deploy script
## Deploy
### Automatic (CI/CD)
On merge to `main`:
1. forgejo webhook fires
2. Talos deploy script pulls latest commit
3. Rebuilds Docker image
4. Stops old container, starts new
5. Runs health check
6. Rolls back on failure
### Manual
```bash
ssh netcup
cd /srv/pryscraper
git pull origin main
docker build -t pryscraper:latest .
docker stop pryscraper
docker rm pryscraper
docker run -d --name pryscraper --restart unless-stopped --network host -p 8005:8005 pryscraper:latest
```
## Health Check
```bash
curl -fsS http://localhost:8005/health
```
## Rollback
```bash
ssh netcup
cd /srv/pryscraper
git log --oneline -5 # find last good commit
git checkout <commit-hash>
docker build -t pryscraper:latest .
docker restart pryscraper
```
## Logs
```bash
docker logs pryscraper --tail 100 -f
```
## Monitoring
- Prometheus: `/metrics` endpoint
- Grafana: dashboards in fleet-infra
- Loki: log aggregation
## Secrets
All secrets in gopass:
- `rmi/pryscraper/admin_key`
- `rmi/pryscraper/db_password`
- _see AGENTS.md for full list_
Loaded via docker `--env-file` or systemd `EnvironmentFile`.
## Firewall
- Public: 80, 443 (nginx)
- Internal: 8005 (localhost only, behind nginx)
- Tailscale: 8005 for admin access
## Backup
- Code: forgejo (source of truth) + Hydra bare mirror (daily 04:00)
- Data: DB snapshot to R2 (daily)
- Config: gopass

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# DEVELOPMENT.md — PryScraper
> Dev workflow. Install, code, test, commit, PR.
## Setup
### Prerequisites
- Python 3.12+ / Node 20+
- `gopass` for secrets
- `mise` for tool version mgmt (or manual)
- `pre-commit` for hooks
### Install
```bash
git clone https://git.rugmunch.io/RugMunchMedia/pryscraper.git
cd pryscraper
make install
pre-commit install
```
### Environment
```bash
# Required env vars (loaded from gopass on deploy, .env locally)
# See .env.example
cp .env.example .env
$EDITOR .env # fill in test values
```
## Workflow
### 1. Create a branch
```bash
git checkout -b feat/my-feature
# or fix/my-bug, docs/my-doc, chore/my-chore
```
### 2. Make changes
- Write code
- Add tests
- Update docs (AGENTS.md, ARCHITECTURE.md, STATUS.md)
### 3. Run pre-commit locally
```bash
make lint
make test
make typecheck
make security
```
### 4. Commit (conventional)
```bash
make commit # interactive
# or:
git commit -m "feat(scope): add new feature"
```
Types: feat, fix, docs, style, refactor, perf, test, build, ci, chore, ops, security
### 5. Push + PR
```bash
git push -u origin feat/my-feature
# Open PR on forgejo: https://git.rugmunch.io/RugMunchMedia/pryscraper/pulls/new
```
### 6. Wait for CI
- All checks must pass
- Review by CODEOWNERS
- Squash-merge to main
- Auto-deploys to Talos (via forgejo webhook)
## Daily End-of-Day
```bash
make status # show what's changed
fleet-commit # commit helper with checklist
```
## Code Style
- Python: ruff (lint + format), mypy strict
- TypeScript: eslint + prettier
- Shell: shellcheck
- Markdown: vale
See [standards/CONVENTIONS.md](https://git.rugmunch.io/RugMunchMedia/standards/raw/branch/main/CONVENTIONS.md).

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# Pry — Multi-stage Docker build
# Stage 1: Build deps + Playwright + Tesseract
FROM python:3.12-slim AS builder
RUN apt-get update && apt-get install -y --no-install-recommends \
wget curl gnupg ca-certificates \
libnss3 libnspr4 libatk1.0-0 libatk-bridge2.0-0 \
libcups2 libdrm2 libdbus-1-3 libxcb1 libxkbcommon0 \
libxcomposite1 libxdamage1 libxrandr2 libgbm1 \
libpango-1.0-0 libcairo2 libasound2 \
tesseract-ocr tesseract-ocr-eng \
&& rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
RUN python3 -m playwright install chromium 2>&1 | tail -1
# Stage 2: Runtime
FROM python:3.12-slim
RUN apt-get update && apt-get install -y --no-install-recommends \
libnss3 libnspr4 libatk1.0-0 libatk-bridge2.0-0 \
libcups2 libdrm2 libdbus-1-3 libxcb1 libxkbcommon0 \
libxcomposite1 libxdamage1 libxrandr2 libgbm1 \
libpango-1.0-0 libcairo2 libasound2 \
tesseract-ocr tesseract-ocr-eng \
curl \
&& rm -rf /var/lib/apt/lists/*
COPY --from=builder /usr/local/lib/python3.12/site-packages /usr/local/lib/python3.12/site-packages
COPY --from=builder /usr/local/bin /usr/local/bin
COPY --from=builder /root/.cache/ms-playwright /root/.cache/ms-playwright
WORKDIR /app
COPY *.py ./
RUN mkdir -p /app/sessions
EXPOSE 8002
HEALTHCHECK --interval=15s --timeout=5s --start-period=10s --retries=3 \
CMD curl -sf http://localhost:8002/health || exit 1
CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "8002", \
"--workers", "2", "--timeout-keep-alive", "120", "--limit-concurrency", "20"]

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---
title: Pry Feature Catalog
status: canonical
owner: Rug Munch Media LLC Engineering
last_updated: 2026-06-30
---
# Pry Feature Catalog
## Scraping & Crawling
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Single URL scrape | `scraper.py` | `POST /v1/scrape` | ✅ |
| Multi-page crawl | `scraper.py` | `POST /v1/crawl` | ✅ |
| Batch scrape (parallel) | `advanced.py` | `POST /v1/batch` | ✅ |
| Batch from file + template | `advanced.py` | `POST /v1/batch-file` | ✅ |
| URL discovery (sitemap) | `scraper.py` | `POST /v1/map` | ✅ |
| Link analysis | `scraper.py` | `POST /v1/links` | ✅ |
## Anti-Detection & Bypass
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| 10-tier fallback scraper | `ultimate_scraper.py` | Internal | ✅ |
| Block detection | `adaptive.py` | `POST /v1/detect-block` | ✅ |
| Adaptive strategy rotation | `adaptive.py` | Internal | ✅ |
| Browser pool + pre-warming | `browser_pool.py` | Internal | ✅ |
| Stealth engine (6 JS scripts) | `stealth_engine.py` | Internal | ✅ |
| Stealth scripts | `stealth_scripts/` | Injected | ✅ |
| Tor proxy routing | `network.py` | `POST /v1/config/profile/tor` | ✅ |
| SOCKS5 proxy support | `network.py` | Config | ✅ |
| User-agent rotation | `scraper.py` | Internal | ✅ |
## Document Parsing
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| PDF parsing | `parser.py` | `POST /v1/parse` | ✅ |
| DOCX parsing | `parser.py` | `POST /v1/parse` | ✅ |
| Image OCR | `parser.py` | `POST /v1/parse` | ✅ |
| CSV parsing | `parser.py` | `POST /v1/parse` | ✅ |
| JSON parsing | `parser.py` | `POST /v1/parse` | ✅ |
| Shadow DOM extraction | `shadow_dom.py` | `POST /v1/shadow-dom` | ✅ |
| Lazy load / infinite scroll | `lazy_load.py` | `POST /v1/capture/lazy` | ✅ |
## Extraction & Structured Data
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| JSON schema extraction | `extractor.py` | `POST /v1/extract` | ✅ |
| LLM extraction + chunking | `extraction.py` | `POST /v1/extract/llm` | ✅ |
| Schema.org / JSON-LD | `extractor.py` | `POST /v1/schema` | ✅ |
| Email address extraction | `extractor.py` | `POST /v1/emails` | ✅ |
| Structured field extraction | `extraction.py` | `POST /v1/extract/fields` | ✅ |
| Field suggestion (AI) | `advanced.py` | `POST /v1/suggest` | ✅ |
## AI & Vision
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Vision AI (OpenRouter, 5-model fallback) | `advanced.py` | `POST /v1/vision` | ✅ |
| AI summarization (Ollama) | `advanced.py` | `POST /v1/summarize` | ✅ |
| AI categorization | `advanced.py` | `POST /v1/categorize` | ✅ |
| GPT Action manifest | `ai_plugin.py` | `GET /v1/ai/gpt-manifest` | ✅ |
| MCP server config | `ai_plugin.py` | `GET /v1/ai/mcp-config` | ✅ |
| MCP tool discovery | `mcp_server.py` | `GET /mcp/tools` | ✅ |
| MCP tool execution | `mcp_server.py` | `POST /mcp/call` | ✅ |
## Browser Automation
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Step-based automation | `automator.py` | `POST /v1/automate` | ✅ |
| Screenshot capture | `automator.py` | `POST /v1/screenshot` | ✅ |
| Persistent browser sessions | `sessions.py` | `POST /v1/session/create` | ✅ |
| Session list | `sessions.py` | `GET /v1/sessions` | ✅ |
| Session save/restore | `sessions.py` | `POST /v1/session/save` | ✅ |
| Session close | `sessions.py` | `POST /v1/session/close` | ✅ |
| Action recorder | `advanced.py` | `POST /v1/record/start` | ✅ |
## CAPTCHA Solving
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| CAPTCHA solving (6 providers) | `captcha_solver.py` | `POST /v1/auth/captcha` | ✅ |
| Auto-fallback across providers | `captcha_solver.py` | Internal | ✅ |
## Authentication
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Credential vault (encrypted) | `auth_connector.py` | `POST /v1/auth/credentials` | ✅ |
| SSO login script generation | `auth_connector.py` | `POST /v1/auth/sso` | ✅ |
| Session health check | `auth_connector.py` | `POST /v1/auth/session/health` | ✅ |
| Signup automation | `signup_automator.py` | Internal | ✅ |
| Account pool management | `account_manager.py` | Internal | ✅ |
## Monitoring & Change Detection
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Page watch / change detection | `freshness.py` | `POST /v1/watch` | ✅ |
| Diff tracking | `advanced.py` | `POST /v1/diff` | ✅ |
| Content freshness dashboard | `freshness.py` | `GET /v1/freshness/dashboard` | ✅ |
| Scheduled monitors (cron) | `monitor.py` | `POST /v1/monitor` | ✅ |
| Monitor run on-demand | `monitor.py` | `POST /v1/monitor/run` | ✅ |
| Monitor list / delete | `monitor.py` | `GET /v1/monitors` | ✅ |
| Structure monitoring | `structure_monitor.py` | `POST /v1/structure/check` | ✅ |
## Alerts & Notifications
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Multi-channel alerts | `alerter.py` | `POST /v1/alert/send` | ✅ |
| Supported channel listing | `alerter.py` | `GET /v1/alert/channels` | ✅ |
| Webhook support | `destinations.py` | Config | ✅ |
| Slack integration | `destinations.py` | Config | ✅ |
## Quality & Review
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Content quality scoring | `quality.py` | `POST /v1/quality/check` | ✅ |
| Quality history | `quality.py` | `GET /v1/quality/history` | ✅ |
| Human review queue | `review.py` | `POST /v1/review/submit` | ✅ |
| Review approve/reject | `review.py` | `POST /v1/review/{id}/approve` | ✅ |
| Review queue listing | `review.py` | `GET /v1/reviews` | ✅ |
## Data Export & Integration
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Multi-format export (JSON, CSV, RSS, TXT, SQL) | `destinations.py` | `POST /v1/export` | ✅ |
| Webhook delivery | `destinations.py` | Config | ✅ |
| S3 upload | `destinations.py` | Config | ✅ |
| GCS upload | `destinations.py` | Config | ✅ |
| SFTP upload | `destinations.py` | Config | ✅ |
| Data transformation | `advanced.py` | `POST /v1/transform` | ✅ |
## Commerce & CRM
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| WooCommerce product sync | `commerce_sync.py` | `POST /v1/commerce/sync` | ✅ |
| Shopify product sync | `commerce_sync.py` | `POST /v1/commerce/sync` | ✅ |
| Shopify app backend | `shopify-app/` | External | ✅ |
| Salesforce CRM sync | `crm_sync.py` | `POST /v1/crm/sync` | ✅ |
| HubSpot CRM sync | `crm_sync.py` | `POST /v1/crm/sync` | ✅ |
| Zoho CRM sync | `crm_sync.py` | `POST /v1/crm/sync` | ✅ |
## Pipelines
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Pipeline definition validation | `pipelines.py` | `POST /v1/pipelines/validate` | ✅ |
| Pipeline execution | `pipelines.py` | `POST /v1/pipelines/run` | ✅ |
| Pipeline CRUD | `pipelines.py` | CRUD endpoints | ✅ |
| Hook registration | `pipeline.py` | `POST /v1/pipeline/hook` | ✅ |
| Hook execution | `pipeline.py` | `POST /v1/pipeline/run` | ✅ |
| Schema pipeline | `pipelines.py` | `POST /v1/pipelines/schema` | ✅ |
## Intelligence & Competitive Analysis
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Competitor snapshot | `intelligence.py` | `POST /v1/intel/snapshot` | ✅ |
| Intel comparison (diff) | `intelligence.py` | `POST /v1/intel/compare` | ✅ |
| Intel report generation | `intelligence.py` | `GET /v1/intel/report` | ✅ |
| Tech stack detection | `enrichment.py` | `POST /v1/enrichment/analyze` | ✅ |
## Compliance & GDPR
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Compliance check (URL) | `compliance.py` | `POST /v1/compliance/check` | ✅ |
| Consent recording | `gdpr.py` | `POST /v1/gdpr/consent` | ✅ |
| Consent check / revoke | `gdpr.py` | `GET /v1/gdpr/consent/{id}` | ✅ |
| Data deletion request | `gdpr.py` | `POST /v1/gdpr/deletion/request` | ✅ |
| Deletion execution | `gdpr.py` | `POST /v1/gdpr/deletion/execute` | ✅ |
| Data retention policy | `gdpr.py` | `GET /v1/gdpr/retention` | ✅ |
| Data portability (export) | `gdpr.py` | `POST /v1/gdpr/export` | ✅ |
| Compliance audit log | `gdpr.py` | `GET /v1/gdpr/audit` | ✅ |
## Training Data
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| PII/copyright stripping | `training_data.py` | `POST /v1/training/clean` | ✅ |
| Dataset generation | `training_data.py` | `POST /v1/training/dataset` | ✅ |
| Dataset CRUD | `training_data.py` | CRUD endpoints | ✅ |
| License classification | `training_data.py` | Internal | ✅ |
## Agency / White-Label
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Agency profile creation | `agency.py` | `POST /v1/agency/create` | ✅ |
| Agency branding | `agency.py` | `PUT /v1/agency/{id}/branding` | ✅ |
| Client sub-account management | `agency.py` | `POST /v1/agency/{id}/clients` | ✅ |
| Usage analytics | `agency.py` | `GET /v1/agency/{id}/analytics` | ✅ |
| Client quota enforcement | `agency.py` | `GET /v1/client/{id}/quota` | ✅ |
## Reporting
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Report generation | `reports.py` | `POST /v1/reports/generate` | ✅ |
| Report list / get | `reports.py` | `GET /v1/reports` | ✅ |
## Templates
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| Template listing | `template_engine.py` | `GET /v1/templates` | ✅ |
| Template detail | `template_engine.py` | `GET /v1/templates/{id}` | ✅ |
| Template generation | `template_engine.py` | `POST /v1/templates/generate` | ✅ |
| Pre-built templates | 110 JSON files | `templates/` | ✅ |
## SEO
| Feature | Module | Endpoint | Status |
|---------|--------|----------|--------|
| SEO analysis | `seo_monitor.py` | `POST /v1/seo/analyze` | ✅ |
| SEO change tracking | `seo_monitor.py` | `POST /v1/seo/track` | ✅ |
| Keyword analysis | `seo_monitor.py` | `POST /v1/seo/keywords` | ✅ |
## Infrastructure
| Feature | Module | Status |
|---------|--------|--------|
| Health probes (liveness/readiness) | `api.py` | ✅ |
| Prometheus metrics | `api.py` | ✅ |
| Rate limiting (token bucket) | `ratelimit.py` | ✅ |
| LRU cache + Redis | `cache.py` | ✅ |
| Circuit breaker per-domain | `advanced.py` | ✅ |
| WebSocket streaming | `pryextras.py` | ✅ |
| Async job queue | `jobqueue.py` | ✅ |
| Docker Compose deployment | `docker-compose.yml` | ✅ |
| CI/CD (GitHub Actions) | `.github/workflows/ci.yml` | ✅ |
| Pre-commit hooks | `.pre-commit-config.yaml` | ✅ |
| Browser extension (Chrome) | `browser-extension/` | ✅ |
| WordPress plugin | `wordpress-plugin/` | ✅ |

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# PROPRIETARY LICENSE — Rug Munch Media LLC
Copyright (c) 2026 Rug Munch Media LLC. All rights reserved.
This software and associated documentation files (the "Software") are
PROPRIETARY and CONFIDENTIAL to Rug Munch Media LLC.
UNAUTHORIZED USE, COPYING, MODIFICATION, MERGING, PUBLISHING,
DISTRIBUTING, SUBLICENSING, AND/OR SELLING COPIES OF THE SOFTWARE ARE
STRICTLY PROHIBITED WITHOUT PRIOR WRITTEN CONSENT FROM RUG MUNCH MEDIA LLC.
For licensing inquiries: licensing@rugmunch.io
For commercial licensing: enterprise@rugmunch.io
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

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# RUG MUNCH MEDIA LLC — LICENSING & PRICING STRATEGY
> **Complete decisions for every system.**
> Last updated: 2026-07-01
> Goal: maximize profit, maintain trust, be first-mover in the market.
---
## 1. LICENSE DECISIONS (Per System)
| System | License | Why |
|--------|---------|-----|
| **WalletConnect integration** (in WalletPress) | **MIT** | Trust requires open source. Community audits wallet security code. No competitive advantage in the protocol itself. |
| **WalletPress core** (self-hosted backend) | **BSL 1.1** (Business Source) | Readable, auditable, but can't commercially clone. Converts to MIT on 2029-01-01. |
| **WalletPress x402 marketplace** (pay-per-wallet) | **Proprietary** | Our revenue engine. Don't show competitors how we price/route payments. |
| **PryScraper** | **Proprietary** | Our competitive moat. Stealth browser, anti-detection — we don't want anyone copying. |
| **RMI (Rug Munch Intelligence)** | **Open Core** (MIT core + Proprietary pro) | Trust is #1 in crypto. MIT detector framework builds community. Proprietary platform = revenue. |
| **rmi-mcp-x402** (MCP server) | **MIT** (for community) + **x402 pay-per-call** (revenue) | MCP servers SHOULD be open source for adoption. Revenue comes from x402 usage, not licensing. |
---
## 2. WALLETPRESS — DETAILED PROFIT MODEL
WalletPress is **THREE products** that need different strategies:
### 2.1 WalletConnect Integration Layer (MIT)
**What it is:** The wallet connection protocol/dApp bridge inside WalletPress.
**License:** MIT — fully open source.
**Why:** Trust. If users connect their wallets through our code, they need to verify it's safe. Closed source = no trust = no users.
**Revenue from this:** NONE directly. This is a **loss leader** that makes the rest of WalletPress trustworthy.
**What goes in this layer:**
- dApp connector (WalletConnect v2 protocol)
- Multi-chain address derivation (BIP-44/49/84)
- Address validation
- ENS/Unstoppable Domains resolution
- Hardware wallet support (Ledger, Trezor)
### 2.2 WalletPress Self-Hosted Core (BSL 1.1)
**What it is:** The main `main.py` FastAPI backend. Users self-host on their own infrastructure.
**License:** BSL 1.1 (Business Source License). Convert to MIT on 2029-01-01.
**Why BSL not MIT:**
- If MIT, anyone can rebrand and sell "WalletPress Pro" competing with us
- BSL allows: read the code, modify for personal use, contribute back
- BSL forbids: selling the software as a competing product
**Pricing — Dual System:**
| Tier | Price | What You Get |
|------|-------|--------------|
| **Community (BSL)** | Free | Full self-hosted backend, all 14 chains, CLI, API, WP plugin |
| **Self-Hosted Pro** | $99/mo | Priority support, auto-updates, advanced features, multi-user |
| **Self-Hosted Enterprise** | $2,400/yr | SSO, audit logs, SLA, dedicated support engineer |
**Revenue projection (Year 1):**
- 200 Community users (free, builds network)
- 50 Pro users × $99 = $4,950/mo = $59,400/yr
- 10 Enterprise × $2,400 = $24,000/yr
- **Self-hosted total: $83,400/yr**
### 2.3 WalletPress x402 Marketplace (Proprietary)
**What it is:** Standalone pay-per-wallet service at `walletpress.cc`. No account, no subscription. Bots/developers pay USDC per wallet generation.
**License:** Proprietary. Don't show competitors our pricing algorithms.
**Pricing — Pay-per-wallet:**
| Service | Price | Use Case |
|---------|-------|----------|
| **Generate wallet** | $0.10/wallet | Bot needs fresh wallet per user |
| **Generate HD batch** (100 wallets) | $5.00 | Bulk wallet generation |
| **Generate HD batch** (1000 wallets) | $25.00 | Enterprise bulk |
| **Derive address from mnemonic** | $0.02/derive | Read-only address extraction |
| **Check balance** | $0.01/check | Wallet monitoring |
| **Sign message** | $0.05/sign | Bot authentication |
| **Send transaction** | $0.10 + gas | Automated payouts |
| **Full transaction suite** | $0.50/tx | Multi-sig, scheduling, batching |
**Revenue projection (Year 1):**
- Average usage: 50,000 wallet generations/mo × $0.10 = $5,000/mo
- Power users: 5 × $500/mo = $2,500/mo
- Enterprise: 2 × $2,000/mo = $4,000/mo
- **x402 marketplace total: $138,000/yr**
### 2.4 WalletPress Cloud (Hosted SaaS)
**What it is:** We host WalletPress for users who don't want to self-host. Same features, managed by us.
**License:** Proprietary SaaS.
**Pricing — Subscription tiers:**
| Tier | Price | Features |
|------|-------|----------|
| **Starter** | $29/mo | 100 wallets, 14 chains, basic API |
| **Growth** | $99/mo | 1,000 wallets, x402 enabled, priority support |
| **Business** | $299/mo | 10,000 wallets, team features, SSO |
| **Enterprise** | $999/mo | Unlimited wallets, dedicated support, custom chains |
**Revenue projection (Year 1):**
- 100 Starter × $29 = $2,900/mo
- 30 Growth × $99 = $2,970/mo
- 10 Business × $299 = $2,990/mo
- 3 Enterprise × $999 = $2,997/mo
- **Cloud total: $142,000/yr**
### 2.5 WalletPress TOTAL Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| Self-hosted Pro | $59K | $180K | $360K |
| Self-hosted Enterprise | $24K | $60K | $120K |
| x402 marketplace | $138K | $400K | $1M |
| Cloud SaaS | $142K | $500K | $1.2M |
| **TOTAL** | **$363K** | **$1.14M** | **$2.68M** |
---
## 3. PRYSCRAPER — DETAILED PROFIT MODEL
### 3.1 License: PROPRIETARY (CONFIRMED)
**Why:**
- Competitive moat is our stealth browser, anti-detection, and bypass techniques
- If competitors see our code, they can replicate in days
- Crypto scrapers are a race — whoever has the best stealth wins
### 3.2 Pricing — Three Models
**Model A: SaaS (Primary)**
- Hosted API at `pry.dev`
- Pay-per-request with x402 micropayments
- Free tier: 1,000 requests/month
- Pro: $49/mo for 100K requests
- Enterprise: Custom pricing for high volume
| Tier | Price | Volume |
|------|-------|--------|
| **Free** | $0 | 1,000 req/mo |
| **Pro** | $49/mo | 100,000 req/mo |
| **Scale** | $199/mo | 500,000 req/mo |
| **Enterprise** | Custom | 5M+ req/mo |
**Model B: x402 Pay-per-call**
- No account needed
- Pay USDC per API call
- AI agents pay automatically
| Endpoint | Price |
|----------|-------|
| `/scrape` | $0.005/call |
| `/crawl` | $0.02/call |
| `/extract` | $0.01/call |
| `/screenshot` | $0.003/call |
| `/stealth_browser` | $0.05/minute |
**Model C: White-label Enterprise**
- Deploy PryScraper on your infrastructure
- Your branding, your data
- Annual license
| Tier | Price |
|------|-------|
| **Startup** | $10K/yr (up to 1M req/mo) |
| **Growth** | $50K/yr (up to 10M req/mo) |
| **Enterprise** | $200K+/yr (unlimited) |
### 3.3 PryScraper Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| SaaS subscriptions | $80K | $300K | $600K |
| x402 pay-per-call | $30K | $120K | $400K |
| White-label Enterprise | $200K | $600K | $1.2M |
| **TOTAL** | **$310K** | **$1.02M** | **$2.2M** |
---
## 4. RMI (RUG MUNCH INTELLIGENCE) — DETAILED PROFIT MODEL
### 4.1 License: OPEN CORE (CONFIRMED)
| Layer | License |
|-------|---------|
| RUI Core (8 basic detectors, public API) | MIT |
| RUI Pro (32 detectors, x402, MCP, RAG) | Commercial |
| RUI Enterprise (on-premise) | BSL 1.1 |
| RUI Cloud (managed) | SaaS |
| Detector framework (community-built) | MIT |
| Rug Munch Verified badge | Proprietary Terms |
### 4.2 Pricing — Already Designed in LICENSING_STRATEGY.md
**Pro tier: $99/mo**
**Team tier: $499/mo**
**Enterprise: $10K+/yr**
**Cloud: Pay-per-use**
### 4.3 RMI Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| Pro subscriptions | $120K | $600K | $1.2M |
| Team subscriptions | $60K | $300K | $600K |
| Enterprise contracts | $90K | $360K | $900K |
| x402 pay-per-call | $30K | $180K | $500K |
| Cloud managed | $0 | $120K | $400K |
| Verified badges | $80K | $200K | $400K |
| **TOTAL** | **$380K** | **$1.76M** | **$4.0M** |
---
## 5. RMI-MCP-X402 — DETAILED PROFIT MODEL
### 5.1 License: MIT + x402 Pay-per-call
**Why MIT:** MCP servers are ecosystem infrastructure. The more people use them, the more the ecosystem grows. Revenue comes from x402 usage, not licensing.
### 5.2 Pricing — x402 Pay-per-call (No subscriptions)
| Tool | Price | Description |
|------|-------|-------------|
| `rugmunch_scan_token` | $0.001 | Full token scan (32 detectors) |
| `rugmunch_wallet_forensics` | $0.01 | Wallet behavior analysis |
| `rugmunch_rug_probability` | $0.005 | AI rug prediction |
| `rugmunch_contract_audit` | $0.05 | Smart contract security |
| `rugmunch_threat_intel` | $0.002 | Threat intelligence lookup |
| `rugmunch_real_time_alert` | $0.001/min | Real-time monitoring |
| `rugmunch_address_labels` | $0.001 | Wallet label lookup |
| `rugmunch_chain_info` | Free | Multi-chain info |
### 5.3 rmi-mcp-x402 Revenue Projection
| Stream | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| x402 pay-per-call | $20K | $150K | $500K |
| Enterprise MCP hosting | $0 | $50K | $200K |
| **TOTAL** | **$20K** | **$200K** | **$700K** |
---
## 6. SPECIFIC IMPROVEMENTS PER SYSTEM
### 6.1 WalletPress Improvements (Self-Hosted BSL)
**Priority 1 (This Week):**
1. **Add WalletConnect v2 integration** — dApp connector for 300+ wallets
2. **Hardware wallet support** — Ledger, Trezor, GridPlus
3. **Multi-sig wallets** — Gnosis Safe integration for 2-of-3, 3-of-5
4. **Address book encryption** — Encrypted contact storage
5. **ENS/Unstoppable Domains** — Human-readable address resolution
**Priority 2 (This Month):**
6. **x402 marketplace UI** — pricing page at walletpress.cc/x402
7. **Stripe billing** — for self-hosted Pro subscriptions
8. **Auto-update mechanism** — Pro users get automatic updates
9. **License key system** — for Pro/Enterprise features
10. **Audit log API** — for Enterprise compliance
**Priority 3 (This Quarter):**
11. **WalletConnect v2 certified** — official WC integration
12. **Multi-user teams** — Organizations, permissions, roles
13. **Transaction scheduling** — Recurring payments, vesting
14. **Gas optimization** — EIP-1559, batch transactions
15. **Mobile SDK** — React Native, Flutter
### 6.2 PryScraper Improvements (Proprietary)
**Priority 1 (This Week):**
1. **camoufox integration** — Firefox-based anti-detection
2. **TLS fingerprint randomization** — Per-request unique fingerprints
3. **Cookie warming** — Pre-aged cookies for trust signals
4. **Residential proxy pool** — 100+ rotating IPs
5. **CAPTCHA solver integration** — 2captcha, anti-captcha
**Priority 2 (This Month):**
6. **JavaScript rendering improvements** — Better React/Vue/Angular support
7. **PDF extraction upgrade** — OCR for scanned documents
8. **Structured data extraction** — Schema.org, JSON-LD, microdata
9. **Screenshot comparison** — Visual diffing for change detection
10. **Rate limiting intelligence** — Per-domain adaptive limits
**Priority 3 (This Quarter):**
11. **AI-powered extraction v2** — Better LLM prompts, structured outputs
12. **Browser extension** — Chrome/Firefox scraping tool
13. **Shopify/WooCommerce integration** — E-commerce scraping
14. **Real-time monitoring** — Webhook + Slack/Discord alerts
15. **Multi-region deployment** — US, EU, APAC for speed
### 6.3 RMI Improvements (Open Core)
**Priority 1 (This Week):**
1. **Split codebase** — core/ (MIT) + pro/ (commercial)
2. **Add LICENSE headers** — Every file has SPDX identifier
3. **MCP tool naming** — rugmunch_scan_token (clear + discoverable)
4. **Verified badge system** — Already built! ✅
5. **Live demo at rugmunch.io** — Paste address → see 32 detector scores
**Priority 2 (This Month):**
6. **Add 8 more detectors** — Currently have 32, add 8 more
7. **RAG investigation reports** — AI-powered forensic analysis
8. **Real-time webhook alerts** — Token launches, deployer activity
9. **Chrome extension "Rug Munch Shield"** — Warns before visiting phishing sites
10. **YouTube demo series** — "How to detect a rug in 30 seconds"
**Priority 3 (This Quarter):**
11. **Threat intel feeds to exchanges** — $10K/mo per exchange
12. **DAO treasury protection** — $5K/mo per DAO
13. **Verified badge at scale** — $500/token, 100 tokens = $50K/mo
14. **Bug bounty program** — $50K for finding wrong safe verdict
15. **AI agent marketplace** — Agents built on top of RMI
### 6.4 rmi-mcp-x402 Improvements (MIT + x402)
**Priority 1 (This Week):**
1. **PyPI package** — `pip install rugmunch-mcp`
2. **Register on pulsemcp.com** — MCP server directory
3. **Register on glama.ai** — Codeberg's MCP registry
4. **Register on mcp.so** — Smithery registry
5. **MCP tool names** — Clear, discoverable, consistent
**Priority 2 (This Month):**
6. **8+ MCP tools** — Already have the framework
7. **x402 payment integration** — USDC on Base, Solana
8. **Streaming responses** — For long-running scans
9. **Batch operations** — Scan multiple tokens in one call
10. **Webhook subscriptions** — Real-time alerts via MCP
**Priority 3 (This Quarter):**
11. **MCP server hosting** — Managed MCP at mcp.rugmunch.io
12. **Custom tool builder** — Let users add their own tools
13. **Tool analytics** — Usage stats, popular tools
14. **Multi-MCP routing** — One request, multiple MCPs
15. **MCP marketplace** — Third-party tools on our platform
---
## 7. UNIFIED REVENUE PROJECTION (All Systems)
| System | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| **RMI (Rug Munch Intelligence)** | $380K | $1.76M | $4.0M |
| **WalletPress** (self-hosted + x402 + cloud) | $363K | $1.14M | $2.68M |
| **PryScraper** (SaaS + x402 + white-label) | $310K | $1.02M | $2.2M |
| **rmi-mcp-x402** (x402 pay-per-call) | $20K | $200K | $700K |
| **TOTAL** | **$1.07M** | **$4.12M** | **$9.58M** |
---
## 8. GO-TO-MARKET SEQUENCE
### Phase 1: Trust Foundation (Month 1-3)
- Launch RUI Core as MIT (open source)
- Launch PryScraper as SaaS (no source)
- Launch WalletPress Community (BSL, free self-hosted)
- Goal: 1,000 GitHub stars, 100 SaaS users
### Phase 2: Revenue (Month 4-6)
- Launch RUI Pro ($99/mo)
- Launch PryScraper Pro ($49/mo)
- Launch WalletPress x402 marketplace ($0.10/wallet)
- Goal: $50K MRR
### Phase 3: Enterprise (Month 7-12)
- Launch RUI Enterprise ($10K+/yr)
- Launch WalletPress Enterprise ($2,400/yr)
- Launch PryScraper White-label ($10K+/yr)
- Goal: $1M ARR
### Phase 4: Scale (Year 2)
- Launch RUI Cloud (managed SaaS)
- Launch WalletPress Cloud (hosted)
- Launch MCP marketplace
- Goal: $4M ARR
### Phase 5: Dominate (Year 3)
- First-mover advantage compounds
- Network effects (more users = better data = better product)
- Goal: $10M ARR
---
## 9. COMPETITIVE POSITIONING
### WalletPress vs Competition
| Competitor | Our Advantage |
|------------|---------------|
| Trust Wallet | Open source, auditable, 14 chains vs 10 |
| MetaMask | Self-hostable, institutional features |
| Exodus | BSL means we can build features they can't copy |
| Coinbase Wallet | We don't have their KYC baggage |
### PryScraper vs Competition
| Competitor | Our Advantage |
|------------|---------------|
| ScrapingBee | Proprietary = we don't show them how |
| Bright Data | x402 pay-per-call, no minimums |
| ScraperAPI | $0.005/call vs $0.10/call, 20x cheaper |
| Apify | We have AI extraction built in |
### RMI vs Competition
| Competitor | Our Advantage |
|------------|---------------|
| GoPlus | Open core = verifiable, x402 = AI agents |
| De.Fi | Open source = trustworthy |
| Token Sniffer | 32 detectors vs their 5, 96 chains |
| Chainalysis | 100x cheaper |
---
## 10. THE FIRST-MOVER ADVANTAGE
Why we win in 2026:
1. **RUI is the first open-core crypto intelligence platform** — competitors are all closed
2. **PryScraper is the first x402-native scraper** — competitors charge $0.10/call, we charge $0.005
3. **WalletPress is the first BSL wallet** — community can audit, competitors can't clone
4. **rmi-mcp-x402 is the first MCP server for crypto** — AI agents will use us by default
5. **The Rug Munch Verified badge is the first honest assessment** — others are paid shills
We are in the right place at the right time. The only thing that can stop us is execution.
---
## 11. NEXT STEPS (Immediate)
### This Week
- [ ] Decide on WalletConnect = MIT (done above)
- [ ] Add WalletConnect v2 to WalletPress
- [ ] Build `pip install rugmunch-mcp` package
- [ ] Register on pulsemcp.com + glama.ai
- [ ] Split RMI code into core/ (MIT) + pro/ (commercial)
### This Month
- [ ] Launch PryScraper Pro tier ($49/mo)
- [ ] Launch WalletPress x402 marketplace UI
- [ ] Launch RUI Pro tier ($99/mo)
- [ ] Create rugmunch.io live demo
- [ ] Start content marketing (YouTube, blog)
### This Quarter
- [ ] Launch Verified Badge program
- [ ] Launch PryScraper White-label
- [ ] Launch RUI Cloud
- [ ] Launch WalletPress Cloud
- [ ] Enterprise sales (DAOs, exchanges)
---
**The decisions are made. The licenses are set. The pricing is designed. The first-mover window is open. Now we ship.**

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---
title: Pry MCP + x402 Audit & 20-Win Improvement Plan
status: canonical
owner: Engineering
last_updated: 2026-07-01
---
# Pry MCP + x402 Audit & 20-Win Improvement Plan
> Researched current standards (modelcontextprotocol.io, coinbase/x402 GitHub), audited RMI's x402 system, and identified 20 critical improvements.
## Implementation Status
| # | Win | Status | Notes |
|---|-----|--------|-------|
| 1 | Fix MCP `tools/call` response format | **Done** | `mcp_production.py` returns `{"content": [...], "isError": bool}` |
| 2 | Add `prompts/list` and `prompts/get` | **Done** | 7 real prompts with argument injection |
| 3 | Real x402 facilitator integration | **Done** | `x402.py` calls Coinbase + PayAI `/verify` and `/settle` |
| 4 | Multi-chain x402 support | **Done** | 13 chains, asset-aware `accepts` array |
| 5 | MCP `outputSchema` for every tool | **Done** | All 12 tools declare output schemas |
| 6 | x402 `PaymentRequirements` with multiple `accepts` | **Done** | Per-chain USDC/USDT/DAI/etc. options |
| 7 | Smithery + Glama + PulseMCP directory listings | **Done** | `smithery.yaml`, `glama.json`, `pulsemcp.json` |
| 8 | Cloudflare Worker deployment | **Done** | `workers/mcp-worker.js` + `wrangler.toml` |
| 9 | Resource subscriptions | **Done** | `resources/subscribe`, `notifications/resources/updated/list_changed` |
| 10 | `completion/complete` endpoint | **Done** | Template ID, max_pages, format autocompletion |
| 11 | HTTP+SSE transport for MCP | **Done** | `/mcp/sse` + `/mcp/messages/{session_id}`; integration-tested with uvicorn; middleware is pure ASGI to avoid buffering |
| 13 | Multi-facilitator smart router | **Done** | `FacilitatorRouter` with health tracking + EIP-7702 fallback |
| 14 | x402 batch payments | **Done** | `POST /v1/x402/batch-payment` + `/v1/x402/batch-verify` |
| 15 | `prompts/get` with embedded resources | **Partial** | Prompts reference resources by URI in text; full embedded resource spec pending |
| 16 | x402 EIP-7702 self-verify mode | **Done** | EVM receipt polling fallback |
| 17 | MCP `logging` capability | **Done** | `logging/setLevel`, `notifications/message` forwarding |
| 18 | x402 settlement receipt | **Done** | `PAYMENT-RESPONSE` header with tx/block/facilitator |
| 19 | MCP server health check endpoint | **Done** | `GET /mcp/health` |
| 20 | `annotations` on all resources | **Done** | audience + priority on 4 resources |
**Remaining:** MCP Apps (#12) — UI widget spec is still stabilizing; will revisit once modelcontextprotocol.io finalizes the schema.
---
## Current State vs. Standards
### MCP (Model Context Protocol)
| Standard Requires | Pry Has | Gap |
|-------------------|---------|-----|
| JSON-RPC 2.0 protocol over stdio | ✅ Fallback implemented | Official SDK class used opportunistically |
| `initialize` with protocolVersion | ✅ Returns `2024-11-05` | OK |
| `tools/list` with proper JSON Schema | ✅ 12 tools | OK |
| `tools/call` with content array | ✅ Returns proper content array | OK |
| `resources/list` and `resources/read` | ✅ Implemented with live data | OK |
| `prompts/list` and `prompts/get` | ✅ 7 prompts | OK |
| `notifications/resources/updated` | ✅ Via notification observers | OK |
| `completion/complete` | ✅ Implemented | OK |
| `logging/setLevel` | ✅ Implemented + log forwarding | OK |
| `roots/list` | ❌ Missing | Intentionally omitted — Pry is server-side |
| **FastMCP** decorator pattern | ⚠️ Fallback dict-of-tools | Official SDK path stubbed; not required for compliance |
| `outputSchema` for tools | ✅ All tools | OK |
| `annotations` on resources | ✅ All resources | OK |
| Resource subscriptions | ✅ `resources/subscribe` + `unsubscribe` | OK |
| HTTP+SSE transport | ✅ `/mcp/sse` + `/mcp/messages/{sid}` | OK |
**Our MCP score: 9/10 — spec-compliant on all required methods except roots/list (out of scope).**
### x402 (HTTP 402 Payment Required)
| Standard Requires | Pry Has | Gap |
|------------------|---------|-----|
| HTTP 402 status code with PaymentRequired body | ✅ Returns 402 | OK |
| `x402Version: 1` in response | ✅ | OK |
| `accepts` array (multiple payment options) | ✅ Multiple chains/assets | OK |
| `PaymentRequirements` schema | ✅ Full schema | OK |
| `PaymentPayload` signing | ✅ `PAYMENT-SIGNATURE` header decoded | OK |
| `PAYMENT-REQUIRED` header (Base64 JSON) | ✅ | OK |
| `PAYMENT-RESPONSE` header on success | ✅ Settlement receipt | OK |
| Facilitator integration (`/verify`, `/settle`) | ✅ Coinbase + PayAI | OK |
| Multiple chains | ✅ 13 chains | OK |
| USDC amounts in atomic units (6 decimals) | ✅ | OK |
| `network` per chain | ✅ | OK |
| `scheme: "exact"` | ✅ | OK |
| `extra` field | ✅ Includes operation, batch_id | OK |
| **Coinbase facilitator** | ✅ Via `x402.org/facilitator` | OK |
| **EIP-7702 self-verify** | ✅ Fallback RPC polling | OK |
| Settlement receipt | ✅ tx hash, block, facilitator | OK |
| Multi-facilitator smart router | ✅ Health tracking + fallback | OK |
| Batch payments | ✅ `/v1/x402/batch-payment` | OK |
**Our x402 score: 9/10 — production-ready payment flow with fallback paths.**
## Top 20 Wins (Ranked by Revenue Impact)
### Tier 1: Immediate Revenue ($100K+ MRR potential)
| # | Win | Why | Effort |
|---|-----|-----|--------|
| 1 | **Fix MCP `tools/call` response format** — Return `{"content": [{"type": "text", "text": "..."}]}` not raw dict | AI agents can't parse our responses. One-line fix. | 1h |
| 2 | **Add `prompts/list` and `prompts/get`** — Pre-built prompt templates like "research a company", "compare products", "extract all emails" | Prompts are how AI users discover server capabilities. RMI doesn't have these. | 4h |
| 3 | **Real x402 facilitator integration** — Call real Coinbase/PayAI facilitator to verify + settle payments | Without this, payments don't actually work. The current "verify" just records a payment. | 1d |
| 4 | **Multi-chain x402 support** — Match RMI's 13 chains (Base, Solana, ETH, Polygon, Arbitrum, TRON, BTC, etc.) | Each chain is a market. Currently we only have Base. | 1d |
| 5 | **MCP `outputSchema` for every tool** — Declare what each tool returns | AI agents can validate responses. Standard requirement. | 4h |
| 6 | **x402 `PaymentRequirements` with multiple `accepts`** — Per-call chain selection | Customers want to pay in their preferred token. One chain = lost revenue. | 4h |
### Tier 2: Adoption (the network effect)
| # | Win | Why | Effort |
|---|-----|-----|--------|
| 7 | **Smithery + Glama + PulseMCP directory listings** — Like RMI has (sol.rugmunch.io, base.rugmunch.io) | Discovery = users. These are THE MCP directories. | 2h |
| 8 | **Cloudflare Worker deployment** — Run the MCP server on Cloudflare Workers like RMI does (sol.rugmunch.io pattern) | Users can't self-host easily. Hosted = revenue. | 1d |
| 9 | **Resource subscriptions** — Push updates to clients when monitors fire | Real-time value. AI agents can react to changes. | 1d |
| 10 | **`completion/complete` endpoint** — Autocomplete parameter values (e.g., suggest URLs, template IDs) | Better UX in AI clients. Standard requires this. | 4h |
| 11 | **HTTP+SSE transport for MCP** — Not just stdio | Claude web, n8n, Zapier integrations need HTTP transport | 1d |
| 12 | **MCP Apps (UI elements)** — Interactive widgets in chat that show scraped data | The newest MCP spec feature. Huge UX win. | 2d |
### Tier 3: Premium features (the moat)
| # | Win | Why | Effort |
|---|-----|-----|--------|
| 13 | **Multi-facilitator smart router** — Auto-pick best facilitator (lowest fee, fastest confirmation) per chain | RMI has this. Without it, our x402 is fragile. | 2d |
| 14 | **x402 batch payments** — Pay for 100 scrapes with one transaction | Bulk users want this. Standard supports it via `PaymentPayload` with multiple `PaymentRequirements`. | 1d |
| 15 | **`prompts/get` with embedded resources** — "Compare products" prompt that auto-injects the catalog resource | Showcases full MCP capability. Differentiator. | 1d |
| 16 | **x402 EIP-7702 self-verify mode** — No facilitator needed, direct on-chain verification | Zero dependencies. RMI supports this. | 1d |
| 17 | **MCP `logging` capability** — Server sends structured log messages to client | Critical for debugging. Standard. | 4h |
### Tier 4: Polish
| # | Win | Why | Effort |
|---|-----|-----|--------|
| 18 | **x402 settlement receipt** — Return tx hash, block number, network, timestamp in `PAYMENT-RESPONSE` header | Customers need receipts. Standard requires this. | 2h |
| 19 | **MCP server health check endpoint** — `/health` reports MCP server status, tools count, version | Production ops need this. | 2h |
| 20 | **`annotations` on all resources** — `audience: ["user", "assistant"]`, `priority: 0.8` | Better AI client UX. Standard feature. | 1h |
## How to Ship This (Practical Order)
| Week | Deliverables | Expected MRR |
|------|-------------|--------------|
| **Week 1** | Fix #1, #5, #18, #19, #20 (MCP response format, output schemas, x402 receipt, health, annotations) | $0 → enables usage tracking |
| **Week 2** | #3, #4, #6, #13, #16 (Real facilitator, multi-chain, multi-accept, smart router, EIP-7702) | $0 → first revenue |
| **Week 3** | #2, #10, #11, #15, #17 (Prompts, completion, HTTP+SSE, embedded resources, logging) | $5K → adoption |
| **Week 4** | #7, #8, #9, #12, #14 (Directory listings, Cloudflare Worker, subscriptions, MCP Apps, batch payments) | $20K+ → scale |
## What I'd Build RIGHT NOW (Top 5 highest leverage)
1. **Fix MCP `tools/call` format** — AI agents can't even use our current server
2. **Add `prompts/list` and `prompts/get`** — Biggest differentiator, zero competitors have it
3. **Real x402 facilitator integration** — Without this, no real revenue
4. **Multi-chain `accepts` array** — Capture all payment networks
5. **Cloudflare Worker deployment** — Self-hosting is friction
## Where the Money Is
- **Per-call pricing**: $0.001 (scrape) to $0.05 (browser automation) — x402 standard pricing
- **Volume**: Apify does $100M+ ARR. Pry can capture 1-5% as a lighter alternative = $1-5M ARR
- **Real revenue starts**: Week 2 (after real facilitator)
- **Profitability**: 90%+ (no compute, no labor, pure infrastructure)
- **Competitive moat**: 110 templates, 9-tier fallback, referral network = hard to replicate

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SHELL := /bin/bash
PYTHON := python3
.PHONY: help install dev lint format typecheck test security check clean commit precommit ci
help:
@echo "Pry Makefile"
@echo ""
@echo " install Install dependencies"
@echo " dev Start dev server"
@echo " lint Run ruff check"
@echo " format Run ruff format"
@echo " typecheck Run mypy"
@echo " test Run pytest"
@echo " security Run bandit + safety"
@echo " check Full audit (lint + typecheck + test)"
@echo " clean Remove build artifacts"
@echo " precommit Run ruff + mypy + bandit"
install:
pip install -e ".[dev]"
dev:
uvicorn api:app --reload --host 0.0.0.0 --port 8005
lint:
ruff check .
format:
ruff format .
typecheck:
mypy .
test:
pytest tests/ -v --cov=. --cov-report=term-missing
security:
bandit -r . -x tests/,.venv,__pycache__
check: lint typecheck test
clean:
rm -rf __pycache__ .ruff_cache .mypy_cache .pytest_cache *.egg-info build dist
precommit: lint format typecheck security
ci: precommit test

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# PLAN.md — PryScraper
> Current sprint. 21-day cycles per ADR-0012.
## Status
current_sprint=2026-Q3-S1 · started=2026-07-01 · ends=2026-07-21
## Sprint Goals
1. _TODO: top 1-3 goals for this sprint_
## In Progress
- [ ] TODO: in-progress items
## Backlog (this sprint)
- [ ] TODO: planned items
## Risks
- TODO: anything blocking progress
## Done (this sprint)
- _None yet — sprint just started._
## Carry-over from previous sprint
- _None._
---
## Definition of Done
- [ ] Code complete
- [ ] Tests written (>80% coverage on changed lines)
- [ ] Pre-commit hooks pass (ruff, mypy, gitleaks, bandit)
- [ ] CI passes on forgejo
- [ ] Conventional commit message
- [ ] PR reviewed + merged
- [ ] Deployed to staging
- [ ] Smoke-tested in production
- [ ] Observability in place (metrics, logs, alerts)
- [ ] Docs updated (AGENTS.md, ARCHITECTURE.md, README.md)
- [ ] STATUS.md updated

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# Pry — Open any website
Self-hosted web scraping + browser automation API. Cloudflare bypass, document parsing, AI summarization. No API keys needed.
## Quickstart
```bash
# Install
pip install pry
# Start the server
pry serve
# Scrape a URL
pry open https://example.com
# With JSON extraction
pry open https://store.com/product --json --schema product.json
# Crawl a site
pry crawl https://docs.com --max-pages 20 -o data.json
```
## Docker
```bash
docker compose up -d
# Pry on :8002, FlareSolverr on :8191
```
## CLI Reference
| Command | Description |
|---------|-------------|
| `pry open <url>` | Scrape URL to clean markdown |
| `pry watch <url>` | Monitor page for changes |
| `pry crawl <url>` | Crawl multiple pages |
| `pry batch <file>` | Batch scrape URLs from file |
| `pry parse <url>` | Parse PDF/DOCX/image |
| `pry ss <url>` | Take screenshot |
| `pry run [pry.yml]` | Execute job file |
| `pry serve` | Start API server |
## API
### `POST /v1/scrape`
```json
{"url": "https://example.com", "bypassCloudflare": true, "formats": ["markdown"]}
```
### `POST /v1/crawl`
```json
{"url": "https://docs.com", "maxPages": 10, "maxDepth": 2}
```
### `POST /v1/automate`
```json
{"steps": [{"action": "navigate", "url": "https://...", {"action": "click", "selector": "#btn"}]]}
```
### `POST /v1/vision`
Analyze images with free OpenRouter vision models. Auto-fallback across 5 models.
```json
{"url": "https://...", "question": "What is shown?"}
```
### `POST /v1/extract`
Extract structured data with JSON schema + optional AI fallback.
### `POST /v1/batch`
Scrape up to 50 URLs in parallel.
### `POST /v1/compare`
Diff two URLs.
### `POST /v1/summarize`
AI summarization via local Ollama (free, private).
### `GET /health`
Service health + cache stats + active sessions.
## Python SDK
```python
from pry_sdk import PryCrawl
mc = PryCrawl("http://localhost:8002")
result = await mc.scrape("https://example.com")
print(result["data"]["markdown"])
```
```python
from pry_sdk import PryCrawlSync
mc = PryCrawlSync("http://localhost:8002")
result = mc.scrape("https://example.com")
```
### SDK Methods
| Method | Description |
|--------|-------------|
| `scrape(url, **opts)` | Scrape to markdown |
| `scrape_json(url, schema, **opts)` | Scrape with JSON extraction |
| `crawl(url, max_pages, **opts)` | Crawl site |
| `map(url, limit)` | Discover URLs |
| `parse(url)` | Parse document |
| `automate(steps, **opts)` | Browser automation |
| `screenshot(url)` | Screenshot page |
| `health()` | Service health |
## MCP (AI Agent Integration)
```json
POST /mcp/call
{"name": "pry_scrape", "arguments": {"url": "https://..."}}
```
Compatible with Claude, Hermes, Cursor, and any MCP client.
## Features
- **4-tier anti-detection**: Direct → FlareSolverr → Playwright → Googlebot
- **Cloudflare bypass**: Automatic via FlareSolverr
- **Document parsing**: PDF, DOCX, images (OCR), CSV, JSON
- **Browser automation**: Login flows, form filling, sessions
- **Vision AI**: Free OpenRouter vision models with auto-fallback
- **Diff tracking**: Page change monitoring with webhooks
- **Batch processing**: Parallel scrape with templates
- **SEO analysis**: Title, meta, headings, keywords, readability
- **Schema extraction**: JSON-LD, Open Graph, microdata
- **Export formats**: JSON, CSV, RSS, TXT, SQL
- **Rate limiting**: Per-IP token bucket (default 120 RPM)
- **Caching**: LRU + Redis with TTL-based invalidation
- **WebSocket streaming**: Real-time job progress
- **Circuit breaker**: Per-domain backoff on failures
## Architecture
```
┌─────────────┐ ┌──────────────┐ ┌──────────┐
│ Client │→ │ Pry API │→ │ Scraper │
│ (CLI/SDK) │ │ (FastAPI) │ │ Engine │
└─────────────┘ └──────┬───────┘ └────┬─────┘
│ ├─ Direct HTTP
│ ├─ FlareSolverr
│ ├─ Playwright
│ └─ Googlebot
│
┌────┴────┐
│ Cache │
│ Redis │
│ Rate │
└─────────┘
```
## Deployment
### Docker Compose (recommended)
```bash
docker compose up -d
```
### Bare metal
```bash
pip install pry
playwright install chromium
# Optional: docker run -d -p 8191:8191 ghcr.io/flaresolverr/flaresolverr
pry serve
```
### Environment
| Variable | Default | Description |
|----------|---------|-------------|
| `PRY_URL` | `http://localhost:8005` | API endpoint for CLI |
| `TOR_ENABLED` | `false` | Enable Tor routing |
| `PROXY_URL` | — | HTTP/SOCKS proxy URL |
| `RATE_LIMIT_RPM` | `120` | Requests per minute |
| `OPENROUTER_API_KEY` | — | For vision endpoint |
## Development
```bash
make install # Install with dev deps
make dev # Start hot-reload server
make lint # ruff check
make format # ruff format
make typecheck # mypy
make test # pytest
make check # Full audit
```
## License
MIT — Rug Munch Media LLC

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---
title: Pry Roadmap & Status
status: canonical
owner: Rug Munch Media LLC Engineering
last_updated: 2026-06-30
---
# Pry Roadmap
## Current Status: v3.0.0 — Active Development
Pry is a mature, feature-complete self-hosted web scraping platform. This roadmap tracks remaining gaps and planned improvements.
## What's Built (✅ Complete)
### Core Scraping Engine
- ✅ 10-tier fallback scraper (direct → FlareSolverr → Playwright → Tor → cache)
- ✅ Block detection with adaptive strategy rotation
- ✅ Document parsing (PDF, DOCX, OCR, CSV, JSON)
- ✅ Shadow DOM extraction
- ✅ Lazy load / infinite scroll handling
### Anti-Detection
- ✅ Cloudflare bypass via FlareSolverr
- ✅ undetected-chromedriver integration
- ✅ Stealth engine (6 anti-detection JS scripts)
- ✅ User-agent rotation
- ✅ Tor proxy routing
- ✅ SOCKS5 proxy support
- ✅ Browser pool with pre-warming
### API (115 endpoints)
- ✅ REST API with 46 endpoint groups
- ✅ Health probes (liveness, readiness)
- ✅ Prometheus metrics
- ✅ Rate limiting (token bucket)
- ✅ Circuit breaker (per-domain)
- ✅ WebSocket streaming
- ✅ Async job queue
- ✅ MCP server (Claude/Hermes/Cursor compatible)
### Auth & Automation
- ✅ CAPTCHA solver (6 providers, auto-fallback)
- ✅ Credential vault (encrypted)
- ✅ SSO login script generation
- ✅ Session management (create, save, restore, close)
- ✅ Browser action recorder
- ✅ Signup automation
- ✅ Account pool management
### Extraction & Analysis
- ✅ JSON schema extraction
- ✅ LLM extraction with chunking strategies
- ✅ Schema.org/JSON-LD extraction
- ✅ Email extraction (Gmail, Outlook, raw)
- ✅ AI summarization (Ollama)
- ✅ AI categorization
- ✅ Vision AI (OpenRouter, 5-model fallback)
- ✅ SEO analysis (title, meta, headings, keywords, readability)
- ✅ Tech stack detection
- ✅ Field suggestion (AI)
### Monitoring & Alerts
- ✅ Page change detection (content fingerprinting)
- ✅ Diff tracking between versions
- ✅ Scheduled monitors (cron-based)
- ✅ Content freshness dashboard
- ✅ Structure change monitoring
- ✅ Multi-channel alerts (webhook, Slack, email, SMS)
### Data Pipeline
- ✅ Pipeline definition, validation, and execution
- ✅ Hook registration and execution
- ✅ Data transformation (JSON, CSV, RSS, TXT, SQL)
- ✅ Multi-format export
- ✅ Webhook delivery, S3, GCS, SFTP
### Quality & Review
- ✅ Content quality scoring (completeness, accuracy, freshness)
- ✅ Human review queue (submit, approve, reject)
- ✅ Quality history tracking
### Commerce & CRM
- ✅ WooCommerce product sync
- ✅ Shopify product sync (with Shopify app backend)
- ✅ Salesforce CRM sync
- ✅ HubSpot CRM sync
- ✅ Zoho CRM sync
### Compliance
- ✅ GDPR consent management
- ✅ Data retention policies
- ✅ Data deletion (right to be forgotten)
- ✅ Data portability (export)
- ✅ Compliance audit log
- ✅ Sensitive data detection
- ✅ PII/copyright stripping for training data
### Agency
- ✅ White-label agency profiles
- ✅ Client sub-account management
- ✅ Usage analytics
- ✅ Quota enforcement
### Training Data
- ✅ Dataset generation from scraped content
- ✅ PII and copyright stripping
- ✅ License classification
- ✅ Dataset CRUD
### Templates (110+)
- ✅ Pre-built scraper templates for major sites
- ✅ Template engine with parameterization
- ✅ Template CRUD
### Intelligence
- ✅ Competitive intelligence snapshots
- ✅ Competitor comparison/diff
- ✅ Intel report generation
### Integrations
- ✅ Webhook support
- ✅ Zapier integration
- ✅ Make (formerly Integromat) integration
- ✅ Browser extension (Chrome)
- ✅ WordPress plugin
- ✅ Shopify app
- ✅ GPT Action manifest
- ✅ MCP server config
- ✅ CLI with autocomplete (bash, zsh, fish)
### Infrastructure
- ✅ Docker Compose deployment
- ✅ Dockerfile
- ✅ GitHub Actions CI/CD
- ✅ Pre-commit hooks (ruff, mypy, bandit, gitleaks)
- ✅ Makefile with standardized targets
- ✅ pyproject.toml with ruff/mypy/pytest config
## What's Remaining (🔜 Planned)
### High Priority
- [ ] Comprehensive test coverage > 80% (current: ~266 tests, ~40% coverage)
- [ ] End-to-end integration tests with FlareSolverr + Playwright
- [ ] OpenAPI specification cleanup and versioning
- [ ] Type annotations across all modules (mypy strict)
### Medium Priority
- [ ] Redis-based distributed rate limiting
- [ ] PostgreSQL-backed job queue (beyond filesystem)
- [ ] Web dashboard (beyond current `/dashboard` health view)
- [ ] API key authentication for multi-user deployments
- [ ] OpenTelemetry tracing instrumentation
- [ ] Prometheus metrics for all modules (currently only in `api.py`)
### Low Priority
- [ ] GraphQL API alternative
- [ ] Webhook retry with exponential backoff
- [ ] Template marketplace / sharing
- [ ] i18n for multi-language content extraction
- [ ] Mobile app (React Native) companion
- [ ] Desktop app (Electron) companion
## Audit Metrics (2026-06-30)
| Metric | Value |
|--------|-------|
| Version | 3.0.0 |
| Python source files | 101 |
| Total lines of Python | 18,567 |
| Test files | 43 |
| Total test functions | 266 |
| API endpoints | 115 |
| Endpoint groups (tags) | 46 |
| JSON scraper templates | 110 |
| HTML templates | 2 |
| Stealth JS scripts | 6 |
| Browser extension files | 5 |
| Shopify app files | 3 |
| WordPress plugin files | 1 |
| Config/support files | 8 |
| Total functions (all modules) | 455 |
| Total classes | 48 |

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# SECURITY.md — PryScraper
> Threat model, secrets, auth, rate limiting, vuln disclosure.
## Threat Model
### Assets
- User wallets (private keys never touch our servers — verified by signature)
- API keys (gopass)
- DB credentials (gopass)
- Reputation (RMM LLC brand)
### Threats
1. **Secret leak to git** — gitleaks pre-commit hook + history scan on incidents
2. **SQL injection** — SQLAlchemy ORM + parameter binding only
3. **XSS** — React auto-escapes; CSP headers on nginx
4. **CSRF** — SameSite=Strict cookies + Bearer tokens
5. **DDoS** — Cloudflare proxy in front
6. **Supply chain** — renovate keeps deps updated; pip-audit weekly
## Secrets
**Storage**: gopass (`rmi/pryscraper/*`)
**Loading**:
- Local dev: `.env` file (gitignored)
- Production: docker `--env-file`, systemd `EnvironmentFile`
**Rotation**:
- API tokens: quarterly
- DB passwords: quarterly
- Signing keys: on personnel change
**Audit**: any secret leak triggers rotation within 1 hour.
## Authentication
- Bearer tokens for API
- OAuth2 (Telegram, etc) for user-facing bots
- x402 micropayments for paid endpoints (signing via Solana/EVM wallet)
## Authorization
- RBAC for admin endpoints
- Rate limiting per IP/token (sliding window)
- Admin endpoints require `cfg.admin_key` check
## Input Validation
- Pydantic models for ALL API inputs (no `dict`/`any` types)
- Length limits, regex validation, enum constraints
- Reject any input containing mainnet addresses/keys (pre-commit hook)
## Rate Limiting
- Default: 60 req/min per IP
- Burst: 100 req
- Authenticated: 600 req/min
- Returns 429 with `Retry-After` header
## Dependencies
- `pip-audit` weekly (CI)
- `safety check` weekly (CI)
- Renovate bot for auto-PR updates
## Vulnerability Disclosure
Email: security@rugmunch.io (PGP key in gopass)
Response within 24 hours.
Patch within 7 days for critical, 30 days for medium.
## Audit Log
Every state-changing action logged to:
- `app/state/audit/` (JSONL, append-only)
- ClickHouse (analytics, 90-day retention)
- WORM storage (R2 with object lock, 7-year retention)
## Incident Response
1. Detect (alert from monitoring)
2. Contain (revoke compromised secrets, block IPs)
3. Eradicate (patch vuln, deploy fix)
4. Recover (verify systems clean, restore from backup if needed)
5. Learn (post-mortem, update SECURITY.md)

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# STATUS.md — PryScraper
> Where we are RIGHT NOW. Update before every commit.
## Last Updated
2026-07-02 (template, replace on first commit)
## Deployment Status
- **Last deploy**: TBD
- **Current version**: TBD
- **Health**: TBD
- **Uptime (30d)**: TBD
- **Active branch**: `main`
## Open Issues
- _None — track in forgejo issues_
## Recent Activity
- 2026-07-02: Repository created from `fleet-template`
## Known Issues / Tech Debt
- _None yet_
## Quick Links
- [Live URL](https://pryscraper.rugmunch.io)
- [API docs](https://pryscraper.rugmunch.io/docs)
- [Health](https://pryscraper.rugmunch.io/health)
- [Repo](https://git.rugmunch.io/RugMunchMedia/pryscraper)
## Status Indicators
Use these in commit messages + status updates:
- 🟢 healthy — deployed, monitored, no issues
- 🟡 degraded — running but with known issues
- 🔴 down — service unavailable
- 🚧 in-development — pre-production, expect breakage
- ⏸️ paused — work intentionally stopped

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# TESTING.md — PryScraper
> Test strategy. Unit, integration, e2e, fixtures.
## Test Pyramid
```
/\
/ \ E2E (slow, few)
/----\
/ \ Integration (medium, more)
/--------\
/ \ Unit (fast, many)
/____________\
```
- **Unit tests**: test individual functions/classes in isolation
- **Integration tests**: test components together (DB, API, auth)
- **E2E tests**: test full user flows (slow, run before deploy)
## Structure
```
tests/
├── unit/ # fast, no external deps
├── integration/ # uses test DB, mocks external APIs
├── e2e/ # full stack, runs against staging
├── fixtures/ # test data (mainnet guard: no real addresses/keys)
└── conftest.py # pytest config
```
## Running
```bash
make test # all tests
make test-unit # only unit
make test-integration # only integration
make test-cov # with coverage report
pytest tests/unit/test_X.py -v # single file
```
## Fixtures
Use pytest fixtures. Examples:
- `clean_db` — fresh DB per test
- `mock_helius` — mock Helius API responses
- `auth_headers` — Bearer token for test user
- `sample_token` — fake token (test address, NOT mainnet)
**NEVER use mainnet data in tests.** Pre-commit hook blocks this.
## Coverage
- Target: >80% on changed lines
- Enforced in CI via `--cov-fail-under=80`
## Mocking
- `httpx` for HTTP mocking (use `respx` or `pytest-httpx`)
- `unittest.mock` for general mocking
- VCR.py for recording/replaying API calls
## Performance Tests
- `locust` for load testing (run against staging)
- Profile with `py-spy` or `cProfile`
## CI
Every PR runs:
1. Lint (ruff)
2. Type check (mypy strict)
3. Unit tests + coverage
4. Integration tests (test DB)
5. Security scan (bandit, gitleaks)
6. Build Docker image (verify it compiles)
Merge blocked if any fails.

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---
title: Pry Usage Guide
status: canonical
owner: Rug Munch Media LLC Engineering
last_updated: 2026-06-30
---
# Pry Usage Guide
## Quickstart
```bash
# Install
pip install pry
# Start the server
pry serve
# Scrape a URL
pry open https://example.com
# With JSON extraction
pry open https://store.com/product --json --schema product.json
# Crawl a site
pry crawl https://docs.com --max-pages 20 -o data.json
```
## Docker
```bash
docker compose up -d
# Pry on :8002, FlareSolverr on :8191
```
## CLI Reference
| Command | Description |
|---------|-------------|
| `pry open <url>` | Scrape URL to clean markdown |
| `pry watch <url>` | Monitor page for changes |
| `pry crawl <url>` | Crawl multiple pages |
| `pry batch <file>` | Batch scrape URLs from file |
| `pry parse <url>` | Parse PDF/DOCX/image |
| `pry ss <url>` | Take screenshot |
| `pry run [pry.yml]` | Execute job file |
| `pry serve` | Start API server |
| `pry completions` | Install shell autocomplete |
### CLI Options
| Flag | Applies to | Description |
|------|-----------|-------------|
| `--json` | `open` | Output as JSON |
| `--schema <file>` | `open` | JSON schema file for extraction |
| `--timeout <sec>` | `open` | Request timeout |
| `--webhook <url>` | `watch` | Webhook URL for change notifications |
| `--max-pages <n>` | `crawl` | Maximum pages to crawl |
| `--template <json>` | `batch` | Extraction template as JSON string |
| `-o <file>` | `crawl`, `ss` | Output file path |
| `--port <n>` | `serve` | Server port (default: 8005) |
## API Reference
Base URL: `http://localhost:8005` (configurable via `PRY_URL` env var)
### Authentication
No authentication required for local use. For production, deploy behind a reverse proxy with auth.
### Core Endpoints
#### `POST /v1/scrape`
Scrape a single URL.
```json
{
"url": "https://example.com",
"bypassCloudflare": true,
"formats": ["markdown"],
"timeout": 30
}
```
#### `POST /v1/crawl`
Crawl multiple pages from a starting URL.
```json
{
"url": "https://docs.com",
"maxPages": 10,
"maxDepth": 2,
"timeout": 120
}
```
#### `POST /v1/extract`
Extract structured data with JSON schema.
```json
{
"url": "https://store.com/product/123",
"schema": {
"name": "string",
"price": "string",
"description": "string",
"availability": "string"
}
}
```
#### `POST /v1/batch`
Scrape up to 50 URLs in parallel.
```json
{
"urls": ["https://example1.com", "https://example2.com"],
"bypassCloudflare": true,
"formats": ["markdown"]
}
```
#### `POST /v1/automate`
Execute browser automation steps.
```json
{
"steps": [
{"action": "navigate", "url": "https://login.example.com"},
{"action": "type", "selector": "#username", "value": "user"},
{"action": "type", "selector": "#password", "value": "pass"},
{"action": "click", "selector": "#login-btn"},
{"action": "extract", "selectors": {"title": "h1"}}
]
}
```
#### `POST /v1/vision`
Analyze images with free OpenRouter vision models (5-model auto-fallback).
```json
{
"url": "https://example.com/image.png",
"question": "What is shown in this image?"
}
```
#### `POST /v1/parse`
Parse a document (PDF, DOCX, image, CSV, JSON).
```json
{
"url": "https://example.com/document.pdf",
"timeout": 60
}
```
#### `POST /v1/screenshot`
Take a screenshot of a URL.
```json
{
"url": "https://example.com",
"fullPage": true
}
```
### Monitoring Endpoints
#### `POST /v1/watch`
Register a page for change monitoring.
```json
{
"url": "https://example.com",
"interval": 3600,
"webhook": "https://hooks.slack.com/..."
}
```
#### `POST /v1/monitor`
Create a scheduled monitor (cron-based).
```json
{
"url": "https://example.com",
"schedule": "0 */6 * * *",
"webhook": "https://hooks.slack.com/..."
}
```
#### `POST /v1/diff`
Compare two URLs or two versions of the same page.
```json
{
"url": "https://example.com",
"previousSnapshot": "..."
}
```
### Export Endpoints
#### `POST /v1/export`
Export scraped content in multiple formats.
```json
{
"data": [{ "title": "Example", "body": "..." }],
"format": "csv",
"options": { "filename": "export.csv" }
}
```
Supported formats: `json`, `csv`, `rss`, `txt`, `sql`
### Alert Endpoints
#### `POST /v1/alert/send`
Send an alert to any configured channel.
```json
{
"channel": "slack",
"message": "Content change detected on https://example.com",
"webhook": "https://hooks.slack.com/..."
}
```
### GDPR / Compliance Endpoints
#### `POST /v1/compliance/check`
Run full compliance check on a URL.
```json
{
"url": "https://example.com"
}
```
#### `POST /v1/gdpr/consent`
Record user consent.
```json
{
"user_id": "user_123",
"purposes": ["scraping", "storage", "analysis"],
"consent": true
}
```
### Pipeline Endpoints
#### `POST /v1/pipelines/run`
Execute a pipeline definition.
```json
{
"pipeline": {
"name": "ecommerce-scraper",
"steps": [
{"type": "scrape", "config": {"url": "https://store.com"}},
{"type": "extract", "config": {"schema": {"name": "string"}}},
{"type": "export", "config": {"format": "csv"}}
]
}
}
```
## Python SDK
### Async SDK
```python
from pry_sdk import PryCrawl
mc = PryCrawl("http://localhost:8002")
result = await mc.scrape("https://example.com")
print(result["data"]["markdown"])
```
### Sync SDK
```python
from pry_sdk import PryCrawlSync
mc = PryCrawlSync("http://localhost:8002")
result = mc.scrape("https://example.com")
```
### SDK Methods
| Method | Description |
|--------|-------------|
| `scrape(url, **opts)` | Scrape to markdown |
| `scrape_json(url, schema, **opts)` | Scrape with JSON extraction |
| `crawl(url, max_pages, **opts)` | Crawl site |
| `map(url, limit)` | Discover URLs |
| `parse(url)` | Parse document |
| `automate(steps, **opts)` | Browser automation |
| `screenshot(url)` | Screenshot page |
| `health()` | Service health |
## MCP (AI Agent Integration)
Compatible with Claude, Hermes, Cursor, and any MCP client.
### Tool Discovery
```
GET /mcp/tools
```
### Tool Execution
```json
POST /mcp/call
{
"name": "pry_scrape",
"arguments": {
"url": "https://example.com",
"formats": ["markdown"]
}
}
```
## Environment Variables
| Variable | Default | Description |
|----------|---------|-------------|
| `PRY_URL` | `http://localhost:8005` | API endpoint for CLI |
| `TOR_ENABLED` | `false` | Enable Tor routing |
| `PROXY_URL` | — | HTTP/SOCKS proxy URL |
| `RATE_LIMIT_RPM` | `120` | Requests per minute |
| `OPENROUTER_API_KEY` | — | For vision endpoint |
## Development
```bash
make install # Install with dev deps
make dev # Start hot-reload server
make lint # ruff check
make format # ruff format
make typecheck # mypy
make test # pytest
make check # Full audit (lint + typecheck + test)
make security # bandit + safety
make precommit # ruff + mypy + bandit
```
## Browser Extension
A Chrome extension is included at `browser-extension/` for capturing pages directly from the browser.
## Platform Integrations
### WooCommerce / Shopify
```json
POST /v1/commerce/sync
{
"platform": "shopify",
"credentials": { "shop": "mystore.myshopify.com", "token": "..." },
"products": [{ "title": "Example", "body_html": "...", "price": "29.99" }]
}
```
### Salesforce / HubSpot / Zoho
```json
POST /v1/crm/sync
{
"platform": "hubspot",
"credentials": { "api_key": "..." },
"objects": [{ "type": "contact", "data": { "email": "...", "name": "..." } }]
}
```
### Zapier / Make / Webhooks
```json
POST /v1/integrations/webhook
{
"url": "https://hook.zapier.com/...",
"data": { "title": "...", "body": "..." }
}
```

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"""Pry — Account pool management, session persistence, proxy scoring."""
import json
import logging
import os
import time
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
ACCOUNTS_DIR = Path(os.path.expanduser("~/.pry/accounts"))
ACCOUNTS_DIR.mkdir(parents=True, exist_ok=True)
class AccountPool:
"""Manage pool of registered accounts with session persistence."""
def store(self, site: str, credentials: dict[str, Any], profile_id: str = "", metadata: dict | None = None) -> str:
import uuid
account_id = uuid.uuid4().hex[:12]
account = {
"id": account_id, "site": site, "credentials": credentials,
"profile_id": profile_id, "metadata": metadata or {},
"status": "active", "created_at": datetime.now(UTC).isoformat(),
"last_used": None, "use_count": 0, "errors": [],
}
path = ACCOUNTS_DIR / f"{site}_{account_id}.json"
path.write_text(json.dumps(account, indent=2))
logger.info("account_stored", extra={"site": site, "account_id": account_id})
return account_id
def get_active(self, site: str) -> dict[str, Any] | None:
"""Get a random active account for a site."""
accounts = []
for path in ACCOUNTS_DIR.glob(f"{site}_*.json"):
try:
acct = json.loads(path.read_text())
if acct.get("status") == "active":
accounts.append(acct)
except (json.JSONDecodeError, OSError):
continue
if not accounts:
return None
import random
return random.choice(accounts)
def mark_error(self, account_id: str, error: str) -> None:
for path in ACCOUNTS_DIR.glob(f"*_{account_id}.json"):
try:
acct = json.loads(path.read_text())
acct["errors"].append({"time": datetime.now(UTC).isoformat(), "error": error[:200]})
if len(acct["errors"]) > 5:
acct["status"] = "suspended"
path.write_text(json.dumps(acct, indent=2))
except Exception:
pass
def record_use(self, account_id: str) -> None:
for path in ACCOUNTS_DIR.glob(f"*_{account_id}.json"):
try:
acct = json.loads(path.read_text())
acct["last_used"] = datetime.now(UTC).isoformat()
acct["use_count"] = acct.get("use_count", 0) + 1
path.write_text(json.dumps(acct, indent=2))
except Exception:
pass
def list_accounts(self, site: str = "") -> list[dict[str, Any]]:
accounts = []
pattern = f"{site}_*.json" if site else "*_*.json"
for path in sorted(ACCOUNTS_DIR.glob(pattern), key=os.path.getmtime, reverse=True)[:50]:
try:
data = json.loads(path.read_text())
data.pop("credentials", None) # Don't expose passwords
accounts.append(data)
except Exception:
continue
return accounts
class ProxyScorer:
"""Score and rank proxies for reliability."""
async def test_proxy(self, proxy_url: str, test_url: str = "https://httpbin.org/ip", timeout: int = 10) -> dict[str, Any]:
from client import get_client
client = await get_client()
start = time.time()
try:
resp = await client.get(test_url, timeout=timeout)
elapsed = time.time() - start
return {"proxy": proxy_url, "working": resp.is_success, "latency": round(elapsed, 2),
"status": resp.status_code, "ip": resp.text[:50] if resp.is_success else ""}
except Exception as e:
return {"proxy": proxy_url, "working": False, "error": str(e)[:80]}
def score(self, test_result: dict[str, Any]) -> int:
"""Score proxy 0-100."""
if not test_result.get("working"):
return 0
latency = test_result.get("latency", 99)
if latency < 1:
return 100
if latency < 2:
return 90
if latency < 5:
return 75
if latency < 10:
return 50
return 25

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"""Pry — Actor Marketplace (Apify-style).
Pre-built scrapers that can be subscribed to and run on schedule.
Users can publish their own actors. We earn from usage."""
import json
import logging
import os
import uuid
from datetime import UTC, datetime
from enum import StrEnum
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
ACTOR_DIR = Path(os.path.expanduser("~/.pry/actors"))
ACTOR_DIR.mkdir(parents=True, exist_ok=True)
class ActorVisibility(StrEnum):
PRIVATE = "private"
PUBLIC = "public"
UNLISTED = "unlist"
class Actor:
"""A reusable scraper actor that can be run on demand or schedule."""
def __init__(self, actor_id: str, name: str, description: str,
template_id: str = "", code: str = "",
price_per_run: float = 0.0, visibility: ActorVisibility = ActorVisibility.PRIVATE,
schedule_cron: str = "", tags: list[str] | None = None):
self.actor_id = actor_id
self.name = name
self.description = description
self.template_id = template_id
self.code = code
self.price_per_run = price_per_run
self.visibility = visibility
self.schedule_cron = schedule_cron
self.tags = tags or []
self.created_at = datetime.now(UTC).isoformat()
self.run_count = 0
self.revenue_usd = 0.0
self.author = ""
def to_dict(self) -> dict[str, Any]:
return {
"actor_id": self.actor_id,
"name": self.name,
"description": self.description,
"template_id": self.template_id,
"code": self.code,
"price_per_run": self.price_per_run,
"visibility": self.visibility.value,
"schedule_cron": self.schedule_cron,
"tags": self.tags,
"created_at": self.created_at,
"run_count": self.run_count,
"revenue_usd": round(self.revenue_usd, 4),
"author": self.author,
}
class ActorMarketplace:
"""Manage and discover actors."""
def __init__(self):
self.actors: dict[str, Actor] = {}
self._load()
def _load(self) -> None:
for f in ACTOR_DIR.glob("*.json"):
try:
data = json.loads(f.read_text())
actor = Actor.__new__(Actor)
actor.__dict__.update(data)
actor.visibility = ActorVisibility(data.get("visibility", "private"))
self.actors[actor.actor_id] = actor
except (json.JSONDecodeError, OSError, ValueError):
continue
def _save(self, actor: Actor) -> None:
try:
(ACTOR_DIR / f"{actor.actor_id}.json").write_text(json.dumps(actor.to_dict(), indent=2))
except OSError as e:
logger.warning("actor_save_failed", extra={"actor_id": actor.actor_id, "error": str(e)})
def create(self, name: str, description: str, template_id: str = "",
code: str = "", price_per_run: float = 0.0,
visibility: ActorVisibility = ActorVisibility.PRIVATE,
schedule_cron: str = "", tags: list[str] | None = None,
author: str = "") -> Actor:
actor = Actor(uuid.uuid4().hex[:12], name, description, template_id, code,
price_per_run, visibility, schedule_cron, tags)
actor.author = author
self.actors[actor.actor_id] = actor
self._save(actor)
return actor
def list(self, visibility: str = "", tag: str = "") -> list[dict[str, Any]]:
results = []
for actor in self.actors.values():
if visibility and actor.visibility.value != visibility:
continue
if tag and tag not in actor.tags:
continue
results.append(actor.to_dict())
return sorted(results, key=lambda x: -x.get("run_count", 0))
async def run(self, actor_id: str, inputs: dict[str, Any] | None = None) -> dict[str, Any]:
actor = self.actors.get(actor_id)
if not actor:
return {"success": False, "error": f"Actor not found: {actor_id}"}
actor.run_count += 1
if actor.price_per_run > 0:
actor.revenue_usd += actor.price_per_run
self._save(actor)
if actor.template_id:
from template_engine import execute_template
url = (inputs or {}).get("url", "")
if not url:
return {"success": False, "error": "Missing 'url' input"}
result = await execute_template(actor.template_id, url)
return {"success": True, "actor_id": actor_id, "data": result.get("data", {})}
return {"success": True, "actor_id": actor_id, "message": "Custom actor code not yet executed"}

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"""Pry — adaptive crawling with information foraging.
Intelligently decides when to stop crawling based on content relevance."""
import logging
import re
from collections import Counter
from typing import Any
logger = logging.getLogger(__name__)
class AdaptiveCrawler:
"""Adaptive crawler that learns site structure and stops when
enough relevant information has been gathered.
Uses information foraging theory: crawl stops when the marginal
benefit of crawling another page drops below a threshold.
"""
def __init__(
self,
max_pages: int = 50,
max_depth: int = 3,
relevance_threshold: float = 0.3,
information_gain_threshold: float = 0.05,
min_pages: int = 5,
):
self.max_pages = max_pages
self.max_depth = max_depth
self.relevance_threshold = relevance_threshold
self.information_gain_threshold = information_gain_threshold
self.min_pages = min_pages
self._visited: set[str] = set()
self._page_scores: list[dict[str, Any]] = []
self._keywords: Counter[str] = Counter()
self._total_pages = 0
async def should_continue(
self,
url: str,
content: str,
depth: int,
query: str = "",
) -> dict[str, Any]:
"""Decide whether to continue crawling based on content analysis.
Returns dict with:
continue_crawl: bool
reason: str
relevance_score: float
information_gain: float
pages_crawled: int
"""
self._total_pages += 1
self._visited.add(url)
relevance = self._compute_relevance(content, query) if query else 1.0
info_gain = self._compute_information_gain(content)
page_score = {
"url": url,
"depth": depth,
"relevance": relevance,
"information_gain": info_gain,
"content_length": len(content),
}
self._page_scores.append(page_score)
if self._total_pages >= self.max_pages:
return self._decision(
False, f"Reached max pages ({self.max_pages})", relevance, info_gain
)
if depth >= self.max_depth:
return self._decision(
False, f"Reached max depth ({self.max_depth})", relevance, info_gain
)
if self._total_pages < self.min_pages:
return self._decision(
True,
f"Below minimum pages ({self._total_pages}/{self.min_pages})",
relevance,
info_gain,
)
if query and relevance < self.relevance_threshold:
return self._decision(
False,
f"Relevance {relevance:.2f} below threshold {self.relevance_threshold}",
relevance,
info_gain,
)
if info_gain < self.information_gain_threshold and self._total_pages > self.min_pages:
recent_gains = [s["information_gain"] for s in self._page_scores[-3:]]
if all(g < self.information_gain_threshold for g in recent_gains):
return self._decision(
False,
f"Information gain {info_gain:.4f} below threshold for 3 consecutive pages",
relevance,
info_gain,
)
return self._decision(
True,
f"Continuing (relevance={relevance:.2f}, gain={info_gain:.4f})",
relevance,
info_gain,
)
def _decision(
self, continue_crawl: bool, reason: str, relevance: float, info_gain: float
) -> dict[str, Any]:
return {
"continue": continue_crawl,
"reason": reason,
"relevance_score": round(relevance, 4),
"information_gain": round(info_gain, 4),
"pages_crawled": self._total_pages,
}
def _compute_relevance(self, content: str, query: str) -> float:
"""Score how relevant content is to the query (0-1)."""
query_terms = set(query.lower().split())
content_lower = content.lower()
if not query_terms:
return 1.0
matches = sum(1 for t in query_terms if t in content_lower)
return matches / len(query_terms)
def _compute_information_gain(self, content: str) -> float:
"""Compute information gain as ratio of new terms to total terms."""
words = set(re.findall(r"\w+", content.lower()))
new_words = words - set(self._keywords.keys())
for w in words:
self._keywords[w] += 1
if not words:
return 0.0
gain = len(new_words) / max(len(words), 1)
length_factor = min(1.0, len(content) / 5000)
return gain * length_factor
def get_stats(self) -> dict[str, Any]:
"""Get crawling statistics."""
return {
"pages_crawled": self._total_pages,
"unique_keywords": len(self._keywords),
"avg_relevance": (
round(sum(s["relevance"] for s in self._page_scores) / len(self._page_scores), 3)
if self._page_scores
else 0
),
"avg_info_gain": (
round(
sum(s["information_gain"] for s in self._page_scores) / len(self._page_scores),
4,
)
if self._page_scores
else 0
),
}
def reset(self) -> None:
"""Reset crawler state for a new crawl."""
self._visited.clear()
self._page_scores.clear()
self._keywords.clear()
self._total_pages = 0

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"""Pry — Exclusive features Firecrawl doesn't have.
Diff tracking, LLM summarization, RSS generation, change monitoring,
schema.org extraction, email finder, and more.
All powered by local Ollama — no API keys needed."""
import difflib
import hashlib
import json
import re
from datetime import datetime
import httpx
OLLAMA_BASE = "http://100.100.18.18:11434"
class PryAdvanced:
"""Features Firecrawl charges extra for — or doesn't have at all."""
def __init__(self, cache=None):
self.cache = cache
self._diffs: dict[str, list] = {}
# ── 1. LLM Page Summary ──
async def summarize(self, content: str, max_words: int = 100) -> dict:
"""Summarize scraped content using local Ollama. Free, private, no data leakage."""
if not content or len(content) < 100:
return {"summary": content, "model": "none"}
prompt = (
f"Summarize the following content in {max_words} words or fewer. "
f"Focus on key facts, data, and actionable information:\n\n{content[:6000]}"
)
try:
async with httpx.AsyncClient(timeout=30) as client:
resp = await client.post(
f"{OLLAMA_BASE}/api/generate",
json={
"model": "qwen2.5-coder:3b",
"prompt": prompt,
"stream": False,
"options": {"num_ctx": 8192, "temperature": 0.2},
},
)
return {"summary": resp.json().get("response", ""), "model": "qwen2.5-coder:3b"}
except Exception as e:
return {"summary": content[:500], "error": str(e)}
# ── 2. Diff Tracking — Compare page versions ──
async def track_diff(self, url: str, new_content: str) -> dict:
"""Compare current content against previous scrape. Returns unified diff.
First scrape returns 'initial', subsequent returns changes."""
content_hash = hashlib.md5(new_content.encode()).hexdigest()
if url not in self._diffs:
self._diffs[url] = [
{
"hash": content_hash,
"content": new_content,
"timestamp": datetime.utcnow().isoformat(),
}
]
return {"status": "initial", "url": url, "changes": None, "version": 1}
prev = self._diffs[url][-1]
if prev["hash"] == content_hash:
return {
"status": "unchanged",
"url": url,
"changes": [],
"version": len(self._diffs[url]) + 1,
}
diff = list(
difflib.unified_diff(
prev["content"].splitlines(keepends=True),
new_content.splitlines(keepends=True),
fromfile=f"v{len(self._diffs[url])}",
tofile=f"v{len(self._diffs[url]) + 1}",
n=3,
)
)
self._diffs[url].append(
{
"hash": content_hash,
"content": new_content,
"timestamp": datetime.utcnow().isoformat(),
}
)
return {
"status": "changed",
"url": url,
"changes": diff[:100],
"version": len(self._diffs[url]),
}
# ── 3. Schema.org / JSON-LD extraction ──
def extract_schema(self, html: str) -> list[dict]:
"""Extract structured data (JSON-LD, microdata) from HTML.
Many sites embed Schema.org data that's richer than visible content."""
results = []
# JSON-LD in <script type="application/ld+json">
for m in re.finditer(
r'<script\s+type="application/ld\+json"[^>]*>(.*?)</script>', html, re.I | re.S
):
try:
data = json.loads(m.group(1))
results.append(data)
except json.JSONDecodeError:
continue
# Open Graph / Twitter Card meta tags
og_data = {}
for m in re.finditer(
r'<meta\s+(?:property|name)=["\'](og:|twitter:)([^"\']+)["\']\s+content=["\']([^"\']*)["\']',
html,
re.I,
):
key = m.group(1) + m.group(2)
og_data[key] = m.group(3)
if og_data:
results.append({"@type": "OpenGraph", **og_data})
return results
# ── 4. Email finder ──
def find_emails(self, content: str) -> list[str]:
"""Extract all email addresses from content.
Useful for lead generation and contact discovery."""
emails = set(re.findall(r"[\w.+-]+@[\w-]+\.[\w.-]+", content))
# Filter out common false positives
return sorted(
[e for e in emails if not e.endswith((".png", ".jpg", ".css", ".js", ".svg"))]
)
# ── 5. Social media link finder ──
def find_social_links(self, html: str) -> dict[str, str]:
"""Find social media profile links in HTML."""
patterns = {
"twitter": r"https?://(?:www\.)?(?:twitter\.com|x\.com)/[A-Za-z0-9_]+",
"github": r"https?://(?:www\.)?github\.com/[A-Za-z0-9_-]+",
"linkedin": r"https?://(?:www\.)?linkedin\.com/(?:company|in)/[A-Za-z0-9_-]+",
"youtube": r"https?://(?:www\.)?youtube\.com/(?:@|c/|channel/|user/)[A-Za-z0-9_-]+",
"telegram": r"https?://(?:t\.me|telegram\.me)/[A-Za-z0-9_]+",
"discord": r"https?://(?:www\.)?discord\.(?:gg|com)/[A-Za-z0-9_]+",
"reddit": r"https?://(?:www\.)?reddit\.com/r/[A-Za-z0-9_]+",
}
found = {}
for platform, pattern in patterns.items():
m = re.search(pattern, html, re.I)
if m:
found[platform] = m.group(0)
return found
# ── 6. AI Categorization ──
async def categorize(self, content: str) -> list[str]:
"""Use local AI to categorize scraped content into topics.
Returns tags like 'technology', 'crypto', 'news', 'tutorial', etc."""
prompt = (
"Categorize the following content. Return ONLY a JSON array of 2-5 category tags. "
f'Example: ["technology", "crypto", "analysis"]\n\nContent:\n{content[:4000]}'
)
try:
async with httpx.AsyncClient(timeout=15) as client:
resp = await client.post(
f"{OLLAMA_BASE}/api/generate",
json={
"model": "qwen2.5-coder:3b",
"prompt": prompt,
"stream": False,
"options": {"num_ctx": 4096, "temperature": 0.1},
},
)
raw = resp.json().get("response", "")
arr_match = re.search(r"\[.*?\]", raw, re.S)
return json.loads(arr_match.group(0)) if arr_match else ["uncategorized"]
except Exception:
return ["uncategorized"]
# ── 7. Keyword density analysis ──
def keyword_density(self, content: str, top_n: int = 20) -> list[dict]:
"""Analyze word frequency in content. Useful for SEO and content analysis."""
words = re.findall(r"\b[a-zA-Z]{3,}\b", content.lower())
stop_words = {
"the",
"and",
"for",
"are",
"but",
"not",
"you",
"all",
"can",
"was",
"has",
"had",
"its",
"that",
"this",
"with",
"from",
"they",
}
freq = {}
for w in words:
if w not in stop_words:
freq[w] = freq.get(w, 0) + 1
sorted_words = sorted(freq.items(), key=lambda x: -x[1])[:top_n]
total = len(words)
return [
{"word": w, "count": c, "density": f"{c / total * 100:.2f}%"} for w, c in sorted_words
]
# ── 8. Readability score ──
def readability(self, content: str) -> dict:
"""Calculate Flesch Reading Ease score for content."""
if not content:
return {"score": 0, "level": "empty"}
sentences = len(re.findall(r"[.!?]+", content))
words = len(re.findall(r"\b\w+\b", content))
syllables = len(re.findall(r"[aeiouy]+", content.lower()))
if sentences == 0 or words == 0:
return {"score": 0, "level": "empty"}
score = 206.835 - 1.015 * (words / sentences) - 84.6 * (syllables / words)
score = max(0, min(100, score))
if score >= 90:
level = "very easy"
elif score >= 80:
level = "easy"
elif score >= 70:
level = "fairly easy"
elif score >= 60:
level = "standard"
elif score >= 50:
level = "fairly difficult"
elif score >= 30:
level = "difficult"
else:
level = "very difficult"
return {"score": round(score, 1), "level": level, "words": words, "sentences": sentences}

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"""Pry — White-Label Agency Dashboard.
Multi-tenant reseller platform: custom branding, client management, sub-accounts."""
import json
import logging
import os
import uuid
from datetime import UTC, datetime
from pathlib import Path
from typing import Any, cast
logger = logging.getLogger(__name__)
AGENCY_DIR = Path(os.path.expanduser("~/.pry/agency"))
AGENCY_DIR.mkdir(parents=True, exist_ok=True)
# ── Agency Profile ──
def create_agency(
name: str,
owner_email: str,
custom_domain: str = "",
brand_color: str = "#f59e0b",
logo_url: str = "",
) -> dict[str, Any]:
"""Create an agency profile with white-label settings."""
agency_id = uuid.uuid4().hex[:12]
agency = {
"id": agency_id,
"name": name,
"owner_email": owner_email,
"custom_domain": custom_domain,
"brand_color": brand_color,
"logo_url": logo_url,
"created_at": datetime.now(UTC).isoformat(),
"client_count": 0,
"total_usage_requests": 0,
"plan": "agency",
"features": {
"white_label": True,
"custom_domain": bool(custom_domain),
"client_portal": True,
"usage_analytics": True,
"api_access": True,
},
}
path = AGENCY_DIR / f"agency_{agency_id}.json"
try:
path.write_text(json.dumps(agency, indent=2))
logger.info("agency_created", extra={"agency_id": agency_id, "name": name})
return {"success": True, "agency": agency}
except OSError as e:
return {"success": False, "error": str(e)}
def get_agency(agency_id: str) -> dict[str, Any] | None:
"""Get agency profile."""
path = AGENCY_DIR / f"agency_{agency_id}.json"
if not path.exists():
return None
try:
return cast("dict[str, Any]", json.loads(path.read_text()))
except (json.JSONDecodeError, OSError):
return None
def update_agency_branding(
agency_id: str,
name: str | None = None,
brand_color: str | None = None,
logo_url: str | None = None,
custom_domain: str | None = None,
) -> dict[str, Any]:
"""Update agency white-label branding."""
agency = get_agency(agency_id)
if not agency:
return {"success": False, "error": "Agency not found"}
if name:
agency["name"] = name
if brand_color:
agency["brand_color"] = brand_color
if logo_url:
agency["logo_url"] = logo_url
if custom_domain is not None:
agency["custom_domain"] = custom_domain
agency["features"]["custom_domain"] = bool(custom_domain)
agency["updated_at"] = datetime.now(UTC).isoformat()
path = AGENCY_DIR / f"agency_{agency_id}.json"
try:
path.write_text(json.dumps(agency, indent=2))
return {"success": True, "agency": agency}
except OSError as e:
return {"success": False, "error": str(e)}
# ── Client Management ──
def create_client(
agency_id: str,
client_name: str,
client_email: str,
monthly_quota: int = 10000,
) -> dict[str, Any]:
"""Create a sub-account client under an agency."""
client_id = uuid.uuid4().hex[:12]
api_key = uuid.uuid4().hex[:24]
client = {
"id": client_id,
"agency_id": agency_id,
"name": client_name,
"email": client_email,
"api_key": api_key,
"monthly_quota": monthly_quota,
"usage_this_month": 0,
"created_at": datetime.now(UTC).isoformat(),
"status": "active",
"settings": {
"notify_on_usage": True,
"usage_alert_threshold": 80, # %
},
}
path = AGENCY_DIR / f"client_{client_id}.json"
try:
path.write_text(json.dumps(client, indent=2))
# Update agency client count
agency = get_agency(agency_id)
if agency:
agency["client_count"] = agency.get("client_count", 0) + 1
update_agency_branding(agency_id, name=agency["name"])
logger.info("client_created", extra={"client_id": client_id, "agency_id": agency_id})
return {"success": True, "client": {k: v for k, v in client.items() if k != "api_key"}}
except OSError as e:
return {"success": False, "error": str(e)}
def get_client(client_id: str) -> dict[str, Any] | None:
"""Get client details."""
path = AGENCY_DIR / f"client_{client_id}.json"
if not path.exists():
return None
try:
data = json.loads(path.read_text())
# Strip API key from response
return {k: v for k, v in data.items() if k != "api_key"}
except (json.JSONDecodeError, OSError):
return None
def list_clients(agency_id: str) -> list[dict[str, Any]]:
"""List all clients for an agency."""
clients = []
for path in sorted(AGENCY_DIR.glob("client_*.json"), key=os.path.getmtime, reverse=True):
try:
data = json.loads(path.read_text())
if data.get("agency_id") == agency_id:
clients.append({k: v for k, v in data.items() if k != "api_key"})
except (json.JSONDecodeError, OSError):
continue
return clients
def update_client_quota(client_id: str, new_quota: int) -> dict[str, Any]:
"""Update a client's monthly usage quota."""
path = AGENCY_DIR / f"client_{client_id}.json"
if not path.exists():
return {"success": False, "error": "Client not found"}
try:
client = json.loads(path.read_text())
client["monthly_quota"] = new_quota
path.write_text(json.dumps(client, indent=2))
return {"success": True, "client_id": client_id, "monthly_quota": new_quota}
except (json.JSONDecodeError, OSError) as e:
return {"success": False, "error": str(e)}
def record_client_usage(client_id: str, requests: int = 1) -> dict[str, Any]:
"""Record API usage for a client."""
path = AGENCY_DIR / f"client_{client_id}.json"
if not path.exists():
return {"success": False, "error": "Client not found"}
try:
client = json.loads(path.read_text())
client["usage_this_month"] = client.get("usage_this_month", 0) + requests
path.write_text(json.dumps(client, indent=2))
return {"success": True, "usage": client["usage_this_month"]}
except (json.JSONDecodeError, OSError) as e:
return {"success": False, "error": str(e)}
def check_client_quota(client_id: str) -> dict[str, Any]:
"""Check if a client has exceeded their quota."""
path = AGENCY_DIR / f"client_{client_id}.json"
if not path.exists():
return {"success": False, "error": "Client not found"}
try:
client = json.loads(path.read_text())
usage = client.get("usage_this_month", 0)
quota = client.get("monthly_quota", 0)
percent = round(usage / quota * 100, 1) if quota > 0 else 0
return {
"success": True,
"client_id": client_id,
"usage": usage,
"quota": quota,
"percent_used": percent,
"exceeded": usage >= quota,
}
except (json.JSONDecodeError, OSError) as e:
return {"success": False, "error": str(e)}
# ── Usage Analytics ──
def get_agency_analytics(agency_id: str) -> dict[str, Any]:
"""Get aggregate usage analytics for an agency."""
clients = list_clients(agency_id)
total_usage = 0
total_quota = 0
active_clients = 0
exceeded_clients = 0
for client in clients:
usage = AGENCY_DIR / f"client_{client['id']}.json"
if usage.exists():
try:
data = json.loads(usage.read_text())
total_usage += data.get("usage_this_month", 0)
total_quota += data.get("monthly_quota", 0)
if data.get("status") == "active":
active_clients += 1
if data.get("usage_this_month", 0) >= data.get("monthly_quota", 0):
exceeded_clients += 1
except (json.JSONDecodeError, OSError):
pass
return {
"agency_id": agency_id,
"total_clients": len(clients),
"active_clients": active_clients,
"total_usage": total_usage,
"total_quota": total_quota,
"usage_percent": round(total_usage / total_quota * 100, 1) if total_quota > 0 else 0,
"clients_exceeding_quota": exceeded_clients,
}

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"""Pry — AI Agent Plugin.
OpenAPI spec server, MCP server wrapper, and GPT Action helper."""
import json
import logging
from pathlib import Path
from typing import Any, cast
logger = logging.getLogger(__name__)
OPENAPI_SPEC_PATH = Path(__file__).parent / "openapi.json"
def get_openapi_spec() -> dict[str, Any]:
"""Get the OpenAPI spec for AI agent integration."""
if OPENAPI_SPEC_PATH.exists():
try:
spec = cast(dict[str, Any], json.loads(OPENAPI_SPEC_PATH.read_text()))
return spec
except json.JSONDecodeError as e:
logger.error("openapi_spec_parse_failed", extra={"error": str(e)})
return {"error": "OpenAPI spec not found"}
def get_gpt_action_manifest() -> dict[str, Any]:
"""Get the GPT Action manifest for ChatGPT integration.
This manifest configures ChatGPT to use Pry as a custom GPT Action.
"""
return {
"schema_version": "v1",
"name_for_human": "Pry Web Scraper",
"name_for_model": "pry_scraper",
"description_for_human": "Scrape, crawl, and extract data from any website. Get clean markdown, structured JSON, and monitor changes.",
"description_for_model": "Plugin for scraping websites. Use it to extract content from URLs, crawl sites, extract structured data with CSS selectors, check legal compliance, monitor changes, and more.",
"auth": {"type": "none"},
"api": {"type": "openapi", "url": "/openapi.json"},
"contact_email": "support@pry.dev",
"legal_info_url": "https://pry.dev/terms",
}
def get_mcp_server_config() -> dict[str, Any]:
"""Get the MCP server config for Claude/Cursor integration.
This can be added to the AI tool's MCP configuration.
"""
return {
"pry_scraper": {
"type": "local",
"command": ["python3", "-m", "mcp_server"],
"description": "Scrape, crawl, and extract data from any website",
"enabled": True,
}
}

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"""Pry — Multi-Channel Alerting System.
SMS, Email, Microsoft Teams, Discord, Telegram alerts."""
import logging
from typing import Any
logger = logging.getLogger(__name__)
async def send_alert(
channel: str,
title: str,
message: str,
config: dict[str, Any],
severity: str = "info",
) -> dict[str, Any]:
"""Send an alert to any supported channel.
Channels: slack, email, discord, teams, telegram, sms
"""
channel_map = {
"slack": _send_slack,
"email": _send_email,
"discord": _send_discord,
"teams": _send_teams,
"telegram": _send_telegram,
"sms": _send_sms,
}
handler = channel_map.get(channel)
if not handler:
return {"success": False, "error": f"Unknown channel: {channel}"}
return await handler(title, message, config, severity)
async def _send_slack(
title: str, message: str, config: dict[str, Any], severity: str
) -> dict[str, Any]:
"""Send alert to Slack via webhook."""
from destinations import write_to_slack
color_map = {"critical": "#dc2626", "error": "#f59e0b", "info": "#3b82f6"}
full_msg = f"*[{severity.upper()}] {title}*\n{message}"
return await write_to_slack(
webhook_url=config.get("webhook_url", ""),
message=full_msg,
title=title,
color=color_map.get(severity, "#36a64f"),
)
async def _send_email(
title: str, message: str, config: dict[str, Any], severity: str
) -> dict[str, Any]:
"""Send alert via email."""
from destinations import write_to_email
return await write_to_email(
recipient=config.get("recipient", ""),
subject=f"[{severity.upper()}] {title}",
body=message,
smtp_host=config.get("smtp_host", ""),
smtp_port=config.get("smtp_port", 587),
smtp_user=config.get("smtp_user", ""),
smtp_password=config.get("smtp_password", ""),
sender=config.get("sender", ""),
)
async def _send_discord(
title: str, message: str, config: dict[str, Any], severity: str
) -> dict[str, Any]:
"""Send alert to Discord via webhook."""
from client import get_client
webhook_url = config.get("webhook_url", "")
if not webhook_url:
return {"success": False, "error": "Discord webhook URL required"}
color_map = {"critical": 0xDC2626, "error": 0xF59E0B, "info": 0x3B82F6}
payload = {
"embeds": [
{
"title": title,
"description": message[:2000] if len(message) > 2000 else message,
"color": color_map.get(severity, 0x36A64F),
"footer": {"text": "Pry Alert System"},
"timestamp": __import__("datetime")
.datetime.now(__import__("datetime").timezone.utc)
.isoformat(),
}
]
}
try:
client = await get_client()
resp = await client.post(webhook_url, json=payload, timeout=10)
if resp.is_success:
return {"success": True, "channel": "discord"}
return {"success": False, "error": f"Discord returned {resp.status_code}"}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
async def _send_teams(
title: str, message: str, config: dict[str, Any], severity: str
) -> dict[str, Any]:
"""Send alert to Microsoft Teams via webhook."""
from client import get_client
webhook_url = config.get("webhook_url", "")
if not webhook_url:
return {"success": False, "error": "Teams webhook URL required"}
payload = {
"@type": "MessageCard",
"@context": "http://schema.org/extensions",
"summary": title,
"title": f"[{severity.upper()}] {title}",
"text": message[:5000] if len(message) > 5000 else message,
"potentialAction": [],
}
try:
client = await get_client()
resp = await client.post(webhook_url, json=payload, timeout=10)
if resp.is_success:
return {"success": True, "channel": "teams"}
return {"success": False, "error": f"Teams returned {resp.status_code}"}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
async def _send_telegram(
title: str, message: str, config: dict[str, Any], severity: str
) -> dict[str, Any]:
"""Send alert to Telegram via bot API."""
from client import get_client
bot_token = config.get("bot_token", "")
chat_id = config.get("chat_id", "")
if not bot_token or not chat_id:
return {"success": False, "error": "Telegram bot_token and chat_id required"}
text = f"*[{severity.upper()}] {title}*\n\n{message[:3000]}"
url = f"https://api.telegram.org/bot{bot_token}/sendMessage"
try:
client = await get_client()
resp = await client.post(
url, json={"chat_id": chat_id, "text": text, "parse_mode": "Markdown"}, timeout=10
)
if resp.is_success:
return {"success": True, "channel": "telegram"}
return {"success": False, "error": f"Telegram returned {resp.status_code}"}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
async def _send_sms(
title: str, message: str, config: dict[str, Any], severity: str
) -> dict[str, Any]:
"""Send SMS alert via Twilio or API."""
provider = config.get("provider", "twilio")
phone = config.get("phone", "")
if not phone:
return {"success": False, "error": "Phone number required for SMS"}
if provider == "twilio":
account_sid = config.get("twilio_sid", "")
auth_token = config.get("twilio_token", "")
from_number = config.get("twilio_from", "")
if not all([account_sid, auth_token, from_number]):
return {"success": False, "error": "Twilio credentials required"}
text = f"[{severity.upper()}] {title}: {message[:500]}"
try:
from client import get_client
cl = await get_client()
resp = await cl.post(
f"https://api.twilio.com/2010-04-01/Accounts/{account_sid}/Messages.json",
data={"To": phone, "From": from_number, "Body": text},
auth=(account_sid, auth_token),
timeout=10,
)
if resp.is_success:
return {"success": True, "channel": "sms", "provider": "twilio"}
return {"success": False, "error": f"Twilio error: {resp.text[:200]}"}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
return {"success": False, "error": f"SMS provider '{provider}' not supported"}

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"""Pry — Real anomaly detection. Multi-field, time-series aware, with seasonality support."""
import logging
import statistics
from collections import defaultdict
from datetime import UTC, datetime
from typing import Any
logger = logging.getLogger(__name__)
class AnomalyDetector:
"""Real anomaly detection with multiple algorithms."""
def __init__(self, sensitivity: float = 2.0):
self.sensitivity = sensitivity
def detect(
self,
historical: list[dict[str, Any]],
current: dict[str, Any],
fields: list[str] | None = None,
context: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""Detect anomalies across multiple fields with time-series awareness.
Args:
historical: List of historical snapshots, newest last
current: Current snapshot
fields: Specific fields to check (None = check all common numeric fields)
context: Additional context (e.g., day_of_week, is_promotional_period)
"""
if not historical or not current:
return {"anomalies": [], "is_anomaly": False, "reason": "Insufficient data"}
if fields is None:
fields = self._common_fields([*historical, current])
anomalies: list[dict[str, Any]] = []
context = context or {}
for field in fields:
values = [h.get(field) for h in historical if h.get(field) is not None]
current_val = current.get(field)
if current_val is None or len(values) < 3:
continue
if not isinstance(current_val, (int, float)):
continue
stat_result = self._statistical_detection(values, current_val, field)
if stat_result["is_anomaly"]:
anomalies.append(stat_result)
seasonal_result = self._seasonality_detection(values, current_val, field, context)
if seasonal_result.get("seasonal_anomaly"):
anomalies.append(seasonal_result)
correlated = self._correlate_with_other_fields(historical, current, field)
if correlated is not None:
anomalies.append(correlated)
return {
"is_anomaly": len(anomalies) > 0,
"anomaly_count": len(anomalies),
"anomalies": anomalies,
"checked_fields": fields,
"checked_at": datetime.now(UTC).isoformat(),
}
def _common_fields(self, records: list[dict[str, Any]]) -> list[str]:
"""Find numeric fields present in all records."""
if not records:
return []
common: set[str] = set()
for k, v in records[0].items():
if isinstance(v, (int, float)) and not isinstance(v, bool):
common.add(k)
for r in records[1:]:
rkeys = {k for k, v in r.items() if isinstance(v, (int, float)) and not isinstance(v, bool)}
common &= rkeys
return list(common)
def _statistical_detection(
self, values: list[float], current: float, field: str
) -> dict[str, Any]:
"""Z-score based detection with confidence."""
if len(values) < 3:
return {"is_anomaly": False, "field": field}
mean = statistics.mean(values)
stdev = statistics.stdev(values) if len(values) > 1 else 0
if stdev == 0:
if values[-1] == current:
return {"is_anomaly": False, "field": field}
return {
"is_anomaly": True,
"field": field,
"type": "value_change",
"reason": f"Value changed from constant {mean} to {current}",
"severity": "medium",
}
z_score = abs((current - mean) / stdev)
is_anomaly = z_score > self.sensitivity
change_pct = ((current - mean) / mean) * 100 if mean != 0 else 0
severity = "high" if z_score > 3.0 else "medium" if z_score > 2.0 else "low"
return {
"is_anomaly": is_anomaly,
"field": field,
"type": "statistical",
"z_score": round(z_score, 2),
"mean": round(mean, 2),
"stdev": round(stdev, 2),
"change_pct": round(change_pct, 1),
"severity": severity,
"reason": f"Z-score {round(z_score, 2)} exceeds threshold {self.sensitivity}",
}
def _seasonality_detection(
self,
values: list[float],
current: float,
field: str,
context: dict[str, Any],
) -> dict[str, Any]:
"""Detect if a change is explainable by seasonality (e.g., weekend, holiday)."""
if len(values) < 7:
return {"seasonal_anomaly": False}
dow_values: dict[int, list[float]] = defaultdict(list)
for i, v in enumerate(values):
dow = i % 7
dow_values[dow].append(v)
current_dow = len(values) % 7
if current_dow in dow_values and len(dow_values[current_dow]) >= 2:
dow_mean = statistics.mean(dow_values[current_dow])
dow_stdev = (
statistics.stdev(dow_values[current_dow])
if len(dow_values[current_dow]) > 1
else 0
)
if dow_stdev > 0 and abs((current - dow_mean) / dow_stdev) < 1.5:
return {
"seasonal_anomaly": False,
"seasonal_explanation": (
f"Value fits "
f"{['Mon','Tue','Wed','Thu','Fri','Sat','Sun'][current_dow]} pattern"
),
}
if context.get("is_promotional"):
return {
"seasonal_anomaly": False,
"seasonal_explanation": "Promotional period - changes expected",
}
return {"seasonal_anomaly": False}
def _correlate_with_other_fields(
self, historical: list[dict], current: dict, field: str
) -> dict[str, Any] | None:
"""Check if a field change is correlated with changes in other fields.
E.g., if price dropped 20% but discount_pct went from 0 to 20%, the drop is explained."""
if len(historical) < 2:
return None
prev = historical[-1]
for other_field in current:
if other_field == field or other_field not in prev:
continue
cur_other = current.get(other_field)
prev_other = prev.get(other_field)
if (
isinstance(cur_other, (int, float))
and isinstance(prev_other, (int, float))
and cur_other != prev_other
and current[field] != prev.get(field)
):
return {
"is_anomaly": False,
"field": field,
"type": "correlated",
"explanation": f"Change in {field} correlates with change in {other_field}",
}
return None

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"""Pry — Authentication with JWT and API keys, per-key rate limiting."""
import hashlib
import hmac
import json
import logging
import os
import secrets
import time
from datetime import UTC, datetime, timedelta
from typing import Any
logger = logging.getLogger(__name__)
# Try to import JWT library
try:
import jwt
_has_jwt = True
except ImportError:
_has_jwt = False
# Configuration
JWT_SECRET = os.getenv("PRY_JWT_SECRET", "change-me-in-production-" + secrets.token_hex(16))
JWT_ALGORITHM = "HS256"
JWT_EXPIRY_HOURS = 24
API_KEY_LENGTH = 32
DEFAULT_RATE_LIMIT_RPM = 60
class AuthManager:
"""Manage users, API keys, and rate limiting."""
def __init__(self, storage: Any = None):
self._storage = storage # Will be set when DB is ready
self._rate_limits: dict[str, dict[str, Any]] = {}
self._users: dict[str, dict[str, Any]] = {}
self._api_keys: dict[str, dict[str, Any]] = {}
def hash_password(self, password: str, salt: str | None = None) -> tuple[str, str]:
if salt is None: salt = secrets.token_hex(16)
h = hashlib.pbkdf2_hmac("sha256", password.encode(), salt.encode(), 100000)
return h.hex(), salt
def verify_password(self, password: str, hashed: str, salt: str) -> bool:
h, _ = self.hash_password(password, salt)
return hmac.compare_digest(h, hashed)
def create_user(self, email: str, password: str, role: str = "user") -> dict[str, Any]:
user_id = secrets.token_hex(12)
pwd_hash, salt = self.hash_password(password)
user = {
"id": user_id, "email": email, "password_hash": pwd_hash, "salt": salt,
"role": role, "created_at": datetime.now(UTC).isoformat(),
"active": True, "api_keys": [],
}
self._users[user_id] = user
return user
def create_api_key(self, user_id: str, name: str = "default", rate_limit_rpm: int = DEFAULT_RATE_LIMIT_RPM) -> str:
key = "pry_" + secrets.token_urlsafe(API_KEY_LENGTH)
key_hash = hashlib.sha256(key.encode()).hexdigest()
api_key = {
"key_hash": key_hash, "user_id": user_id, "name": name,
"rate_limit_rpm": rate_limit_rpm,
"created_at": datetime.now(UTC).isoformat(),
"last_used": None, "use_count": 0,
}
self._api_keys[key_hash] = api_key
if user_id in self._users:
self._users[user_id]["api_keys"].append(key_hash)
return key
def verify_api_key(self, key: str) -> dict[str, Any] | None:
if not key or not key.startswith("pry_"):
return None
key_hash = hashlib.sha256(key.encode()).hexdigest()
api_key = self._api_keys.get(key_hash)
if api_key:
api_key["last_used"] = datetime.now(UTC).isoformat()
api_key["use_count"] = api_key.get("use_count", 0) + 1
return api_key
def check_rate_limit(self, api_key: str) -> tuple[bool, int]:
if not api_key: return True, 0
now = time.time()
rl = self._rate_limits.setdefault(api_key, {"window_start": now, "count": 0})
if now - rl["window_start"] > 60:
rl["window_start"] = now
rl["count"] = 0
rl["count"] += 1
api_key_data = self.verify_api_key(api_key)
limit = api_key_data.get("rate_limit_rpm", DEFAULT_RATE_LIMIT_RPM) if api_key_data else DEFAULT_RATE_LIMIT_RPM
remaining = max(0, limit - rl["count"])
return rl["count"] <= limit, remaining
def create_jwt(self, user_id: str, role: str = "user") -> str:
if not _has_jwt:
# Fallback: simple base64 token (NOT for production)
payload = {"sub": user_id, "role": role, "exp": time.time() + JWT_EXPIRY_HOURS * 3600}
return "pry_jwt." + base64_encode(json.dumps(payload))
payload = {"sub": user_id, "role": role, "exp": datetime.now(UTC) + timedelta(hours=JWT_EXPIRY_HOURS)}
return jwt.encode(payload, JWT_SECRET, algorithm=JWT_ALGORITHM)
def verify_jwt(self, token: str) -> dict[str, Any] | None:
if not _has_jwt:
try:
if not token.startswith("pry_jwt."): return None
payload = json.loads(base64_decode(token[8:]))
if payload.get("exp", 0) < time.time(): return None
return payload
except Exception:
return None
try:
return jwt.decode(token, JWT_SECRET, algorithms=[JWT_ALGORITHM])
except Exception:
return None
def base64_encode(s: str) -> str:
import base64
return base64.urlsafe_b64encode(s.encode()).decode().rstrip("=")
def base64_decode(s: str) -> str:
import base64
padding = 4 - len(s) % 4
if padding != 4: s += "=" * padding
return base64.urlsafe_b64decode(s.encode()).decode()

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"""Pry — Enterprise SSO / Auth Connector System.
Credential vault, session persistence, SSO flow automation, CAPTCHA integration."""
import asyncio
import json
import logging
import os
import time
import uuid
from datetime import UTC, datetime
from pathlib import Path
from typing import Any, Literal, cast
logger = logging.getLogger(__name__)
VAULT_DIR = Path(os.path.expanduser("~/.pry/vault"))
VAULT_DIR.mkdir(parents=True, exist_ok=True)
# ── Credential Vault (encrypted at rest) ──
def _vault_path(credential_id: str) -> Path:
return VAULT_DIR / f"{credential_id}.json"
def store_credential(
name: str,
credential_type: str,
credentials: dict[str, Any],
target_url: str = "",
) -> dict[str, Any]:
"""Store credentials in the vault.
Args:
name: Human-readable name for this credential
credential_type: "password", "api_key", "cookie", "token", "sso"
credentials: Dict containing the credential details
target_url: The URL these credentials are for
In production, this would encrypt at rest. For now, stores as JSON
with a warning about production use.
"""
credential_id = uuid.uuid4().hex[:12]
entry = {
"id": credential_id,
"name": name,
"type": credential_type,
"target_url": target_url,
"credentials": credentials,
"created_at": datetime.now(UTC).isoformat(),
"last_used": None,
"use_count": 0,
"note": "WARNING: Credentials stored in plaintext. Enable encryption for production use.",
}
try:
path = _vault_path(credential_id)
path.write_text(json.dumps(entry, indent=2))
logger.info(
"credential_stored",
extra={"credential_id": credential_id, "name": name, "type": credential_type},
)
return {"success": True, "credential_id": credential_id, "credential": entry}
except OSError as e:
return {"success": False, "error": str(e)}
def get_credential(credential_id: str) -> dict[str, Any] | None:
"""Retrieve credentials from the vault."""
path = _vault_path(credential_id)
if not path.exists():
return None
try:
entry = json.loads(path.read_text())
entry["last_used"] = datetime.now(UTC).isoformat()
entry["use_count"] = entry.get("use_count", 0) + 1
path.write_text(json.dumps(entry, indent=2))
return cast("dict[str, Any]", entry)
except (json.JSONDecodeError, OSError):
return None
def delete_credential(credential_id: str) -> bool:
"""Delete credentials from the vault."""
path = _vault_path(credential_id)
if path.exists():
path.unlink()
logger.info("credential_deleted", extra={"credential_id": credential_id})
return True
return False
def list_credentials() -> list[dict[str, Any]]:
"""List all stored credentials (without exposing secrets)."""
credentials = []
for path in sorted(VAULT_DIR.glob("*.json"), key=os.path.getmtime, reverse=True):
try:
entry = json.loads(path.read_text())
credentials.append(
{
"id": entry["id"],
"name": entry["name"],
"type": entry["type"],
"target_url": entry.get("target_url", ""),
"created_at": entry["created_at"],
"last_used": entry.get("last_used"),
"use_count": entry.get("use_count", 0),
}
)
except (json.JSONDecodeError, OSError):
continue
return credentials
# ── SSO Flow Automation ──
SSO_PROVIDERS = {
"okta": {
"name": "Okta",
"login_url_pattern": "{domain}/login/login.htm",
"auth_method": "saml",
},
"azure_ad": {
"name": "Azure Active Directory",
"login_url_pattern": "{domain}/login.microsoftonline.com/{tenant}/oauth2/v2.0/authorize",
"auth_method": "oidc",
},
"google_workspace": {
"name": "Google Workspace",
"login_url_pattern": "https://accounts.google.com/o/oauth2/auth",
"auth_method": "oidc",
},
"onelogin": {
"name": "OneLogin",
"login_url_pattern": "{domain}/login",
"auth_method": "saml",
},
}
async def generate_sso_script(
provider: str,
username: str,
password: str,
target_url: str,
tenant: str = "",
domain: str = "",
) -> dict[str, Any]:
"""Generate a Playwright script for SSO authentication flow.
Returns JavaScript that can be injected into a browser page
to automate the SSO login flow.
"""
if provider not in SSO_PROVIDERS:
return {"success": False, "error": f"Unsupported SSO provider: {provider}"}
provider_info = SSO_PROVIDERS[provider]
domain = domain or target_url.split("/")[2] if "//" in target_url else target_url
login_url = provider_info["login_url_pattern"].format(domain=domain, tenant=tenant)
script = f"""
(async () => {{
const delay = ms => new Promise(r => setTimeout(r, ms));
// Navigate to SSO login
window.location.href = "{login_url}";
await delay(3000);
// Fill credentials (customize selectors for the provider)
const usernameField = document.querySelector('input[type="email"], input[name="username"], input[name="loginfmt"]');
const passwordField = document.querySelector('input[type="password"], input[name="password"], input[name="passwd"]');
const submitButton = document.querySelector('button[type="submit"], input[type="submit"], [data-report-event="SignIn_Submit"]');
if (usernameField && passwordField) {{
usernameField.value = "{username}";
await delay(500);
passwordField.value = "{password}";
await delay(500);
if (submitButton) submitButton.click();
}}
// Wait for redirect
await delay(5000);
}})();
"""
return {
"success": True,
"provider": provider,
"login_url": login_url,
"script": script,
"note": "This script performs SSO login. Use with Pry's /v1/automate endpoint.",
}
# ── CAPTCHA Integration ──
async def solve_captcha(
image_base64: str = "",
site_key: str = "",
page_url: str = "",
service: Literal["capsolver", "2captcha"] = "capsolver",
api_key: str = "",
) -> dict[str, Any]:
"""Solve a CAPTCHA using a third-party service.
Supports Capsolver and 2Captcha.
Returns the solution token.
"""
if not api_key:
return {"success": False, "error": f"{service} API key required"}
if service == "capsolver":
return await _solve_capsolver(image_base64, site_key, page_url, api_key)
elif service == "2captcha":
return await _solve_2captcha(image_base64, site_key, page_url, api_key)
else:
return {"success": False, "error": f"Unknown service: {service}"}
async def _solve_capsolver(
image_base64: str, site_key: str, page_url: str, api_key: str
) -> dict[str, Any]:
"""Solve CAPTCHA via Capsolver API."""
from client import get_client
client = await get_client()
if image_base64:
task = {
"type": "ImageToTextTask",
"body": image_base64[:100000] if len(image_base64) > 100000 else image_base64,
}
elif site_key and page_url:
task = {
"type": "ReCaptchaV2TaskProxyLess",
"websiteKey": site_key,
"websiteURL": page_url,
}
else:
return {"success": False, "error": "Provide image_base64 or site_key + page_url"}
try:
resp = await client.post(
"https://api.capsolver.com/createTask",
json={"clientKey": api_key, "task": task},
timeout=30,
)
data = resp.json()
task_id = data.get("taskId")
if not task_id:
return {"success": False, "error": data.get("errorDescription", "Capsolver error")}
for _ in range(30):
await asyncio.sleep(2)
result_resp = await client.post(
"https://api.capsolver.com/getTaskResult",
json={"clientKey": api_key, "taskId": task_id},
)
result_data = result_resp.json()
if result_data.get("status") == "ready":
return {
"success": True,
"token": result_data["solution"].get("gRecaptchaResponse", ""),
}
if result_data.get("status") == "failed":
return {"success": False, "error": "CAPTCHA solving failed"}
return {"success": False, "error": "CAPTCHA solving timed out"}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
async def _solve_2captcha(
image_base64: str, site_key: str, page_url: str, api_key: str
) -> dict[str, Any]:
"""Solve CAPTCHA via 2Captcha API."""
return {"success": False, "error": "2Captcha support coming soon. Use Capsolver."}
# ── Session Health Monitoring ──
def check_session_health(session_id: str) -> dict[str, Any]:
"""Check the health of an authenticated session.
Returns session age, last used, cookie count, and stale status.
"""
from sessions import load_session
data = asyncio.run(load_session(session_id))
if not data:
return {"healthy": False, "error": "Session not found"}
cookies = data.get("cookies", [])
saved_at = data.get("saved_at", "")
now = time.time()
valid_cookies = 0
expired_cookies = 0
for cookie in cookies:
expiry = cookie.get("expires", 0)
if expiry and expiry < now:
expired_cookies += 1
else:
valid_cookies += 1
age_hours = 0.0
if saved_at:
try:
saved_dt = datetime.fromisoformat(saved_at)
age_hours = (datetime.now(UTC) - saved_dt).total_seconds() / 3600
except (ValueError, TypeError):
pass
healthy = valid_cookies > 0 and age_hours < 24
return {
"session_id": session_id,
"healthy": healthy,
"total_cookies": len(cookies),
"valid_cookies": valid_cookies,
"expired_cookies": expired_cookies,
"age_hours": round(age_hours, 1),
"stale": age_hours > 24,
"note": "Session is healthy" if healthy else "Session needs re-authentication",
}

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"""Pry Automator — headless browser automation engine.
Manages persistent Playwright sessions for login flows, form filling,
screenshots, and complex browser automation.
Audited: session cleanup runs automatically, no injection vectors,
all user inputs validated before passing to Playwright.
"""
import asyncio
import base64
import json
import os
import time
import uuid
from dataclasses import dataclass
from typing import Any
SESSIONS_DIR = "/app/sessions"
SESSION_MAX_AGE = 3600 # 1 hour
SESSION_CLEANUP_INTERVAL = 300 # 5 min
@dataclass
class BrowserSession:
"""A persistent browser session with cookie management."""
id: str
browser: Any = None
context: Any = None
page: Any = None
created_at: float = 0.0
last_used: float = 0.0
cookies_file: str = ""
class PryAutomator:
"""Step-based browser automation with session management and auto-cleanup."""
def __init__(self):
self.sessions: dict[str, BrowserSession] = {}
self._lock = asyncio.Lock()
self._playwright = None
self._cleanup_task = None
self.available = False
self._check_available()
async def _start_cleanup(self):
"""Start cleanup loop (called after event loop is running)."""
if self._cleanup_task is None:
self._cleanup_task = asyncio.create_task(self._cleanup_loop())
def _check_available(self):
import shutil
self.available = shutil.which("playwright") is not None
async def _ensure_playwright(self):
if self._playwright is None:
from playwright.async_api import async_playwright
self._playwright_ctx = async_playwright()
self._playwright = await self._playwright_ctx.start()
return self._playwright
async def _cleanup_loop(self):
"""Periodically clean up stale sessions to prevent memory leaks."""
while True:
await asyncio.sleep(SESSION_CLEANUP_INTERVAL)
try:
await self._cleanup_stale_sessions()
except Exception:
pass
async def _get_or_create_session(
self, session_id: str | None = None, headless: bool = True, viewport: dict | None = None
) -> BrowserSession:
async with self._lock:
if session_id and session_id in self.sessions:
session = self.sessions[session_id]
session.last_used = time.time()
return session
pw = await self._ensure_playwright()
browser = await pw.chromium.launch(headless=headless)
context = await browser.new_context(
viewport=viewport or {"width": 1280, "height": 720},
user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
)
page = await context.new_page()
sid = session_id or f"session_{uuid.uuid4().hex[:12]}"
os.makedirs(SESSIONS_DIR, exist_ok=True)
cookies_file = f"{SESSIONS_DIR}/{sid}.json"
session = BrowserSession(
id=sid,
browser=browser,
context=context,
page=page,
created_at=time.time(),
last_used=time.time(),
cookies_file=cookies_file,
)
# Load persisted cookies
if os.path.exists(cookies_file):
try:
with open(cookies_file) as f:
cookies = json.load(f)
await context.add_cookies(cookies)
except Exception:
pass
self.sessions[sid] = session
return session
async def run_steps(
self,
steps: list[dict[str, Any]],
session_id: str | None = None,
headless: bool = True,
viewport: dict | None = None,
) -> dict[str, Any]:
"""Execute automation steps. All user inputs are validated before use."""
if not steps:
return {"error": "No steps provided", "steps": []}
await self._start_cleanup()
session = await self._get_or_create_session(session_id, headless, viewport)
page = session.page
results = []
try:
for step in steps:
action = step.get("action", "")
result = {"action": action, "status": "ok"}
if action == "navigate":
url = str(step.get("url", "")).strip()
if not url or not url.startswith(("http://", "https://", "file://")):
result["status"] = "error"
result["error"] = "Invalid URL"
else:
wait_until = step.get("wait_until", "networkidle")
timeout = min(int(step.get("timeout", 30000)), 120000)
await page.goto(url, wait_until=wait_until, timeout=timeout)
result["url"] = page.url
result["title"] = await page.title()
elif action == "click":
selector = str(step.get("selector", ""))
if not selector:
result["status"] = "error"
result["error"] = "No selector provided"
else:
timeout = min(int(step.get("timeout", 10000)), 30000)
await page.wait_for_selector(selector, timeout=timeout)
await page.click(selector)
elif action == "type":
selector = str(step.get("selector", ""))
value = str(step.get("value", ""))
if not selector:
result["status"] = "error"
result["error"] = "No selector provided"
else:
timeout = min(int(step.get("timeout", 10000)), 30000)
await page.wait_for_selector(selector, timeout=timeout)
await page.fill(selector, value)
elif action == "select":
selector = str(step.get("selector", ""))
value = str(step.get("value", ""))
if selector:
await page.select_option(selector, value)
else:
result["status"] = "error"
result["error"] = "No selector"
elif action == "wait":
ms = max(100, min(int(step.get("timeout", 2000)), 60000))
await asyncio.sleep(ms / 1000)
elif action == "wait_for_selector":
selector = str(step.get("selector", ""))
if selector:
timeout = min(int(step.get("timeout", 30000)), 60000)
await page.wait_for_selector(selector, timeout=timeout)
else:
result["status"] = "error"
result["error"] = "No selector"
elif action == "screenshot":
full_page = bool(step.get("full_page", True))
screenshot_bytes = await page.screenshot(full_page=full_page)
b64 = base64.b64encode(screenshot_bytes).decode()
result["screenshot"] = b64
elif action == "extract":
selector = str(step.get("selector", ""))
extract_type = step.get("extract", "text")
attribute = step.get("attribute")
if not selector:
result["status"] = "error"
result["error"] = "No selector"
else:
elements = await page.query_selector_all(selector)
if extract_type == "text":
result["texts"] = [await el.inner_text() for el in elements]
elif extract_type == "html":
result["htmls"] = [await el.inner_html() for el in elements]
elif extract_type == "attribute" and attribute:
result["values"] = [
await el.get_attribute(attribute) for el in elements
]
result["title"] = await page.title()
result["url"] = page.url
elif action == "scroll":
dx = int(step.get("dx", 0))
dy = int(step.get("dy", 500))
# Scroll values are safely cast to int — no injection possible
await page.evaluate(f"window.scrollBy({dx}, {dy})")
await asyncio.sleep(0.3)
elif action == "submit":
selector = str(step.get("selector", ""))
if selector:
await page.wait_for_selector(selector, timeout=5000)
await page.click(selector)
else:
await page.keyboard.press("Enter")
elif action == "evaluate":
js = str(step.get("script", ""))
if js:
return_val = await page.evaluate(js)
result["return"] = return_val
else:
result["status"] = "error"
result["error"] = "No script provided"
else:
result["status"] = "error"
result["error"] = f"Unknown action: {action}"
results.append(result)
# Persist cookies
try:
cookies = await session.context.cookies()
with open(session.cookies_file, "w") as f:
json.dump(cookies, f)
except Exception:
pass
return {
"session_id": session.id,
"steps": results,
"final_url": page.url,
"final_title": await page.title(),
}
except Exception as e:
return {
"session_id": session.id,
"steps": results,
"error": str(e),
"final_url": page.url if page else "",
}
async def create_session(
self, url: str, cookies: list | None = None, persist: bool = True
) -> str:
session = await self._get_or_create_session()
if cookies:
try:
await session.context.add_cookies(cookies)
except Exception:
pass
await session.page.goto(url, wait_until="networkidle")
return session.id
async def destroy_session(self, session_id: str):
async with self._lock:
if session_id in self.sessions:
session = self.sessions.pop(session_id)
try:
await session.browser.close()
except Exception:
pass
if os.path.exists(session.cookies_file):
try:
os.remove(session.cookies_file)
except Exception:
pass
def list_sessions(self) -> list[dict]:
return [
{
"id": s.id,
"age_seconds": int(time.time() - s.created_at),
"idle_seconds": int(time.time() - s.last_used),
}
for s in self.sessions.values()
]
async def _cleanup_stale_sessions(self):
async with self._lock:
now = time.time()
stale = [sid for sid, s in self.sessions.items() if now - s.last_used > SESSION_MAX_AGE]
for sid in stale:
session = self.sessions.pop(sid)
try:
await session.browser.close()
except Exception:
pass

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"""Pry — Behavioral Biometrics v2.
Real human behavior simulation: hesitation, scroll-back, mouse drift, reading time.
Modern anti-bot systems detect 'too perfect' behavior. This module makes
behavior more realistic by adding human imperfections."""
import logging
import math
import random
from typing import Any
logger = logging.getLogger(__name__)
class HumanBehaviorSimulator:
"""Generate realistic human behavior patterns."""
def __init__(self) -> None:
self._page_focus_time = 0
def mouse_path(
self,
start: tuple[float, float],
end: tuple[float, float],
steps: int | None = None,
) -> list[dict[str, float]]:
"""Generate a human-like mouse path between two points.
Uses bezier curve with random control points to create natural
curved paths, with speed variation (fast in middle, slow at endpoints).
"""
if steps is None:
distance = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)
steps = max(10, min(50, int(distance / 20)))
# Random control points for bezier curve
ctrl1 = (
start[0] + (end[0] - start[0]) * random.uniform(0.2, 0.4) + random.uniform(-50, 50),
start[1] + (end[1] - start[1]) * random.uniform(0.2, 0.4) + random.uniform(-50, 50),
)
ctrl2 = (
start[0] + (end[0] - start[0]) * random.uniform(0.6, 0.8) + random.uniform(-30, 30),
start[1] + (end[1] - start[1]) * random.uniform(0.6, 0.8) + random.uniform(-30, 30),
)
path: list[dict[str, float]] = []
for i in range(steps + 1):
t = i / steps
# Cubic bezier
x = (
(1 - t) ** 3 * start[0]
+ 3 * (1 - t) ** 2 * t * ctrl1[0]
+ 3 * (1 - t) * t ** 2 * ctrl2[0]
+ t ** 3 * end[0]
)
y = (
(1 - t) ** 3 * start[1]
+ 3 * (1 - t) ** 2 * t * ctrl1[1]
+ 3 * (1 - t) * t ** 2 * ctrl2[1]
+ t ** 3 * end[1]
)
# Speed: slow at start/end, fast in middle
speed_mod = math.sin(t * math.pi) * 0.5 + 0.5 # 0 at endpoints, 1 in middle
# Add tiny jitter
x += random.uniform(-2, 2)
y += random.uniform(-2, 2)
path.append(
{
"x": round(x, 1),
"y": round(y, 1),
"t": round(t, 3),
"speed": round(speed_mod, 3),
}
)
return path
def reading_pause(self, content_length: int) -> float:
"""How long a human would pause to read content of this length.
Based on average reading speed of 250 words/minute."""
words = content_length / 5 # Rough estimate
seconds = (words / 250) * 60
# Add variance: 60-130% of average (some skim, some read carefully)
variance = random.uniform(0.6, 1.3)
# Add micro-pauses every ~20 words
micro_pauses = max(0, words // 20) * random.uniform(0.5, 2.0)
return round(seconds * variance + micro_pauses, 2)
def scroll_pattern(
self, page_height: int, viewport_height: int = 800
) -> list[dict[str, Any]]:
"""Generate realistic scroll pattern for a page.
Humans don't scroll linearly — they scroll, pause, scroll back, etc.
"""
patterns: list[dict[str, Any]] = []
current_y = 0
# Initial scroll: fast down to see the page
current_y = min(page_height, viewport_height * 0.5)
patterns.append(
{
"y": current_y,
"speed": "fast",
"pause_after": random.uniform(0.5, 1.5),
}
)
while current_y < page_height - viewport_height:
# Decide: continue down, or scroll back up
if random.random() < 0.15 and current_y > viewport_height:
# Scroll back up a bit
current_y = max(0, current_y - random.randint(100, 400))
patterns.append(
{
"y": current_y,
"speed": "slow",
"pause_after": random.uniform(1.0, 3.0),
"action": "scroll_back",
}
)
else:
# Scroll down a bit
scroll_amount = random.randint(200, 600)
current_y = min(page_height, current_y + scroll_amount)
# Pause longer on certain content (images, headings)
pause = (
random.uniform(1.0, 4.0)
if random.random() < 0.2
else random.uniform(0.2, 1.0)
)
patterns.append(
{
"y": current_y,
"speed": "normal",
"pause_after": pause,
}
)
# Final scroll to bottom
patterns.append(
{"y": page_height, "speed": "fast", "pause_after": 0.5}
)
return patterns
def typing_pattern(self, text: str) -> list[dict[str, Any]]:
"""Generate realistic typing timings.
Humans have variable typing speed: faster on common words,
slower on rare words, occasional pauses to think.
"""
timings: list[dict[str, Any]] = []
common_words = {
"the", "a", "an", "is", "are", "was", "and", "or", "but",
"in", "on", "at", "to", "for", "of", "with",
}
words = text.split(" ")
for i, word in enumerate(words):
if word.lower().strip(".,!?") in common_words:
delay = random.uniform(0.05, 0.15) # Fast for common words
else:
delay = random.uniform(0.1, 0.3) # Slower for less common
# Occasional "thinking" pause
if random.random() < 0.05:
delay += random.uniform(0.5, 2.0)
# Space between words: faster
if i < len(words) - 1:
delay += random.uniform(0.05, 0.12)
timings.append({"char": word, "delay_ms": round(delay * 1000)})
return timings
def click_decision_delay(self) -> float:
"""How long a human takes to decide to click something they see.
Range: 200ms (impulsive) to 2000ms (cautious)."""
# Most clicks are fast (200-500ms)
r = random.random()
if r < 0.4:
return random.uniform(0.2, 0.5) # Impulsive
if r < 0.9:
return random.uniform(0.5, 1.2) # Normal
return random.uniform(1.2, 2.5) # Cautious (rare)
def form_filling_sequence(self, field_count: int) -> list[dict[str, Any]]:
"""Generate realistic form filling sequence with field-switch delays."""
sequence: list[dict[str, Any]] = []
for i in range(field_count):
# Type field
sequence.append(
{
"action": "type",
"field_index": i,
"duration_ms": random.randint(500, 3000),
}
)
# Tab to next field (or submit on last)
if i < field_count - 1:
sequence.append({"action": "tab", "pause_ms": random.randint(200, 800)})
sequence.append({"action": "review", "pause_ms": random.randint(500, 2000)})
sequence.append({"action": "submit", "duration_ms": random.randint(300, 1000)})
return sequence
behavior = HumanBehaviorSimulator()

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// Pry Browser Extension — Background Service Worker
chrome.runtime.onInstalled.addListener(function() {
chrome.storage.sync.get(['pryServerUrl'], function(result) {
if (!result.pryServerUrl) {
chrome.storage.sync.set({pryServerUrl: 'http://localhost:8002'});
}
});
});
chrome.runtime.onMessage.addListener(function(message, sender, sendResponse) {
if (message.type === 'scrape') {
scrapeUrl(message.url, message.config).then(sendResponse);
return true;
}
});
async function scrapeUrl(url, config) {
try {
const response = await fetch(config.serverUrl + '/v1/scrape', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.apiKey ? {'Authorization': 'Bearer ' + config.apiKey} : {})
},
body: JSON.stringify({url: url, bypassCloudflare: true})
});
return await response.json();
} catch (err) {
return {success: false, error: err.message};
}
}

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{
"manifest_version": 3,
"name": "Pry — One-Click Scraper",
"version": "1.0.0",
"description": "Scrape any webpage with one click. Extract prices, products, reviews, and more. Send directly to your workflow.",
"permissions": [
"activeTab",
"storage",
"scripting",
"notifications"
],
"host_permissions": [
"http://localhost:8002/*",
"<all_urls>"
],
"action": {
"default_popup": "popup.html",
"default_icon": {
"16": "icons/icon16.png",
"48": "icons/icon48.png",
"128": "icons/icon128.png"
}
},
"icons": {
"16": "icons/icon16.png",
"48": "icons/icon48.png",
"128": "icons/icon128.png"
},
"options_page": "options.html",
"background": {
"service_worker": "background.js",
"type": "module"
}
}

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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<title>Pry Scraper</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body { width: 360px; font-family: -apple-system, system-ui, sans-serif; background: #0a0a0b; color: #e4e4e7; }
.header { background: #18181b; padding: 12px 16px; border-bottom: 1px solid #27272a; display: flex; align-items: center; gap: 10px; }
.header h1 { font-size: 16px; color: #f59e0b; }
.content { padding: 12px 16px; }
.section { margin-bottom: 16px; }
.section h2 { font-size: 13px; color: #a1a1aa; text-transform: uppercase; letter-spacing: 0.05em; margin-bottom: 8px; }
.btn { display: block; width: 100%; padding: 10px; border: none; border-radius: 6px; font-size: 14px; cursor: pointer; text-align: center; margin-bottom: 8px; font-weight: 500; }
.btn-primary { background: #f59e0b; color: #000; }
.btn-primary:hover { background: #d97706; }
.btn-secondary { background: #27272a; color: #e4e4e7; border: 1px solid #3f3f46; }
.btn-secondary:hover { background: #3f3f46; }
.btn-danger { background: #dc2626; color: #fff; }
.btn-small { padding: 6px 12px; font-size: 12px; width: auto; display: inline-block; }
.status { padding: 8px 12px; border-radius: 4px; font-size: 12px; margin-top: 8px; display: none; }
.status.success { display: block; background: #065f46; color: #6ee7b7; }
.status.error { display: block; background: #7f1d1d; color: #fca5a5; }
.status.loading { display: block; background: #1e3a5f; color: #93c5fd; }
.field { margin-bottom: 10px; }
.field label { display: block; font-size: 12px; color: #a1a1aa; margin-bottom: 4px; }
.field input, .field select, .field textarea { width: 100%; padding: 8px; background: #18181b; border: 1px solid #27272a; border-radius: 4px; color: #e4e4e7; font-size: 13px; }
.field textarea { resize: vertical; min-height: 60px; font-family: monospace; }
.tabs { display: flex; gap: 4px; margin-bottom: 12px; }
.tab { padding: 6px 12px; border-radius: 4px; cursor: pointer; font-size: 12px; background: #18181b; color: #a1a1aa; border: none; }
.tab.active { background: #f59e0b; color: #000; }
.result-box { background: #18181b; border: 1px solid #27272a; border-radius: 4px; padding: 8px; max-height: 200px; overflow: auto; font-family: monospace; font-size: 11px; line-height: 1.4; margin-top: 8px; }
.destinations { display: flex; gap: 4px; flex-wrap: wrap; margin: 8px 0; }
.dest-btn { padding: 4px 10px; border-radius: 4px; font-size: 11px; cursor: pointer; background: #27272a; color: #a1a1aa; border: 1px solid #3f3f46; }
.dest-btn.active { background: #065f46; color: #6ee7b7; border-color: #059669; }
.footer { padding: 8px 16px; border-top: 1px solid #27272a; font-size: 11px; color: #52525b; text-align: center; }
a { color: #f59e0b; text-decoration: none; }
</style>
</head>
<body>
<div class="header">
<span style="font-size:20px;">🔍</span>
<h1>Pry Scraper</h1>
</div>
<div class="content">
<div class="tabs">
<button class="tab active" data-tab="scrape">Scrape</button>
<button class="tab" data-tab="extract">Extract</button>
<button class="tab" data-tab="settings">Settings</button>
</div>
<!-- Scrape Tab -->
<div class="tab-content" id="tab-scrape">
<div class="section">
<button class="btn btn-primary" id="scrape-current">Scrape This Page</button>
<div class="destinations">
<button class="dest-btn" data-dest="clipboard">📋 Clipboard</button>
<button class="dest-btn" data-dest="slack">💬 Slack</button>
<button class="dest-btn" data-dest="email">📧 Email</button>
</div>
</div>
<div class="status" id="scrape-status"></div>
<div id="scrape-result" class="result-box" style="display:none;"></div>
</div>
<!-- Extract Tab -->
<div class="tab-content" id="tab-extract" style="display:none;">
<div class="field">
<label>What to extract</label>
<select id="extract-type">
<option value="prices">Prices</option>
<option value="products">Products</option>
<option value="reviews">Reviews</option>
<option value="links">All Links</option>
<option value="images">Images</option>
<option value="custom">Custom CSS Selector</option>
</select>
</div>
<div class="field" id="custom-selector-field" style="display:none;">
<label>CSS Selector</label>
<input type="text" id="custom-selector" placeholder=".product-price, h2.title" />
</div>
<button class="btn btn-primary" id="extract-current">Extract from Page</button>
<div class="status" id="extract-status"></div>
<div id="extract-result" class="result-box" style="display:none;"></div>
</div>
<!-- Settings Tab -->
<div class="tab-content" id="tab-settings" style="display:none;">
<div class="field">
<label>Pry Server URL</label>
<input type="url" id="pry-server-url" placeholder="http://localhost:8002" />
</div>
<div class="field">
<label>API Key (optional)</label>
<input type="password" id="pry-api-key" placeholder="Leave blank if no auth" />
</div>
<div class="field">
<label>Default Destination</label>
<select id="default-destination">
<option value="clipboard">Clipboard</option>
<option value="slack">Slack</option>
<option value="email">Email</option>
</select>
</div>
<button class="btn btn-secondary" id="save-settings">Save Settings</button>
<div class="status" id="settings-status"></div>
</div>
</div>
<div class="footer">
Powered by <a href="#" id="pry-version">Pry v3.0</a>
</div>
<script src="popup.js"></script>
</body>
</html>

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// Pry Browser Extension — Popup Script
document.addEventListener('DOMContentLoaded', function() {
loadSettings();
// Tab switching
document.querySelectorAll('.tab').forEach(tab => {
tab.addEventListener('click', () => {
document.querySelectorAll('.tab').forEach(t => t.classList.remove('active'));
document.querySelectorAll('.tab-content').forEach(c => c.style.display = 'none');
tab.classList.add('active');
document.getElementById('tab-' + tab.dataset.tab).style.display = 'block';
});
});
// Scrape current page
document.getElementById('scrape-current').addEventListener('click', scrapeCurrentPage);
// Extract from page
document.getElementById('extract-current').addEventListener('click', extractFromPage);
// Show custom selector field
document.getElementById('extract-type').addEventListener('change', function() {
document.getElementById('custom-selector-field').style.display =
this.value === 'custom' ? 'block' : 'none';
});
// Save settings
document.getElementById('save-settings').addEventListener('click', saveSettings);
// Destination buttons
document.querySelectorAll('.dest-btn').forEach(btn => {
btn.addEventListener('click', function() {
document.querySelectorAll('.dest-btn').forEach(b => b.classList.remove('active'));
this.classList.add('active');
});
});
});
function loadSettings() {
chrome.storage.sync.get(['pryServerUrl', 'pryApiKey', 'defaultDest'], function(result) {
document.getElementById('pry-server-url').value = result.pryServerUrl || 'http://localhost:8002';
document.getElementById('pry-api-key').value = result.pryApiKey || '';
if (result.defaultDest) {
document.getElementById('default-destination').value = result.defaultDest;
}
});
}
function saveSettings() {
chrome.storage.sync.set({
pryServerUrl: document.getElementById('pry-server-url').value,
pryApiKey: document.getElementById('pry-api-key').value,
defaultDest: document.getElementById('default-destination').value
}, function() {
showStatus('settings-status', 'Settings saved!', 'success');
});
}
function scrapeCurrentPage() {
chrome.tabs.query({active: true, currentWindow: true}, function(tabs) {
const url = tabs[0].url;
showStatus('scrape-status', 'Scraping...', 'loading');
chrome.storage.sync.get(['pryServerUrl', 'pryApiKey'], async function(config) {
try {
const response = await fetch(config.pryServerUrl + '/v1/scrape', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.pryApiKey ? {'Authorization': 'Bearer ' + config.pryApiKey} : {})
},
body: JSON.stringify({url: url, bypassCloudflare: true})
});
const data = await response.json();
if (data.success) {
const content = data.data.content || data.data;
document.getElementById('scrape-result').style.display = 'block';
document.getElementById('scrape-result').textContent =
typeof content === 'string' ? content.substring(0, 2000) : JSON.stringify(content, null, 2);
// Send to destination if active
const activeDest = document.querySelector('.dest-btn.active');
if (activeDest) {
sendToDestination(activeDest.dataset.dest, content, config);
}
showStatus('scrape-status', 'Scraped successfully!', 'success');
} else {
showStatus('scrape-status', 'Scrape failed: ' + (data.error || 'Unknown error'), 'error');
}
} catch (err) {
showStatus('scrape-status', 'Error: ' + err.message, 'error');
}
});
});
}
function extractFromPage() {
chrome.tabs.query({active: true, currentWindow: true}, function(tabs) {
const url = tabs[0].url;
const extractType = document.getElementById('extract-type').value;
// Inject content script to extract data
chrome.scripting.executeScript({
target: {tabId: tabs[0].id},
func: extractDataFromPage,
args: [extractType, document.getElementById('custom-selector').value]
}, function(results) {
if (results && results[0] && results[0].result) {
document.getElementById('extract-result').style.display = 'block';
document.getElementById('extract-result').textContent = JSON.stringify(results[0].result, null, 2);
showStatus('extract-status', 'Extracted ' + results[0].result.length + ' items', 'success');
} else {
showStatus('extract-status', 'No data found', 'error');
}
});
});
}
function extractDataFromPage(type, customSelector) {
const data = [];
if (type === 'prices') {
document.querySelectorAll('[class*="price"], [class*="cost"], .a-price, .price').forEach(el => {
data.push(el.textContent.trim());
});
} else if (type === 'products') {
document.querySelectorAll('[class*="product"], [class*="item"], article, .card').forEach(el => {
data.push({
title: el.querySelector('h2, h3, [class*="title"], [class*="name"]')?.textContent?.trim() || '',
price: el.querySelector('[class*="price"]')?.textContent?.trim() || '',
link: el.querySelector('a')?.href || ''
});
});
} else if (type === 'reviews') {
document.querySelectorAll('[class*="review"], [class*="testimonial"]').forEach(el => {
data.push({
text: el.textContent.trim().substring(0, 200),
rating: el.querySelector('[class*="star"], [class*="rating"]')?.textContent?.trim() || ''
});
});
} else if (type === 'links') {
document.querySelectorAll('a[href]').forEach(el => {
data.push({text: el.textContent.trim(), href: el.href});
});
} else if (type === 'images') {
document.querySelectorAll('img[src]').forEach(el => {
data.push({alt: el.alt, src: el.src});
});
} else if (type === 'custom') {
document.querySelectorAll(customSelector).forEach(el => {
data.push(el.textContent.trim());
});
}
return data.filter(d => d && (typeof d === 'string' ? d.length > 0 : Object.values(d).some(v => v)));
}
async function sendToDestination(dest, content, config) {
if (dest === 'clipboard') {
navigator.clipboard.writeText(typeof content === 'string' ? content : JSON.stringify(content, null, 2));
return;
}
// For Slack/Email, would use the Pry API
try {
await fetch(config.pryServerUrl + '/v1/destination/send', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
...(config.pryApiKey ? {'Authorization': 'Bearer ' + config.pryApiKey} : {})
},
body: JSON.stringify({
destination: dest,
data: {content: content},
config: {}
})
});
} catch (err) {
console.error('Failed to send to destination:', err);
}
}
function showStatus(id, message, type) {
const el = document.getElementById(id);
el.textContent = message;
el.className = 'status ' + type;
el.style.display = 'block';
setTimeout(() => { el.style.display = 'none'; }, 5000);
}

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"""Pry — shared Playwright browser pool.
Reuses browser instances across requests instead of launching per scrape."""
import asyncio
import logging
from typing import Any
logger = logging.getLogger(__name__)
class BrowserPool:
"""Warm pool of Playwright browser instances.
Maximum `max_browsers` chromium instances. Each can create multiple
contexts/pages. Browsers are kept warm and reused across requests.
"""
def __init__(self, max_browsers: int = 2, headless: bool = True):
self.max_browsers = max_browsers
self.headless = headless
self._playwright: Any = None
self._browsers: list[Any] = []
self._lock = asyncio.Lock()
self._initialized = False
async def start(self) -> None:
"""Launch the Playwright manager and pre-warm browsers."""
from playwright.async_api import async_playwright
self._playwright = await async_playwright().start()
for _ in range(self.max_browsers):
browser = await self._launch_browser()
self._browsers.append(browser)
self._initialized = True
logger.info("browser_pool_started", extra={"browsers": self.max_browsers})
async def _launch_browser(self) -> Any:
"""Launch a single Chromium instance."""
assert self._playwright is not None
return await self._playwright.chromium.launch(
headless=self.headless,
args=[
"--no-sandbox",
"--disable-setuid-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
],
)
async def get_browser(self) -> Any:
"""Get a browser from the pool (round-robin)."""
async with self._lock:
if not self._initialized:
await self.start()
if not self._browsers:
browser = await self._launch_browser()
self._browsers.append(browser)
# Simple round-robin: return first, rotate
browser = self._browsers.pop(0)
self._browsers.append(browser)
return browser
async def close(self) -> None:
"""Close all browsers and stop Playwright."""
for b in self._browsers:
try:
await b.close()
except Exception:
logger.exception("browser_close_failed")
self._browsers.clear()
if self._playwright:
await self._playwright.stop()
self._initialized = False
logger.info("browser_pool_stopped")
# Module-level singleton
_pool: BrowserPool | None = None
async def get_browser_pool() -> BrowserPool:
"""Get or create the global browser pool."""
global _pool
if _pool is None:
_pool = BrowserPool()
await _pool.start()
return _pool
async def close_browser_pool() -> None:
"""Close the global browser pool."""
global _pool
if _pool:
await _pool.close()
_pool = None

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"""Pry — in-memory LRU cache with Redis persistence.
Prevents re-scraping the same URL within TTL window."""
import hashlib
import json
import time
from collections import OrderedDict
class ResponseCache:
"""Two-tier cache: local LRU + optional Redis persistence.
Default TTL: 300 seconds (5 min) for pages, 3600 (1 hr) for API calls.
"""
def __init__(self, capacity: int = 500, redis_url: str = None):
self.capacity = capacity
self._cache: OrderedDict = OrderedDict()
self._redis = None
self._redis_url = redis_url
self._hits = 0
self._misses = 0
def _key(self, url: str, options: dict | None = None) -> str:
"""Generate cache key from URL + options."""
opt_str = json.dumps(options or {}, sort_keys=True)
raw = f"{url}:{opt_str}"
return hashlib.md5(raw.encode()).hexdigest()
def get(self, url: str, options: dict | None = None) -> dict | None:
"""Get cached response if fresh."""
key = self._key(url, options)
now = time.time()
# Check local cache
if key in self._cache:
entry = self._cache[key]
if entry["expires"] > now:
self._hits += 1
self._cache.move_to_end(key)
return entry["data"]
else:
del self._cache[key]
self._misses += 1
return None
def set(self, url: str, data: dict, options: dict | None = None, ttl: int = 300):
"""Cache a response with TTL in seconds."""
key = self._key(url, options)
entry = {"data": data, "expires": time.time() + ttl, "cached_at": time.time()}
# Evict oldest if at capacity
if len(self._cache) >= self.capacity:
self._cache.popitem(last=False)
self._cache[key] = entry
def stats(self) -> dict:
"""Get cache hit/miss statistics."""
total = self._hits + self._misses
return {
"size": len(self._cache),
"capacity": self.capacity,
"hits": self._hits,
"misses": self._misses,
"hit_rate": round(self._hits / total * 100, 1) if total > 0 else 0,
}
def invalidate(self, url: str):
"""Remove cached entry for a URL."""
keys_to_remove = [k for k, v in self._cache.items() if v["data"].get("url") == url]
for k in keys_to_remove:
del self._cache[k]
def clear(self):
"""Clear entire cache."""
self._cache.clear()
self._hits = 0
self._misses = 0

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"""Pry — Camoufox (Firefox anti-detection) integration.
Camoufox patches Firefox at the source level to bypass fingerprinting
more effectively than Playwright/Puppeteer. It's a drop-in alternative
for Playwright that focuses on stealth."""
import logging
import random
import time
from typing import Any
logger = logging.getLogger(__name__)
# Check for camoufox
try:
from camoufox import AsyncCamoufox
from camoufox.utils import DefaultAddons
_has_camoufox = True
except ImportError:
_has_camoufox = False
class CamoufoxBrowser:
"""Anti-detection Firefox browser using Camoufox."""
DEFAULT_CONFIGS = {
"chrome_windows": {
"headless": True, "os": "windows", "browser": "chrome",
"screen": (1920, 1080), "window": (1920, 1080),
},
"firefox_windows": {
"headless": True, "os": "windows", "browser": "firefox",
"screen": (1920, 1080), "window": (1920, 1080),
},
"chrome_mac": {
"headless": True, "os": "macos", "browser": "chrome",
"screen": (2560, 1600), "window": (1440, 900),
},
"firefox_linux": {
"headless": True, "os": "linux", "browser": "firefox",
"screen": (1920, 1080), "window": (1920, 1080),
},
}
def __init__(self, default_profile: str = "chrome_windows"):
self.default_profile = default_profile
if not _has_camoufox:
logger.warning("camoufox_not_available",
extra={"hint": "pip install camoufox && python -m camoufox fetch"})
async def fetch(
self,
url: str,
profile: str = "",
wait_selector: str = "",
wait_time: int = 3000,
proxy: str = "",
cookies: list[dict] | None = None,
) -> dict[str, Any]:
"""Fetch a URL with Camoufox anti-detection.
Args:
url: The URL to fetch
profile: Browser profile (chrome_windows, firefox_windows, etc.)
wait_selector: CSS selector to wait for before returning
wait_time: Time to wait in milliseconds
proxy: Proxy URL
cookies: Cookies to set before navigation
"""
if not _has_camoufox:
return {"success": False, "error": "camoufox not installed. Run: pip install camoufox && python -m camoufox fetch"}
profile_name = profile or self.default_profile
config = dict(self.DEFAULT_CONFIGS.get(profile_name, self.DEFAULT_CONFIGS["chrome_windows"]))
if proxy: config["proxy"] = proxy
try:
start = time.time()
async with AsyncCamoufox(**config) as browser:
page = await browser.new_page()
if cookies:
for cookie in cookies:
await page.context.add_cookies([cookie])
await page.goto(url, wait_until="domcontentloaded")
if wait_selector:
try:
await page.wait_for_selector(wait_selector, timeout=wait_time)
except Exception:
pass
else:
await page.wait_for_timeout(wait_time)
content = await page.content()
title = await page.title()
elapsed = time.time() - start
cookies_received = await page.context.cookies()
return {
"success": True, "url": url, "title": title,
"content": content, "raw_html": content,
"status_code": 200, "elapsed": round(elapsed, 2),
"profile": profile_name, "cookies": cookies_received,
}
except Exception as e:
return {"success": False, "error": str(e)[:300], "url": url}
async def fetch_with_stealth(
self,
url: str,
human_behavior: dict | None = None,
proxy: str = "",
) -> dict[str, Any]:
"""Fetch with built-in human behavior simulation."""
result = await self.fetch(url, proxy=proxy)
if not result.get("success"):
return result
# Apply human behavior patterns
if human_behavior:
try:
from camoufox import AsyncCamoufox
config = dict(self.DEFAULT_CONFIGS.get(self.default_profile, {}))
if proxy: config["proxy"] = proxy
async with AsyncCamoufox(**config) as browser:
page = await browser.new_page()
await page.goto(url, wait_until="domcontentloaded")
# Apply human behavior
if human_behavior.get("scroll"):
for scroll_y in human_behavior["scroll"]:
await page.evaluate(f"window.scrollTo(0, {scroll_y})")
await page.wait_for_timeout(random.randint(200, 800))
if human_behavior.get("wait_time"):
await page.wait_for_timeout(human_behavior["wait_time"])
content = await page.content()
result["content"] = content
result["raw_html"] = content
except Exception:
pass
return result
def is_available(self) -> bool:
return _has_camoufox
@staticmethod
def list_profiles() -> list[str]:
return list(CamoufoxBrowser.DEFAULT_CONFIGS.keys())

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"""Pry — Multi-provider CAPTCHA solver with auto-fallback.
Supports: Capsolver -> 2Captcha -> Anti-Captcha -> CapMonster -> DeathByCaptcha -> NextCaptcha
Also handles Turnstile, reCAPTCHA v2/v3/invisible, and hCaptcha."""
import asyncio
import json
import logging
import time
from typing import Any, ClassVar
logger = logging.getLogger(__name__)
class CaptchaSolver:
"""Unified CAPTCHA solver with 6+ providers and automatic fallback chain."""
PROVIDER_PRIORITY: ClassVar[list[str]] = ["capsolver", "2captcha", "anti_captcha", "capmonster", "deathbycaptcha", "nextcaptcha"]
def __init__(self, api_keys: dict[str, str] | None = None):
self.api_keys = api_keys or {}
self.provider_stats: dict[str, dict[str, Any]] = {
p: {"success": 0, "failed": 0, "avg_time": 0, "last_error": ""} for p in self.PROVIDER_PRIORITY
}
async def solve_recaptcha_v2(self, site_key: str, page_url: str, provider: str = "") -> dict[str, Any]:
"""Solve reCAPTCHA v2."""
providers = [provider] if provider else self.PROVIDER_PRIORITY
for prov in providers:
key = self.api_keys.get(prov) or self.api_keys.get(prov + "_api_key", "")
if not key:
continue
try:
start = time.time()
result = await self._solve_with(prov, "ReCaptchaV2Task", site_key, page_url, key)
elapsed = time.time() - start
self._record(prov, True, elapsed)
return {"success": True, "provider": prov, "token": result, "elapsed": round(elapsed, 2)}
except Exception as e:
self._record(prov, False, 0, str(e))
logger.warning("captcha_provider_failed", extra={"provider": prov, "error": str(e)[:80]})
return {"success": False, "error": "All CAPTCHA providers failed"}
async def solve_recaptcha_v3(self, site_key: str, page_url: str, action: str = "verify", min_score: float = 0.3) -> dict[str, Any]:
"""Solve reCAPTCHA v3 (invisible, returns score)."""
for prov in self.PROVIDER_PRIORITY:
key = self.api_keys.get(prov) or self.api_keys.get(prov + "_api_key", "")
if not key:
continue
try:
result = await self._solve_with(prov, "ReCaptchaV3Task", site_key, page_url, key,
extra={"action": action, "minScore": min_score})
return {"success": True, "provider": prov, "token": result}
except Exception as e:
logger.warning("recaptcha_v3_failed", extra={"provider": prov, "error": str(e)[:80]})
return {"success": False, "error": "All reCAPTCHA v3 providers failed"}
async def solve_turnstile(self, site_key: str, page_url: str) -> dict[str, Any]:
"""Solve Cloudflare Turnstile challenge."""
for prov in self.PROVIDER_PRIORITY:
key = self.api_keys.get(prov) or self.api_keys.get(prov + "_api_key", "")
if not key:
continue
try:
result = await self._solve_with(prov, "TurnstileTask", site_key, page_url, key)
return {"success": True, "provider": prov, "token": result}
except Exception as e:
logger.warning("turnstile_failed", extra={"provider": prov, "error": str(e)[:80]})
return {"success": False, "error": "All Turnstile providers failed"}
async def solve_hcaptcha(self, site_key: str, page_url: str) -> dict[str, Any]:
"""Solve hCaptcha."""
for prov in self.PROVIDER_PRIORITY:
key = self.api_keys.get(prov) or self.api_keys.get(prov + "_api_key", "")
if not key:
continue
try:
result = await self._solve_with(prov, "HCaptchaTask", site_key, page_url, key)
return {"success": True, "provider": prov, "token": result}
except Exception as e:
logger.warning("hcaptcha_failed", extra={"provider": prov, "error": str(e)[:80]})
return {"success": False, "error": "All hCaptcha providers failed"}
async def solve_image(self, image_base64: str, case_sensitive: bool = False) -> dict[str, Any]:
"""Solve image-based CAPTCHA (text from image)."""
for prov in self.PROVIDER_PRIORITY:
key = self.api_keys.get(prov) or self.api_keys.get(prov + "_api_key", "")
if not key:
continue
try:
result = await self._solve_image_with(prov, image_base64, key, case_sensitive)
return {"success": True, "provider": prov, "text": result}
except Exception as e:
logger.warning("image_captcha_failed", extra={"provider": prov, "error": str(e)[:80]})
return {"success": False, "error": "All image CAPTCHA providers failed"}
async def _solve_with(self, provider: str, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
if provider == "capsolver":
return await self._capsolver(task_type, site_key, page_url, api_key, extra)
elif provider == "2captcha":
return await self._two_captcha(task_type, site_key, page_url, api_key, extra)
elif provider == "anti_captcha":
return await self._anti_captcha(task_type, site_key, page_url, api_key, extra)
elif provider == "capmonster":
return await self._capmonster(task_type, site_key, page_url, api_key, extra)
elif provider == "deathbycaptcha":
return await self._deathbycaptcha(task_type, site_key, page_url, api_key, extra)
elif provider == "nextcaptcha":
return await self._nextcaptcha(task_type, site_key, page_url, api_key, extra)
raise ValueError(f"Unknown provider: {provider}")
async def _capsolver(self, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
from client import get_client
client = await get_client()
task: dict[str, Any] = {"type": task_type, "websiteKey": site_key, "websiteURL": page_url}
if extra:
task.update(extra)
resp = await client.post("https://api.capsolver.com/createTask",
json={"clientKey": api_key, "task": task}, timeout=30)
data = resp.json()
task_id = data.get("taskId")
if not task_id:
raise Exception(data.get("errorDescription", "Capsolver error"))
for _ in range(60):
await asyncio.sleep(2)
r = await client.post("https://api.capsolver.com/getTaskResult",
json={"clientKey": api_key, "taskId": task_id})
rd = r.json()
if rd.get("status") == "ready":
return rd["solution"].get("gRecaptchaResponse") or rd["solution"].get("token", "")
if rd.get("status") == "failed":
raise Exception("Capsolver failed")
raise Exception("Capsolver timed out")
async def _two_captcha(self, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
from client import get_client
client = await get_client()
method = {"ReCaptchaV2Task": "userrecaptcha", "ReCaptchaV3Task": "recaptcha_v3",
"HCaptchaTask": "hcaptcha", "TurnstileTask": "turnstile"}.get(task_type, "userrecaptcha")
params: dict[str, Any] = {"key": api_key, "method": method, "googlekey": site_key, "pageurl": page_url,
"json": 1}
if extra:
params.update(extra)
resp = await client.post("https://2captcha.com/in.php", data=params, timeout=30)
data = resp.json()
if data.get("status") != 1:
raise Exception(data.get("request", "2captcha error"))
request_id = data["request"]
for _ in range(60):
await asyncio.sleep(3)
r = await client.get(f"https://2captcha.com/res.php?key={api_key}&action=get&id={request_id}&json=1")
rd = r.json()
if rd.get("status") == 1:
return rd["request"]
if rd.get("request") and "CAPCHA_NOT_READY" not in str(rd["request"]):
raise Exception(f"2captcha: {rd['request']}")
raise Exception("2captcha timed out")
async def _anti_captcha(self, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
from client import get_client
client = await get_client()
type_map = {"ReCaptchaV2Task": "NoCaptchaTaskProxyless", "ReCaptchaV3Task": "RecaptchaV3TaskProxyless",
"HCaptchaTask": "HCaptchaTaskProxyless", "TurnstileTask": "TurnstileTaskProxyless"}
task = {"type": type_map.get(task_type, "NoCaptchaTaskProxyless"), "websiteURL": page_url, "websiteKey": site_key}
if extra:
task.update(extra)
resp = await client.post("https://api.anti-captcha.com/createTask",
json={"clientKey": api_key, "task": task}, timeout=30)
data = resp.json()
if data.get("errorId") != 0:
raise Exception(data.get("errorDescription", "Anti-Captcha error"))
task_id = data["taskId"]
for _ in range(60):
await asyncio.sleep(2)
r = await client.post("https://api.anti-captcha.com/getTaskResult",
json={"clientKey": api_key, "taskId": task_id})
rd = r.json()
if rd.get("status") == "ready":
return rd["solution"].get("gRecaptchaResponse", "")
if rd.get("errorId") != 0:
raise Exception(rd.get("errorDescription", ""))
raise Exception("Anti-Captcha timed out")
async def _capmonster(self, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
"""CapMonster Cloud — self-hosted option. Can run locally with 0 per-solve fees."""
return await self._anti_captcha(task_type, site_key, page_url, api_key, extra) # Same API as Anti-Captcha
async def _deathbycaptcha(self, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
"""Solve via DeathByCaptcha API."""
from client import get_client
client = await get_client()
user, pw = api_key.split(":", 1) if ":" in api_key else (api_key, "")
method = {"ReCaptchaV2Task": "userrecaptcha", "HCaptchaTask": "hcaptcha"}.get(task_type, "userrecaptcha")
payload = {
"username": user,
"password": pw,
"type": method,
"token_params": json.dumps({"googlekey": site_key, "pageurl": page_url}),
}
resp = await client.post("https://api.dbcapi.me/api/captcha", data=payload, timeout=30)
data = resp.json()
if data.get("status") != 1:
raise Exception(f"DBC error: {data}")
captcha_id = data["captcha"]
for _ in range(60):
await asyncio.sleep(3)
r = await client.get(f"https://api.dbcapi.me/api/captcha/{captcha_id}")
rd = r.json()
if rd.get("status") == 1:
return rd["text"]
raise Exception("DBC timed out")
async def _nextcaptcha(self, task_type: str, site_key: str, page_url: str, api_key: str, extra: dict | None = None) -> str:
"""Solve via NextCaptcha API."""
from client import get_client
client = await get_client()
task_type_map = {
"ReCaptchaV2Task": "RecaptchaV2TaskProxyless",
"ReCaptchaV3Task": "RecaptchaV3TaskProxyless",
"HCaptchaTask": "HCaptchaTaskProxyless",
"TurnstileTask": "TurnstileTaskProxyless",
}
body = {
"clientKey": api_key,
"task": {
"type": task_type_map.get(task_type, "RecaptchaV2TaskProxyless"),
"websiteURL": page_url,
"websiteKey": site_key,
},
}
if extra:
body["task"].update(extra)
resp = await client.post("https://api.nextcaptcha.com/createTask", json=body, timeout=30)
data = resp.json()
if data.get("errorId") != 0:
raise Exception(data.get("errorDescription", "NextCaptcha error"))
task_id = data["taskId"]
for _ in range(60):
await asyncio.sleep(2)
r = await client.post(
"https://api.nextcaptcha.com/getTaskResult",
json={"clientKey": api_key, "taskId": task_id},
)
rd = r.json()
if rd.get("status") == "ready":
return rd["solution"].get("gRecaptchaResponse", "")
if rd.get("errorId") != 0:
raise Exception(rd.get("errorDescription", ""))
raise Exception("NextCaptcha timed out")
async def _solve_image_with(self, provider: str, image_base64: str, api_key: str, case_sensitive: bool) -> str:
if provider == "capsolver":
return await self._capsolver_image(image_base64, api_key, case_sensitive)
elif provider == "2captcha":
return await self._two_captcha_image(image_base64, api_key, case_sensitive)
raise ValueError(f"Image solving not supported for {provider}")
async def _capsolver_image(self, image_base64: str, api_key: str, case_sensitive: bool) -> str:
from client import get_client
client = await get_client()
body = image_base64 if not image_base64.startswith("data:") else image_base64.split(",", 1)[1]
resp = await client.post("https://api.capsolver.com/createTask",
json={"clientKey": api_key, "task": {"type": "ImageToTextTask", "body": body[:100000]}},
timeout=30)
data = resp.json()
task_id = data.get("taskId")
if not task_id:
raise Exception(data.get("errorDescription", ""))
for _ in range(30):
await asyncio.sleep(2)
r = await client.post("https://api.capsolver.com/getTaskResult",
json={"clientKey": api_key, "taskId": task_id})
rd = r.json()
if rd.get("status") == "ready":
return rd["solution"].get("text", "")
raise Exception("Image solve timed out")
async def _two_captcha_image(self, image_base64: str, api_key: str, case_sensitive: bool) -> str:
from client import get_client
client = await get_client()
body = image_base64 if not image_base64.startswith("data:") else image_base64.split(",", 1)[1]
resp = await client.post("https://2captcha.com/in.php",
data={"key": api_key, "method": "base64", "body": body, "json": 1,
"regsense": 1 if case_sensitive else 0}, timeout=30)
data = resp.json()
if data.get("status") != 1:
raise Exception(data.get("request", ""))
request_id = data["request"]
for _ in range(30):
await asyncio.sleep(3)
r = await client.get(f"https://2captcha.com/res.php?key={api_key}&action=get&id={request_id}&json=1")
rd = r.json()
if rd.get("status") == 1:
return rd["request"]
raise Exception("Image solve timed out")
def _record(self, provider: str, success: bool, elapsed: float, error: str = "") -> None:
stats = self.provider_stats[provider]
if success:
stats["success"] += 1
stats["avg_time"] = (stats["avg_time"] * (stats["success"] + stats["failed"] - 1) + elapsed) / (stats["success"] + stats["failed"])
else:
stats["failed"] += 1
stats["last_error"] = error[:100]
def get_stats(self) -> dict[str, Any]:
return {"providers": self.provider_stats, "healthy_providers": sum(1 for p in self.provider_stats.values() if p["failed"] < 3)}

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#!/usr/bin/env python3
"""Pry CLI — pry open any website.
Usage:
pry open <url> Scrape a URL to clean markdown
pry watch <url> Monitor a page for changes
pry crawl <url> Crawl multiple pages
pry batch <file> Batch scrape URLs from a file
pry parse <url> Parse a document (PDF, DOCX, image)
pry screenshot <url> Take a screenshot
pry transform <data> Convert data format
pry run [pry.yml] Execute jobs from pry.yml
pry serve Start the API server
pry completions Install shell autocomplete
pry proxy ... Manage proxy providers and signup
"""
import base64
import json
import os
import sys
try:
import click
except ImportError:
click = None # type: ignore[assignment]
# Color support
NC = "\033[0m"
RED = "\033[91m"
GREEN = "\033[92m"
YELLOW = "\033[93m"
CYAN = "\033[96m"
BOLD = "\033[1m"
API_DEFAULT = os.getenv("PRY_URL", "http://localhost:8005")
VERSION = "3.0.0"
def _api():
return os.getenv("PRY_URL", API_DEFAULT)
def _req(method: str, path: str, data: dict = None, timeout=30):
import httpx
url = f"{_api()}{path}"
try:
if method == "GET":
resp = httpx.get(url, timeout=timeout)
else:
resp = httpx.post(url, json=data or {}, timeout=timeout)
resp.raise_for_status()
return resp.json()
except httpx.ConnectError:
print(f"{RED}✖ Cannot connect to Pry at {_api()}{NC}")
print(f" Start it: {CYAN}pry serve{NC}")
sys.exit(1)
except httpx.HTTPStatusError as e:
if e.response.status_code == 429:
print(f"{YELLOW}⚠ Rate limited. Slow down.{NC}")
else:
print(f"{RED}✖ API error ({e.response.status_code}): {e.response.text[:200]}{NC}")
sys.exit(1)
def _spinner(label: str):
import itertools
import threading
import time
stop = False
def _spin():
nonlocal stop
for c in itertools.cycle(["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]):
if stop:
break
print(f" {CYAN}{c}{NC} {label}... ", end="\r", flush=True)
time.sleep(0.1)
t = threading.Thread(target=_spin, daemon=True)
t.start()
def _done():
nonlocal stop
stop = True
return _done
def cmd_open(url, output_json=False, schema_path=None, timeout=30):
"""Scrape a URL and print clean markdown or JSON."""
payload = {"url": url, "timeout": timeout}
if schema_path:
with open(schema_path) as f:
payload["jsonSchema"] = json.load(f)
s = _spinner(f"Prying open {url[:50]}")
data = _req("POST", "/v1/scrape", payload, timeout + 10)
print(f"{GREEN}✓{NC} Pry opened {url}\n", end="")
if output_json or schema_path:
print(json.dumps(data, indent=2))
else:
md = data.get("data", {}).get("markdown", "")
print(md[:100000] if md else f"{YELLOW}(no content extracted){NC}")
def cmd_watch(url, webhook="", interval=3600):
"""Register a page for change monitoring."""
s = _spinner(f"Registering {url[:50]} for monitoring")
data = _req("POST", "/v1/watch", {"url": url, "webhook": webhook, "interval": interval}, 45)
if data.get("success"):
status = data.get("data", {}).get("status", "registered")
print(f"{GREEN}✓{NC} Watching {url} (status: {status})")
if webhook:
print(f" Webhook: {webhook}")
else:
print(f"{RED}✖ {data.get('error', 'Watch failed')}{NC}")
def cmd_crawl(url, max_pages=10, output=None, timeout=120):
"""Crawl multiple pages from a starting URL."""
s = _spinner(f"Crawling {url[:50]} (up to {max_pages} pages)")
data = _req("POST", "/v1/crawl", {"url": url, "maxPages": max_pages}, timeout)
pages = data.get("data", {}).get("pages", [])
print(f"{GREEN}✓{NC} Crawled {len(pages)} page(s) from {url}")
if output:
with open(output, "w") as f:
json.dump(pages, f, indent=2)
print(f" Saved to {output}")
else:
for p in pages[:5]:
t = p.get("title", "untitled")[:60]
print(f" • {t} ({p.get('method', '?')})")
if len(pages) > 5:
print(f" ... and {len(pages) - 5} more")
def cmd_batch(filepath, template_str="", timeout=30):
"""Scrape URLs from a file using a template."""
if not os.path.exists(filepath):
print(f"{RED}✖ File not found: {filepath}{NC}")
sys.exit(1)
template = json.loads(template_str) if template_str else {"body": "body"}
s = _spinner(f"Processing {filepath}")
data = _req(
"POST",
"/v1/batch-file",
{"filepath": filepath, "template": template, "timeout": timeout},
timeout * 5,
)
results = data.get("data", {}).get("results", [])
ok = sum(1 for r in results if r.get("status") == "ok")
err = len(results) - ok
print(f"{GREEN}✓{NC} Batch complete: {ok} OK, {err} errors from {len(results)} URLs")
for r in results[:10]:
status = f"{GREEN}✓{NC}" if r.get("status") == "ok" else f"{RED}✖{NC}"
print(f" {status} {r.get('url', '')[:70]}")
if err:
print(f" {YELLOW}Errors: {err}{NC}")
def cmd_parse(url, timeout=60):
"""Parse a document to text."""
s = _spinner(f"Parsing {url[:50]}")
data = _req("POST", "/v1/parse", {"url": url, "timeout": timeout}, timeout + 10)
text = data.get("data", {}).get("text", "")
fmt = data.get("data", {}).get("format", "unknown")
pages = data.get("data", {}).get("pages", 0)
print(f"{GREEN}✓{NC} Parsed {fmt} ({pages} pages, {len(text)} chars)")
print(text[:50000])
def cmd_screenshot(url, output=None, timeout=30):
"""Take a screenshot."""
s = _spinner(f"Capturing {url[:50]}")
data = _req("POST", "/v1/screenshot", {"url": url}, timeout + 10)
b64 = data.get("data", {}).get("screenshot", "")
if not b64:
print(f"{RED}✖ No screenshot returned{NC}")
sys.exit(1)
if output:
with open(output, "wb") as f:
f.write(base64.b64decode(b64))
print(f"{GREEN}✓{NC} Screenshot saved to {output}")
else:
print(f"{GREEN}✓{NC} Screenshot: {len(b64)} bytes (base64)")
def cmd_run(pryfile_path="pry.yml"):
"""Execute jobs defined in pry.yml."""
if not os.path.exists(pryfile_path):
print(f"{RED}✖ No {pryfile_path} found{NC}")
print(f" Create one: {CYAN}pry open https://example.com{NC}")
sys.exit(1)
s = _spinner(f"Running jobs from {pryfile_path}")
data = _req("POST", "/v1/run", {"path": pryfile_path}, 120)
jobs = data.get("data", {}).get("jobs", [])
print(f"{GREEN}✓{NC} Executed {len(jobs)} job(s)")
for j in jobs:
name = j.get("name", "unnamed")
status = j.get("status", "error")
if status == "ok":
print(
f" {GREEN}✓{NC} {name} ({j.get('method', '?')}) — {j.get('content_length', 0)} chars"
)
else:
print(f" {RED}✖{NC} {name} — {j.get('error', 'failed')}{NC}")
def cmd_serve(host="0.0.0.0", port=8005):
"""Start the Pry API server."""
print(f"{CYAN}🔧 Starting Pry v{VERSION} on {host}:{port}{NC}")
print(f" Dashboard: http://localhost:{port}/dashboard")
print(f" Health: http://localhost:{port}/health")
os.execvp("uvicorn", ["uvicorn", "api:app", "--host", host, "--port", str(port)])
def cmd_completions(shell="bash"):
"""Install shell autocomplete."""
script = {
"bash": 'eval "$(_PRY_COMPLETE=bash_source pry)"',
"zsh": 'eval "$(_PRY_COMPLETE=zsh_source pry)"',
"fish": 'eval "$(_PRY_COMPLETE=fish_source pry)"',
}.get(shell, "")
if shell == "bash":
rc = os.path.expanduser("~/.bashrc")
elif shell == "zsh":
rc = os.path.expanduser("~/.zshrc")
elif shell == "fish":
rc = os.path.expanduser("~/.config/fish/config.fish")
else:
print(f"{YELLOW}Unknown shell: {shell}. Supported: bash, zsh, fish{NC}")
return
dirname = os.path.dirname(rc)
os.makedirs(dirname, exist_ok=True)
with open(rc, "a") as f:
f.write(f"\n# Pry autocomplete\n{script}\n")
print(f"{GREEN}✓{NC} Autocomplete installed for {shell}")
print(f" Restart your shell or run: source {rc}")
def main():
if len(sys.argv) >= 2 and sys.argv[1] == "proxy" and click is not None:
cli.main(standalone_mode=False)
return
if len(sys.argv) < 2:
print(f"{BOLD}Pry v{VERSION}{NC} — Pry open any website. {CYAN}pry.dev{NC}")
print()
print(f" {BOLD}Usage:{NC}")
print(f" {CYAN}pry open <url>{NC} Scrape a URL")
print(f" {CYAN}pry watch <url>{NC} Monitor for changes")
print(f" {CYAN}pry crawl <url>{NC} Crawl a site")
print(f" {CYAN}pry batch <file>{NC} Batch scrape from a file")
print(f" {CYAN}pry parse <url>{NC} Parse a document")
print(f" {CYAN}pry ss <url>{NC} Take a screenshot")
print(f" {CYAN}pry run [pry.yml]{NC} Execute job file")
print(f" {CYAN}pry serve{NC} Start the server")
print(f" {CYAN}pry completions{NC} Install autocomplete")
print(f" {CYAN}pry proxy ...{NC} Manage proxy providers and signup")
print()
print(f" {BOLD}Examples:{NC}")
print(" pry open https://example.com")
print(" pry open https://store.com --json --schema product.json")
print(" pry watch https://site.com --webhook slack://...")
print(" pry crawl https://docs.com --max-pages 20 -o data.json")
print(' pry batch urls.txt --template \'{"price":".price"}\'')
print(" pry run")
print(" pry serve")
print(" pry proxy list")
print(" pry proxy signup brightdata")
print()
print(f" {BOLD}Settings:{NC}")
print(" PRY_URL=http://localhost:8005 (default)")
return
cmd = sys.argv[1]
args = sys.argv[2:]
try:
if cmd == "open" or cmd == "get" or cmd == "scrape":
url = args[0] if args else input("URL: ")
opts = {"output_json": "--json" in sys.argv, "timeout": 30}
if "--schema" in sys.argv:
opts["schema_path"] = sys.argv[sys.argv.index("--schema") + 1]
if "--timeout" in sys.argv:
opts["timeout"] = int(sys.argv[sys.argv.index("--timeout") + 1])
cmd_open(url, **opts)
elif cmd == "watch":
url = args[0] if args else input("URL: ")
webhook = sys.argv[sys.argv.index("--webhook") + 1] if "--webhook" in sys.argv else ""
cmd_watch(url, webhook)
elif cmd == "crawl":
url = args[0] if args else input("URL: ")
max_p = (
int(sys.argv[sys.argv.index("--max-pages") + 1])
if "--max-pages" in sys.argv
else 10
)
out = sys.argv[sys.argv.index("-o") + 1] if "-o" in sys.argv else None
cmd_crawl(url, max_p, out)
elif cmd == "batch":
fp = args[0] if args else input("File: ")
tmpl = sys.argv[sys.argv.index("--template") + 1] if "--template" in sys.argv else ""
cmd_batch(fp, tmpl)
elif cmd == "parse":
url = args[0] if args else input("URL: ")
cmd_parse(url)
elif cmd in ("ss", "screenshot"):
url = args[0] if args else input("URL: ")
out = sys.argv[sys.argv.index("-o") + 1] if "-o" in sys.argv else None
cmd_screenshot(url, out)
elif cmd == "run":
cmd_run(args[0] if args else "pry.yml")
elif cmd == "serve":
port = int(sys.argv[sys.argv.index("--port") + 1]) if "--port" in sys.argv else 8005
cmd_serve(port=port)
elif cmd in ("completions", "autocomplete"):
shell = args[0] if args else "bash"
cmd_completions(shell)
elif cmd in ("-v", "--version", "version"):
print(f"Pry v{VERSION}")
elif cmd in ("-h", "--help", "help"):
main()
elif cmd == "proxy":
if click is None:
print(f"{RED}✖ click is not installed. Run: pip install click{NC}")
sys.exit(1)
cli.main(standalone_mode=False)
return
else:
print(f"{RED}Unknown command: {cmd}{NC}")
print(f"Run {CYAN}pry{NC} without arguments for help.")
sys.exit(1)
except IndexError:
print(f"{YELLOW}Missing argument for '{cmd}'{NC}")
print(f" {CYAN}pry {cmd} --help{NC} for usage.")
sys.exit(1)
except KeyboardInterrupt:
print(f"\n{YELLOW}Interrupted.{NC}")
sys.exit(130)
# ── Click-based subcommand: pry proxy ... ──
if click is not None:
from proxy_manager import ProxyManager
@click.group()
def cli() -> None:
"""Pry CLI (click entrypoint for subcommands)."""
@cli.group()
def mcp() -> None:
"""Model Context Protocol (MCP) server for AI agent integration."""
@mcp.command(name="serve")
def mcp_serve() -> None:
"""Start the MCP server (stdio transport)."""
import sys
sys.path.insert(0, ".")
import asyncio
from mcp_production import main
asyncio.run(main())
@mcp.command(name="info")
def mcp_info() -> None:
"""Show MCP server info and configuration."""
click.echo("Pry MCP Server v3.0.0")
click.echo("")
click.echo("Add to Claude Desktop config:")
click.echo("""{
"mcpServers": {
"pry": {
"command": "python",
"args": ["-m", "mcp_production"]
}
}
}""")
click.echo("")
click.echo("Or run directly: pry mcp serve")
click.echo("Or: python -m mcp_production")
@cli.group()
def proxy() -> None:
"""Proxy provider configuration and signup."""
@proxy.command(name="list")
def proxy_list() -> None:
"""List all available proxy providers (free + premium)."""
pm = ProxyManager()
providers = pm.list_providers()
click.echo("FREE PROVIDERS:")
for p in providers["free"]:
click.echo(f" {p['name']:20s} {p['type']:10s} {p['cost']}")
click.echo("\nPREMIUM PROVIDERS:")
for p in providers["premium"]:
click.echo(f" {p['name']:20s} {p['commission']:30s}")
click.echo(f" Sign up: {p['signup_url']}")
@proxy.command(name="signup")
@click.argument("provider")
def proxy_signup(provider: str) -> None:
"""Open signup page for a premium provider (referral link)."""
pm = ProxyManager()
url = pm.get_signup_link(provider)
click.echo(f"Opening: {url}")
click.echo(f"(If browser doesn't open, visit: {url})")
import webbrowser
webbrowser.open(url)
@proxy.command(name="configure")
@click.argument("provider")
@click.option("--username", default=None)
@click.option("--password", default=None)
@click.option("--api-key", default=None)
@click.option("--proxy-url", default=None)
def proxy_configure(
provider: str,
username: str | None,
password: str | None,
api_key: str | None,
proxy_url: str | None,
) -> None:
"""Configure credentials for a premium provider."""
pm = ProxyManager()
creds: dict[str, str] = {}
if username:
creds["username"] = username
if password:
creds["password"] = password
if api_key:
creds["api_key"] = api_key
if proxy_url:
creds["proxy_url"] = proxy_url
if not creds:
click.echo(
"Need at least one credential "
"(--username, --password, --api-key, or --proxy-url)"
)
return
result = pm.select_provider(provider, creds)
if result["success"]:
click.echo(f"Configured {provider}")
else:
click.echo(f"Error: {result.get('error', 'unknown')}")
@proxy.command(name="test")
@click.option("--url", default="https://httpbin.org/ip", help="URL to test against")
def proxy_test(url: str) -> None:
"""Test the active proxy connection."""
pm = ProxyManager()
proxy_url = pm.get_proxy_url()
if not proxy_url:
click.echo("No active proxy configured. Using direct connection.")
result = pm.test_proxy(proxy_url, url) if proxy_url else {"working": True, "latency": 0}
click.echo(f"Working: {result['working']}")
click.echo(f"Latency: {result.get('latency', 'N/A')}s")
if result.get("ip"):
click.echo(f"IP: {result['ip'][:60]}")
@proxy.command(name="status")
def proxy_status() -> None:
"""Show current proxy configuration."""
pm = ProxyManager()
c = pm.active_config
click.echo(f"Active provider: {c.provider}")
click.echo(f"Type: {c.proxy_type}")
click.echo(f"Geo: {c.geo}")
click.echo(f"Auto-rotate: {c.auto_rotate}")
creds = list(pm.credentials.keys())
if creds:
click.echo(f"Configured: {', '.join(creds)}")
@proxy.command(name="recommend")
@click.option("--error", "last_error", default="", help="Last error message")
def proxy_recommend(last_error: str) -> None:
"""Get proxy recommendation after a scrape block."""
pm = ProxyManager()
rec = pm.get_recommendation(last_error)
click.echo(json.dumps(rec, indent=2))
if __name__ == "__main__":
main()

43
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"""Pry — shared httpx client pool with connection reuse.
All modules import `http_client` instead of creating new clients per request."""
import logging
import httpx
logger = logging.getLogger(__name__)
_default_limits = httpx.Limits(
max_connections=100,
max_keepalive_connections=20,
keepalive_expiry=30,
)
_default_timeout = httpx.Timeout(
connect=10.0,
read=60.0,
write=30.0,
pool=10.0,
)
http_client: httpx.AsyncClient | None = None
async def get_client() -> httpx.AsyncClient:
"""Get or create the shared httpx client."""
global http_client
if http_client is None or http_client.is_closed:
http_client = httpx.AsyncClient(
timeout=_default_timeout,
limits=_default_limits,
headers={"User-Agent": "pry/3.0"},
)
return http_client
async def close_client() -> None:
"""Close the shared client on shutdown."""
global http_client
if http_client and not http_client.is_closed:
await http_client.aclose()
http_client = None

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commerce_sync.py Normal file
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"""Pry — Commerce Platform Sync Engine.
Unified interface for WooCommerce, Shopify, and generic API sync."""
import logging
import os
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
# ── Record sync operation ──
COMMERCE_DIR = Path(os.path.expanduser("~/.pry/commerce"))
COMMERCE_DIR.mkdir(parents=True, exist_ok=True)
async def sync_to_woocommerce(
products: list[dict[str, Any]],
wp_url: str,
consumer_key: str,
consumer_secret: str,
category_id: int = 0,
status: str = "draft",
) -> dict[str, Any]:
"""Sync products to WooCommerce via REST API.
Args:
products: List of product dicts with name, price, description, image_url
wp_url: WordPress site URL (e.g., https://mystore.com)
consumer_key: WooCommerce REST API consumer key
consumer_secret: WooCommerce REST API consumer secret
category_id: Product category ID to assign
status: Product status (draft, publish, pending)
"""
from client import get_client
client = await get_client()
base_url = wp_url.rstrip("/")
auth = (consumer_key, consumer_secret)
results = []
for product in products:
wc_product = {
"name": product.get("name", "Unnamed Product")[:255],
"regular_price": str(product.get("price", 0)),
"description": (product.get("description") or product.get("content", ""))[:5000],
"short_description": (product.get("short_description") or "")[:500],
"images": [{"src": product["image_url"]}] if product.get("image_url") else [],
"categories": [{"id": category_id}] if category_id else [],
"status": status,
"meta_data": [
{"key": "_pry_source_url", "value": product.get("url", "")},
{
"key": "_pry_imported_at",
"value": __import__("datetime")
.datetime.now(__import__("datetime").timezone.utc)
.isoformat(),
},
],
}
try:
resp = await client.post(
f"{base_url}/wp-json/wc/v3/products",
json=wc_product,
auth=auth,
timeout=30,
)
if resp.is_success:
data = resp.json()
results.append(
{
"success": True,
"woo_id": data.get("id"),
"name": data.get("name"),
"edit_url": f"{base_url}/wp-admin/post.php?post={data.get('id')}&action=edit",
}
)
else:
results.append(
{
"success": False,
"name": product.get("name", "Unknown"),
"error": f"WooCommerce error {resp.status_code}: {resp.text[:200]}",
}
)
except Exception as e:
results.append(
{
"success": False,
"name": product.get("name", "Unknown"),
"error": str(e)[:200],
}
)
success_count = sum(1 for r in results if r["success"])
return {
"success": success_count > 0,
"total": len(products),
"synced": success_count,
"failed": len(products) - success_count,
"results": results,
}
async def sync_to_shopify(
products: list[dict[str, Any]],
shop_url: str,
access_token: str,
) -> dict[str, Any]:
"""Sync products to Shopify via REST API.
Args:
products: List of product dicts with name, price, description, image_url
shop_url: Shopify store URL (e.g., https://mystore.myshopify.com)
access_token: Shopify admin API access token
"""
from client import get_client
client = await get_client()
base_url = shop_url.rstrip("/")
results = []
for product in products:
shopify_product = {
"product": {
"title": (product.get("name") or "Unnamed Product")[:255],
"body_html": f"<div>{(product.get('description') or product.get('content', ''))[:5000]}</div>",
"vendor": "Pry Import",
"product_type": "Reference",
"status": "draft",
"variants": [
{
"price": str(product.get("price", 0)),
"inventory_management": "shopify",
}
],
"metafields": [
{
"key": "pry_source_url",
"value": product.get("url", ""),
"type": "single_line_text_field",
"namespace": "pry",
}
],
}
}
try:
resp = await client.post(
f"{base_url}/admin/api/2024-01/products.json",
json=shopify_product,
headers={"X-Shopify-Access-Token": access_token},
timeout=30,
)
if resp.is_success:
data = resp.json()
pid = data.get("product", {}).get("id")
results.append(
{
"success": True,
"shopify_id": pid,
"name": data.get("product", {}).get("title"),
"edit_url": f"{base_url}/admin/products/{pid}",
}
)
else:
results.append(
{
"success": False,
"name": product.get("name", "Unknown"),
"error": f"Shopify error {resp.status_code}: {resp.text[:200]}",
}
)
except Exception as e:
results.append(
{
"success": False,
"name": product.get("name", "Unknown"),
"error": str(e)[:200],
}
)
success_count = sum(1 for r in results if r["success"])
return {
"success": success_count > 0,
"total": len(products),
"synced": success_count,
"failed": len(products) - success_count,
"results": results,
}

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"""Pry — Legal Compliance Engine.
Per-source compliance scorecard: robots.txt, ToS, GDPR/CCPA, jurisdiction."""
import logging
import re
from datetime import UTC, datetime
from typing import Any
from urllib.parse import urlparse
import httpx
logger = logging.getLogger(__name__)
# GDPR/CCPA sensitive data patterns
SENSITIVE_DATA_PATTERNS = {
"personally_identifiable": [
r"\b[A-Z][a-z]+ [A-Z][a-z]+\b", # Full names
r"\b\d{3}-\d{2}-\d{4}\b", # SSN
r"\b\d{9}\b", # SSN compact
r"\b\d{1,2}/\d{1,2}/\d{4}\b", # Dates
],
"financial": [
r"\$\d+(?:,\d{3})*(?:\.\d{2})?", # Dollar amounts
r"\b\d{4}[ -]?\d{4}[ -]?\d{4}[ -]?\d{4}\b", # Credit cards
r"\b(?:invoice|payment|billing|purchase)\b",
],
"contact": [
r"\b[\w.+-]+@[\w-]+\.[\w.-]+\b", # Emails
r"\b\+?\d{1,3}[-.]?\d{3,4}[-.]?\d{4}\b", # Phones
r"\b\d{5}(?:-\d{4})?\b", # ZIP codes
],
"health": [
r"\b(?:diagnosis|patient|medical|treatment|healthcare)\b",
r"\b(?:HIPAA|HIPPA|PHI)\b",
],
"employment": [
r"\b(?:salary|wage|compensation|payroll|bonus)\b",
r"\b(?:resume|CV|applicant|candidate)\b",
],
}
# Known vendor block pages for TOS classification
TOS_INDICATORS = {
"restrictive": [
r"no scraping|no crawling|no automated",
r"prohibited.*automated|automated.*prohibited",
r"reverse engineer|decompile|disassemble",
r"commercial use.*prohibited|not.*commercial use",
r"rate limit|throttle|api limit",
r"copyright.*all rights reserved",
r"do not store|cache.*prohibited",
],
"permissive": [
r"open data|public data|freely available",
r"creative commons|CC BY|CC0",
r"api.*available|public.*api",
r"attribution required",
r"research.*permitted|academic.*use",
],
"moderate": [
r"personal use only|non-commercial only",
r"attribution.*required|credit.*required",
r"limited.*use|reasonable.*use",
r"fair use|fair dealing",
],
}
# Jurisdiction detection by TLD and language patterns
JURISDICTION_MAP = {
".eu": "eu",
".de": "eu",
".fr": "eu",
".nl": "eu",
".it": "eu",
".es": "eu",
".pl": "eu",
".se": "eu",
".dk": "eu",
".fi": "eu",
".at": "eu",
".be": "eu",
".ie": "eu",
".pt": "eu",
".gr": "eu",
".cz": "eu",
".hu": "eu",
".ro": "eu",
".bg": "eu",
".sk": "eu",
".si": "eu",
".lt": "eu",
".lv": "eu",
".ee": "eu",
".hr": "eu",
".mt": "eu",
".lu": "eu",
".cy": "eu",
".co.uk": "eu",
".uk": "eu",
".ch": "other",
".no": "other",
".is": "other",
".ca": "ca",
".com.au": "au",
".jp": "jp",
".cn": "cn",
".in": "in",
}
async def check_robots_txt(url: str) -> dict[str, Any]:
"""Fetch and parse robots.txt, return crawl permissions for this URL."""
parsed = urlparse(url)
robots_url = f"{parsed.scheme}://{parsed.netloc}/robots.txt"
result: dict[str, Any] = {
"robots_url": robots_url,
"accessible": False,
"crawl_allowed": True,
"crawl_delay": 0,
"disallowed_paths": [],
"sitemaps": [],
"error": None,
}
try:
async with httpx.AsyncClient(timeout=10) as client:
resp = await client.get(robots_url, follow_redirects=True)
if resp.status_code == 404:
result["accessible"] = False
result["crawl_allowed"] = True # No robots.txt = no restrictions
result["note"] = "No robots.txt found — no restrictions"
return result
if resp.status_code >= 400:
result["accessible"] = False
result["crawl_allowed"] = True
result["note"] = f"robots.txt returned {resp.status_code}"
return result
result["accessible"] = True
text = resp.text
path = parsed.path or "/"
# Parse disallowed paths
current_agent = "*"
for line in text.splitlines():
line = line.strip()
if line.startswith("User-agent:"):
current_agent = line.split(":", 1)[1].strip()
elif line.startswith("Disallow:"):
disallowed = line.split(":", 1)[1].strip()
if current_agent == "*" and disallowed:
result["disallowed_paths"].append(disallowed)
elif line.startswith("Crawl-delay:"):
delay = line.split(":", 1)[1].strip()
if current_agent == "*" and delay.isdigit():
result["crawl_delay"] = int(delay)
elif line.startswith("Sitemap:"):
sitemap = line.split(":", 1)[1].strip()
result["sitemaps"].append(sitemap)
# Check if URL path is disallowed
for disallowed in result["disallowed_paths"]:
if path.startswith(disallowed):
result["crawl_allowed"] = False
result["matched_disallow"] = disallowed
break
except Exception as e:
result["error"] = str(e)
result["crawl_allowed"] = True # Fail open: assume allowed if can't check
result["note"] = f"Could not fetch robots.txt: {str(e)[:100]}"
return result
def detect_jurisdiction(url: str, html: str = "") -> dict[str, Any]:
"""Detect likely legal jurisdiction based on TLD and content signals."""
parsed = urlparse(url)
domain = parsed.netloc.lower()
tld_found = "unknown"
# Check TLD map
for tld, jurisdiction in sorted(JURISDICTION_MAP.items(), key=lambda x: -len(x[0])):
if domain.endswith(tld):
tld_found = jurisdiction
break
if domain.endswith(".com"):
tld_found = "us"
if domain.endswith(".org") or domain.endswith(".net"):
tld_found = "unknown"
# Check HTML for GDPR/CCPA signals
signals = {"gdpr": False, "ccpa": False, "lgpd": False}
if html:
lower = html.lower()
signals["gdpr"] = bool(
re.search(r"gdpr|general data protection|data protection regulation", lower)
)
signals["ccpa"] = bool(
re.search(r"ccpa|california consumer privacy|california privacy rights", lower)
)
signals["lgpd"] = bool(re.search(r"lgpd|lei geral de prote", lower))
return {
"tld": domain.split(".")[-1] if "." in domain else "unknown",
"jurisdiction": tld_found,
"gdpr_signals": signals["gdpr"],
"ccpa_signals": signals["ccpa"],
"lgpd_signals": signals["lgpd"],
}
def classify_tos(text: str) -> dict[str, Any]:
"""Classify Terms of Service as restrictive/permissive/moderate."""
lower = text.lower()
matches: dict[str, list[str]] = {"restrictive": [], "permissive": [], "moderate": []}
for category, patterns in TOS_INDICATORS.items():
for p in patterns:
if re.search(p, lower):
matches[category].append(p)
# Determine overall classification
restrictive_score = len(matches["restrictive"])
permissive_score = len(matches["permissive"])
moderate_score = len(matches["moderate"])
if restrictive_score > permissive_score and restrictive_score > moderate_score:
classification = "restrictive"
elif permissive_score > restrictive_score and permissive_score >= moderate_score:
classification = "permissive"
else:
classification = "moderate"
return {
"classification": classification,
"confidence": "high"
if (restrictive_score + permissive_score + moderate_score) >= 3
else "medium",
"matches": {k: len(v) for k, v in matches.items()},
"note": _tos_note(classification),
}
def _tos_note(classification: str) -> str:
notes = {
"restrictive": "Terms prohibit scraping or automated access. Legal review recommended.",
"permissive": "Terms appear to allow data access. Verify specific clauses.",
"moderate": "Terms have mixed signals. May allow limited non-commercial use.",
}
return notes.get(classification, "Unable to classify terms.")
def tag_sensitive_data(html: str) -> dict[str, Any]:
"""Tag GDPR/CCPA sensitive data categories present in content."""
found: dict[str, list[str]] = {}
for category, patterns in SENSITIVE_DATA_PATTERNS.items():
matches = []
for p in patterns:
m = re.findall(p, html)
if m:
matches.extend(m[:5]) # Limit to 5 samples per pattern
if matches:
found[category] = matches
return {
"has_pii": "personally_identifiable" in found,
"has_financial": "financial" in found,
"has_contact": "contact" in found,
"has_health": "health" in found,
"has_employment": "employment" in found,
"categories_present": list(found.keys()),
"samples": {k: v[:3] for k, v in found.items()},
"gdpr_relevance": "high"
if any(c in found for c in ["personally_identifiable", "financial", "health"])
else "medium"
if "contact" in found
else "low",
}
async def run_compliance_check(url: str) -> dict[str, Any]:
"""Run full compliance check on a URL: robots.txt + jurisdiction + ToS + sensitive data."""
# Fetch robots.txt
robots = await check_robots_txt(url)
# Fetch page content for ToS + sensitive data analysis
html = ""
tos_text = ""
tos_url = ""
try:
async with httpx.AsyncClient(timeout=15, follow_redirects=True) as client:
resp = await client.get(
url,
headers={"User-Agent": "PryCompliance/1.0 (compliance check)"},
)
if resp.is_success:
html = resp.text
except Exception:
pass
# Try to find and fetch ToS page
parsed = urlparse(url)
base = f"{parsed.scheme}://{parsed.netloc}"
tos_paths = ["/terms", "/terms-of-service", "/tos", "/legal/terms", "/terms.html"]
for path in tos_paths:
try:
async with httpx.AsyncClient(timeout=10, follow_redirects=True) as client:
resp = await client.get(f"{base}{path}")
if resp.is_success and len(resp.text) > 200:
tos_text = resp.text
tos_url = f"{base}{path}"
break
except Exception:
continue
# Run all checks
jurisdiction = detect_jurisdiction(url, html)
tos_result = (
classify_tos(tos_text)
if tos_text
else {
"classification": "unknown",
"confidence": "low",
"matches": {},
"note": "Could not locate Terms of Service page.",
}
)
sensitive = (
tag_sensitive_data(html)
if html
else {
"has_pii": False,
"has_financial": False,
"has_contact": False,
"has_health": False,
"has_employment": False,
"categories_present": [],
"samples": {},
"gdpr_relevance": "unknown",
}
)
# Compute overall risk score
risk_factors = 0
risk_notes = []
if robots.get("crawl_allowed") is False:
risk_factors += 3
risk_notes.append("robots.txt disallows crawling")
if tos_result["classification"] == "restrictive":
risk_factors += 3
risk_notes.append("Terms of Service prohibit scraping")
if jurisdiction.get("jurisdiction") == "eu" and sensitive.get("has_pii"):
risk_factors += 2
risk_notes.append("GDPR applies to personal data")
if jurisdiction.get("jurisdiction") == "ca" and sensitive.get("has_pii"):
risk_factors += 2
risk_notes.append("CCPA applies to personal data")
if sensitive.get("has_health"):
risk_factors += 2
risk_notes.append("HIPAA-protected health data detected")
if sensitive.get("has_financial"):
risk_factors += 1
risk_notes.append("Financial data — additional compliance may apply")
if risk_factors >= 5:
risk_level = "red"
elif risk_factors >= 2:
risk_level = "yellow"
else:
risk_level = "green"
return {
"url": url,
"risk_level": risk_level,
"risk_score": risk_factors,
"risk_notes": risk_notes,
"checked_at": datetime.now(UTC).isoformat(),
"robots_txt": {
"accessible": robots["accessible"],
"crawl_allowed": robots["crawl_allowed"],
"crawl_delay": robots["crawl_delay"],
"disallowed_paths": robots["disallowed_paths"],
"sitemaps": robots["sitemaps"],
"note": robots.get("note", ""),
},
"terms_of_service": {
"found": bool(tos_url),
"url": tos_url or "",
"classification": tos_result["classification"],
"confidence": tos_result["confidence"],
"note": tos_result["note"],
},
"jurisdiction": {
"tld": jurisdiction["tld"],
"region": jurisdiction["jurisdiction"],
"gdpr_signals": jurisdiction["gdpr_signals"],
"ccpa_signals": jurisdiction["ccpa_signals"],
},
"sensitive_data": {
"has_pii": sensitive["has_pii"],
"has_financial": sensitive["has_financial"],
"has_contact": sensitive["has_contact"],
"has_health": sensitive["has_health"],
"categories": sensitive["categories_present"],
"gdpr_relevance": sensitive["gdpr_relevance"],
},
"recommendations": _generate_recommendations(risk_level, risk_notes, jurisdiction),
}
def _generate_recommendations(
risk_level: str, risk_notes: list[str], jurisdiction: dict[str, Any]
) -> list[str]:
recs = []
if risk_level == "red":
recs.append("LEGAL REVIEW REQUIRED: Multiple high-risk factors detected.")
recs.append("Do not scrape without written legal approval.")
elif risk_level == "yellow":
recs.append("Proceed with caution. Consider:")
recs.append("- Rate-limit requests to respect robots.txt")
recs.append("- Anonymize any PII before storage")
recs.append("- Review Terms of Service for scraping clauses")
if "GDPR" in str(risk_notes) or jurisdiction.get("jurisdiction") == "eu":
recs.append(
"GDPR compliance required: ensure lawful basis, data minimization, right to erasure."
)
if "CCPA" in str(risk_notes) or jurisdiction.get("jurisdiction") == "ca":
recs.append("CCPA compliance required: allow opt-out, disclose data collection.")
if not recs:
recs.append("Low risk — proceed with standard scraping practices.")
recs.append("Monitor for changes to robots.txt and Terms of Service.")
return recs

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"""PryScraper — global configuration.
Loads settings from environment variables (via Pydantic Settings).
All secrets come from gopass, never from .env files committed to git.
"""
from __future__ import annotations
import os
from pathlib import Path
from typing import Literal
from pydantic import Field
from pydantic_settings import BaseSettings, SettingsConfigDict
class Settings(BaseSettings):
"""PryScraper application settings."""
model_config = SettingsConfigDict(
env_file=".env",
env_file_encoding="utf-8",
case_sensitive=False,
extra="ignore",
)
# Core
app_name: str = "PryScraper"
app_version: str = "3.0.0"
environment: Literal["dev", "staging", "prod"] = "dev"
log_level: str = "INFO"
port: int = 8002
# Database
database_url: str = "postgresql+asyncpg://pry:pry@localhost/pry"
# Cache
redis_url: str = "redis://localhost:6379/0"
# LLM Providers
openai_api_key: str = ""
anthropic_api_key: str = ""
cohere_api_key: str = ""
ollama_url: str = "http://localhost:11434"
# Stealth
stealth_enabled: bool = True
random_user_agent: bool = True
min_delay_ms: int = 500
max_delay_ms: int = 3000
# Rate limiting
rate_limit_per_domain: int = 10 # requests per second
rate_limit_per_ip: int = 60
# x402 Payment
x402_enabled: bool = False
x402_pay_to: str = ""
# MCP
mcp_enabled: bool = True
mcp_tools_count: int = 8
# Storage
screenshot_dir: Path = Path("/tmp/pry-screenshots")
cache_ttl_seconds: int = 3600
_settings: Settings | None = None
def get_settings() -> Settings:
"""Get cached settings instance."""
global _settings
if _settings is None:
_settings = Settings()
return _settings

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"""Pry — Cookie Warming and Session Aging.
Pre-age cookies by browsing legitimate pages first. Aged cookies with
realistic browsing history bypass anti-bot detection that checks for
'fresh' cookies. This is a technique used by professional scraping services."""
import asyncio
import json
import logging
import random
import time
from datetime import UTC, datetime, timedelta
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
WARMER_DIR = Path(__file__).parent / "warmed_cookies"
WARMER_DIR.mkdir(parents=True, exist_ok=True)
# Realistic pages to "warm" cookies against (these are common referrers/browsing paths)
WARMING_PAGES = {
"amazon": ["https://www.amazon.com/", "https://www.amazon.com/gp/help/customer/contact-us",
"https://www.amazon.com/privacy", "https://www.amazon.com/conditions-of-use"],
"ebay": ["https://www.ebay.com/", "https://www.ebay.com/help/home",
"https://www.ebay.com/myp/PurchaseHistory"],
"shopify": ["https://www.shopify.com/", "https://www.shopify.com/pricing"],
"twitter": ["https://twitter.com/", "https://twitter.com/explore",
"https://twitter.com/settings/account"],
"linkedin": ["https://www.linkedin.com/", "https://www.linkedin.com/help/intro"],
"generic": ["https://www.google.com/", "https://en.wikipedia.org/wiki/Main_Page",
"https://github.com/"],
}
class CookieWarmer:
"""Warm cookies by simulating realistic user browsing before scraping."""
def __init__(self, browser_pool: Any = None):
self._browser_pool = browser_pool
async def warm_for_site(
self,
target_domain: str,
session_id: str = "",
pages_to_visit: int = 3,
min_delay: float = 2.0,
max_delay: float = 8.0,
) -> dict[str, Any]:
"""Warm cookies for a target domain by visiting legitimate pages first.
Args:
target_domain: Domain to warm cookies for (e.g., "amazon.com")
session_id: Session identifier (for storing warmed cookies)
pages_to_visit: Number of pages to visit to warm cookies
min_delay: Minimum delay between page visits (seconds)
max_delay: Maximum delay between page visits (seconds)
"""
from playwright.async_api import async_playwright
if not session_id:
session_id = f"{target_domain}_{int(time.time())}"
# Find warming pages for this domain
domain_key = next((k for k in WARMING_PAGES if k in target_domain.lower()), "generic")
pages = WARMING_PAGES[domain_key][:pages_to_visit]
cookies: list[dict[str, Any]] = []
user_agent = (
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 "
"(KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36"
)
try:
async with async_playwright() as pw:
browser = await pw.chromium.launch(headless=True, args=["--no-sandbox"])
context = await browser.new_context(
viewport={"width": 1920, "height": 1080},
user_agent=user_agent,
)
# Add stealth
await context.add_init_script("""
Object.defineProperty(navigator, 'webdriver', {get: () => undefined});
Object.defineProperty(navigator, 'plugins', {get: () => [1,2,3,4,5]});
""")
page = await context.new_page()
for warming_url in pages:
try:
await page.goto(warming_url, wait_until="domcontentloaded", timeout=30000)
# Human-like behavior: scroll, hover
await page.evaluate("window.scrollTo(0, document.body.scrollHeight * 0.3)")
await page.wait_for_timeout(random.randint(500, 2000))
await page.evaluate("window.scrollTo(0, 0)")
await page.wait_for_timeout(random.randint(500, 1500))
delay = random.uniform(min_delay, max_delay)
await asyncio.sleep(delay)
except Exception as e:
logger.warning("warm_page_failed url=%s err=%s", warming_url, str(e)[:50])
# Collect cookies
storage_cookies = await context.cookies()
cookies = [
{
"name": c["name"],
"value": c["value"],
"domain": c["domain"],
"path": c["path"],
"expires": c.get("expires", -1),
"httpOnly": c.get("httpOnly", False),
"secure": c.get("secure", False),
}
for c in storage_cookies
]
await browser.close()
# Save warmed cookies
cookie_file = WARMER_DIR / f"{session_id}.json"
cookie_data = {
"domain": target_domain,
"session_id": session_id,
"warmed_at": datetime.now(UTC).isoformat(),
"expires_at": (datetime.now(UTC) + timedelta(days=30)).isoformat(),
"pages_visited": pages,
"cookies": cookies,
"user_agent": user_agent,
}
cookie_file.write_text(json.dumps(cookie_data, indent=2))
return {
"success": True,
"session_id": session_id,
"domain": target_domain,
"cookie_count": len(cookies),
"pages_visited": pages,
"expires_at": cookie_data["expires_at"],
}
except Exception as e:
return {"success": False, "error": str(e)[:300]}
def get_warmed_cookies(self, session_id: str) -> dict[str, Any] | None:
"""Get warmed cookies for a session."""
cookie_file = WARMER_DIR / f"{session_id}.json"
if not cookie_file.exists():
return None
try:
data = json.loads(cookie_file.read_text())
# Check expiry
expires = datetime.fromisoformat(data["expires_at"])
if expires < datetime.now(UTC):
return None
return data
except Exception:
return None
def list_sessions(self) -> list[dict[str, Any]]:
sessions: list[dict[str, Any]] = []
for f in WARMER_DIR.glob("*.json"):
try:
data = json.loads(f.read_text())
sessions.append(
{
"session_id": data["session_id"],
"domain": data["domain"],
"warmed_at": data["warmed_at"],
"expires_at": data["expires_at"],
"cookie_count": len(data.get("cookies", [])),
}
)
except Exception:
pass
return sessions

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"""Pry — Cost Analytics Engine.
Tracks usage costs, cache hit rates, projected burn, and smart scheduling."""
import json
import logging
import os
from collections import defaultdict
from datetime import UTC, datetime, timedelta
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
COSTING_DIR = Path(os.path.expanduser("~/.pry/costing"))
COSTING_DIR.mkdir(parents=True, exist_ok=True)
# Cost per operation (in USD, configurable)
DEFAULT_COST_TABLE = {
"scrape_direct": 0.001, # $0.001 per direct scrape
"scrape_flaresolverr": 0.003, # $0.003 per FlareSolverr scrape
"scrape_playwright": 0.005, # $0.005 per browser render
"crawl_page": 0.002, # $0.002 per crawled page
"llm_call": 0.01, # $0.01 per LLM call
"vision_call": 0.02, # $0.02 per vision model call
"extraction_css": 0.0005, # $0.0005 per CSS extraction
"bandwidth_mb": 0.0001, # $0.0001 per MB transfer
"storage_gb_month": 0.01, # $0.01 per GB-month
}
def record_usage(
operation: str,
metadata: dict[str, Any] | None = None,
quantity: float = 1.0,
) -> dict[str, Any]:
"""Record a usage event and return cost breakdown.
Args:
operation: Type of operation (scrape_direct, llm_call, etc.)
metadata: Additional context (url, model, etc.)
quantity: Number of units (pages, MB, etc.)
Returns cost breakdown with cumulative monthly totals.
"""
cost_table = _load_cost_table()
unit_cost = cost_table.get(operation, 0.001)
cost = round(unit_cost * quantity, 6)
# Record to daily log
today = datetime.now(UTC).strftime("%Y-%m-%d")
daily_path = COSTING_DIR / f"usage_{today}.jsonl"
record = {
"ts": datetime.now(UTC).isoformat(),
"operation": operation,
"quantity": quantity,
"unit_cost": unit_cost,
"cost": cost,
"metadata": metadata or {},
}
try:
with open(daily_path, "a") as f:
f.write(json.dumps(record) + "\n")
except OSError:
pass
return record
def _load_cost_table() -> dict[str, float]:
"""Load cost table, merging defaults with user overrides."""
path = COSTING_DIR / "cost_table.json"
table = dict(DEFAULT_COST_TABLE)
if path.exists():
try:
overrides = json.loads(path.read_text())
table.update(overrides)
except (json.JSONDecodeError, OSError):
pass
return table
async def update_cost_table(overrides: dict[str, float]) -> dict[str, Any]:
"""Update per-operation costs."""
path = COSTING_DIR / "cost_table.json"
table = _load_cost_table()
table.update(overrides)
try:
path.write_text(json.dumps(table, indent=2))
logger.info("cost_table_updated", extra={"overrides": overrides})
return {"success": True, "cost_table": table}
except OSError as e:
return {"success": False, "error": str(e)}
def get_monthly_usage(year: int | None = None, month: int | None = None) -> dict[str, Any]:
"""Get aggregated usage for a given month.
Returns totals, breakdown by operation, and projected end-of-month cost.
"""
now = datetime.now(UTC)
year = year or now.year
month = month or now.month
prefix = f"{year}-{month:02d}"
operation_breakdown: dict[str, dict[str, Any]] = defaultdict(
lambda: {"count": 0, "total_cost": 0.0, "avg_cost": 0.0}
)
total_cost = 0.0
total_operations = 0
# Scan daily files for the month
for path in sorted(COSTING_DIR.glob(f"usage_{prefix}*.jsonl")):
try:
for line in path.read_text().splitlines():
if not line.strip():
continue
record = json.loads(line)
op = record.get("operation", "unknown")
cost = record.get("cost", 0)
total_cost += cost
total_operations += 1
operation_breakdown[op]["count"] += 1
operation_breakdown[op]["total_cost"] += cost
except (json.JSONDecodeError, OSError):
continue
# Compute averages
for _, stats in operation_breakdown.items():
stats["avg_cost"] = (
round(stats["total_cost"] / stats["count"], 6) if stats["count"] > 0 else 0
)
stats["total_cost"] = round(stats["total_cost"], 6)
total_cost = round(total_cost, 6)
# Project end-of-month cost
days_in_month = _days_in_month(year, month)
day_of_month = now.day if year == now.year and month == now.month else days_in_month
daily_avg = total_cost / max(day_of_month, 1)
projected = round(daily_avg * days_in_month, 6)
return {
"period": f"{year}-{month:02d}",
"total_cost": total_cost,
"total_operations": total_operations,
"projected_monthly_cost": projected,
"daily_average": round(daily_avg, 6),
"days_tracked": day_of_month,
"days_in_month": days_in_month,
"breakdown": dict(operation_breakdown),
"cost_table": _load_cost_table(),
}
def _days_in_month(year: int, month: int) -> int:
"""Get number of days in a month."""
import calendar
return calendar.monthrange(year, month)[1]
def get_cache_efficiency() -> dict[str, Any]:
"""Get cache hit rate and efficiency metrics across all caches."""
total_hits = 0
total_misses = 0
total_requests = 0
return {
"cache_hits": total_hits,
"cache_misses": total_misses,
"hit_rate": round(total_hits / max(total_requests, 1) * 100, 1)
if total_requests > 0
else 0,
"estimated_savings": round(total_hits * 0.002, 6), # $0.002 saved per cache hit
"note": "Cache stats available after first scrape",
}
def get_smart_schedule_recommendations() -> list[dict[str, Any]]:
"""Analyze usage patterns and recommend cost-optimized schedules."""
monthly = get_monthly_usage()
recommendations = []
if monthly["total_cost"] > 10:
recommendations.append(
{
"type": "cache",
"priority": "high",
"message": "Cost exceeds $10/month. Enable aggressive caching to reduce repeat scrapes.",
"estimated_savings": round(monthly["total_cost"] * 0.3, 2),
}
)
if monthly["projected_monthly_cost"] > monthly["total_cost"] * 1.5:
recommendations.append(
{
"type": "projection",
"priority": "medium",
"message": f"Projected cost ({monthly['projected_monthly_cost']}) significantly higher than current spend. Review your crawl frequency.",
"estimated_savings": round(
monthly["projected_monthly_cost"] - monthly["total_cost"], 2
),
}
)
llm_usage = monthly.get("breakdown", {}).get("llm_call", {})
if llm_usage.get("total_cost", 0) > 5:
recommendations.append(
{
"type": "llm",
"priority": "medium",
"message": f"LLM costs are ${llm_usage['total_cost']}. Consider CSS/XPath extraction for structured data.",
"estimated_savings": round(llm_usage["total_cost"] * 0.7, 2),
}
)
if not recommendations:
recommendations.append(
{
"type": "info",
"priority": "low",
"message": "Usage is within normal range. No optimizations needed.",
"estimated_savings": 0,
}
)
return recommendations
def get_cost_dashboard() -> dict[str, Any]:
"""Get full cost analytics dashboard data."""
monthly = get_monthly_usage()
# Get last 7 days of daily totals
now = datetime.now(UTC)
daily_totals = []
for i in range(6, -1, -1):
day = now - timedelta(days=i)
prefix = day.strftime("%Y-%m-%d")
day_cost = 0.0
day_ops = 0
path = COSTING_DIR / f"usage_{prefix}.jsonl"
if path.exists():
try:
for line in path.read_text().splitlines():
if not line.strip():
continue
r = json.loads(line)
day_cost += r.get("cost", 0)
day_ops += 1
except (json.JSONDecodeError, OSError):
pass
daily_totals.append(
{
"date": prefix,
"cost": round(day_cost, 6),
"operations": day_ops,
}
)
return {
"current_month": monthly,
"daily_totals": daily_totals,
"cache_efficiency": get_cache_efficiency(),
"recommendations": get_smart_schedule_recommendations(),
}

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"""Pry — Reverse ETL to CRM.
Sync scraped data to Salesforce, HubSpot, Pipedrive, and Close.com."""
import logging
from typing import Any
logger = logging.getLogger(__name__)
async def sync_to_salesforce(
objects: list[dict[str, Any]],
object_type: str = "Lead",
instance_url: str = "",
access_token: str = "",
) -> dict[str, Any]:
"""Sync scraped data to Salesforce objects.
Args:
objects: List of records to create/update
object_type: Salesforce object type (Lead, Contact, Account, Opportunity)
instance_url: Salesforce instance URL (e.g., https://yourInstance.salesforce.com)
access_token: Salesforce OAuth2 access token
"""
from client import get_client
client = await get_client()
api_url = instance_url.rstrip("/")
results = []
for obj in objects:
sf_obj = _map_to_salesforce(obj, object_type)
try:
resp = await client.post(
f"{api_url}/services/data/v58.0/sobjects/{object_type}",
json=sf_obj,
headers={
"Authorization": f"Bearer {access_token}",
"Content-Type": "application/json",
},
timeout=30,
)
if resp.is_success:
data = resp.json()
results.append(
{
"success": True,
"salesforce_id": data.get("id"),
"name": obj.get("name") or obj.get("title", "Unknown"),
}
)
else:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": f"Salesforce error {resp.status_code}: {resp.text[:200]}",
}
)
except Exception as e:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": str(e)[:200],
}
)
success_count = sum(1 for r in results if r["success"])
return {
"success": success_count > 0,
"platform": "salesforce",
"object_type": object_type,
"total": len(objects),
"synced": success_count,
"failed": len(objects) - success_count,
"results": results,
}
def _map_to_salesforce(data: dict[str, Any], object_type: str) -> dict[str, Any]:
"""Map common field names to Salesforce standard field names."""
mapping = {
"name": "LastName" if object_type == "Lead" else "Name",
"first_name": "FirstName",
"last_name": "LastName",
"email": "Email",
"phone": "Phone",
"company": "Company",
"title": "Title",
"description": "Description",
"website": "Website",
"industry": "Industry",
"address": "Street",
"city": "City",
"state": "State",
"zip": "PostalCode",
"country": "Country",
"revenue": "AnnualRevenue",
"employees": "NumberOfEmployees",
"source_url": "LeadSource",
}
result = {}
for src_key, sf_key in mapping.items():
if data.get(src_key):
result[sf_key] = data[src_key]
for key, value in data.items():
if key not in mapping and key not in ("_source", "_raw"):
result[key[:120]] = (
value if isinstance(value, (str, int, float, bool)) else str(value)[:255]
)
return result
async def sync_to_hubspot(
objects: list[dict[str, Any]],
object_type: str = "contacts",
api_key: str = "",
) -> dict[str, Any]:
"""Sync scraped data to HubSpot CRM.
Args:
objects: List of contact/company/deal records
object_type: contacts, companies, deals
api_key: HubSpot Private App API key
"""
from client import get_client
client = await get_client()
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
}
results = []
api_paths = {
"contacts": "/crm/v3/objects/contacts",
"companies": "/crm/v3/objects/companies",
"deals": "/crm/v3/objects/deals",
}
api_path = api_paths.get(object_type, f"/crm/v3/objects/{object_type}")
for obj in objects:
properties = _map_to_hubspot(obj, object_type)
payload = {"properties": properties}
try:
resp = await client.post(
f"https://api.hubapi.com{api_path}",
json=payload,
headers=headers,
timeout=30,
)
if resp.is_success:
data = resp.json()
results.append(
{
"success": True,
"hubspot_id": data.get("id"),
"name": obj.get("name") or obj.get("email", "Unknown"),
}
)
else:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": f"HubSpot error {resp.status_code}: {resp.text[:200]}",
}
)
except Exception as e:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": str(e)[:200],
}
)
success_count = sum(1 for r in results if r["success"])
return {
"success": success_count > 0,
"platform": "hubspot",
"object_type": object_type,
"total": len(objects),
"synced": success_count,
"failed": len(objects) - success_count,
"results": results,
}
def _map_to_hubspot(data: dict[str, Any], object_type: str) -> dict[str, str]:
"""Map common fields to HubSpot property names."""
mapping: dict[str, str] = {
"name": "firstname" if object_type == "contacts" else "name",
"first_name": "firstname",
"last_name": "lastname",
"email": "email",
"phone": "phone",
"company": "company",
"title": "jobtitle",
"website": "website",
"description": "description",
"address": "address",
"city": "city",
"state": "state",
"zip": "zip",
"country": "country",
"industry": "industry",
}
properties = {}
for src_key, hs_key in mapping.items():
if data.get(src_key):
properties[hs_key] = str(data[src_key])[:500]
return properties
async def sync_to_pipedrive(
objects: list[dict[str, Any]],
object_type: str = "person",
api_token: str = "",
domain: str = "",
) -> dict[str, Any]:
"""Sync scraped data to Pipedrive CRM."""
from client import get_client
client = await get_client()
domain = domain or "mycompany"
results = []
api_paths = {
"person": "/v1/persons",
"organization": "/v1/organizations",
"deal": "/v1/deals",
"lead": "/v1/leads",
}
path = api_paths.get(object_type, "/v1/persons")
base = f"https://{domain}.pipedrive.com/api{path}"
for obj in objects:
fields = _map_to_pipedrive(obj, object_type)
fields["api_token"] = api_token
try:
resp = await client.post(base, json=fields, timeout=30)
if resp.is_success:
data = resp.json()
results.append(
{
"success": True,
"pipedrive_id": data.get("data", {}).get("id"),
"name": obj.get("name", "Unknown"),
}
)
else:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": f"Pipedrive error {resp.status_code}",
}
)
except Exception as e:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": str(e)[:200],
}
)
success_count = sum(1 for r in results if r["success"])
return {
"success": success_count > 0,
"platform": "pipedrive",
"total": len(objects),
"synced": success_count,
"failed": len(objects) - success_count,
"results": results,
}
def _map_to_pipedrive(data: dict[str, Any], object_type: str) -> dict[str, Any]:
mapping = {
"name": "name",
"email": "email",
"phone": "phone",
"company": "org_name",
"title": "title",
}
result: dict[str, Any] = {}
for src_key, pd_key in mapping.items():
if data.get(src_key):
result[pd_key] = str(data[src_key])[:500]
if object_type == "person" and "email" in result:
result["email"] = [{"value": result["email"], "primary": True}]
return result
async def sync_to_close(
objects: list[dict[str, Any]],
object_type: str = "lead",
api_key: str = "",
) -> dict[str, Any]:
"""Sync scraped data to Close.com CRM."""
import base64
from client import get_client
client = await get_client()
auth = base64.b64encode(f"{api_key}:".encode()).decode()
results = []
for obj in objects:
close_obj = {
"name": obj.get("name") or obj.get("title", "Unknown"),
"description": (obj.get("description") or obj.get("content", ""))[:500],
"url": obj.get("url", ""),
}
if obj.get("email"):
close_obj["contacts"] = [{"emails": [{"email": obj["email"]}]}]
try:
resp = await client.post(
"https://api.close.com/api/v1/lead/",
json=close_obj,
headers={"Authorization": f"Basic {auth}", "Content-Type": "application/json"},
timeout=30,
)
if resp.is_success:
data = resp.json()
results.append(
{
"success": True,
"close_id": data.get("id"),
"name": obj.get("name", "Unknown"),
}
)
else:
results.append(
{
"success": False,
"name": obj.get("name", "Unknown"),
"error": f"Close error {resp.status_code}: {resp.text[:200]}",
}
)
except Exception as e:
results.append(
{"success": False, "name": obj.get("name", "Unknown"), "error": str(e)[:200]}
)
success_count = sum(1 for r in results if r["success"])
return {
"success": success_count > 0,
"platform": "close",
"total": len(objects),
"synced": success_count,
"failed": len(objects) - success_count,
"results": results,
}

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"""Pry — Database layer using SQLAlchemy (SQLite/PostgreSQL).
Replaces JSON file storage for production safety."""
import logging
import os
from collections.abc import Generator
from contextlib import contextmanager
from datetime import UTC, datetime
from typing import Any
logger = logging.getLogger(__name__)
# Try to import SQLAlchemy
try:
from sqlalchemy import (
JSON,
Boolean,
Column,
DateTime,
Float,
Integer,
String,
Text,
create_engine,
)
from sqlalchemy.dialects.postgresql import JSONB
from sqlalchemy.orm import Session, declarative_base, sessionmaker
_has_sqlalchemy = True
Base = declarative_base()
except ImportError:
_has_sqlalchemy = False
Base = None
if _has_sqlalchemy:
class ApiKey(Base):
__tablename__ = "api_keys"
id = Column(Integer, primary_key=True, autoincrement=True)
key_hash = Column(String(64), unique=True, nullable=False, index=True)
user_id = Column(String(32), nullable=False, index=True)
name = Column(String(100), default="default")
rate_limit_rpm = Column(Integer, default=60)
created_at = Column(DateTime, default=lambda: datetime.now(UTC))
last_used = Column(DateTime, nullable=True)
use_count = Column(Integer, default=0)
active = Column(Boolean, default=True)
class User(Base):
__tablename__ = "users"
id = Column(String(32), primary_key=True)
email = Column(String(255), unique=True, nullable=False, index=True)
password_hash = Column(String(128), nullable=False)
salt = Column(String(64), nullable=False)
role = Column(String(20), default="user")
created_at = Column(DateTime, default=lambda: datetime.now(UTC))
active = Column(Boolean, default=True)
metadata_json = Column("metadata", JSON, default=dict)
class UsageRecord(Base):
__tablename__ = "usage_records"
id = Column(Integer, primary_key=True, autoincrement=True)
api_key_id = Column(Integer, index=True)
operation = Column(String(50), nullable=False, index=True)
quantity = Column(Float, default=1.0)
cost_usd = Column(Float, default=0.0)
timestamp = Column(DateTime, default=lambda: datetime.now(UTC), index=True)
metadata_json = Column("metadata", JSON, default=dict)
class QualityCheckRecord(Base):
__tablename__ = "quality_checks"
id = Column(Integer, primary_key=True, autoincrement=True)
url_hash = Column(String(16), index=True)
url = Column(String(2048), nullable=False)
data = Column(JSON, default=dict)
quality_score = Column(Float, default=0.0)
anomaly_count = Column(Integer, default=0)
checked_at = Column(DateTime, default=lambda: datetime.now(UTC))
class ReviewRequest(Base):
__tablename__ = "review_requests"
id = Column(String(12), primary_key=True)
user_id = Column(String(32), index=True)
status = Column(String(20), default="pending", index=True)
data = Column(JSON, default=dict)
confidence_score = Column(Float, default=0.0)
flagged_fields = Column(JSON, default=list)
created_at = Column(DateTime, default=lambda: datetime.now(UTC))
reviewed_at = Column(DateTime, nullable=True)
reviewed_by = Column(String(100), nullable=True)
review_notes = Column(Text, default="")
class MonitorRecord(Base):
__tablename__ = "monitors"
id = Column(String(12), primary_key=True)
user_id = Column(String(32), index=True)
name = Column(String(200), nullable=False)
target_url = Column(String(2048), nullable=False)
schedule_cron = Column(String(50), default="0 */6 * * *")
goal = Column(Text, default="")
webhook_url = Column(String(2048), default="")
status = Column(String(20), default="active")
created_at = Column(DateTime, default=lambda: datetime.now(UTC))
last_run_at = Column(DateTime, nullable=True)
total_checks = Column(Integer, default=0)
total_changes = Column(Integer, default=0)
class AgencyClient(Base):
__tablename__ = "agency_clients"
id = Column(String(12), primary_key=True)
agency_id = Column(String(32), index=True)
name = Column(String(200), nullable=False)
email = Column(String(255), nullable=False)
api_key_hash = Column(String(64), unique=True)
monthly_quota = Column(Integer, default=10000)
usage_this_month = Column(Integer, default=0)
status = Column(String(20), default="active")
created_at = Column(DateTime, default=lambda: datetime.now(UTC))
class Database:
"""SQLAlchemy database wrapper. Supports SQLite (default) and PostgreSQL."""
def __init__(self, url: str = ""):
if not _has_sqlalchemy:
logger.warning("sqlalchemy_not_available")
return
if not url:
url = os.getenv("PRY_DATABASE_URL", "sqlite:///~/.pry/data.db")
# Ensure directory exists
if url.startswith("sqlite:///"):
db_path = url[10:]
os.makedirs(os.path.dirname(os.path.expanduser(db_path)), exist_ok=True)
url = f"sqlite:///{os.path.expanduser(db_path)}"
self.engine = create_engine(url, echo=False, pool_pre_ping=True)
self.Session = sessionmaker(bind=self.engine)
# Create tables
Base.metadata.create_all(self.engine)
logger.info("database_initialized", extra={"url": url.split("@")[-1]})
@contextmanager
def session(self) -> Generator[Any, None, None]:
if not _has_sqlalchemy:
yield None
return
s = self.Session()
try:
yield s
s.commit()
except Exception:
s.rollback()
raise
finally:
s.close()
def is_available(self) -> bool:
return _has_sqlalchemy
_db: Database | None = None
def get_db() -> Database | None:
"""Get or create the global database instance."""
global _db
if _db is None and _has_sqlalchemy:
_db = Database()
return _db

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"""Pry — Result Deduplication using SimHash.
Detects near-duplicate content (e.g., 95% similar pages) using SimHash.
Useful for crawl deduplication, change detection, and content grouping."""
import hashlib
import logging
import re
from typing import Any
logger = logging.getLogger(__name__)
class SimHash:
"""SimHash implementation for near-duplicate detection."""
@staticmethod
def hash(text: str, n_features: int = 128) -> int:
"""Generate a SimHash fingerprint for text."""
tokens = re.findall(r"\w+", text.lower())
if not tokens:
return 0
vector = [0] * n_features
for token in tokens:
token_hash = int(hashlib.md5(token.encode()).hexdigest(), 16)
for i in range(n_features):
if token_hash & (1 << i):
vector[i] += 1
else:
vector[i] -= 1
result = 0
for i in range(n_features):
if vector[i] > 0:
result |= 1 << i
return result
@staticmethod
def hamming_distance(hash1: int, hash2: int) -> int:
"""Count differing bits between two hashes (lower = more similar)."""
x = hash1 ^ hash2
distance = 0
while x:
distance += x & 1
x >>= 1
return distance
@staticmethod
def similarity(hash1: int, hash2: int, n_features: int = 128) -> float:
"""Calculate similarity (0-1) between two SimHashes."""
dist = SimHash.hamming_distance(hash1, hash2)
return 1.0 - (dist / n_features)
class Deduplicator:
"""Find near-duplicate content across documents."""
def __init__(self, threshold: float = 0.85):
self.threshold = threshold
self._hashes: dict[str, int] = {}
def add(self, doc_id: str, text: str) -> int:
"""Add a document and return its SimHash."""
h = SimHash.hash(text)
self._hashes[doc_id] = h
return h
def find_duplicates(self, text: str) -> list[dict[str, Any]]:
"""Find existing documents that are near-duplicates of this text."""
target_hash = SimHash.hash(text)
duplicates: list[dict[str, Any]] = []
for doc_id, doc_hash in self._hashes.items():
sim = SimHash.similarity(target_hash, doc_hash)
if sim >= self.threshold:
duplicates.append(
{
"doc_id": doc_id,
"similarity": round(sim, 3),
"distance": SimHash.hamming_distance(target_hash, doc_hash),
}
)
return sorted(duplicates, key=lambda x: -x["similarity"])
def cluster(self, texts: dict[str, str]) -> dict[str, list[str]]:
"""Cluster texts by similarity. Returns {cluster_id: [doc_ids]}."""
hashes = {doc_id: SimHash.hash(text) for doc_id, text in texts.items()}
clusters: dict[str, list[str]] = {}
visited: set[str] = set()
cluster_id = 0
for doc_id, hash_val in hashes.items():
if doc_id in visited:
continue
cluster = [doc_id]
visited.add(doc_id)
for other_id, other_hash in hashes.items():
if other_id in visited:
continue
if SimHash.similarity(hash_val, other_hash) >= self.threshold:
cluster.append(other_id)
visited.add(other_id)
if cluster:
clusters[f"cluster_{cluster_id}"] = cluster
cluster_id += 1
return clusters
def diff(self, text1: str, text2: str) -> dict[str, Any]:
"""Show what changed between two versions of the same content."""
hash1 = SimHash.hash(text1)
hash2 = SimHash.hash(text2)
sim = SimHash.similarity(hash1, hash2)
distance = SimHash.hamming_distance(hash1, hash2)
return {
"similarity": round(sim, 3),
"hamming_distance": distance,
"changed": sim < self.threshold,
"change_severity": (
"high" if distance > 20
else "medium" if distance > 10
else "low" if distance > 3
else "minimal"
),
}

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"""Pry — One-Click Business Integrations.
Native write destinations: Google Sheets, Slack, Airtable, email."""
import json
import logging
from datetime import UTC, datetime
from typing import Any
from urllib.parse import quote
import httpx
from client import get_client
logger = logging.getLogger(__name__)
# ── Slack ──
async def write_to_slack(
webhook_url: str,
message: str,
title: str = "Pry Data",
color: str = "#36a64f",
) -> dict[str, Any]:
"""Send data to a Slack webhook as a formatted message."""
if not webhook_url:
return {"success": False, "error": "Slack webhook URL is required"}
# Truncate message if too long (Slack limit: 40000 chars)
if len(message) > 35000:
message = message[:35000] + "\n\n*(truncated — data too large for Slack)*"
payload = {
"attachments": [
{
"color": color,
"title": title,
"text": message,
"footer": "Pry — Web Intelligence",
"ts": datetime.now(UTC).timestamp(),
}
]
}
try:
client = await get_client()
resp = await client.post(webhook_url, json=payload, timeout=15)
if resp.status_code >= 400:
error_body = resp.text[:200]
logger.error(
"slack_write_failed",
extra={"status": resp.status_code, "error": error_body},
)
return {"success": False, "error": f"Slack returned {resp.status_code}: {error_body}"}
return {"success": True, "destination": "slack"}
except httpx.InvalidURL:
return {"success": False, "error": "Invalid Slack webhook URL"}
except httpx.RequestError as e:
return {"success": False, "error": f"Slack request failed: {str(e)[:200]}"}
async def write_to_slack_csv(
webhook_url: str,
csv_data: str,
title: str = "Pry Data Export",
) -> dict[str, Any]:
"""Send CSV data to Slack as a formatted code block."""
if len(csv_data) > 35000:
csv_data = csv_data[:35000] + "\n...(truncated)"
message = f"```\n{csv_data}\n```"
return await write_to_slack(webhook_url, message, title=title)
# ── Google Sheets (via public API — requires service account) ──
async def write_to_googlesheets(
spreadsheet_id: str,
range_name: str,
values: list[list[Any]],
credentials_json: str | None = None,
) -> dict[str, Any]:
"""Write data to Google Sheets using the Sheets API.
Requires a service account credentials JSON string.
Falls back to a public CSV-export approach if no credentials.
"""
if credentials_json:
return await _write_to_sheets_api(spreadsheet_id, range_name, values, credentials_json)
# Without credentials, generate a shareable CSV link
csv_lines = "\n".join([",".join(str(c) for c in row) for row in values])
data_url = f"https://docs.google.com/spreadsheets/d/{spreadsheet_id}/edit"
return {
"success": True,
"destination": "googlesheets",
"note": "Credentials required for automatic write. CSV preview available.",
"csv_preview": csv_lines[:500],
"manual_url": data_url,
}
async def _write_to_sheets_api(
spreadsheet_id: str,
range_name: str,
values: list[list[Any]],
credentials_json: str,
) -> dict[str, Any]:
"""Write to Google Sheets using the Sheets API v4."""
try:
creds = json.loads(credentials_json)
access_token = creds.get("access_token") or creds.get("token") or ""
if not access_token:
return {"success": False, "error": "No access_token found in credentials"}
client = await get_client()
body = {"values": values, "majorDimension": "ROWS"}
url = f"https://sheets.googleapis.com/v4/spreadsheets/{spreadsheet_id}/values/{quote(range_name)}?valueInputOption=USER_ENTERED"
resp = await client.put(
url,
json=body,
headers={"Authorization": f"Bearer {access_token}"},
timeout=30,
)
if resp.status_code < 400:
return {
"success": True,
"destination": "googlesheets",
"updated_cells": len(values) * len(values[0]) if values else 0,
}
return {
"success": False,
"error": f"Sheets API error: {resp.status_code} {resp.text[:200]}",
}
except json.JSONDecodeError:
return {"success": False, "error": "Invalid credentials JSON"}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
# ── Airtable ──
async def write_to_airtable(
base_id: str,
table_name: str,
records: list[dict[str, Any]],
api_key: str = "",
) -> dict[str, Any]:
"""Write records to an Airtable base/table."""
if not api_key:
return {"success": False, "error": "Airtable API key required"}
client = await get_client()
# Airtable API accepts up to 10 records per request
all_results: list[dict[str, Any]] = []
batch_size = 10
for i in range(0, len(records), batch_size):
batch = records[i : i + batch_size]
payload = {"records": [{"fields": r} for r in batch]}
try:
resp = await client.post(
f"https://api.airtable.com/v0/{base_id}/{quote(table_name)}",
json=payload,
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
timeout=30,
)
if resp.status_code < 400:
data = resp.json()
created = data.get("records", [])
all_results.append(
{
"batch": i // batch_size,
"created": len(created),
"success": True,
}
)
else:
all_results.append(
{
"batch": i // batch_size,
"success": False,
"error": f"Airtable error {resp.status_code}: {resp.text[:200]}",
}
)
except Exception as e:
all_results.append({"batch": i // batch_size, "success": False, "error": str(e)[:200]})
total_created = sum(r.get("created", 0) for r in all_results if r.get("success"))
return {
"success": all(r["success"] for r in all_results),
"destination": "airtable",
"total_records": len(records),
"successfully_written": total_created,
"batches": all_results,
}
# ── Email (SMTP) ──
async def write_to_email(
recipient: str,
subject: str,
body: str,
smtp_host: str = "",
smtp_port: int = 587,
smtp_user: str = "",
smtp_password: str = "",
sender: str = "",
) -> dict[str, Any]:
"""Send data via email using SMTP.
Falls back to generating a mailto: link if SMTP not configured.
"""
if smtp_host and smtp_user and smtp_password:
return await _send_smtp(
recipient, subject, body, smtp_host, smtp_port, smtp_user, smtp_password, sender
)
# Generate mailto link as fallback
body_short = body[:500].replace("\n", "%0A").replace(" ", "%20")
mailto = f"mailto:{recipient}?subject={quote(subject)}&body={body_short}"
return {
"success": True,
"destination": "email",
"note": "SMTP not configured — mailto link generated",
"mailto_link": mailto,
}
async def _send_smtp(
recipient: str,
subject: str,
body: str,
host: str,
port: int,
user: str,
password: str,
sender: str,
) -> dict[str, Any]:
"""Send email via SMTP."""
import smtplib
from email.mime.text import MIMEText
try:
msg = MIMEText(body, "plain" if not body.startswith("<") else "html")
msg["Subject"] = subject
msg["From"] = sender or user
msg["To"] = recipient
with smtplib.SMTP(host, port) as server:
server.starttls()
server.login(user, password)
server.send_message(msg)
return {"success": True, "destination": "email"}
except Exception as e:
return {"success": False, "error": f"SMTP error: {str(e)[:200]}"}
# ── Generic dispatch ──
DESTINATIONS: dict[str, Any] = {
"slack": write_to_slack,
"googlesheets": write_to_googlesheets,
"airtable": write_to_airtable,
"email": write_to_email,
}
SUPPORTED_DESTINATIONS = list(DESTINATIONS.keys())
async def dispatch(
destination: str,
data: dict[str, Any],
config: dict[str, Any],
) -> dict[str, Any]:
"""Dispatch data to a configured destination.
Args:
destination: One of: slack, googlesheets, airtable, email
data: Data to send (will be formatted for the destination)
config: Destination-specific config (webhook_url, api_key, etc.)
"""
handler = DESTINATIONS.get(destination)
if not handler:
return {
"success": False,
"error": f"Unknown destination: {destination}. Supported: {SUPPORTED_DESTINATIONS}",
}
# Format data for destination
if destination == "slack":
message = json.dumps(data, indent=2, default=str)
return await handler( # type: ignore[no-any-return]
webhook_url=config.get("webhook_url", ""),
message=message,
title=config.get("title", "Pry Data"),
)
if destination == "googlesheets":
rows = _data_to_rows(data)
return await handler( # type: ignore[no-any-return]
spreadsheet_id=config.get("spreadsheet_id", ""),
range_name=config.get("range", "Sheet1!A1"),
values=rows,
credentials_json=config.get("credentials_json"),
)
if destination == "airtable":
records = data if isinstance(data, list) else [data]
return await handler( # type: ignore[no-any-return]
base_id=config.get("base_id", ""),
table_name=config.get("table_name", "Table 1"),
records=records,
api_key=config.get("api_key", ""),
)
if destination == "email":
body = json.dumps(data, indent=2, default=str)
return await handler( # type: ignore[no-any-return]
recipient=config.get("recipient", ""),
subject=config.get("subject", "Pry Data Export"),
body=body,
smtp_host=config.get("smtp_host", ""),
smtp_port=config.get("smtp_port", 587),
smtp_user=config.get("smtp_user", ""),
smtp_password=config.get("smtp_password", ""),
sender=config.get("sender", ""),
)
return {"success": False, "error": f"Unhandled destination: {destination}"}
def _data_to_rows(data: dict[str, Any] | list[Any]) -> list[list[Any]]:
"""Convert extracted data to spreadsheet rows (header row + data rows)."""
if isinstance(data, list):
if not data:
return [["No data"]]
if isinstance(data[0], dict):
headers = list(data[0].keys())
rows = [[str(v) for v in r.values()] for r in data]
return [headers, *rows]
return [[str(item) for item in data]]
if isinstance(data, dict):
headers = list(data.keys())
values = [str(v) for v in data.values()]
return [headers, values]
return [["value"], [str(data)]]

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services:
pry:
build: .
container_name: pry
restart: unless-stopped
ports:
- "127.0.0.1:8005:8002"
volumes:
- pry-sessions:/app/sessions
environment:
- PYTHONUNBUFFERED=1
- PRY_TIMEOUT=60
- FLARESOLVERR_URL=http://flaresolverr:8191/v1
- TOR_ENABLED=0
- TOR_SOCKS5_HOST=tor
- TOR_SOCKS5_PORT=9050
- PROXY_URL=
- PROXY_TYPE=http
- IP_ROTATION=off
- MAX_RETRIES=3
- MIN_QUALITY=20
- RATE_LIMIT_RPM=120
healthcheck:
test: ["CMD", "curl", "-sf", "http://localhost:8002/health"]
interval: 15s
timeout: 5s
retries: 3
start_period: 30s
deploy:
resources:
limits:
memory: 2G
cpus: "2"
depends_on:
flaresolverr:
condition: service_healthy
flaresolverr:
image: ghcr.io/flaresolverr/flaresolverr:latest
container_name: pry-flaresolverr
restart: unless-stopped
ports:
- "127.0.0.1:8191:8191"
environment:
- LOG_LEVEL=info
- CAPTCHA_SOLVER=none
healthcheck:
test: ["CMD", "curl", "-sf", "http://localhost:8191/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 10s
deploy:
resources:
limits:
memory: 512M
cpus: "1"
tor:
image: dperson/torproxy:latest
container_name: pry-tor
restart: unless-stopped
ports:
- "127.0.0.1:9050:9050"
- "127.0.0.1:9051:9051"
deploy:
resources:
limits:
memory: 128M
cpus: "0.5"
profiles:
- tor
volumes:
pry-sessions:

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# ADR-NNNN: <Short Title>
## Status
Proposed | Accepted | Deprecated | Superseded by ADR-XXXX
## Context
What's the issue? What's the pressure for a decision?
## Decision
What did we choose?
## Consequences
What becomes easier? What becomes harder?
## Alternatives
What else did we consider? Why didn't we pick them?

22
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# ADR-0001: Initial Architecture
## Status
Accepted · 2026-07-02
## Context
First commit of PryScraper. Need to document the foundational choices.
## Decision
- **Language**: Python 3.12 + FastAPI
- **Port**: 8005
- **Deployment**: Docker on Talos behind nginx
- **Source of truth**: forgejo
- **CI**: forgejo Actions
- **Secrets**: gopass
## Consequences
- All future decisions build on this foundation
- Breaking changes to these need a new ADR
## Alternatives
- _TBD_

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"""Pry — Email Inbox Scraping.
Connect Gmail/Outlook, extract structured data from emails."""
import base64
import logging
import re
from datetime import UTC, datetime, timedelta
from typing import Any
logger = logging.getLogger(__name__)
# ── Email Data Extraction Patterns ──
ORDER_PATTERNS = {
"order_number": [
r"order\s*(?:#|number|id|no)[:\s]*([A-Z0-9\-]{5,20})",
r"(?:order|ord)\s*[#:]?\s*([A-Z0-9]{5,20})",
r"confirmation\s*(?:#|number|id|no)[:\s]*([A-Z0-9\-]{5,20})",
],
"total_amount": [
r"total[:\s]*\$?([0-9,.]+)",
r"amount[:\s]*\$?([0-9,.]+)",
r"charged[:\s]*\$?([0-9,.]+)",
],
"tracking_number": [
r"tracking\s*(?:#|number|id|no)[:\s]*(?:is\s+)?([A-Z0-9]{8,30})",
r"track\s*(?:#|number)[:\s]*(?:is\s+)?([A-Z0-9]{8,30})",
r"shipment\s*(?:#|number|id)[:\s]*(?:is\s+)?([A-Z0-9]{8,30})",
],
"product_name": [
r"(?:product|item|goods)[:\s]*(.+?)(?:\n|$)",
r"purchased[:\s]*(.+?)(?:\n|$)",
r"ordered[:\s]*(.+?)(?:\n|$)",
],
"shipping_address": [
r"shipping\s*(?:to|address)[:\s]*([A-Za-z0-9\s,\-]{10,100})",
r"deliver\s*(?:to|address)[:\s]*([A-Za-z0-9\s,\-]{10,100})",
],
}
INVOICE_PATTERNS = {
"invoice_number": [
r"invoice\s*(?:#|number|id|no)[:\s]*([A-Z0-9\-]{5,25})",
r"inv\s*(?:#|no)[:\s]*([A-Z0-9\-]{5,25})",
],
"due_date": [
r"due\s*(?:date|by)[:\s]*([A-Za-z0-9\s,]+)",
r"payment\s*(?:due|by)[:\s]*([A-Za-z0-9\s,]+)",
],
"vendor": [
r"(?:from|vendor|supplier)[:\s]*(.+?)(?:\n|$)",
r"billed\s*(?:from|by)[:\s]*(.+?)(?:\n|$)",
],
}
SHIPPING_PATTERNS = {
"tracking_number": [
r"tracking\s*(?:#|number|id|no)[:\s]*(?:is\s+)?([A-Z0-9]{8,30})",
r"track\s*(?:#|number)[:\s]*(?:is\s+)?([A-Z0-9]{8,30})",
r"shipment\s*(?:#|number|id)[:\s]*(?:is\s+)?([A-Z0-9\-]{8,30})",
],
"carrier": [
r"(?:carrier|shipped\s*via|courier)[:\s]*(.+?)(?:\n|$)",
r"(?:USPS|UPS|FedEx|DHL|Canada\s*Post|Royal\s*Mail)",
],
"estimated_delivery": [
r"estimated\s*(?:delivery|arrival)[:\s]*(.+?)(?:\n|$)",
r"delivery\s*(?:date|by|on)[:\s]*([A-Za-z0-9\s,]+)",
r"arriving\s*(?:on|by)[:\s]*([A-Za-z0-9\s,]+)",
],
}
RECEIPT_PATTERNS = {
"receipt_number": [
r"receipt\s*(?:#|number)[:\s]*([A-Z0-9\-]{5,25})",
r"transaction\s*(?:#|id)[:\s]*([A-Z0-9\-]{5,30})",
],
"payment_method": [
r"payment\s*method[:\s]*(.+?)(?:\n|$)",
r"paid\s*via[:\s]*(.+?)(?:\n|$)",
r"card[:\s]*([A-Za-z0-9\s]{4,30})",
],
"store_name": [
r"(?:store|merchant|seller|retailer)[:\s]*(.+?)(?:\n|$)",
r"purchased\s*(?:from|at)[:\s]*(.+?)(?:\n|$)",
r"thank\s*you\s*for\s*(?:your\s*)?purchase\s*(?:from|at)?[:\s]*(.+?)(?:\n|$)",
],
}
def extract_email_data(subject: str, body: str, sender: str) -> dict[str, Any]:
"""Extract structured data from an email body.
Returns extracted order/invoice/receipt information with confidence scores.
"""
result: dict[str, Any] = {
"sender": sender,
"subject": subject,
"email_type": _classify_email(subject, body),
"extracted_data": {},
}
email_type = result["email_type"]
patterns = _get_patterns_for_type(email_type)
for field, regexes in patterns.items():
for pattern in regexes:
match = re.search(pattern, body, re.IGNORECASE)
if match:
result["extracted_data"][field] = match.group(1).strip()
break
# Extract monetary amounts
amounts = re.findall(r"\$?([0-9]+(?:\.[0-9]{2})?)", body)
if amounts and "total_amount" not in result["extracted_data"]:
# Take the largest amount as most significant
numeric = [float(a) for a in amounts if re.match(r"^\d+\.?\d*$", a)]
if numeric:
result["extracted_data"]["potential_amount"] = max(numeric)
# Extract dates
dates = re.findall(r"(\d{1,2}[/\-]\d{1,2}[/\-]\d{2,4})", body)
if dates:
result["extracted_data"]["dates_found"] = dates[:3]
# Extract URLs
urls = re.findall(r'https?://(?:www\.)?[^\s<>"]+', body)
if urls:
result["extracted_data"]["urls_found"] = urls[:5]
return result
def _classify_email(subject: str, body: str) -> str:
"""Classify an email as order_confirmation, invoice, receipt, or other."""
combined = (subject + " " + body[:500]).lower()
if any(
w in combined for w in ["order confirmation", "your order", "order received", "order #"]
):
return "order_confirmation"
if any(w in combined for w in ["invoice", "payment due", "billing statement", "invoice #"]):
return "invoice"
if any(
w in combined for w in ["receipt", "your receipt", "payment receipt", "transaction receipt"]
):
return "receipt"
if any(w in combined for w in ["shipping", "shipment", "tracking", "on its way", "dispatched"]):
return "shipping_notification"
if any(w in combined for w in ["subscription", "renewal", "billed"]):
return "subscription"
return "other"
def _get_patterns_for_type(email_type: str) -> dict[str, list[str]]:
"""Get extraction patterns for the classified email type."""
if email_type == "order_confirmation":
return ORDER_PATTERNS
if email_type == "invoice":
return INVOICE_PATTERNS
if email_type == "receipt":
return RECEIPT_PATTERNS
if email_type == "shipping_notification":
return SHIPPING_PATTERNS
return {}
# ── Gmail Integration ──
async def fetch_gmail_emails(
access_token: str,
max_results: int = 20,
query: str = "",
since_days: int = 7,
) -> dict[str, Any]:
"""Fetch emails from Gmail via the Gmail API.
Args:
access_token: Gmail OAuth2 access token
max_results: Maximum number of emails to fetch
query: Gmail search query (e.g., "from:amazon subject:order")
since_days: Fetch emails from this many days ago
Returns list of emails with extracted data.
"""
from client import get_client
client = await get_client()
headers = {"Authorization": f"Bearer {access_token}"}
# Build query
since_date = (datetime.now(UTC) - timedelta(days=since_days)).strftime("%Y/%m/%d")
full_query = f"after:{since_date} {query}".strip()
try:
# List messages
resp = await client.get(
"https://gmail.googleapis.com/gmail/v1/users/me/messages",
params={"q": full_query, "maxResults": min(max_results, 50)},
headers=headers,
timeout=15,
)
if not resp.is_success:
return {
"success": False,
"error": f"Gmail API error {resp.status_code}: {resp.text[:200]}",
}
data = resp.json()
messages = data.get("messages", [])
if not messages:
return {
"success": True,
"total": 0,
"emails": [],
"note": "No emails found matching query",
}
# Fetch each message
emails = []
for msg in messages[:max_results]:
msg_id = msg["id"]
try:
msg_resp = await client.get(
f"https://gmail.googleapis.com/gmail/v1/users/me/messages/{msg_id}",
headers=headers,
timeout=15,
)
if not msg_resp.is_success:
continue
msg_data = msg_resp.json()
email = _parse_gmail_message(msg_data)
if email:
emails.append(email)
except Exception as e:
logger.warning(
"gmail_fetch_failed", extra={"msg_id": msg_id, "error": str(e)[:100]}
)
continue
return {"success": True, "total": len(emails), "emails": emails}
except Exception as e:
return {"success": False, "error": str(e)[:300]}
def _parse_gmail_message(msg_data: dict[str, Any]) -> dict[str, Any] | None:
"""Parse a single Gmail message into a structured email object."""
payload = msg_data.get("payload", {})
headers = {h["name"].lower(): h["value"] for h in payload.get("headers", [])}
subject = headers.get("subject", "")
sender = headers.get("from", "")
date_str = headers.get("date", "")
body = _get_gmail_body(payload)
if not body:
return None
extracted = extract_email_data(subject, body[:5000], sender)
return {
"id": msg_data.get("id"),
"thread_id": msg_data.get("threadId"),
"subject": subject[:200],
"from": sender[:200],
"date": date_str[:50],
"snippet": msg_data.get("snippet", "")[:200],
"email_type": extracted["email_type"],
"extracted_data": extracted["extracted_data"],
}
def _get_gmail_body(payload: dict[str, Any]) -> str:
"""Extract body text from a Gmail message payload."""
if payload.get("mimeType") == "text/plain" and payload.get("body", {}).get("data"):
data = payload["body"]["data"]
try:
return base64.urlsafe_b64decode(data).decode("utf-8", errors="replace")
except Exception:
return ""
# Check parts
parts = payload.get("parts", [])
for part in parts:
if part.get("mimeType") == "text/plain" and part.get("body", {}).get("data"):
data = part["body"]["data"]
try:
return base64.urlsafe_b64decode(data).decode("utf-8", errors="replace")
except Exception:
continue
# Recursive check
if part.get("parts"):
result = _get_gmail_body(part)
if result:
return result
return ""
# ── Microsoft Graph / Outlook Integration ──
async def fetch_outlook_emails(
access_token: str,
max_results: int = 20,
query: str = "",
since_days: int = 7,
) -> dict[str, Any]:
"""Fetch emails from Outlook/Office 365 via Microsoft Graph API."""
from client import get_client
client = await get_client()
headers = {"Authorization": f"Bearer {access_token}"}
since_date = (datetime.now(UTC) - timedelta(days=since_days)).isoformat()
try:
params: dict[str, Any] = {
"$top": min(max_results, 50),
"$orderby": "receivedDateTime DESC",
"$filter": f"receivedDateTime ge {since_date}",
}
if query:
params["$search"] = f'"{query}"'
resp = await client.get(
"https://graph.microsoft.com/v1.0/me/messages",
params=params,
headers=headers,
timeout=15,
)
if not resp.is_success:
return {"success": False, "error": f"Outlook API error {resp.status_code}"}
data = resp.json()
messages = data.get("value", [])
emails = []
for msg in messages[:max_results]:
subject = msg.get("subject", "")
sender = msg.get("from", {}).get("emailAddress", {}).get("address", "")
body_content = msg.get("body", {}).get("content", "")
# Strip HTML
body_text = re.sub(r"<[^>]+>", " ", body_content)
extracted = extract_email_data(subject, body_text[:5000], sender)
emails.append(
{
"id": msg.get("id"),
"subject": subject[:200],
"from": sender[:200],
"date": msg.get("receivedDateTime", "")[:25],
"email_type": extracted["email_type"],
"extracted_data": extracted["extracted_data"],
}
)
return {"success": True, "total": len(emails), "emails": emails}
except Exception as e:
return {"success": False, "error": str(e)[:300]}

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"""Pry — Data Enrichment Pipeline.
Enrich scraped data with company info, social profiles, tech stack detection."""
import logging
import re
from typing import Any
logger = logging.getLogger(__name__)
# ── Tech Stack Detection ──
TECH_PATTERNS: dict[str, list[str]] = {
"wordpress": [r"wp-content", r"wp-includes", r"/wp-json/", r"wordpress"],
"shopify": [r"shopify\.com", r"myshopify\.com", r"Shopify", r"cdn\.shopify"],
"woocommerce": [r"woocommerce", r"wc-", r"add-to-cart"],
"wix": [r"wix\.com", r"Wix\.com", r"wixstatic\.com"],
"squarespace": [r"squarespace\.com", r"Squarespace"],
"webflow": [r"webflow\.com", r"Webflow"],
"magento": [r"magento", r"Magento", r"mage\-"],
"laravel": [r"laravel", r"Laravel"],
"django": [r"django", r"Django", r"csrfmiddlewaretoken", r"csrftoken"],
"rails": [r"rails", r"Ruby on Rails", r"_rails"],
"nextjs": [r"_next/static", r"__next_data__", r"next\.js"],
"nuxt": [r"__NUXT__", r"nuxt"],
"gatsby": [r"gatsby", r"Gatsby"],
"react": [r"react\.js", r"react-dom", r"React", r"create-react-app"],
"vue": [r"vue\.js", r"Vue", r"vue-router"],
"angular": [r"angular\.js", r"Angular", r"ng-"],
"cloudflare": [r"cloudflare", r"cf-ray", r"__cfduid"],
"fastly": [r"fastly", r"Fastly"],
"cloudfront": [r"cloudfront\.net", r"CloudFront"],
"google_analytics": [r"google-analytics\.com", r"gtag", r"ga\.js"],
"facebook_pixel": [r"facebook\.com/tr", r"fbq\("],
"hotjar": [r"hotjar", r"Hotjar"],
"intercom": [r"intercom\.io", r"Intercom"],
"hubspot": [r"hubspot\.com", r"HubSpot", r"hs-scripts"],
"stripe": [r"stripe\.com", r"Stripe", r"pk_live"],
"paypal": [r"paypal\.com", r"PayPal"],
}
def detect_tech_stack(html: str, headers: dict[str, str] | None = None) -> dict[str, Any]:
"""Detect technologies used on a website from HTML and headers."""
detected: dict[str, bool] = {}
lower_html = html.lower()
for tech, patterns in TECH_PATTERNS.items():
for p in patterns:
if re.search(p, lower_html) or (
headers and any(re.search(p, str(v).lower()) for v in headers.values())
):
detected[tech] = True
break
# Categorize
categories: dict[str, list[str]] = {
"cms": [
t
for t in [
"wordpress",
"shopify",
"woocommerce",
"wix",
"squarespace",
"webflow",
"magento",
]
if detected.get(t)
],
"framework": [
t for t in ["laravel", "django", "rails", "nextjs", "nuxt", "gatsby"] if detected.get(t)
],
"frontend": [t for t in ["react", "vue", "angular"] if detected.get(t)],
"hosting_cdn": [t for t in ["cloudflare", "fastly", "cloudfront"] if detected.get(t)],
"analytics": [
t
for t in ["google_analytics", "facebook_pixel", "hotjar", "intercom", "hubspot"]
if detected.get(t)
],
"payments": [t for t in ["stripe", "paypal"] if detected.get(t)],
}
return {
"detected": list(detected.keys()),
"count": len(detected),
"categories": categories,
}
# ── Social Profile Extraction ──
SOCIAL_PATTERNS: dict[str, str] = {
"twitter": r"(?:twitter\.com|x\.com)/([A-Za-z0-9_]{1,30})/?",
"linkedin": r"linkedin\.com/(?:company|in)/([A-Za-z0-9\-]+)/?",
"facebook": r"facebook\.com/([A-Za-z0-9\.\-]+)/?",
"instagram": r"instagram\.com/([A-Za-z0-9_\.]+)/?",
"youtube": r"youtube\.com/@?([A-Za-z0-9_\-]+)/?",
"github": r"github\.com/([A-Za-z0-9\-]+)/?",
"crunchbase": r"crunchbase\.com/(?:organization|person)/([A-Za-z0-9\-]+)/?",
"angellist": r"angel\.co/([A-Za-z0-9\-]+)/?",
"producthunt": r"producthunt\.com/@?([A-Za-z0-9_\-]+)/?",
}
def extract_social_profiles(html: str, url: str = "") -> dict[str, Any]:
"""Extract social media profile links from HTML."""
profiles: dict[str, list[str]] = {}
lower_html = html.lower()
for platform, pattern in SOCIAL_PATTERNS.items():
matches = re.findall(pattern, lower_html)
if matches:
profiles[platform] = list(set(matches[:3]))
# Also check URL itself
if url:
lower_url = url.lower()
for platform, pattern in SOCIAL_PATTERNS.items():
if platform not in profiles:
m = re.search(pattern, lower_url)
if m:
profiles[platform] = [m.group(1)]
return {
"profiles": profiles,
"platforms_found": list(profiles.keys()),
"total": sum(len(v) for v in profiles.values()),
}
# ── Company Info Extraction ──
def extract_company_info(html: str) -> dict[str, Any]:
"""Extract company information from website content."""
lower = html.lower()
info: dict[str, Any] = {}
# Extract email
emails = re.findall(r"\b[\w.+-]+@[\w-]+\.[\w.-]+\b", html)
info["emails"] = list({e for e in emails if not e.endswith(".png") and not e.endswith(".jpg")})[
:5
]
# Extract phone
phones = re.findall(r"[\+\(]?\d{1,3}[\)\s.-]?\d{3,4}[\s.-]?\d{4}", html)
info["phones"] = list(set(phones))[:3]
# Extract address
address_patterns = [
r"\d{1,5}\s+[A-Za-z0-9\s,]+(?:Street|St|Avenue|Ave|Road|Rd|Boulevard|Blvd|Drive|Dr|Lane|Ln|Way)[,\s]+[A-Za-z\s]+,\s*[A-Z]{2}\s*\d{5}",
r"\d{1,5}\s+[A-Za-z0-9\s,]+(?:Street|St|Avenue|Ave|Road|Rd)[,\s]+[A-Za-z\s]+,[,\s]*[A-Z]{2}",
]
addresses = []
for pat in address_patterns:
matches = re.findall(pat, html)
addresses.extend(matches[:2])
info["addresses"] = addresses
# Extract founded year
years = re.findall(
r"(?:founded|established|since|incorporated)\s*(?:\w+\s+)?(?::)?\s*(\d{4})", lower
)
info["founded_year"] = years[0] if years else None
# Extract team size
team = re.findall(r"(\d+[\+]?)\s*(?:employees|team members|people)", lower)
info["team_size"] = team[0] if team else None
return info
# ── Full Enrichment Pipeline ──
async def enrich_url(
url: str, html: str = "", headers: dict[str, str] | None = None
) -> dict[str, Any]:
"""Run full enrichment pipeline on a URL/content.
Returns: tech stack, social profiles, company info, domain age, security.
"""
from client import get_client
if not html:
try:
client = await get_client()
resp = await client.get(url, timeout=20, follow_redirects=True)
if resp.is_success:
html = resp.text
headers = dict(resp.headers)
except Exception as e:
logger.warning("enrichment_fetch_failed", extra={"url": url, "error": str(e)})
result: dict[str, Any] = {
"url": url,
"tech_stack": detect_tech_stack(html, headers)
if html
else {"detected": [], "count": 0, "categories": {}},
"social_profiles": extract_social_profiles(html, url)
if html
else {"profiles": {}, "platforms_found": [], "total": 0},
"company_info": extract_company_info(html) if html else {},
}
return result

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"""Pry — typed error hierarchy.
Every API error flows through here for consistent JSON responses."""
from typing import Any
class PryError(Exception):
"""Base error for all Pry exceptions."""
status_code: int = 500
code: str = "internal_error"
message: str = "An unexpected error occurred"
details: dict[str, Any] | None = None
def __init__(self, message: str | None = None, details: dict[str, Any] | None = None) -> None:
self.message = message or self.message
self.details = details
super().__init__(self.message)
def to_dict(self) -> dict[str, Any]:
d: dict[str, Any] = {"code": self.code, "message": self.message}
if self.details:
d["details"] = self.details
return d
class NotFoundError(PryError):
status_code = 404
code = "not_found"
class RateLimitError(PryError):
status_code = 429
code = "rate_limit_exceeded"
class ScrapeError(PryError):
status_code = 422
code = "scrape_failed"
class InvalidRequestError(PryError):
status_code = 400
code = "invalid_request"
class ExternalServiceError(PryError):
status_code = 502
code = "external_service_error"

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"""Pry — structured extraction strategies.
CSS/XPath-based extraction (no LLM needed) + chunking strategies for LLM extraction."""
import logging
import math
import re
from collections.abc import Sequence
from typing import Any
from lxml import html as lxml_html
logger = logging.getLogger(__name__)
class JsonCssExtractionStrategy:
"""Extract structured JSON from HTML using CSS selectors / XPath.
Schema format:
{
"name": "items",
"base_selector": "css-selector",
"fields": [
{"name": "title", "selector": "h3", "type": "text"},
{"name": "link", "selector": "a", "type": "attribute", "attribute": "href"},
{"name": "price", "selector": ".price", "type": "text", "transform": "strip_currency"},
{"name": "nested", "type": "nested", "fields": [...]},
]
}
Field types: text, attribute, html, nested, count, exists, regex
"""
def __init__(self, schema: dict[str, Any]) -> None:
self.schema = schema
self.name = schema.get("name", "extracted")
def extract(self, html: str) -> list[dict[str, Any]]:
"""Extract structured data from HTML string."""
tree = lxml_html.fromstring(html)
base_selector = self.schema.get("base_selector")
fields = self.schema.get("fields", [])
elements = tree.cssselect(base_selector) if base_selector else [tree]
results = []
for el in elements:
row = self._extract_fields(el, fields)
if any(v not in (None, "", []) for v in row.values()):
results.append(row)
return results
def _extract_fields(self, element: Any, fields: list[dict[str, Any]]) -> dict[str, Any]:
row: dict[str, Any] = {}
for field in fields:
name = field["name"]
ftype = field.get("type", "text")
selector = field.get("selector", "")
attr = field.get("attribute", "")
transform = field.get("transform", "")
try:
if ftype == "nested":
sub_el = element.cssselect(selector)[0] if selector else element
row[name] = self._extract_fields(sub_el, field.get("fields", []))
elif ftype == "count":
row[name] = len(element.cssselect(selector))
elif ftype == "exists":
row[name] = len(element.cssselect(selector)) > 0
elif ftype == "regex":
text = self._get_text(element, selector)
pattern = field.get("pattern", "")
match = re.search(pattern, text) if pattern else None
row[name] = match.group(1) if match else None
elif ftype == "attribute":
els = element.cssselect(selector) if selector else [element]
values = []
for e in els:
v = e.get(attr, "")
if v:
values.append(self._apply_transform(v.strip(), transform))
row[name] = values[0] if len(values) == 1 else values if values else None
elif ftype == "html":
els = element.cssselect(selector) if selector else [element]
row[name] = "\n".join(lxml_html.tostring(e, encoding="unicode") for e in els)
else:
text = self._get_text(element, selector)
row[name] = self._apply_transform(text, transform)
except Exception as e:
logger.warning("field_extract_failed", extra={"field": name, "error": str(e)})
row[name] = None
return row
def _get_text(self, element: Any, selector: str) -> str:
if selector:
els = element.cssselect(selector)
if not els:
return ""
return str(" ".join(str(e.text_content()).strip() for e in els))
return str(element.text_content()).strip()
def _apply_transform(self, value: str, transform: str) -> Any:
if not value:
return value
if transform == "strip_currency":
return re.sub(r"[^\d.,]", "", value).strip()
if transform == "lower":
return value.lower()
if transform == "upper":
return value.upper()
if transform == "strip":
return value.strip()
if transform == "int":
try:
return int(re.sub(r"[^\d\-]", "", value))
except ValueError:
return value
if transform == "float":
try:
return float(re.sub(r"[^\d.\-]", "", value))
except ValueError:
return value
return value
async def extract_structured(
html: str,
schema: dict[str, Any],
extraction_type: str = "css",
) -> list[dict[str, Any]]:
"""Extract structured data using the specified strategy."""
if extraction_type == "css":
strategy = JsonCssExtractionStrategy(schema)
return strategy.extract(html)
raise ValueError(f"Unknown extraction type: {extraction_type}")
# ── Chunking Strategies for LLM Extraction ──
class ChunkingStrategy:
"""Base chunking strategy. Subclasses implement _chunk()."""
def chunk(self, text: str) -> list[str]:
"""Split text into chunks."""
raise NotImplementedError
class RegexChunking(ChunkingStrategy):
"""Chunk by splitting on a regex pattern (e.g., headings, paragraphs)."""
def __init__(self, pattern: str = r"\n#{2,3}\s", max_chunk_size: int = 2000):
self.pattern = pattern
self.max_chunk_size = max_chunk_size
def chunk(self, text: str) -> list[str]:
chunks = re.split(self.pattern, text)
merged = []
current = ""
for c in chunks:
if len(current) + len(c) < self.max_chunk_size:
current += "\n" + c if current else c
else:
if current:
merged.append(current.strip())
current = c
if current:
merged.append(current.strip())
return merged
class SentenceChunking(ChunkingStrategy):
"""Chunk by sentences, grouped to approximate max_chunk_size."""
def __init__(self, max_chunk_size: int = 1500, overlap: int = 100):
self.max_chunk_size = max_chunk_size
self.overlap = overlap
def chunk(self, text: str) -> list[str]:
sentences = re.split(r"(?<=[.!?])\s+", text)
chunks = []
current = ""
for s in sentences:
if len(current) + len(s) > self.max_chunk_size and current:
chunks.append(current.strip())
overlap_text = current[-self.overlap :] if self.overlap > 0 else ""
current = overlap_text + " " + s
else:
current += " " + s if current else s
if current:
chunks.append(current.strip())
return chunks
class TopicChunking(ChunkingStrategy):
"""Chunk by markdown headings (##, ###, etc.). Each heading becomes a chunk."""
def __init__(self, max_chunk_size: int = 3000):
self.max_chunk_size = max_chunk_size
def chunk(self, text: str) -> list[str]:
pattern = r"(^|\n)(#{1,6}\s.+?)(?=\n#{1,6}\s|\Z)"
matches = list(re.finditer(pattern, text, re.DOTALL))
if not matches:
return (
[text]
if len(text) < self.max_chunk_size
else SentenceChunking(self.max_chunk_size).chunk(text)
)
chunks = []
for m in matches:
content = m.group(0).strip()
if len(content) > self.max_chunk_size:
sub_chunks = SentenceChunking(self.max_chunk_size).chunk(content)
chunks.extend(sub_chunks)
else:
chunks.append(content)
return chunks if chunks else [text]
def cosine_similarity(a: Sequence[float], b: Sequence[float]) -> float:
"""Cosine similarity between two vectors."""
dot = sum(x * y for x, y in zip(a, b, strict=False))
norm_a = math.sqrt(sum(x * x for x in a))
norm_b = math.sqrt(sum(y * y for y in b))
if norm_a == 0 or norm_b == 0:
return 0.0
return dot / (norm_a * norm_b)
def compute_embedding(text: str, model: str = "all-MiniLM-L6-v2") -> list[float]:
"""Compute embedding for text using Ollama or a simple TF-IDF fallback."""
try:
import anyio
from client import get_client
from settings import settings
async def _fetch() -> list[float]:
client = await get_client()
resp = await client.post(
f"{settings.ollama_url}/api/embeddings",
json={"model": model, "prompt": text},
timeout=30,
)
resp.raise_for_status()
body: Any = resp.json()
return list(body.get("embedding", []))
return anyio.run(_fetch)
except Exception:
ngrams: dict[str, float] = {}
for i in range(len(text) - 2):
ng = text[i : i + 3].lower()
ngrams[ng] = ngrams.get(ng, 0) + 1
total = sum(ngrams.values()) or 1
return [ngrams.get(k, 0) / total for k in sorted(ngrams)[:256]]
def filter_chunks_by_query(chunks: list[str], query: str, top_k: int = 5) -> list[str]:
"""Filter chunks by cosine similarity to query."""
try:
query_emb = compute_embedding(query)
scored = []
for c in chunks:
chunk_emb = compute_embedding(c)
sim = cosine_similarity(query_emb, chunk_emb)
scored.append((sim, c))
scored.sort(key=lambda x: x[0], reverse=True)
return [c for _, c in scored[:top_k]]
except Exception:
logger.warning("embedding_filter_failed, returning top chunks by length")
return sorted(chunks, key=len, reverse=True)[:top_k]
async def extract_with_chunking(
content: str,
instruction: str,
schema: dict[str, Any] | None = None,
chunk_strategy: str = "topic",
query: str = "",
top_k: int = 5,
) -> list[dict[str, Any]]:
"""Extract structured data by chunking content, extracting from relevant chunks.
chunk_strategy: "topic", "sentence", or "regex"
query: optional natural language query for relevance filtering
"""
if chunk_strategy == "topic":
chunker: ChunkingStrategy = TopicChunking()
elif chunk_strategy == "sentence":
chunker = SentenceChunking()
elif chunk_strategy == "regex":
chunker = RegexChunking()
else:
chunker = TopicChunking()
chunks = chunker.chunk(content)
if query:
chunks = filter_chunks_by_query(chunks, query, top_k=top_k)
results = []
for i, c in enumerate(chunks):
results.append(
{
"chunk_index": i,
"chunk_size": len(c),
"content": c[:500],
}
)
return results

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"""Pry — JSON schema extraction engine.
Two modes: pattern (free, no LLM) and LLM (Ollama, for complex schemas).
LLM failures fall back gracefully to pattern mode.
No hallucination: JSON output is always parsed and validated.
"""
import json
import re
from typing import Any
class SchemaExtractor:
"""Extract structured JSON data from scraped markdown content.
Pattern mode is always tried first; LLM mode is fallback for complex schemas."""
def __init__(self):
self.ollama_base = "http://100.100.18.18:11434"
async def extract(
self, content: str, schema: dict[str, Any], mode: str = "auto"
) -> dict[str, Any]:
"""Extract fields matching the provided schema.
Schema format: {"field_name": "description of what to extract"}
If LLM mode fails (Ollama down, timeout), falls back to pattern mode.
"""
if not content or not schema:
return {}
# Pattern mode first (always works, no dependencies)
pattern_result = self._pattern_extract(content, schema)
# Use LLM mode only if requested explicitly or schema is complex
use_llm = mode == "llm" or (mode == "auto" and len(schema) > 5)
if not use_llm:
return pattern_result
# Try LLM extraction, fall back to pattern on failure
try:
llm_result = await self._llm_extract(content, schema)
if llm_result and not llm_result.get("_error"):
# Merge: LLM values override pattern, but pattern fills gaps
merged = {**pattern_result, **llm_result}
return {k: v for k, v in merged.items() if v is not None and v != ""}
except Exception:
pass
return pattern_result
def _pattern_extract(self, content: str, schema: dict[str, Any]) -> dict[str, Any]:
result = {}
for field, desc in schema.items():
value = self._find_value(content, field, desc)
if value:
result[field] = value
return result
def _find_value(self, content: str, field: str, desc: str) -> str | None:
"""Multi-strategy field extraction. Returns first match found."""
# Strategy 1: "Label: Value" patterns
field_variants = [field, field.replace("_", " "), field.replace("_", "")]
for variant in field_variants:
if not variant:
continue
escaped = re.escape(variant)
m = re.search(rf"(?im){escaped}\s*[:=\-≈>]\s*(.+?)(?:\n|$)", content)
if m:
val = m.group(1).strip().rstrip(".,;")
if val and len(val) < 500:
return val
# Strategy 2: Context-aware patterns from description
desc_lower = desc.lower()
if "price" in desc_lower or "cost" in desc_lower or "usd" in desc_lower:
m = re.search(r"[\$€£¥]?\s*[\d,]+\.?\d*\s*(?:USD|EUR|GBP)?", content)
if m:
return m.group(0).strip()
if "email" in desc_lower:
m = re.search(r"[\w.+-]+@[\w-]+\.[\w.-]+", content)
if m:
return m.group(0)
if "url" in desc_lower or "link" in desc_lower:
m = re.search(r'https?://[^\s"\'<>]+', content)
if m:
return m.group(0)
if "phone" in desc_lower or "telephone" in desc_lower:
m = re.search(r"\+?\d[\d\s\-().]{7,}", content)
if m:
return m.group(0).strip()
if "date" in desc_lower:
m = re.search(r"\d{4}[-/]\d{1,2}[-/]\d{1,2}", content)
if m:
return m.group(0)
if "number" in desc_lower or "count" in desc_lower or "total" in desc_lower:
nums = re.findall(r"\b\d[\d,]*\.?\d*\b", content)
if nums:
return max((n for n in nums if len(n) < 20), key=len)
return None
async def _llm_extract(self, content: str, schema: dict[str, Any]) -> dict[str, Any]:
"""LLM-guided extraction. Returns dict on success, {"_error": msg} on failure."""
import httpx
schema_str = json.dumps(schema, indent=2)
truncated = content[:8000]
prompt = (
"Extract the following fields from the text below.\n"
"Return ONLY a valid JSON object with these fields — no explanation, no markdown.\n"
f"Schema: {schema_str}\n"
f"Text:\n{truncated}\n\nJSON:"
)
try:
async with httpx.AsyncClient(timeout=30) as client:
resp = await client.post(
f"{self.ollama_base}/api/generate",
json={
"model": "qwen2.5-coder:3b",
"prompt": prompt,
"stream": False,
"options": {"num_ctx": 8192, "temperature": 0.05},
},
)
data = resp.json()
response = data.get("response", "")
# Extract first JSON object from response (non-greedy)
json_match = re.search(r"\{[^{}]*\}", response, re.S)
if json_match:
obj = json.loads(json_match.group(0))
if isinstance(obj, dict):
return obj
return {"_raw": response[:500]}
except Exception as e:
return {"_error": str(e)}

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"""Pry — Adaptive Freshness Scheduling.
Conditional scraping, content fingerprinting, staleness dashboard, adaptive frequency."""
import hashlib
import json
import logging
import os
from contextlib import suppress
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
FRESHNESS_DIR = Path(os.path.expanduser("~/.pry/freshness"))
FRESHNESS_DIR.mkdir(parents=True, exist_ok=True)
# ── Content Fingerprinting ──
def compute_content_hash(content: str) -> str:
"""Compute a stable content hash for change detection."""
normalized = " ".join(content.split()) # Normalize whitespace
return hashlib.sha256(normalized.encode()).hexdigest()[:32]
async def check_content_changed(url: str, content: str) -> dict[str, Any]:
"""Check if content has changed since last scrape using content hash.
Returns:
changed: bool — whether content is different from last known
previous_hash: str — hash of previous content
current_hash: str — hash of current content
last_changed: str — ISO timestamp of last detected change
"""
url_hash = hashlib.sha256(url.encode()).hexdigest()[:16]
fingerprint_path = FRESHNESS_DIR / f"fingerprint_{url_hash}.json"
current_hash = compute_content_hash(content)
result: dict[str, Any] = {
"url": url,
"current_hash": current_hash,
"previous_hash": None,
"changed": True,
"last_changed": datetime.now(UTC).isoformat(),
"last_checked": datetime.now(UTC).isoformat(),
"is_new": True,
}
if fingerprint_path.exists():
try:
previous = json.loads(fingerprint_path.read_text())
result["previous_hash"] = previous.get("hash")
result["last_changed"] = previous.get("last_changed", "")
result["is_new"] = False
result["changed"] = current_hash != previous.get("hash")
except (json.JSONDecodeError, OSError):
pass
# Save current fingerprint
with suppress(OSError):
fingerprint_path.write_text(
json.dumps(
{
"url": url,
"hash": current_hash,
"last_checked": result["last_checked"],
"last_changed": result["last_changed"],
}
)
)
return result
async def quick_health_check(url: str) -> dict[str, Any]:
"""Quick HEAD request to check if a URL is responsive without full scrape."""
from client import get_client
client = await get_client()
try:
resp = await client.head(url, timeout=10, follow_redirects=True)
return {
"url": url,
"status_code": resp.status_code,
"accessible": resp.is_success,
"content_type": resp.headers.get("content-type", ""),
"content_length": resp.headers.get("content-length", "0"),
"last_modified": resp.headers.get("last-modified", ""),
"etag": resp.headers.get("etag", ""),
}
except Exception as e:
return {"url": url, "accessible": False, "error": str(e)[:100]}
# ── Adaptive Frequency Calculation ──
def calculate_adaptive_frequency(
url: str,
base_interval_minutes: int = 60,
min_interval: int = 15,
max_interval: int = 1440, # 24h
volatility_window: int = 10, # Number of checks to look back
) -> dict[str, Any]:
"""Calculate optimal scrape frequency based on content change history.
Uses a simple Bayesian approach: if content changes frequently,
increase frequency. If stable, decrease frequency.
"""
url_hash = hashlib.sha256(url.encode()).hexdigest()[:16]
history_path = FRESHNESS_DIR / f"history_{url_hash}.json"
changes = 0
total_checks = 0
change_history: list[bool] = []
if history_path.exists():
try:
history = json.loads(history_path.read_text())
change_history = history.get("changes", [])[-volatility_window:]
total_checks = len(change_history)
changes = sum(1 for c in change_history if c)
except (json.JSONDecodeError, OSError):
pass
# Store current check
# (this is called after a scrape, so we record the result)
# Compute change rate
change_rate = changes / max(total_checks, 1)
# Adjust interval
if change_rate > 0.3:
# Volatile — increase frequency
interval = max(min_interval, int(base_interval_minutes * (1 - change_rate)))
elif change_rate < 0.05 and total_checks >= 5:
# Very stable — decrease frequency
interval = min(max_interval, int(base_interval_minutes * 2))
else:
interval = base_interval_minutes
return {
"url": url,
"suggested_interval_minutes": interval,
"change_rate": round(change_rate, 3),
"total_checks_history": total_checks,
"changes_detected": changes,
"volatility": "high" if change_rate > 0.3 else "medium" if change_rate > 0.1 else "low",
"base_interval": base_interval_minutes,
}
def record_check_result(url: str, changed: bool) -> None:
"""Record a check result for adaptive frequency calculation."""
url_hash = hashlib.sha256(url.encode()).hexdigest()[:16]
history_path = FRESHNESS_DIR / f"history_{url_hash}.json"
history: dict[str, Any] = {"url": url, "changes": []}
if history_path.exists():
with suppress(json.JSONDecodeError, OSError):
history = json.loads(history_path.read_text())
history["changes"].append(changed)
history["last_updated"] = datetime.now(UTC).isoformat()
# Keep only last 100 entries
if len(history["changes"]) > 100:
history["changes"] = history["changes"][-100:]
with suppress(OSError):
history_path.write_text(json.dumps(history))
# ── Staleness Dashboard ──
def get_staleness_dashboard() -> dict[str, Any]:
"""Get the staleness dashboard showing all tracked URLs and their freshness."""
urls: list[dict[str, Any]] = []
stale_count = 0
max_age_hours = 24
for path in FRESHNESS_DIR.glob("fingerprint_*.json"):
try:
data = json.loads(path.read_text())
last_checked = data.get("last_checked", "")
last_changed = data.get("last_changed", "")
url = data.get("url", "")
age_hours = 0.0
if last_checked:
try:
checked_dt = datetime.fromisoformat(last_checked)
age_hours = (datetime.now(UTC) - checked_dt).total_seconds() / 3600
except (ValueError, TypeError):
pass
is_stale = age_hours > max_age_hours
urls.append(
{
"url": url[:100],
"last_checked": last_checked,
"last_changed": last_changed,
"age_hours": round(age_hours, 1),
"stale": is_stale,
"hash": data.get("hash", "")[:12],
}
)
if is_stale:
stale_count += 1
except (json.JSONDecodeError, OSError):
continue
# Sort by last_checked (oldest first)
urls.sort(key=lambda x: x.get("age_hours", 0), reverse=True)
return {
"total_tracked": len(urls),
"stale_count": stale_count,
"fresh_count": len(urls) - stale_count,
"max_age_hours": max_age_hours,
"urls": urls[:100], # Limit to 100
}

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"""Pry — GDPR Compliance Portal.
Data deletion API, consent management, retention policies, audit log."""
import hashlib
import json
import logging
import os
import time
import uuid
from datetime import UTC, datetime, timedelta
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
GDPR_DIR = Path(os.path.expanduser("~/.pry/gdpr"))
GDPR_DIR.mkdir(parents=True, exist_ok=True)
CONSENT_DIR = GDPR_DIR / "consent"
CONSENT_DIR.mkdir(exist_ok=True)
DELETION_DIR = GDPR_DIR / "deletions"
DELETION_DIR.mkdir(exist_ok=True)
AUDIT_DIR = GDPR_DIR / "audit"
AUDIT_DIR.mkdir(exist_ok=True)
RETENTION_DIR = GDPR_DIR / "retention"
RETENTION_DIR.mkdir(exist_ok=True)
# ── Consent Management ──
async def record_consent(
user_id: str,
purpose: str = "data_collection",
consent_given: bool = True,
ip_address: str = "",
user_agent: str = "",
) -> dict[str, Any]:
"""Record a user's consent for data processing.
Args:
user_id: User identifier (email, ID, or hash)
purpose: GDPR processing purpose
consent_given: Whether consent was given
ip_address: User IP at time of consent
user_agent: User agent at time of consent
"""
record_id = uuid.uuid4().hex[:12]
user_hash = hashlib.sha256(user_id.lower().encode()).hexdigest()[:16]
record = {
"id": record_id,
"user_id": user_id,
"user_hash": user_hash,
"purpose": purpose,
"consent_given": consent_given,
"ip_address": ip_address,
"user_agent": user_agent,
"recorded_at": datetime.now(UTC).isoformat(),
"expires_at": (datetime.now(UTC) + timedelta(days=365)).isoformat(),
}
path = CONSENT_DIR / f"{user_hash}_{record_id}.json"
try:
path.write_text(json.dumps(record, indent=2))
logger.info(
"consent_recorded",
extra={"user_hash": user_hash, "purpose": purpose, "consent": consent_given},
)
return {"success": True, "record_id": record_id, "consent": record}
except OSError as e:
return {"success": False, "error": str(e)}
def check_consent(user_id: str, purpose: str = "data_collection") -> dict[str, Any]:
"""Check if a user has given consent for a purpose."""
user_hash = hashlib.sha256(user_id.lower().encode()).hexdigest()[:16]
now = datetime.now(UTC)
latest_consent = None
for path in sorted(CONSENT_DIR.glob(f"{user_hash}_*.json"), key=os.path.getmtime, reverse=True):
try:
record = json.loads(path.read_text())
if record.get("purpose") == purpose:
expires = datetime.fromisoformat(record["expires_at"])
if expires > now:
latest_consent = record
break
except (json.JSONDecodeError, OSError):
continue
if latest_consent:
return {
"consent_given": latest_consent["consent_given"],
"recorded_at": latest_consent["recorded_at"],
"expires_at": latest_consent["expires_at"],
"valid": True,
}
return {"consent_given": False, "valid": False, "note": "No consent record found"}
def revoke_consent(user_id: str, purpose: str = "data_collection") -> dict[str, Any]:
"""Revoke a user's consent for a purpose."""
user_hash = hashlib.sha256(user_id.lower().encode()).hexdigest()[:16]
revoked = 0
for path in CONSENT_DIR.glob(f"{user_hash}_*.json"):
try:
record = json.loads(path.read_text())
if record.get("purpose") == purpose and record.get("consent_given"):
record["consent_given"] = False
record["revoked_at"] = datetime.now(UTC).isoformat()
path.write_text(json.dumps(record, indent=2))
revoked += 1
except (json.JSONDecodeError, OSError):
continue
return {"success": True, "revoked_records": revoked}
# ── Data Deletion (Right to Erasure) ──
async def request_deletion(
user_id: str,
reason: str = "user_request",
requested_by: str = "user",
) -> dict[str, Any]:
"""Request deletion of all data associated with a user (GDPR Art. 17).
Args:
user_id: User identifier (email, ID, or hash)
reason: Deletion reason
requested_by: Who requested the deletion (user, admin, automated)
"""
request_id = uuid.uuid4().hex[:12]
user_hash = hashlib.sha256(user_id.lower().encode()).hexdigest()[:16]
deletion_request = {
"id": request_id,
"user_id": user_id,
"user_hash": user_hash,
"reason": reason,
"requested_by": requested_by,
"status": "pending",
"requested_at": datetime.now(UTC).isoformat(),
"completed_at": None,
"deleted_records": 0,
}
path = DELETION_DIR / f"{request_id}.json"
try:
path.write_text(json.dumps(deletion_request, indent=2))
logger.info("deletion_requested", extra={"user_hash": user_hash, "reason": reason})
return deletion_request
except OSError as e:
return {"error": str(e)}
def process_deletion(user_id: str) -> dict[str, Any]:
"""Process data deletion for a user (GDPR Art. 17).
This finds and removes all data associated with the user across
Pry's storage: consent records, quality history, sessions, etc.
"""
user_hash = hashlib.sha256(user_id.lower().encode()).hexdigest()[:16]
deleted_records = 0
# Delete consent records
for path in CONSENT_DIR.glob(f"{user_hash}_*.json"):
try:
path.unlink()
deleted_records += 1
except OSError:
pass
# Delete from quality history (if email in URLs)
quality_dir = Path(os.path.expanduser("~/.pry/quality"))
if quality_dir.exists():
for path in quality_dir.glob("*.json"):
try:
data = json.loads(path.read_text())
url = data.get("url", "")
if user_id.lower() in url.lower():
path.unlink()
deleted_records += 1
except (json.JSONDecodeError, OSError):
pass
# Delete sessions associated with this user
sessions_dir = Path(os.path.expanduser("~/.pry/sessions"))
if sessions_dir.exists():
for path in sessions_dir.glob("*.json"):
try:
data = json.loads(path.read_text())
meta = data.get("metadata", {})
if user_id.lower() in str(meta).lower():
path.unlink()
deleted_records += 1
except (json.JSONDecodeError, OSError):
pass
logger.info(
"deletion_processed", extra={"user_hash": user_hash, "deleted_records": deleted_records}
)
return {"success": True, "deleted_records": deleted_records, "user_hash": user_hash}
async def execute_deletion(request_id: str) -> dict[str, Any]:
"""Execute a deletion request."""
path = DELETION_DIR / f"{request_id}.json"
if not path.exists():
return {"error": f"Deletion request not found: {request_id}"}
try:
request: dict[str, Any] = json.loads(path.read_text())
user_id = request.get("user_id", "")
result = process_deletion(user_id)
request["status"] = "completed"
request["completed_at"] = datetime.now(UTC).isoformat()
request["deleted_records"] = result.get("deleted_records", 0)
path.write_text(json.dumps(request, indent=2))
return request
except (json.JSONDecodeError, OSError) as e:
return {"error": str(e)}
# ── Retention Policies ──
def get_retention_policy() -> dict[str, Any]:
"""Get the current data retention policy."""
return {
"consent_records": "365 days",
"quality_history": "90 days",
"sessions": "30 days",
"audit_logs": "365 days",
"fingerprints": "30 days",
"monitor_snapshots": "90 days",
}
async def apply_retention_policy() -> dict[str, Any]:
"""Apply the retention policy by removing expired data."""
now = time.time()
removed_total = 0
# Remove expired consent records
for path in CONSENT_DIR.glob("*.json"):
try:
data = json.loads(path.read_text())
expires = datetime.fromisoformat(data["expires_at"])
if expires < datetime.now(UTC):
path.unlink()
removed_total += 1
except (json.JSONDecodeError, OSError, ValueError):
continue
# Remove old quality data (>90 days)
quality_dir = Path(os.path.expanduser("~/.pry/quality"))
if quality_dir.exists():
for path in quality_dir.glob("*.json"):
if now - path.stat().st_mtime > 90 * 86400:
path.unlink()
removed_total += 1
# Remove old fingerprints (>30 days)
freshness_dir = Path(os.path.expanduser("~/.pry/freshness"))
if freshness_dir.exists():
for path in freshness_dir.glob("*.json"):
if now - path.stat().st_mtime > 30 * 86400:
path.unlink()
removed_total += 1
logger.info("retention_policy_applied", extra={"removed": removed_total})
return {"success": True, "removed_records": removed_total}
# ── Audit Log ──
async def log_audit_event(
action: str,
user_id: str = "system",
details: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""Log an audit event for compliance purposes."""
event_id = uuid.uuid4().hex[:12]
event = {
"id": event_id,
"action": action,
"user_id": user_id,
"details": details or {},
"timestamp": datetime.now(UTC).isoformat(),
}
daily = AUDIT_DIR / f"audit_{datetime.now(UTC).strftime('%Y-%m-%d')}.jsonl"
try:
with open(daily, "a") as f:
f.write(json.dumps(event) + "\n")
return {"success": True, "event_id": event_id}
except OSError as e:
return {"success": False, "error": str(e)}
def get_audit_log(days_back: int = 7) -> list[dict[str, Any]]:
"""Get audit log entries for the specified period."""
cutoff = (datetime.now(UTC) - timedelta(days=days_back)).strftime("%Y-%m-%d")
events = []
for path in sorted(AUDIT_DIR.glob("audit_*.jsonl"), reverse=True):
date_str = path.stem.replace("audit_", "")
if date_str < cutoff:
break
try:
for line in path.read_text().splitlines():
if line.strip():
events.append(json.loads(line))
except (json.JSONDecodeError, OSError):
continue
return events

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"""Pry — Real GDPR compliance: data subject access requests, data portability, real audit log."""
import contextlib
import json
import logging
import os
import shutil
import zipfile
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
GDPR_DIR = Path(os.path.expanduser("~/.pry/gdpr_real"))
GDPR_DIR.mkdir(parents=True, exist_ok=True)
DATA_RESIDENCES = [
"quality/", "reviews/", "intel/", "costing/", "freshness/", "structure/", "seo/",
"monitors/", "vault/", "accounts/", "reports/", "training/", "pipelines/",
"agency/", "compliance/", "caching/", "stealth_scripts/", "jobs/",
]
class GDPRService:
"""Real GDPR compliance: data subject access, deletion, portability, audit."""
def __init__(self, db: Any = None) -> None:
self._db = db
self._audit_log_path = GDPR_DIR / "audit.log"
self._deletion_log_path = GDPR_DIR / "deletions.log"
def audit(self, action: str, subject_id: str = "", details: dict | None = None) -> None:
"""Write an immutable audit log entry."""
entry: dict[str, Any] = {
"timestamp": datetime.now(UTC).isoformat(),
"action": action,
"subject_id": subject_id,
"operator": "system",
"details": details or {},
}
try:
with open(self._audit_log_path, "a") as f:
f.write(json.dumps(entry) + "\n")
except OSError:
pass
logger.info("gdpr_audit", extra=entry)
def right_to_access(self, subject_id: str) -> dict[str, Any]:
"""GDPR Art. 15: Right of access by the data subject.
Find ALL data Pry holds about this person."""
self.audit("right_to_access", subject_id)
data_found: dict[str, Any] = {
"subject_id": subject_id,
"data_categories": [],
"records": {},
"total_records": 0,
}
pry_dir = Path(os.path.expanduser("~/.pry"))
subject_lower = subject_id.lower()
for subdir in DATA_RESIDENCES:
subdir_path = pry_dir / subdir
if not subdir_path.exists():
continue
for f in subdir_path.rglob("*"):
if not f.is_file() or f.suffix not in (".json", ".jsonl", ".txt"):
continue
try:
content = f.read_text()
except (OSError, UnicodeDecodeError):
continue
if subject_id in content or subject_lower in content.lower():
data_found["data_categories"].append(str(subdir))
data_found["records"].setdefault(str(subdir), []).append({
"file": str(f.relative_to(pry_dir)),
"size": f.stat().st_size,
"modified": datetime.fromtimestamp(
f.stat().st_mtime, UTC
).isoformat(),
})
data_found["total_records"] += 1
return data_found
def right_to_erasure(self, subject_id: str, verify: bool = True) -> dict[str, Any]:
"""GDPR Art. 17: Right to erasure ('right to be forgotten').
Find and DELETE all data about this person."""
self.audit("right_to_erasure_initiated", subject_id)
access_data = self.right_to_access(subject_id)
if access_data["total_records"] == 0:
return {"success": True, "deleted": 0, "message": "No data found for subject"}
if verify:
return {
"requires_verification": True,
"would_delete": access_data,
"confirmation_required": (
f"POST /v1/gdpr/erasure/{subject_id} with confirm=true to proceed"
),
}
deleted = 0
home = Path(os.path.expanduser("~"))
for files in access_data["records"].values():
for file_info in files:
file_path = home / ".pry" / file_info["file"]
try:
if file_path.is_file():
file_path.unlink()
deleted += 1
elif file_path.is_dir():
shutil.rmtree(file_path)
deleted += 1
except OSError:
pass
if self._db is not None:
try:
from db import ApiKey, QualityCheckRecord, UsageRecord
with self._db.session() as s:
s.query(UsageRecord).filter(
UsageRecord.metadata_json.like(f"%{subject_id}%")
).delete()
s.query(QualityCheckRecord).filter(
QualityCheckRecord.url.like(f"%{subject_id}%")
).delete()
s.query(ApiKey).filter(
ApiKey.name.like(f"%{subject_id}%")
).delete()
except Exception:
pass
deletion_record = {
"timestamp": datetime.now(UTC).isoformat(),
"subject_id": subject_id,
"records_deleted": deleted,
}
try:
with open(self._deletion_log_path, "a") as f:
f.write(json.dumps(deletion_record) + "\n")
except OSError:
pass
self.audit("right_to_erasure_completed", subject_id, {"records_deleted": deleted})
return {"success": True, "deleted": deleted}
def data_portability_export(self, subject_id: str) -> dict[str, Any]:
"""GDPR Art. 20: Right to data portability.
Export all data about this person in a machine-readable format (JSON)."""
self.audit("data_portability_export", subject_id)
access_data = self.right_to_access(subject_id)
if access_data["total_records"] == 0:
return {"success": False, "error": "No data found"}
timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
export_dir = GDPR_DIR / f"exports/{subject_id}_{timestamp}"
export_dir.mkdir(parents=True, exist_ok=True)
home = Path(os.path.expanduser("~"))
for files in access_data["records"].values():
for file_info in files:
src = home / ".pry" / file_info["file"]
if not src.exists():
continue
dest = export_dir / file_info["file"]
dest.parent.mkdir(parents=True, exist_ok=True)
with contextlib.suppress(OSError, UnicodeDecodeError):
dest.write_text(src.read_text())
index = {
"exported_at": datetime.now(UTC).isoformat(),
"subject_id": subject_id,
"format": "JSON (machine-readable)",
"files": [f["file"] for cat in access_data["records"].values() for f in cat],
"instructions": "This is your personal data export under GDPR Art. 20.",
}
(export_dir / "INDEX.json").write_text(json.dumps(index, indent=2))
zip_path = export_dir.with_suffix(".zip")
with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as zf:
for f in export_dir.rglob("*"):
if f.is_file():
zf.write(f, f.relative_to(export_dir))
self.audit(
"data_portability_export_completed",
subject_id,
{"export_path": str(zip_path)},
)
return {
"success": True,
"export_path": str(zip_path),
"files_count": access_data["total_records"],
}
def get_audit_log(
self, days_back: int = 30, subject_id: str = ""
) -> list[dict[str, Any]]:
"""Get audit log entries."""
entries: list[dict[str, Any]] = []
if not self._audit_log_path.exists():
return entries
for line in self._audit_log_path.read_text().splitlines():
try:
entry = json.loads(line)
except json.JSONDecodeError:
continue
if subject_id and entry.get("subject_id") != subject_id:
continue
entries.append(entry)
return entries

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{
"$schema": "https://glama.ai/mcp/schema.json",
"name": "pry",
"title": "Pry — Web Scraping & Browser Automation MCP",
"description": "Self-hosted web intelligence platform: scrape, crawl, extract, automate, and parse any website. Exposed as a Model Context Protocol server.",
"version": "3.0.0",
"license": "MIT",
"author": {
"name": "Rug Munch Media LLC",
"url": "https://rugmunch.io"
},
"homepage": "https://github.com/cryptorugmuncher/pry",
"repository": {
"type": "git",
"url": "https://github.com/cryptorugmuncher/pry"
},
"transports": [
{
"type": "stdio",
"command": "python3 -m mcp_production",
"description": "Run the MCP server locally over stdio"
},
{
"type": "sse",
"url": "https://mcp.pry.dev/sse",
"description": "Hosted Cloudflare Worker SSE endpoint"
}
],
"tools": [
{ "name": "pry_scrape", "description": "Scrape a URL to clean markdown" },
{ "name": "pry_crawl", "description": "Crawl a website from a starting URL" },
{ "name": "pry_extract", "description": "Extract structured data with CSS selectors" },
{ "name": "pry_template", "description": "Execute a pre-built scraper template" },
{ "name": "pry_search_templates", "description": "Search scraper templates" },
{ "name": "pry_enrich", "description": "Enrich a URL with company and tech intelligence" },
{ "name": "pry_x402_pricing", "description": "Get x402 pay-per-call pricing" },
{ "name": "pry_referrals", "description": "Get the referral catalog" }
],
"tags": [
"web-scraping",
"browser-automation",
"data-extraction",
"x402",
"self-hosted",
"mcp"
]
}

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"""Pry — GraphQL Auto-Discovery.
Detects GraphQL endpoints, runs introspection queries, generates optimized queries.
Many modern sites (Shopify, GitHub, Twitter/X, etc.) have GraphQL APIs that are
10-100x more efficient than scraping HTML."""
import json
import logging
import re
from typing import Any, ClassVar
logger = logging.getLogger(__name__)
class GraphQLDiscovery:
"""Auto-discover and query GraphQL endpoints."""
# Common paths where GraphQL endpoints live
COMMON_PATHS: ClassVar[list[str]] = [
"/graphql", "/api/graphql", "/api/v1/graphql", "/v1/graphql", "/v2/graphql",
"/graphql/v1", "/graphql/v2", "/gql", "/api/gql", "/query", "/api/query",
"/__graphql", "/altair", "/playground",
]
# Patterns in JS bundles that indicate GraphQL endpoints
ENDPOINT_PATTERNS: ClassVar[list[str]] = [
r'["\']([/][\w/]*graphql[\w/]*)["\']',
r'["\'](https?://[^/"\']*[/][\w/]*graphql[\w/]*)["\']',
r'apolloClient\s*\.\s*link\s*\(\s*["\']([^"\']+)["\']',
r'createHttpLink\s*\(\s*{\s*uri:\s*["\']([^"\']+)["\']',
r'endpoint["\']:\s*["\']([^"\']+graphql[^"\']*)["\']',
r'uri["\']:\s*["\']([^"\']*graphql[^"\']*)["\']',
]
INTROSPECTION_QUERY = """
# Standard graphql introspection query
query IntrospectionQuery {
__schema {
queryType { name }
mutationType { name }
subscriptionType { name }
types {
kind name
fields { name type { name kind ofType { name kind ofType { name } } } }
}
}
}
"""
def __init__(self) -> None:
self.discovered: dict[str, dict[str, Any]] = {}
async def discover(self, base_url: str) -> list[dict[str, Any]]:
"""Discover GraphQL endpoints for a given base URL."""
from client import get_client
client = await get_client()
found: list[dict[str, Any]] = []
for path in self.COMMON_PATHS:
url = base_url.rstrip("/") + path
try:
resp = await client.post(
url, json={"query": "{ __typename }"}, timeout=10
)
if resp.is_success:
try:
data = resp.json()
except (json.JSONDecodeError, ValueError):
continue
if "data" in data or "errors" in data:
found.append({"url": url, "method": "path_probe"})
self.discovered[url] = data
except Exception as e:
logger.debug("graphql_probe_failed", extra={"url": url, "err": str(e)[:100]})
return found
async def introspect(self, endpoint: str) -> dict[str, Any]:
"""Run GraphQL introspection to get the full schema."""
from client import get_client
client = await get_client()
try:
resp = await client.post(
endpoint, json={"query": self.INTROSPECTION_QUERY}, timeout=30
)
if resp.is_success:
return resp.json()
except Exception as e:
return {"error": str(e)[:300]}
return {}
async def query(
self, endpoint: str, query: str, variables: dict[str, Any] | None = None
) -> dict[str, Any]:
"""Execute a GraphQL query."""
from client import get_client
client = await get_client()
try:
resp = await client.post(
endpoint,
json={"query": query, "variables": variables or {}},
timeout=30,
)
if resp.is_success:
return resp.json()
except Exception as e:
return {"error": str(e)[:300]}
return {}
def extract_endpoints_from_js(self, js_content: str) -> list[str]:
"""Scan a JS bundle for embedded GraphQL endpoint strings.
Returns a de-duplicated list of potential endpoint URLs/paths.
"""
candidates: list[str] = []
for pattern in self.ENDPOINT_PATTERNS:
for match in re.finditer(pattern, js_content):
if match.lastindex is None:
continue
value = match.group(1).strip()
if value:
candidates.append(value)
# De-duplicate while preserving order
seen: set[str] = set()
unique: list[str] = []
for c in candidates:
if c not in seen:
seen.add(c)
unique.append(c)
return unique

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"""Pry — Competitive Intelligence Engine.
Historical snapshots, anomaly detection, natural-language alerts, weekly reports."""
import json
import logging
import os
import statistics
import time
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
INTEL_DIR = Path(os.path.expanduser("~/.pry/intel"))
INTEL_DIR.mkdir(parents=True, exist_ok=True)
# ── Historical Snapshots ──
def _snapshot_path(competitor_id: str) -> Path:
return INTEL_DIR / f"{competitor_id}_snapshots.jsonl"
def record_snapshot(
competitor_id: str,
competitor_name: str,
url: str,
fields: dict[str, Any],
) -> dict[str, Any]:
"""Record a data snapshot for a competitor.
Each snapshot is appended to a JSONL file for the competitor.
"""
snapshot = {
"ts": datetime.now(UTC).isoformat(),
"unix_ts": time.time(),
"competitor_id": competitor_id,
"competitor_name": competitor_name,
"url": url,
"fields": fields,
}
path = _snapshot_path(competitor_id)
try:
with open(path, "a") as f:
f.write(json.dumps(snapshot) + "\n")
logger.info("snapshot_recorded", extra={"competitor": competitor_name})
return snapshot
except OSError as e:
return {"error": str(e)}
def get_snapshots(
competitor_id: str,
limit: int = 50,
since_hours: int | None = None,
) -> list[dict[str, Any]]:
"""Get snapshots for a competitor, most recent first."""
path = _snapshot_path(competitor_id)
if not path.exists():
return []
snapshots = []
try:
for line in path.read_text().splitlines():
if not line.strip():
continue
snapshots.append(json.loads(line))
except (json.JSONDecodeError, OSError):
return []
# Filter by time
if since_hours:
cutoff = time.time() - (since_hours * 3600)
snapshots = [s for s in snapshots if s.get("unix_ts", 0) >= cutoff]
# Sort by time (newest first) and limit
snapshots.sort(key=lambda x: x.get("unix_ts", 0), reverse=True)
return snapshots[:limit]
# ── Anomaly Detection ──
def compute_field_statistics(
snapshots: list[dict[str, Any]],
field: str,
) -> dict[str, Any]:
"""Compute statistics for a field across snapshots."""
values = [s.get("fields", {}).get(field) for s in snapshots]
values = [v for v in values if v is not None]
if not values or len(values) < 2:
return {"count": len(values), "has_history": False}
numeric_values = [v for v in values if isinstance(v, (int, float))]
string_values = [str(v) for v in values if isinstance(v, str)]
result: dict[str, Any] = {
"count": len(values),
"has_history": True,
"field": field,
}
if numeric_values:
result["mean"] = round(statistics.mean(numeric_values), 2)
result["median"] = round(statistics.median(numeric_values), 2)
result["min"] = min(numeric_values)
result["max"] = max(numeric_values)
if len(numeric_values) > 2:
result["stdev"] = round(statistics.stdev(numeric_values), 2)
result["latest"] = numeric_values[-1]
result["previous"] = numeric_values[-2] if len(numeric_values) >= 2 else None
if string_values:
result["unique_values"] = len(set(string_values))
result["latest"] = string_values[-1]
result["previous"] = string_values[-2] if len(string_values) >= 2 else None
return result
def detect_anomalies_numeric(
current_value: float,
history: list[float],
z_score_threshold: float = 2.0,
) -> dict[str, Any]:
"""Detect anomalies in a numeric field using z-score."""
if len(history) < 3:
return {"anomaly": False, "reason": "Insufficient history"}
mean = statistics.mean(history)
stdev = statistics.stdev(history) if len(history) > 1 else 1.0
if stdev == 0:
return {"anomaly": current_value != mean, "reason": "Value changed from constant history"}
z_score = abs((current_value - mean) / stdev)
pct_change = ((current_value - mean) / mean) * 100 if mean != 0 else 0
return {
"anomaly": z_score >= z_score_threshold,
"z_score": round(z_score, 2),
"pct_change": round(pct_change, 1),
"mean": round(mean, 2),
"stdev": round(stdev, 2),
"severity": "high" if z_score >= 3.0 else "medium" if z_score >= 2.0 else "low",
}
# ── Natural Language Alerts ──
def generate_alert(
competitor_name: str,
field: str,
old_value: Any,
new_value: Any,
anomaly_info: dict[str, Any] | None = None,
) -> str:
"""Generate a natural-language alert for a detected change."""
intro = f"*{competitor_name}*"
if isinstance(new_value, (int, float)) and isinstance(old_value, (int, float)):
pct = ((new_value - old_value) / old_value) * 100 if old_value != 0 else 0
direction = "increased" if pct > 0 else "decreased"
change_part = f"{direction} {field} from {old_value} to {new_value} ({abs(pct):.1f}%)"
elif isinstance(new_value, str) and isinstance(old_value, str):
if len(new_value) > 50 or len(old_value) > 50:
change_part = (
f"changed {field} (length: {len(old_value)} \u2192 {len(new_value)} chars)"
)
else:
change_part = f'changed {field}: "{old_value}" \u2192 "{new_value}"'
else:
change_part = f"updated {field}"
severity = ""
if anomaly_info and anomaly_info.get("anomaly"):
severity = " \u26a0\ufe0f *ANOMALY DETECTED*"
alert = f"{intro} {change_part}{severity}"
if anomaly_info and anomaly_info.get("z_score"):
alert += f" (z-score: {anomaly_info['z_score']})"
if anomaly_info and anomaly_info.get("pct_change"):
alert += f" \u2014 unusual change of {anomaly_info['pct_change']}% vs historical average"
return alert
# ── Weekly Reports ──
def generate_weekly_report(
competitors: list[dict[str, Any]],
days_back: int = 7,
) -> dict[str, Any]:
"""Generate a weekly competitive intelligence report."""
cutoff_ts = time.time() - (days_back * 86400)
report_sections: list[dict[str, Any]] = []
for comp in competitors:
comp_id = comp.get("id", comp.get("name", "").lower().replace(" ", "_"))
comp_name = comp.get("name", "Unknown")
snapshots = get_snapshots(comp_id)
weekly = [s for s in snapshots if s.get("unix_ts", 0) >= cutoff_ts]
if not weekly:
continue
# Get fields that changed this week
if len(weekly) >= 2:
latest = weekly[0].get("fields", {})
oldest = weekly[-1].get("fields", {})
changes = []
all_fields = set(latest.keys()) | set(oldest.keys())
for field in all_fields:
old_val = oldest.get(field)
new_val = latest.get(field)
if old_val != new_val:
changes.append(
{
"field": field,
"from": old_val,
"to": new_val,
"alert": generate_alert(comp_name, field, old_val, new_val),
}
)
if changes:
report_sections.append(
{
"competitor": comp_name,
"changes_count": len(changes),
"snapshots_this_week": len(weekly),
"changes": changes,
}
)
# Summary
total_changes = sum(s["changes_count"] for s in report_sections)
most_active = (
max(report_sections, key=lambda x: x["changes_count"]) if report_sections else None
)
return {
"report_period": f"Last {days_back} days",
"generated_at": datetime.now(UTC).isoformat(),
"competitors_tracked": len(competitors),
"competitors_with_changes": len(report_sections),
"total_changes": total_changes,
"most_active_competitor": most_active["competitor"] if most_active else None,
"sections": report_sections,
"summary": _generate_report_summary(report_sections, total_changes, len(competitors)),
}
def _generate_report_summary(
sections: list[dict[str, Any]],
total_changes: int,
total_competitors: int,
) -> str:
"""Generate a text summary of the weekly report."""
if not sections:
return f"No significant changes detected across {total_competitors} tracked competitors."
most_active = max(sections, key=lambda x: x["changes_count"])
lines = [
"Weekly Competitive Intelligence Summary",
"",
f"Tracked {total_competitors} competitors over the past 7 days.",
f"Detected {total_changes} changes across {len(sections)} competitors.",
"",
f"Most active: {most_active['competitor']} with {most_active['changes_count']} changes.",
]
for section in sections[:5]:
lines.append("")
lines.append(f"\u2500\u2500 {section['competitor']} \u2500\u2500")
for change in section["changes"][:3]:
lines.append(f" \u2022 {change['alert']}")
if len(section["changes"]) > 3:
lines.append(f" ... and {len(section['changes']) - 3} more changes")
return "\n".join(lines)

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"""Pry — batch job queue with Redis and webhook callbacks.
Async processing engine inspired by Firecrawl's webhook system."""
import hashlib
import hmac
import json
import uuid
from datetime import datetime
from typing import Any
import httpx
class JobQueue:
"""Redis-backed async job queue for batch scrape/crawl operations.
Features:
- Job creation with unique ID
- Status tracking (pending, running, completed, failed)
- Webhook callbacks on completion
- Job timeout and cleanup
"""
def __init__(self, redis_url: str = "redis://localhost:6379/0"):
self.redis_url = redis_url
self._redis = None
self._local_jobs: dict[str, dict] = {}
async def _get_redis(self):
if self._redis is None:
import redis.asyncio as aioredis
try:
self._redis = await aioredis.from_url(self.redis_url, socket_timeout=3)
await self._redis.ping()
except:
self._redis = None # Fallback to local storage
return self._redis
async def create_job(
self, job_type: str, payload: dict, webhook: str | None = None, timeout: int = 300
) -> str:
"""Create a new job and return its ID."""
job_id = f"job_{uuid.uuid4().hex[:16]}"
job = {
"id": job_id,
"type": job_type,
"payload": payload,
"status": "pending",
"created_at": datetime.utcnow().isoformat(),
"updated_at": datetime.utcnow().isoformat(),
"webhook": webhook,
"timeout": timeout,
"result": None,
"error": None,
}
redis = await self._get_redis()
if redis:
await redis.set(f"pry:job:{job_id}", json.dumps(job), ex=timeout + 60)
await redis.rpush("pry:queue", job_id)
else:
self._local_jobs[job_id] = job
return job_id
async def get_job(self, job_id: str) -> dict | None:
"""Get job status and result."""
redis = await self._get_redis()
if redis:
data = await redis.get(f"pry:job:{job_id}")
return json.loads(data) if data else None
return self._local_jobs.get(job_id)
async def update_job(self, job_id: str, **updates):
"""Update job fields."""
redis = await self._get_redis()
if redis:
data = await redis.get(f"pry:job:{job_id}")
if data:
job = json.loads(data)
job.update(updates)
job["updated_at"] = datetime.utcnow().isoformat()
await redis.set(
f"pry:job:{job_id}", json.dumps(job), ex=job.get("timeout", 300) + 60
)
elif job_id in self._local_jobs:
self._local_jobs[job_id].update(updates)
self._local_jobs[job_id]["updated_at"] = datetime.utcnow().isoformat()
async def complete_job(self, job_id: str, result: Any):
"""Mark job as completed and fire webhook."""
await self.update_job(job_id, status="completed", result=result)
job = await self.get_job(job_id)
if job and job.get("webhook"):
await self._fire_webhook(job["webhook"], job)
async def fail_job(self, job_id: str, error: str):
"""Mark job as failed and fire webhook."""
await self.update_job(job_id, status="failed", error=error)
job = await self.get_job(job_id)
if job and job.get("webhook"):
await self._fire_webhook(job["webhook"], job)
async def _fire_webhook(self, url: str, job: dict):
"""Fire webhook callback with job result."""
try:
signature = hmac.new(
b"pry-webhook-secret", json.dumps(job).encode(), hashlib.sha256
).hexdigest()
async with httpx.AsyncClient(timeout=10) as client:
await client.post(
url,
json=job,
headers={
"X-Pry-Signature": signature,
"Content-Type": "application/json",
},
)
except:
pass # Webhook fire is best-effort

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"""Pry — lazy load and infinite scroll handling."""
import logging
import re
from typing import Any
logger = logging.getLogger(__name__)
def detect_lazy_loading(html: str) -> dict[str, Any]:
"""Detect lazy loading patterns in HTML."""
result: dict[str, Any] = {
"lazy_images": False,
"lazy_frames": False,
"infinite_scroll": False,
"load_more": False,
"intersection_observer": False,
}
# Check for lazy loading images
if re.search(r'loading=["\']lazy["\']', html, re.IGNORECASE):
result["lazy_images"] = True
# Check for lazy loading iframes
if re.search(r'<iframe[^>]*loading=["\']lazy["\']', html, re.IGNORECASE):
result["lazy_frames"] = True
# Check for infinite scroll
if re.search(r"infinite[_-]?scroll|infinitescroll", html, re.IGNORECASE):
result["infinite_scroll"] = True
# Check for "load more" buttons
if re.search(r"load[_-]?more|show[_-]?more|see[_-]?more", html, re.IGNORECASE):
result["load_more"] = True
# Intersection Observer API
if re.search(r"IntersectionObserver", html):
result["intersection_observer"] = True
return result
def generate_scroll_script(max_scrolls: int = 5, delay_ms: int = 1000) -> str:
"""Generate JavaScript to scroll through lazy-loaded content.
Returns JS that scrolls the page in steps, waiting for content to load.
"""
return f"""
(async () => {{
const delay = ms => new Promise(r => setTimeout(r, ms));
let prevHeight = document.body.scrollHeight;
let scrolls = 0;
while (scrolls < {max_scrolls}) {{
window.scrollTo(0, document.body.scrollHeight);
await delay({delay_ms});
const newHeight = document.body.scrollHeight;
if (newHeight === prevHeight) break;
prevHeight = newHeight;
scrolls++;
}}
// Scroll back to top
window.scrollTo(0, 0);
await delay(200);
}})();
"""
def generate_load_more_script(max_clicks: int = 10, delay_ms: int = 1500) -> str:
"""Generate JavaScript to click 'Load More' buttons.
Finds buttons with text containing 'load more', 'show more', etc.
"""
return f"""
(async () => {{
const delay = ms => new Promise(r => setTimeout(r, ms));
const patterns = ['load more', 'show more', 'see more', 'view more', 'load additional'];
let clicks = 0;
while (clicks < {max_clicks}) {{
let clicked = false;
for (const pattern of patterns) {{
const buttons = Array.from(document.querySelectorAll('button, a, [role="button"]'));
for (const btn of buttons) {{
if (btn.textContent.toLowerCase().includes(pattern)) {{
btn.click();
clicked = true;
await delay({delay_ms});
break;
}}
}}
if (clicked) break;
}}
if (!clicked) break;
clicks++;
}}
}})();
"""

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"""Pry — Real LLM-powered features. Replaces regex stubs with actual LLM calls.
Used by compliance, SEO, entity reconciliation, PII redaction, and other AI features."""
import json
import logging
from typing import Any
from llm_providers.registry import get_registry
logger = logging.getLogger(__name__)
def _strip_fence(text: str) -> str:
"""Strip markdown code fences that LLMs commonly wrap JSON in."""
t = text.strip()
if t.startswith("```json"):
t = t[len("```json"):]
elif t.startswith("```"):
t = t[len("```"):]
if t.endswith("```"):
t = t[: -len("```")]
return t.strip()
async def llm_compliance_analyze(text: str, url: str = "") -> dict[str, Any]:
"""Use LLM to actually analyze Terms of Service for compliance risk."""
if not text:
return {"risk_level": "unknown", "reason": "No ToS text provided"}
prompt = f"""Analyze the following Terms of Service for legal compliance risks when scraping the associated website.
URL: {url}
Terms of Service (truncated to 4000 chars):
{text[:4000]}
Return JSON with these fields:
- risk_level: "green" (no restrictions), "yellow" (some restrictions), "red" (prohibits scraping)
- confidence: "high" / "medium" / "low"
- key_restrictions: list of strings describing scraping-related restrictions
- risk_summary: 1-2 sentence summary
- recommendation: what to do before scraping
Respond ONLY with valid JSON, no markdown formatting."""
try:
reg = get_registry()
resp = await reg.complete(
prompt,
system=(
"You are a legal compliance analyst specializing in web scraping. "
"Be concise and accurate."
),
max_tokens=800,
temperature=0.3,
)
result = json.loads(_strip_fence(resp.text))
result["llm_provider"] = resp.provider
result["llm_cost_usd"] = round(resp.cost_usd, 6)
return result
except Exception as e:
logger.warning("llm_compliance_failed", extra={"error": str(e)[:80]})
return {"risk_level": "unknown", "error": str(e)[:200]}
async def llm_seo_analyze(
url: str, content: str, target_keywords: list[str] | None = None
) -> dict[str, Any]:
"""Use LLM to analyze SEO quality of a page and identify optimization opportunities."""
if not content:
return {"score": 0, "recommendations": []}
keywords = ", ".join(target_keywords) if target_keywords else "general relevance"
prompt = f"""Analyze the SEO quality of this page for target keywords: {keywords}
URL: {url}
Page content (truncated):
{content[:3000]}
Return JSON with:
- overall_score: 0-100
- title_quality: "good" / "fair" / "poor"
- content_depth: "comprehensive" / "adequate" / "shallow"
- keyword_presence: {{keyword: "well_optimized" / "under_optimized" / "missing"}}
- recommendations: list of 3-5 specific actionable improvements
- issues: list of SEO problems found
Respond ONLY with valid JSON."""
try:
reg = get_registry()
resp = await reg.complete(prompt, max_tokens=1000, temperature=0.3)
return json.loads(_strip_fence(resp.text))
except Exception as e:
logger.warning("llm_seo_failed", extra={"error": str(e)[:80]})
return {"score": 0, "error": str(e)[:200]}
async def llm_entity_reconcile(records: list[dict], vertical: str = "product") -> dict[str, Any]:
"""Use LLM to semantically match and merge records from different sources."""
if not records or len(records) < 2:
return {"entities": records, "matches": []}
sample = records[:50]
prompt = f"""You are a data reconciliation expert. Given records from multiple sources for the same {vertical} vertical, identify which records refer to the same real-world entity.
Records (JSON):
{json.dumps(sample, indent=2, default=str)[:8000]}
Return JSON with:
- groups: list of groups, each with a "canonical_id" (string) and "record_indices" (list of integers referring to the input records)
- unmatched: list of record indices that don't match any other record
- reasoning: brief explanation of how you matched
Respond ONLY with valid JSON."""
try:
reg = get_registry()
resp = await reg.complete(prompt, max_tokens=2000, temperature=0.2)
return json.loads(_strip_fence(resp.text))
except Exception as e:
logger.warning("llm_reconcile_failed", extra={"error": str(e)[:80]})
return {"entities": records, "matches": [], "error": str(e)[:200]}
async def llm_pii_detect(text: str) -> dict[str, Any]:
"""Use LLM to detect PII that regex might miss (context-aware)."""
if not text or len(text) < 50:
return {"pii_found": [], "redacted_text": text}
prompt = f"""Find all personally identifiable information (PII) in this text that should be redacted for safe AI training data use.
Text:
{text[:4000]}
Return JSON with:
- pii_items: list of {{"text": "the PII", "type": "name/email/phone/ssn/address/other", "start": character_index, "end": character_index}}
- redacted_text: the original text with PII replaced by [REDACTED:TYPE]
Respond ONLY with valid JSON. Use character indices relative to the original text (truncated to 4000 chars)."""
try:
reg = get_registry()
resp = await reg.complete(prompt, max_tokens=2000, temperature=0.1)
return json.loads(_strip_fence(resp.text))
except Exception as e:
logger.warning("llm_pii_failed", extra={"error": str(e)[:80]})
return {"pii_items": [], "redacted_text": text, "error": str(e)[:200]}
async def llm_anomaly_detect(
historical_data: list[dict], current: dict, field: str = "price"
) -> dict[str, Any]:
"""Use LLM to detect anomalies that statistical methods miss (context-aware).
E.g., Black Friday prices dropping 50% is expected, but a 50% drop on a random Tuesday is suspicious."""
if not historical_data or not current:
return {"anomaly": False, "reason": "Insufficient data"}
prompt = f"""Analyze whether this change in '{field}' is a true anomaly or an expected pattern.
Historical data (last 10):
{json.dumps(historical_data[-10:], default=str)}
Current value:
{json.dumps(current, default=str)}
Consider:
- Day of week / seasonal patterns
- Promotional events (Black Friday, holidays)
- Market conditions
- Whether the change is in the expected direction
Return JSON with:
- is_anomaly: bool
- confidence: 0.0-1.0
- reasoning: 1-2 sentences
- context_factors: list of relevant context that explain the change
Respond ONLY with valid JSON."""
try:
reg = get_registry()
resp = await reg.complete(prompt, max_tokens=500, temperature=0.3)
return json.loads(_strip_fence(resp.text))
except Exception as e:
logger.warning("llm_anomaly_failed", extra={"error": str(e)[:80]})
return {"is_anomaly": False, "reason": str(e)[:200]}

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"""Pry — LLM Provider abstraction with referral revenue tracking.
Supports pluggable providers: OpenAI, Anthropic, Google, Cohere, Mistral, Ollama, OpenRouter.
Includes referral/affiliate link tracking for revenue sharing."""
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any
logger = logging.getLogger(__name__)
@dataclass
class LLMResponse:
"""Standard response from any LLM provider."""
text: str
model: str
provider: str
input_tokens: int = 0
output_tokens: int = 0
cost_usd: float = 0.0
referral_id: str = ""
latency_ms: int = 0
raw: dict[str, Any] = field(default_factory=dict)
@dataclass
class ReferralConfig:
"""Referral/affiliate config for revenue sharing."""
enabled: bool = True
program_id: str = "pry-default"
# Provider-specific referral links (with our affiliate ID)
referral_links: dict[str, str] = field(default_factory=dict)
# NEW: link to the full provider catalog
catalog: dict = field(default_factory=dict)
def __post_init__(self):
if not self.referral_links:
from referrals import PROVIDER_CATALOG
for _category, providers in PROVIDER_CATALOG.items():
for p in providers:
self.referral_links[p["tag"]] = p["url"]
self.catalog = PROVIDER_CATALOG
class LLMProvider(ABC):
"""Abstract base class for LLM providers."""
name: str = ""
cost_per_1k_input: float = 0.0
cost_per_1k_output: float = 0.0
referral_url: str = ""
@abstractmethod
async def complete(self, prompt: str, system: str = "", max_tokens: int = 1024,
temperature: float = 0.7, model: str = "") -> LLMResponse:
"""Send completion request to provider."""
raise NotImplementedError
@abstractmethod
async def embed(self, text: str, model: str = "") -> list[float]:
"""Generate embedding for text."""
raise NotImplementedError
def estimate_cost(self, input_tokens: int, output_tokens: int) -> float:
return (input_tokens / 1000) * self.cost_per_1k_input + (output_tokens / 1000) * self.cost_per_1k_output

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"""Concrete LLM provider implementations."""
import logging
import os
from llm_providers.base import LLMProvider, LLMResponse
logger = logging.getLogger(__name__)
class OpenAIProvider(LLMProvider):
name = "openai"
cost_per_1k_input = 0.00015 # gpt-4o-mini
cost_per_1k_output = 0.0006
referral_url = "https://platform.openai.com/signup?via=pry"
def __init__(self, api_key: str = ""):
self.api_key = api_key or os.getenv("OPENAI_API_KEY", "")
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model="gpt-4o-mini"):
from client import get_client
client = await get_client()
messages = []
if system: messages.append({"role": "system", "content": system})
messages.append({"role": "user", "content": prompt})
resp = await client.post("https://api.openai.com/v1/chat/completions",
json={"model": model, "messages": messages, "max_tokens": max_tokens, "temperature": temperature},
headers={"Authorization": f"Bearer {self.api_key}"}, timeout=60)
data = resp.json()
choice = data["choices"][0]
return LLMResponse(text=choice["message"]["content"], model=model, provider=self.name,
input_tokens=data["usage"]["prompt_tokens"],
output_tokens=data["usage"]["completion_tokens"], raw=data)
async def embed(self, text, model="text-embedding-3-small"):
from client import get_client
client = await get_client()
resp = await client.post("https://api.openai.com/v1/embeddings",
json={"input": text, "model": model},
headers={"Authorization": f"Bearer {self.api_key}"}, timeout=30)
return resp.json()["data"][0]["embedding"]
class AnthropicProvider(LLMProvider):
name = "anthropic"
cost_per_1k_input = 0.00025 # claude-3-haiku
cost_per_1k_output = 0.00125
referral_url = "https://console.anthropic.com/?ref=pry"
def __init__(self, api_key: str = ""):
self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY", "")
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model="claude-3-haiku-20240307"):
from client import get_client
client = await get_client()
body = {"model": model, "max_tokens": max_tokens, "temperature": temperature,
"messages": [{"role": "user", "content": prompt}]}
if system: body["system"] = system
resp = await client.post("https://api.anthropic.com/v1/messages",
json=body, headers={"x-api-key": self.api_key, "anthropic-version": "2023-06-01"}, timeout=60)
data = resp.json()
return LLMResponse(text=data["content"][0]["text"], model=model, provider=self.name,
input_tokens=data["usage"]["input_tokens"],
output_tokens=data["usage"]["output_tokens"], raw=data)
async def embed(self, text, model=""):
raise NotImplementedError("Anthropic doesn't have a public embedding API yet")
class GoogleProvider(LLMProvider):
name = "google"
cost_per_1k_input = 0.000125 # gemini-flash
cost_per_1k_output = 0.000375
referral_url = "https://aistudio.google.com/?utm_source=pry"
def __init__(self, api_key: str = ""):
self.api_key = api_key or os.getenv("GOOGLE_API_KEY", "")
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model="gemini-1.5-flash"):
from client import get_client
client = await get_client()
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent?key={self.api_key}"
contents = [{"role": "user", "parts": [{"text": prompt}]}]
body = {"contents": contents, "generationConfig": {"maxOutputTokens": max_tokens, "temperature": temperature}}
if system: body["systemInstruction"] = {"parts": [{"text": system}]}
resp = await client.post(url, json=body, timeout=60)
data = resp.json()
text = data["candidates"][0]["content"]["parts"][0]["text"]
usage = data.get("usageMetadata", {})
return LLMResponse(text=text, model=model, provider=self.name,
input_tokens=usage.get("promptTokenCount", 0),
output_tokens=usage.get("candidatesTokenCount", 0), raw=data)
async def embed(self, text, model="text-embedding-004"):
from client import get_client
client = await get_client()
url = f"https://generativelanguage.googleapis.com/v1beta/models/{model}:embedContent?key={self.api_key}"
resp = await client.post(url, json={"content": {"parts": [{"text": text}]}}, timeout=30)
return resp.json()["embedding"]["values"]
class CohereProvider(LLMProvider):
name = "cohere"
cost_per_1k_input = 0.0001
cost_per_1k_output = 0.0004
referral_url = "https://dashboard.cohere.com/welcome?ref=pry"
def __init__(self, api_key: str = ""):
self.api_key = api_key or os.getenv("COHERE_API_KEY", "")
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model="command-r"):
from client import get_client
client = await get_client()
body = {"model": model, "message": prompt, "max_tokens": max_tokens, "temperature": temperature}
if system: body["preamble"] = system
resp = await client.post("https://api.cohere.ai/v1/chat",
json=body, headers={"Authorization": f"Bearer {self.api_key}"}, timeout=60)
data = resp.json()
return LLMResponse(text=data["text"], model=model, provider=self.name, raw=data)
async def embed(self, text, model="embed-english-v3.0"):
from client import get_client
client = await get_client()
resp = await client.post("https://api.cohere.ai/v1/embed",
json={"texts": [text], "model": model, "input_type": "search_document"},
headers={"Authorization": f"Bearer {self.api_key}"}, timeout=30)
return resp.json()["embeddings"][0]
class MistralProvider(LLMProvider):
name = "mistral"
cost_per_1k_input = 0.0002
cost_per_1k_output = 0.0006
referral_url = "https://console.mistral.ai/?ref=pry"
def __init__(self, api_key: str = ""):
self.api_key = api_key or os.getenv("MISTRAL_API_KEY", "")
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model="mistral-small-latest"):
from client import get_client
client = await get_client()
messages = []
if system: messages.append({"role": "system", "content": system})
messages.append({"role": "user", "content": prompt})
resp = await client.post("https://api.mistral.ai/v1/chat/completions",
json={"model": model, "messages": messages, "max_tokens": max_tokens, "temperature": temperature},
headers={"Authorization": f"Bearer {self.api_key}"}, timeout=60)
data = resp.json()
return LLMResponse(text=data["choices"][0]["message"]["content"], model=model, provider=self.name,
input_tokens=data["usage"]["prompt_tokens"],
output_tokens=data["usage"]["completion_tokens"], raw=data)
async def embed(self, text, model="mistral-embed"):
from client import get_client
client = await get_client()
resp = await client.post("https://api.mistral.ai/v1/embeddings",
json={"model": model, "input": [text]},
headers={"Authorization": f"Bearer {self.api_key}"}, timeout=30)
return resp.json()["data"][0]["embedding"]
class OllamaProvider(LLMProvider):
name = "ollama"
cost_per_1k_input = 0.0 # Self-hosted, free
cost_per_1k_output = 0.0
referral_url = "https://ollama.com" # No referral program, just self-hosted
def __init__(self, base_url: str = "", model: str = "llama3.2"):
self.base_url = base_url or os.getenv("PRY_OLLAMA_URL", "http://localhost:11434")
self.default_model = model
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model=""):
from client import get_client
client = await get_client()
model = model or self.default_model
body = {"model": model, "prompt": prompt, "stream": False, "options": {"temperature": temperature, "num_predict": max_tokens}}
if system: body["system"] = system
resp = await client.post(f"{self.base_url}/api/generate", json=body, timeout=300)
data = resp.json()
return LLMResponse(text=data["response"], model=model, provider=self.name,
input_tokens=data.get("prompt_eval_count", 0),
output_tokens=data.get("eval_count", 0), raw=data)
async def embed(self, text, model=""):
from client import get_client
client = await get_client()
model = model or "nomic-embed-text"
resp = await client.post(f"{self.base_url}/api/embeddings",
json={"model": model, "prompt": text}, timeout=60)
return resp.json()["embedding"]
class OpenRouterProvider(LLMProvider):
name = "openrouter"
cost_per_1k_input = 0.0 # Free models available
cost_per_1k_output = 0.0
referral_url = "https://openrouter.ai/?ref=pry"
def __init__(self, api_key: str = ""):
self.api_key = api_key or os.getenv("OPENROUTER_API_KEY", "")
async def complete(self, prompt, system="", max_tokens=1024, temperature=0.7, model="meta-llama/llama-3.2-3b-instruct:free"):
from client import get_client
client = await get_client()
messages = []
if system: messages.append({"role": "system", "content": system})
messages.append({"role": "user", "content": prompt})
resp = await client.post("https://openrouter.ai/api/v1/chat/completions",
json={"model": model, "messages": messages, "max_tokens": max_tokens, "temperature": temperature},
headers={"Authorization": f"Bearer {self.api_key}"}, timeout=60)
data = resp.json()
usage = data.get("usage", {})
return LLMResponse(text=data["choices"][0]["message"]["content"], model=model, provider=self.name,
input_tokens=usage.get("prompt_tokens", 0),
output_tokens=usage.get("completion_tokens", 0), raw=data)
async def embed(self, text, model=""):
raise NotImplementedError("OpenRouter focuses on chat; use dedicated embedding providers")
def register_default_providers(registry: "LLMRegistry") -> None:
"""Register all providers whose API keys are set in environment."""
api_key_map = {
"openai": os.getenv("OPENAI_API_KEY"),
"anthropic": os.getenv("ANTHROPIC_API_KEY"),
"google": os.getenv("GOOGLE_API_KEY"),
"cohere": os.getenv("COHERE_API_KEY"),
"mistral": os.getenv("MISTRAL_API_KEY"),
"openrouter": os.getenv("OPENROUTER_API_KEY"),
}
provider_classes = {
"openai": OpenAIProvider, "anthropic": AnthropicProvider, "google": GoogleProvider,
"cohere": CohereProvider, "mistral": MistralProvider, "openrouter": OpenRouterProvider,
}
for name, cls in provider_classes.items():
key = api_key_map.get(name)
if key:
registry.register(cls(api_key=key))
# Ollama is always available if running locally
registry.register(OllamaProvider())

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"""LLM provider registry with fallback chain and cost tracking."""
import json
import logging
import os
import time
from collections import defaultdict
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from llm_providers.base import LLMProvider, LLMResponse, ReferralConfig
logger = logging.getLogger(__name__)
USAGE_DIR = Path(os.path.expanduser("~/.pry/llm_usage"))
USAGE_DIR.mkdir(parents=True, exist_ok=True)
class LLMRegistry:
"""Registry of LLM providers with fallback chain, cost tracking, and referral config."""
def __init__(self, referral: ReferralConfig | None = None):
self.providers: dict[str, LLMProvider] = {}
self.referral = referral or ReferralConfig()
self.fallback_chain: list[str] = []
# Usage tracking
self.usage: dict[str, dict[str, Any]] = defaultdict(lambda: {
"calls": 0, "input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0,
"last_used": None,
})
self._load_usage()
def register(self, provider: LLMProvider) -> None:
self.providers[provider.name] = provider
if provider.name not in self.fallback_chain:
self.fallback_chain.append(provider.name)
logger.info("provider_registered", extra={"name": provider.name})
def set_fallback_chain(self, chain: list[str]) -> None:
self.fallback_chain = chain
async def complete(self, prompt: str, system: str = "", provider_name: str = "",
max_tokens: int = 1024, temperature: float = 0.7, model: str = "",
fallback: bool = True) -> LLMResponse:
"""Complete via specified provider or fallback chain."""
names = [provider_name] if provider_name else list(self.fallback_chain)
if not fallback:
names = [provider_name] if provider_name else [self.fallback_chain[0]] if self.fallback_chain else []
last_error = ""
for name in names:
provider = self.providers.get(name)
if not provider:
continue
try:
start = time.time()
response = await provider.complete(prompt, system, max_tokens, temperature, model)
response.latency_ms = int((time.time() - start) * 1000)
response.cost_usd = provider.estimate_cost(response.input_tokens, response.output_tokens)
response.referral_id = self.referral.program_id
self._track(response)
return response
except Exception as e:
last_error = str(e)
logger.warning("llm_provider_failed",
extra={"provider": name, "error": str(e)[:100]})
if not fallback:
break
raise Exception(f"All LLM providers failed. Last: {last_error}")
async def embed(self, text: str, provider_name: str = "", model: str = "") -> list[float]:
names = [provider_name] if provider_name else list(self.fallback_chain)
for name in names:
provider = self.providers.get(name)
if not provider:
continue
try:
return await provider.embed(text, model)
except Exception as e:
logger.warning("embed_provider_failed", extra={"provider": name, "error": str(e)[:80]})
raise Exception("All embedding providers failed")
def _track(self, response: LLMResponse) -> None:
u = self.usage[response.provider]
u["calls"] += 1
u["input_tokens"] += response.input_tokens
u["output_tokens"] += response.output_tokens
u["cost_usd"] += response.cost_usd
u["last_used"] = datetime.now(UTC).isoformat()
# NEW: track in-app LLM usage as referral revenue
try:
from referrals import ReferralTracker
if not hasattr(self, '_referral_tracker'):
self._referral_tracker = ReferralTracker()
# Estimate revenue at 5% of user cost (typical affiliate share)
self._referral_tracker.track_in_app_usage(response.provider, response.cost_usd * 0.05)
except ImportError:
pass
# Persist daily
today = datetime.now(UTC).strftime("%Y-%m-%d")
path = USAGE_DIR / f"usage_{today}.jsonl"
try:
with open(path, "a") as f:
f.write(json.dumps({
"ts": datetime.now(UTC).isoformat(),
"provider": response.provider, "model": response.model,
"input_tokens": response.input_tokens,
"output_tokens": response.output_tokens,
"cost_usd": response.cost_usd,
"referral_id": response.referral_id,
}) + "\n")
except OSError:
pass
def _load_usage(self) -> None:
for path in USAGE_DIR.glob("usage_*.jsonl"):
try:
for line in path.read_text().splitlines():
if line.strip():
r = json.loads(line)
prov = r.get("provider", "unknown")
u = self.usage[prov]
u["calls"] += 1
u["input_tokens"] += r.get("input_tokens", 0)
u["output_tokens"] += r.get("output_tokens", 0)
u["cost_usd"] += r.get("cost_usd", 0.0)
except (json.JSONDecodeError, OSError):
pass
def get_stats(self) -> dict[str, Any]:
return {
"providers": {name: self.usage[p.name] for name, p in self.providers.items()},
"referral": {
"enabled": self.referral.enabled,
"program_id": self.referral.program_id,
"links": self.referral.referral_links,
},
"fallback_chain": self.fallback_chain,
}
# Global registry
_registry: LLMRegistry | None = None
def get_registry() -> LLMRegistry:
"""Get or create the global LLM registry with default providers."""
global _registry
if _registry is None:
_registry = LLMRegistry()
# Register providers lazily (only if API keys are set)
from llm_providers.providers import register_default_providers
register_default_providers(_registry)
return _registry

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"""Pry — markdown generation strategies with content filtering.
Raw, Fit (pruning), and Fit+BM25 (query-relevant) markdown generators."""
import logging
import math
import re
from collections.abc import Sequence
from typing import Any, ClassVar
logger = logging.getLogger(__name__)
class MarkdownGenerator:
"""Base markdown generator. Produces raw markdown from content."""
def generate(self, content: str, url: str = "") -> dict[str, Any]:
"""Generate markdown. Returns dict with raw_markdown and metadata."""
return {"raw_markdown": content, "url": url}
class PruningContentFilter:
"""Remove noise from content: nav bars, ads, sidebars, footers.
Uses heuristics to identify and prune boilerplate sections."""
BOILERPLATE_PATTERNS: ClassVar[list[str]] = [
r"nav|navbar|navigation|menu",
r"footer|copyright",
r"sidebar|aside",
r"advertisement|sponsored|promoted",
r"cookie|consent|gdpr",
r"social.*share|share.*buttons",
r"newsletter|subscribe|sign.?up",
r"comments?.*section",
]
def __init__(self, threshold: float = 0.3, min_word_threshold: int = 50) -> None:
self.threshold = threshold
self.min_word_threshold = min_word_threshold
def filter(self, content: str) -> str:
"""Remove boilerplate sections from content."""
lines = content.split("\n")
kept: list[str] = []
for line in lines:
if self._is_boilerplate(line):
continue
if len(line.strip()) < 3:
continue
kept.append(line)
return "\n".join(kept)
def _is_boilerplate(self, line: str) -> bool:
lower = line.lower().strip()
return any(re.search(p, lower) for p in self.BOILERPLATE_PATTERNS)
def score(self, content: str) -> dict[str, Any]:
"""Score content quality. Higher = better."""
lines = content.split("\n")
total = len(lines)
boilerplate = sum(1 for line in lines if self._is_boilerplate(line))
header_score = sum(1 for line in lines if line.startswith("#"))
link_score = sum(1 for line in lines if "http" in line.lower())
return {
"total_lines": total,
"boilerplate_lines": boilerplate,
"boilerplate_ratio": round(boilerplate / total, 2) if total > 0 else 0,
"headers": header_score,
"links": link_score,
"quality": "high" if boilerplate / total < self.threshold else "low",
}
class BM25ContentFilter:
"""BM25-based content filtering for query-relevant extraction.
Scores each section by relevance to a user query."""
def __init__(self, k1: float = 1.5, b: float = 0.75, threshold: float = 1.0) -> None:
self.k1 = k1
self.b = b
self.threshold = threshold
def filter(self, content: str, query: str) -> str:
"""Filter content to keep only query-relevant sections."""
sections = self._split_sections(content)
if not sections:
return content
scores = self._bm25_scores(sections, query)
kept: list[str] = []
for section, score in zip(sections, scores, strict=False):
if score >= self.threshold:
kept.append(section)
return "\n\n".join(kept) if kept else content
def _split_sections(self, content: str) -> list[str]:
"""Split content into sections by headings."""
sections = re.split(r"\n(?=#+\s)", content)
return [s.strip() for s in sections if s.strip()]
def _bm25_scores(self, sections: Sequence[str], query: str) -> list[float]:
"""Compute BM25 score for each section against query."""
query_terms = query.lower().split()
if not query_terms:
return [1.0] * len(sections)
tokenized = [self._tokenize(s) for s in sections]
avg_len = sum(len(t) for t in tokenized) / max(len(tokenized), 1)
df: dict[str, int] = {}
for tokens in tokenized:
for term in set(tokens):
df[term] = df.get(term, 0) + 1
n = len(sections)
scores: list[float] = []
for tokens in tokenized:
score = 0.0
doc_len = len(tokens)
for term in query_terms:
if term not in df:
continue
tf = tokens.count(term)
idf = math.log((n - df[term] + 0.5) / (df[term] + 0.5) + 1.0)
numerator = tf * (self.k1 + 1)
denominator = tf + self.k1 * (1 - self.b + self.b * doc_len / avg_len)
score += idf * (numerator / denominator)
scores.append(score)
return scores
def _tokenize(self, text: str) -> list[str]:
"""Simple word tokenizer."""
return re.findall(r"\w+", text.lower())
class DefaultMarkdownGenerator(MarkdownGenerator):
"""Markdown generator with configurable content filtering.
Usage:
gen = DefaultMarkdownGenerator(
content_filter=PruningContentFilter(threshold=0.3)
)
result = gen.generate(content, url="https://example.com")
print(result["raw_markdown"])
if "fit_markdown" in result:
print(result["fit_markdown"])
"""
def __init__(
self, content_filter: PruningContentFilter | BM25ContentFilter | None = None
) -> None:
self.content_filter = content_filter
def generate(self, content: str, url: str = "", query: str = "") -> dict[str, Any]:
"""Generate markdown with optional filtering.
Returns:
raw_markdown: Original content as markdown
fit_markdown: Pruned content (if PruningContentFilter)
fit_markdown_bm25: BM25-filtered content (if BM25ContentFilter + query)
metadata: Content quality scores
"""
result: dict[str, Any] = {
"raw_markdown": content,
"url": url,
}
if self.content_filter:
if isinstance(self.content_filter, BM25ContentFilter) and query:
result["fit_markdown_bm25"] = self.content_filter.filter(content, query)
result["filter"] = "bm25"
elif isinstance(self.content_filter, PruningContentFilter):
result["fit_markdown"] = self.content_filter.filter(content)
result["metadata"] = self.content_filter.score(content)
result["filter"] = "pruning"
else:
result["fit_markdown"] = content
return result

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"""Pry Config — runtime configuration for proxy, Tor, VPN, and anti-detection.
Users can configure at startup (env vars) or at runtime (API calls)."""
import json
import os
CONFIG_FILE = "/app/config.json"
DEFAULT_CONFIG = {
"proxy": {"enabled": False, "type": "", "url": "", "username": "", "password": ""},
"tor": {"enabled": False, "socks5_host": "tor", "socks5_port": 9050, "control_port": 9051},
"rotation": {
"user_agent": "rotate",
"ip": "off",
"timing": "random",
"delay_range": [0.5, 3.0],
},
"stealth": {
"webdriver_override": True,
"canvas_noise": True,
"webrtc_disable": True,
"geolocation_spoof": True,
},
"retry": {"max_attempts": 3, "min_quality": 20, "backoff": "exponential"},
"output": {"default_format": "markdown", "max_chars": 100000, "include_links": True},
"rate_limit": {"rpm": 120, "burst": 200},
}
class PryConfig:
"""Configuration manager — loads from env vars, config file, and API overrides."""
def __init__(self):
self.config = json.loads(json.dumps(DEFAULT_CONFIG)) # Deep copy
self._load_env()
self._load_file()
self._resolve_proxy_chain()
def _load_env(self):
"""Environment variables override defaults."""
if os.getenv("TOR_ENABLED", "").lower() in ("1", "true", "yes"):
self.config["tor"]["enabled"] = True
if os.getenv("TOR_SOCKS5_HOST"):
self.config["tor"]["socks5_host"] = os.getenv("TOR_SOCKS5_HOST")
if os.getenv("TOR_SOCKS5_PORT"):
self.config["tor"]["socks5_port"] = int(os.getenv("TOR_SOCKS5_PORT"))
if os.getenv("PROXY_URL"):
self.config["proxy"]["enabled"] = True
self.config["proxy"]["url"] = os.getenv("PROXY_URL")
self.config["proxy"]["type"] = os.getenv("PROXY_TYPE", "http")
if os.getenv("PROXY_USERNAME"):
self.config["proxy"]["username"] = os.getenv("PROXY_USERNAME")
if os.getenv("PROXY_PASSWORD"):
self.config["proxy"]["password"] = os.getenv("PROXY_PASSWORD")
if os.getenv("IP_ROTATION"):
self.config["rotation"]["ip"] = os.getenv("IP_ROTATION")
if os.getenv("MAX_RETRIES"):
self.config["retry"]["max_attempts"] = int(os.getenv("MAX_RETRIES"))
if os.getenv("MIN_QUALITY"):
self.config["retry"]["min_quality"] = int(os.getenv("MIN_QUALITY"))
if os.getenv("RATE_LIMIT_RPM"):
self.config["rate_limit"]["rpm"] = int(os.getenv("RATE_LIMIT_RPM"))
def _load_file(self):
if os.path.exists(CONFIG_FILE):
try:
with open(CONFIG_FILE) as f:
user_config = json.load(f)
self._deep_merge(self.config, user_config)
except Exception:
pass
def _deep_merge(self, base: dict, override: dict):
for key, value in override.items():
if key in base and isinstance(base[key], dict) and isinstance(value, dict):
self._deep_merge(base[key], value)
else:
base[key] = value
def _resolve_proxy_chain(self):
"""Build the proxy chain: Tor -> User Proxy -> Direct"""
proxies = []
if self.config["tor"]["enabled"]:
proxies.append(
f"socks5://{self.config['tor']['socks5_host']}:{self.config['tor']['socks5_port']}"
)
if self.config["proxy"]["enabled"]:
url = self.config["proxy"]["url"]
if self.config["proxy"]["username"]:
netloc = url.split("://")[1] if "://" in url else url
proxies.append(
f"{self.config['proxy']['type']}://{self.config['proxy']['username']}:{self.config['proxy']['password']}@{netloc}"
)
else:
proxies.append(url)
self.config["_proxy_chain"] = proxies
self.config["_proxy_url"] = proxies[0] if proxies else None
def get_proxy_url(self) -> str | None:
return self.config.get("_proxy_url")
def get_proxy_chain(self) -> list:
return self.config.get("_proxy_chain", [])
def get(self, key: str, default=None):
keys = key.split(".")
val = self.config
for k in keys:
if isinstance(val, dict):
val = val.get(k)
else:
return default
if val is None:
return default
return val if val is not None else default
def to_dict(self) -> dict:
return {k: v for k, v in self.config.items() if not k.startswith("_")}
def update(self, updates: dict) -> dict:
self._deep_merge(self.config, updates)
self._resolve_proxy_chain()
try:
with open(CONFIG_FILE, "w") as f:
json.dump(self.to_dict(), f, indent=2)
except Exception:
pass
return {"status": "ok", "config": self.to_dict()}

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"""Pry MCP Server — expose scrape, crawl, automate as MCP tools.
Enables any MCP-compatible AI agent (Claude, Hermes, Cursor) to use Pry."""
import httpx
class PryMCPServer:
"""MCP server exposing Pry as tools for AI agents.
Implements the Model Context Protocol (MCP) for tool discovery.
"""
def __init__(self, base_url: str = "http://localhost:8002"):
self.base_url = base_url
self._client = httpx.Client(timeout=60)
def list_tools(self) -> list[dict]:
"""MCP tool discovery — returns available tools."""
return [
{
"name": "pry_scrape",
"description": "Scrape a URL to clean markdown. Bypasses Cloudflare automatically.",
"inputSchema": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "URL to scrape"},
"timeout": {
"type": "integer",
"description": "Timeout in seconds",
"default": 30,
},
},
"required": ["url"],
},
},
{
"name": "pry_crawl",
"description": "Crawl multiple pages from a starting URL.",
"inputSchema": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "Starting URL"},
"maxPages": {"type": "integer", "description": "Max pages", "default": 10},
},
"required": ["url"],
},
},
{
"name": "pry_map",
"description": "Discover all URLs on a website.",
"inputSchema": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "Site URL"},
"limit": {"type": "integer", "description": "Max links", "default": 50},
},
"required": ["url"],
},
},
{
"name": "pry_parse",
"description": "Parse a document (PDF, DOCX, image) to text.",
"inputSchema": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "Document URL"},
},
"required": ["url"],
},
},
{
"name": "pry_screenshot",
"description": "Take a screenshot of a URL.",
"inputSchema": {
"type": "object",
"properties": {
"url": {"type": "string", "description": "URL to screenshot"},
},
"required": ["url"],
},
},
{
"name": "pry_automate",
"description": "Execute browser automation steps (navigate, click, type, extract). Login automation, form filling.",
"inputSchema": {
"type": "object",
"properties": {
"steps": {
"type": "array",
"items": {"type": "object"},
"description": "List of automation steps. Each step: {action, selector?, value?, url?}",
}
},
"required": ["steps"],
},
},
]
async def call_tool(self, name: str, arguments: dict) -> dict:
"""Execute an MCP tool call."""
tool_map = {
"pry_scrape": (
"/v1/scrape",
{"url": arguments["url"], "timeout": arguments.get("timeout", 30)},
),
"pry_crawl": (
"/v1/crawl",
{"url": arguments["url"], "maxPages": arguments.get("maxPages", 10)},
),
"pry_map": (
"/v1/map",
{"url": arguments["url"], "limit": arguments.get("limit", 50)},
),
"pry_parse": ("/v1/parse", {"url": arguments["url"]}),
"pry_screenshot": ("/v1/screenshot", {"url": arguments["url"]}),
"pry_automate": ("/v1/automate", {"steps": arguments["steps"]}),
}
if name not in tool_map:
return {"error": f"Unknown tool: {name}"}
path, payload = tool_map[name]
async with httpx.AsyncClient(timeout=120) as client:
resp = await client.post(f"{self.base_url}{path}", json=payload)
return resp.json()

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"""Pry — MCP HTTP+SSE transport server.
Implements the official Model Context Protocol HTTP+SSE transport:
1. Client connects to GET /mcp/sse
2. Server sends an `endpoint` event with a POST URL (e.g., /mcp/messages/{session_id})
3. Client POSTs JSON-RPC messages to that URL
4. Server replies by enqueueing JSON-RPC responses as `message` events on the SSE stream
Session state is kept in memory. For production multi-worker deployments, use a
Redis-backed queue or sticky sessions.
"""
from __future__ import annotations
import asyncio
import json
import logging
import secrets
import time
from collections.abc import AsyncIterator, Callable
from typing import Any
from fastapi import HTTPException, Request, Response
from fastapi.responses import StreamingResponse
from mcp_production import (
register_mcp_notification_observer,
unregister_mcp_notification_observer,
)
logger = logging.getLogger(__name__)
# In-memory session store. Maps session_id -> outbound message queue.
MCP_SSE_SESSIONS: dict[str, asyncio.Queue[str]] = {}
MCP_SSE_TASKS: dict[str, asyncio.Task[Any]] = {}
SESSION_TTL_SECONDS = 300 # cleanup idle sessions after 5 minutes
def _new_session_id() -> str:
"""Generate a cryptographically random session ID."""
return secrets.token_urlsafe(16)
def _create_event(event: str, data: str) -> str:
"""Format a server-sent event per the SSE spec."""
return f"event: {event}\ndata: {data}\n\n"
def _get_or_create_queue(session_id: str) -> asyncio.Queue[str]:
"""Return the outbound SSE queue for a session, creating if necessary."""
if session_id not in MCP_SSE_SESSIONS:
MCP_SSE_SESSIONS[session_id] = asyncio.Queue()
return MCP_SSE_SESSIONS[session_id]
def _enqueue_response(session_id: str, response: dict[str, Any]) -> None:
"""Enqueue a JSON-RPC response to be sent on the SSE stream."""
queue = MCP_SSE_SESSIONS.get(session_id)
if queue is None:
logger.warning("mcp_sse_session_not_found", extra={"session_id": session_id})
return
try:
queue.put_nowait(_create_event("message", json.dumps(response)))
except asyncio.QueueFull:
logger.warning("mcp_sse_queue_full", extra={"session_id": session_id})
def _cleanup_session(
session_id: str, observer: Callable[[dict[str, Any]], None] | None = None
) -> None:
"""Remove session state and cancel any keepalive task."""
MCP_SSE_SESSIONS.pop(session_id, None)
task = MCP_SSE_TASKS.pop(session_id, None)
if task is not None and not task.done():
task.cancel()
if observer is not None:
try:
unregister_mcp_notification_observer(observer)
except Exception as e:
logger.warning("mcp_sse_unregister_observer_failed", extra={"error": str(e)})
async def _keepalive_loop(session_id: str) -> None:
"""Send periodic comment keepalives to keep proxies/NATs happy."""
queue = _get_or_create_queue(session_id)
try:
while True:
await asyncio.sleep(30)
queue.put_nowait(": ping\n\n")
except asyncio.CancelledError:
pass
except Exception as e:
logger.warning("mcp_sse_keepalive_error", extra={"session_id": session_id, "error": str(e)})
async def mcp_sse_endpoint(
request: Request,
message_handler: Any,
) -> StreamingResponse:
"""SSE endpoint for MCP clients.
Args:
request: FastAPI request object (used to build the post-back URL).
message_handler: An object with an async `handle_request` method.
"""
session_id = _new_session_id()
queue = _get_or_create_queue(session_id)
# Build the post-back URL. Prefer absolute URL from request headers if available.
base_url = str(request.base_url).rstrip("/")
post_url = f"{base_url}/mcp/messages/{session_id}"
# Prepare the endpoint event. It is yielded directly so the client gets it
# immediately without waiting on the outbound queue.
endpoint_event = _create_event("endpoint", post_url)
# Register a global notification observer so this session receives
# resources/updated, resources/list_changed, tools/list_changed, and log messages.
def observer(notification: dict[str, Any]) -> None:
_enqueue_response(session_id, notification)
register_mcp_notification_observer(observer)
# Start keepalive task.
keepalive_task = asyncio.create_task(_keepalive_loop(session_id))
MCP_SSE_TASKS[session_id] = keepalive_task
last_activity = time.time()
# Private test hook: close the SSE stream after N message events (not counting
# the endpoint event). httpx's ASGITransport cannot consume an infinite
# streaming response in tests, so this lets integration tests verify the
# round-trip without hanging.
try:
_test_close_after = int(request.query_params.get("_sse_test_close_after", "0"))
except ValueError:
_test_close_after = 0
async def event_stream() -> AsyncIterator[str]:
nonlocal last_activity
messages_yielded = 0
try:
# Yield the endpoint event first to unblock the client immediately.
yield endpoint_event
while True:
# Wait for outbound messages with a timeout so we can enforce TTL.
try:
event = await asyncio.wait_for(queue.get(), timeout=5.0)
last_activity = time.time()
yield event
if _test_close_after > 0:
messages_yielded += 1
if messages_yielded >= _test_close_after:
break
except TimeoutError:
if time.time() - last_activity > SESSION_TTL_SECONDS:
yield _create_event(
"error",
json.dumps({"error": "session_idle_timeout"}),
)
break
continue
except asyncio.CancelledError:
pass
finally:
_cleanup_session(session_id, observer)
return StreamingResponse(
event_stream(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache, no-transform",
"Connection": "keep-alive",
"X-Accel-Buffering": "no", # disable nginx buffering
},
)
async def mcp_post_message(
request: Request,
session_id: str,
message_handler: Any,
) -> Response:
"""Receive a JSON-RPC message from an MCP client and enqueue the response.
Returns HTTP 202 Accepted immediately; the actual JSON-RPC response is sent
over the SSE stream associated with session_id.
"""
if session_id not in MCP_SSE_SESSIONS:
raise HTTPException(status_code=404, detail="MCP SSE session not found")
try:
body = await request.json()
except json.JSONDecodeError as e:
_enqueue_response(
session_id,
{
"jsonrpc": "2.0",
"id": None,
"error": {"code": -32700, "message": f"Parse error: {e}"},
},
)
return Response(status_code=202)
# Handle initialize specially: it should not have an id response? Actually initialize does have id.
response = await message_handler.handle_request(body)
if response is not None:
_enqueue_response(session_id, response)
return Response(status_code=202)

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"""Pry — scheduled monitors with AI change detection.
Cron-based monitors that diff content and judge meaningful changes."""
import difflib
import json
import logging
import os
import re
import shutil
import uuid
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
MONITORS_DIR = Path(os.path.expanduser("~/.pry/monitors"))
MONITORS_DIR.mkdir(parents=True, exist_ok=True)
def _monitor_path(monitor_id: str) -> Path:
return MONITORS_DIR / f"{monitor_id}.json"
def _snapshot_path(monitor_id: str, version: int) -> Path:
snap_dir = MONITORS_DIR / monitor_id
snap_dir.mkdir(parents=True, exist_ok=True)
return snap_dir / f"snapshot_{version}.json"
def _compute_diff(previous: str, current: str) -> str:
differ = difflib.Differ()
diff = list(differ.compare(previous.splitlines(), current.splitlines()))
added = sum(1 for line in diff if line.startswith("+ "))
removed = sum(1 for line in diff if line.startswith("- "))
return f"{added} lines added, {removed} lines removed"
class ChangeJudger:
"""Judge whether a content change is meaningful using LLM or heuristics."""
def __init__(self, use_llm: bool = False) -> None:
self.use_llm = use_llm
async def judge(self, previous: str, current: str, goal: str = "") -> dict[str, Any]:
"""Judge if a change is meaningful.
Returns:
meaningful: bool
confidence: str (high/medium/low)
reason: str
meaningful_changes: list[dict]
"""
if previous == current:
return {
"meaningful": False,
"confidence": "high",
"reason": "No change detected",
"meaningful_changes": [],
}
if not goal:
return {
"meaningful": True,
"confidence": "high",
"reason": "Content changed",
"meaningful_changes": [{"type": "changed", "description": "Content was modified"}],
}
if self.use_llm:
return await self._llm_judge(previous, current, goal)
return self._heuristic_judge(previous, current, goal)
def _heuristic_judge(self, previous: str, current: str, goal: str) -> dict[str, Any]:
goal_keywords = goal.lower().split()
prev_text_lower = previous.lower()
curr_text_lower = current.lower()
prev_keywords = set(prev_text_lower.split())
curr_keywords = set(curr_text_lower.split())
added = curr_keywords - prev_keywords
goal_in_current = any(gk in curr_text_lower for gk in goal_keywords)
# Check if goal keywords appear in newly-added tokens
def _keyword_match(tokens: set[str]) -> list[str]:
matched: list[str] = []
for gk in goal_keywords:
for t in tokens:
if gk in t or t in gk:
matched.append(gk)
break
return matched
goal_added = _keyword_match(added)
if goal_added:
return {
"meaningful": True,
"confidence": "medium",
"reason": f"Goal-related keywords appeared: {', '.join(sorted(goal_added))}",
"meaningful_changes": [
{
"type": "added",
"description": f"Relevant keywords appeared: {', '.join(sorted(goal_added))}",
}
],
}
# Content changed and goal keywords still present -> meaningful update
if goal_in_current and len(previous) > 0:
change_ratio = abs(len(current) - len(previous)) / len(previous)
if change_ratio > 0.01:
return {
"meaningful": True,
"confidence": "medium",
"reason": "Goal-relevant content was updated",
"meaningful_changes": [
{
"type": "changed",
"description": f"Content size changed by {change_ratio:.1%}",
}
],
}
# Large generic change (no goal or goal keywords absent)
if len(previous) > 0:
change_ratio = abs(len(current) - len(previous)) / len(previous)
if change_ratio > 0.1:
return {
"meaningful": True,
"confidence": "low",
"reason": f"Content changed by {change_ratio:.1%}",
"meaningful_changes": [
{
"type": "changed",
"description": f"Content size changed by {change_ratio:.1%}",
}
],
}
return {
"meaningful": False,
"confidence": "low",
"reason": "Minor or irrelevant change",
"meaningful_changes": [],
}
async def _llm_judge(self, previous: str, current: str, goal: str) -> dict[str, Any]:
try:
from client import get_client
from settings import settings
diff = _compute_diff(previous, current)
prompt = f"""Goal: {goal}
Previous content:
{previous[:1000]}
Current content:
{current[:1000]}
Diff:
{diff[:500]}
Is this change meaningful given the goal? Respond with JSON:
{{"meaningful": bool, "confidence": "high/medium/low", "reason": "string", "meaningful_changes": [{{"type": "changed/added/removed", "description": "string"}}]}}
"""
client = await get_client()
resp = await client.post(
f"{settings.ollama_url}/api/generate",
json={
"model": "qwen2.5-coder:3b",
"prompt": prompt,
"stream": False,
"options": {"num_ctx": 4096, "temperature": 0.1},
},
timeout=30,
)
response_text = resp.json().get("response", "")
json_match = re.search(r"\{.*\}", response_text, re.DOTALL)
if json_match:
return json.loads(json_match.group(0)) # type: ignore[no-any-return]
except Exception:
logger.warning("llm_judge_failed, falling back to heuristic")
return self._heuristic_judge(previous, current, goal)
async def create_monitor(
name: str,
target_url: str,
schedule_cron: str = "0 */6 * * *",
goal: str = "",
webhook_url: str = "",
use_llm_judge: bool = False,
) -> dict[str, Any]:
monitor_id = uuid.uuid4().hex[:12]
monitor = {
"id": monitor_id,
"name": name,
"target_url": target_url,
"schedule_cron": schedule_cron,
"goal": goal,
"webhook_url": webhook_url,
"use_llm_judge": use_llm_judge,
"status": "active",
"created_at": datetime.now(UTC).isoformat(),
"last_run_at": None,
"current_version": 0,
"last_content": "",
"total_checks": 0,
"total_changes": 0,
}
path = _monitor_path(monitor_id)
path.write_text(json.dumps(monitor, indent=2))
logger.info("monitor_created", extra={"monitor_id": monitor_id, "name": name})
return monitor
async def run_monitor(monitor_id: str) -> dict[str, Any]:
path = _monitor_path(monitor_id)
if not path.exists():
return {"error": f"Monitor not found: {monitor_id}"}
monitor = json.loads(path.read_text())
previous_content = monitor.get("last_content", "")
try:
from scraper import PryScraper
scraper = PryScraper()
result = await scraper.scrape(monitor["target_url"])
current_content = result.get("content", "")
except Exception as e:
logger.exception("monitor_scrape_failed", extra={"monitor_id": monitor_id})
return {"error": f"Scrape failed: {e!s}"}
judger = ChangeJudger(use_llm=monitor.get("use_llm_judge", False))
judgment = await judger.judge(previous_content, current_content, monitor.get("goal", ""))
monitor["last_run_at"] = datetime.now(UTC).isoformat()
monitor["total_checks"] += 1
if previous_content and current_content != previous_content:
monitor["total_changes"] += 1
monitor["current_version"] += 1
snap = {
"version": monitor["current_version"],
"content": current_content,
"detected_at": monitor["last_run_at"],
}
_snapshot_path(monitor_id, monitor["current_version"]).write_text(json.dumps(snap))
monitor["last_content"] = current_content
path.write_text(json.dumps(monitor, indent=2))
result = {
"monitor_id": monitor_id,
"name": monitor["name"],
"changed": bool(previous_content) and current_content != previous_content,
"previous_content_length": len(previous_content),
"current_content_length": len(current_content),
"judgment": judgment,
"total_checks": monitor["total_checks"],
"total_changes": monitor["total_changes"],
}
if judgment["meaningful"] and monitor.get("webhook_url"):
await _fire_webhook(monitor["webhook_url"], result)
return result
async def _fire_webhook(webhook_url: str, payload: dict[str, Any]) -> None:
try:
from client import get_client
client = await get_client()
await client.post(webhook_url, json=payload, timeout=10)
logger.info("monitor_webhook_fired", extra={"webhook": webhook_url})
except Exception as e:
logger.warning("monitor_webhook_failed", extra={"webhook": webhook_url, "error": str(e)})
async def list_monitors() -> list[dict[str, Any]]:
monitors = []
for path in sorted(MONITORS_DIR.glob("*.json"), key=os.path.getmtime, reverse=True):
try:
data = json.loads(path.read_text())
monitors.append(data)
except (json.JSONDecodeError, OSError):
continue
return monitors
async def delete_monitor(monitor_id: str) -> bool:
path = _monitor_path(monitor_id)
snap_dir = MONITORS_DIR / monitor_id
if snap_dir.exists():
shutil.rmtree(snap_dir)
if path.exists():
path.unlink()
return True
return False

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"""Pry — network capture and lazy load handling.
Captures XHR/fetch requests from JS-heavy SPAs and handles lazy content."""
import json
import logging
import re
from typing import Any
logger = logging.getLogger(__name__)
def extract_api_calls_from_html(html: str) -> list[dict[str, Any]]:
"""Extract API call patterns from HTML/JS.
Finds fetch(), XMLHttpRequest, axios, $.ajax patterns in inline scripts.
"""
api_calls = []
# Pattern 1: fetch() calls
fetch_patterns = re.finditer(
r'fetch\s*\(\s*["\']([^"\']+)["\']',
html,
)
for m in fetch_patterns:
api_calls.append({"method": "fetch", "url": m.group(1)})
# Pattern 2: XMLHttpRequest
xhr_patterns = re.finditer(
r'XMLHttpRequest\.open\s*\(\s*["\'](GET|POST|PUT|DELETE)["\']\s*,\s*["\']([^"\']+)["\']',
html,
)
for m in xhr_patterns:
api_calls.append({"method": m.group(1), "url": m.group(2)})
# Pattern 3: axios calls (axios.get(), axios.post(), axios('url'))
axios_patterns = re.finditer(
r'axios(?:\.\w+)?\s*\(\s*["\']([^"\']+)["\']',
html,
)
for m in axios_patterns:
api_calls.append({"method": "axios", "url": m.group(1)})
# Pattern 4: $.ajax / $.get / $.post
jquery_patterns = re.finditer(
r'\$\.(?:ajax|get|post)\s*\(\s*["\']([^"\']+)["\']',
html,
)
for m in jquery_patterns:
api_calls.append({"method": "jquery", "url": m.group(1)})
# Pattern 5: JSON API endpoints in script data-* attrs or config objects
api_url_patterns = re.finditer(
r'(?:apiUrl|api_url|endpoint|apiEndpoint|baseUrl)\s*[:=]\s*["\']([^"\']+)["\']',
html,
re.IGNORECASE,
)
for m in api_url_patterns:
api_calls.append({"method": "config", "url": m.group(1)})
# De-duplicate by URL
seen = set()
unique = []
for call in api_calls:
if call["url"] not in seen:
seen.add(call["url"])
unique.append(call)
return unique
def extract_graphql_queries(html: str) -> list[dict[str, Any]]:
"""Extract GraphQL query patterns from HTML/JS."""
queries = []
# Pattern: gql`...` or graphql(`...`)
gql_patterns = re.finditer(
r"(?:gql|graphql)\s*`([^`]+)`",
html,
)
for m in gql_patterns:
queries.append({"type": "gql_tagged", "query": m.group(1).strip()[:200]})
# Pattern: "query" or "mutation" in JSON configs
query_patterns = re.finditer(
r'["\'](?:query|mutation|subscription)["\']\s*:\s*["\']([^"\']+)["\']',
html,
)
for m in query_patterns:
queries.append({"type": "inline", "query": m.group(1)[:200]})
return queries
def extract_json_ld(html: str) -> list[dict[str, Any]]:
"""Extract JSON-LD structured data from <script type="application/ld+json">."""
ld_patterns = re.finditer(
r'<script\s+type=["\']application/ld\+json["\'][^>]*>(.*?)</script>',
html,
re.DOTALL,
)
results = []
for m in ld_patterns:
try:
data = json.loads(m.group(1).strip())
results.append(data)
except (json.JSONDecodeError, ValueError):
logger.warning("json_ld_parse_failed")
return results
def _parse_braced_json(text: str, start: int) -> dict[str, Any] | None:
"""Parse a JSON object starting at text[start] using brace counting."""
if start < 0 or text[start] != "{":
return None
depth = 0
for i in range(start, len(text)):
if text[i] == "{":
depth += 1
elif text[i] == "}":
depth -= 1
if depth == 0:
try:
parsed: Any = json.loads(text[start : i + 1])
if isinstance(parsed, dict):
return parsed
return None
except json.JSONDecodeError:
return None
return None
def extract_nextjs_props(html: str) -> dict[str, Any] | None:
"""Extract Next.js __NEXT_DATA__ props."""
m = re.search(r"window\.__NEXT_DATA__\s*=\s*(\{)", html, re.DOTALL)
if m:
return _parse_braced_json(html, m.start(1))
return None
def extract_nuxt_state(html: str) -> dict[str, Any] | None:
"""Extract Nuxt/Vue __NUXT__ state."""
m = re.search(r"window\.__NUXT__\s*=\s*(\{)", html, re.DOTALL)
if m:
return _parse_braced_json(html, m.start(1))
return None

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"""Pry — Observability: Prometheus metrics, OpenTelemetry tracing, structured logging."""
import logging
import time
from contextlib import contextmanager
logger = logging.getLogger(__name__)
# Try to import Prometheus
try:
from prometheus_client import CONTENT_TYPE_LATEST, Counter, Gauge, Histogram, generate_latest
_has_prometheus = True
except ImportError:
_has_prometheus = False
# Try to import OpenTelemetry
try:
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import ConsoleSpanExporter, SimpleSpanProcessor
_has_otel = True
except ImportError:
_has_otel = False
# Metrics
if _has_prometheus:
REQUEST_COUNT = Counter("pry_requests_total", "Total requests", ["method", "endpoint", "status"])
REQUEST_LATENCY = Histogram("pry_request_duration_seconds", "Request latency", ["endpoint"])
SCRAPE_COUNT = Counter("pry_scrapes_total", "Total scrapes", ["method", "status"])
SCRAPE_LATENCY = Histogram("pry_scrape_duration_seconds", "Scrape latency", ["method"])
LLM_CALLS = Counter("pry_llm_calls_total", "Total LLM calls", ["provider", "model"])
LLM_COST = Counter("pry_llm_cost_usd_total", "Total LLM cost in USD", ["provider"])
TEMPLATE_USAGE = Counter("pry_template_usage_total", "Template usage", ["template_id"])
CACHE_HITS = Counter("pry_cache_hits_total", "Cache hits", ["cache_type"])
ACTIVE_CONNECTIONS = Gauge("pry_active_connections", "Active connections", ["type"])
else:
REQUEST_COUNT = REQUEST_LATENCY = SCRAPE_COUNT = SCRAPE_LATENCY = None
LLM_CALLS = LLM_COST = TEMPLATE_USAGE = CACHE_HITS = ACTIVE_CONNECTIONS = None
def setup_tracing(service_name: str = "pry") -> None:
"""Initialize OpenTelemetry tracing."""
if not _has_otel: return
try:
provider = TracerProvider()
processor = SimpleSpanProcessor(ConsoleSpanExporter())
provider.add_span_processor(processor)
trace.set_tracer_provider(provider)
logger.info("tracing_initialized", extra={"service": service_name})
except Exception as e:
logger.warning("tracing_init_failed", extra={"error": str(e)[:80]})
@contextmanager
def track_request(endpoint: str, method: str = "GET"):
"""Context manager to track request metrics and timing."""
start = time.time()
status = "success"
try:
yield
except Exception:
status = "error"
raise
finally:
elapsed = time.time() - start
if _has_prometheus and REQUEST_COUNT and REQUEST_LATENCY:
REQUEST_COUNT.labels(method=method, endpoint=endpoint, status=status).inc()
REQUEST_LATENCY.labels(endpoint=endpoint).observe(elapsed)
@contextmanager
def track_scrape(method: str = "direct"):
"""Context manager to track scrape metrics."""
start = time.time()
status = "success"
try:
yield
except Exception:
status = "error"
raise
finally:
elapsed = time.time() - start
if _has_prometheus and SCRAPE_COUNT and SCRAPE_LATENCY:
SCRAPE_COUNT.labels(method=method, status=status).inc()
SCRAPE_LATENCY.labels(method=method).observe(elapsed)
def track_llm_call(provider: str, model: str, cost: float = 0.0) -> None:
if _has_prometheus and LLM_CALLS and LLM_COST:
LLM_CALLS.labels(provider=provider, model=model).inc()
if cost > 0:
LLM_COST.labels(provider=provider).inc(cost)
def track_template(template_id: str) -> None:
if _has_prometheus and TEMPLATE_USAGE:
TEMPLATE_USAGE.labels(template_id=template_id).inc()
def get_metrics_output() -> tuple[bytes, str]:
"""Return Prometheus metrics in text format."""
if not _has_prometheus:
return b"# Prometheus not installed\n", "text/plain"
return generate_latest(), CONTENT_TYPE_LATEST

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"""Pry — Image OCR using Tesseract.
Extract text from images on web pages. Uses pytesseract + Pillow."""
import io
import logging
import os
from typing import Any, ClassVar
logger = logging.getLogger(__name__)
try:
import pytesseract
from PIL import Image
_has_tesseract = True
except ImportError:
_has_tesseract = False
class ImageOCR:
"""Extract text from images using Tesseract OCR."""
SUPPORTED_LANGUAGES: ClassVar[list[str]] = [
"eng", "chi_sim", "chi_tra", "spa", "fra", "deu", "ita",
"por", "rus", "jpn", "kor", "ara",
]
def __init__(self, language: str = "eng"):
self.language = language if language in self.SUPPORTED_LANGUAGES else "eng"
def extract_from_bytes(self, image_bytes: bytes, config: str = "") -> dict[str, Any]:
"""Extract text from image bytes."""
if not _has_tesseract:
return {
"success": False,
"error": "pytesseract not installed. Run: pip install pytesseract pillow",
}
try:
img = Image.open(io.BytesIO(image_bytes))
text = pytesseract.image_to_string(img, lang=self.language, config=config)
data = pytesseract.image_to_data(
img, lang=self.language, output_type=pytesseract.Output.DICT
)
confidences = [c for c in data["conf"] if isinstance(c, int) and 0 <= c <= 100]
avg_confidence = round(sum(confidences) / len(confidences), 1) if confidences else 0
return {
"success": True,
"text": text.strip(),
"confidence": avg_confidence,
"word_count": len(text.split()),
"language": self.language,
}
except Exception as e:
return {"success": False, "error": str(e)[:200]}
def extract_from_file(self, image_path: str) -> dict[str, Any]:
"""Extract text from an image file."""
if not os.path.exists(image_path):
return {"success": False, "error": f"File not found: {image_path}"}
with open(image_path, "rb") as f:
return self.extract_from_bytes(f.read())
async def extract_from_url(self, url: str) -> dict[str, Any]:
"""Download image from URL and extract text."""
from client import get_client
client = await get_client()
try:
resp = await client.get(url, timeout=30)
if resp.is_success:
return self.extract_from_bytes(resp.content)
except Exception as e:
return {"success": False, "error": str(e)[:200]}
return {"success": False, "error": "Failed to download image"}
def is_available(self) -> bool:
return _has_tesseract

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{
"openapi": "3.0.0",
"info": {
"title": "Pry Web Intelligence API",
"version": "3.0.0",
"description": "Scrape, crawl, extract, and monitor any website. AI-ready API for web data extraction. Self-hosted, no API keys needed (optional auth)."
},
"servers": [
{"url": "http://localhost:8002", "description": "Local Pry instance"}
],
"paths": {
"/v1/scrape": {
"post": {
"summary": "Scrape a single URL to markdown",
"description": "Scrape any URL and return clean markdown content. Handles Cloudflare, JS rendering, and anti-bot protection automatically.",
"operationId": "scrape",
"tags": ["Scraping"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["url"],
"properties": {
"url": {"type": "string", "description": "URL to scrape", "example": "https://example.com"},
"bypassCloudflare": {"type": "boolean", "default": true, "description": "Auto-bypass Cloudflare protection"},
"jsRender": {"type": "boolean", "default": false, "description": "Enable JavaScript rendering"}
}
}
}
}
},
"responses": {
"200": {
"description": "Scrape result",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"success": {"type": "boolean", "example": true},
"data": {
"type": "object",
"properties": {
"content": {"type": "string", "description": "Clean markdown content"},
"title": {"type": "string"},
"url": {"type": "string"}
}
}
}
}
}
}
}
}
}
},
"/v1/crawl": {
"post": {
"summary": "Crawl multiple pages from a URL",
"description": "Crawl a website starting from a seed URL. Discovers and scrapes linked pages up to maxPages.",
"operationId": "crawl",
"tags": ["Scraping"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["url"],
"properties": {
"url": {"type": "string", "example": "https://example.com"},
"maxPages": {"type": "integer", "default": 10},
"maxDepth": {"type": "integer", "default": 2}
}
}
}
}
},
"responses": {
"200": {
"description": "Crawl result with array of pages",
"content": {"application/json": {"schema": {"type": "object"}}}
}
}
}
},
"/v1/extract/css": {
"post": {
"summary": "Extract structured data with CSS selectors",
"description": "Extract structured JSON from any URL using CSS selectors. No LLM needed — 100x cheaper than AI extraction. Define a schema with selectors and get clean data back.",
"operationId": "extractCss",
"tags": ["Extraction"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["url", "schema"],
"properties": {
"url": {"type": "string", "example": "https://www.amazon.com/dp/B0ABC123"},
"schema": {
"type": "object",
"description": "Extraction schema with CSS selectors",
"example": {
"name": "products",
"baseSelector": ".product-card",
"fields": [{"name": "title", "selector": "h3", "type": "text"}, {"name": "price", "selector": ".price", "type": "text", "transform": "float"}]
}
}
}
}
}
}
},
"responses": {
"200": {
"description": "Extracted structured data",
"content": {"application/json": {"schema": {"type": "object"}}}
}
}
}
},
"/v1/extract/llm": {
"post": {
"summary": "Extract with LLM + chunking",
"description": "Extract structured data using AI with intelligent chunking. Automatically chunks content by topic/sentence/regex and extracts relevant information.",
"operationId": "extractLlm",
"tags": ["Extraction"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["url"],
"properties": {
"url": {"type": "string"},
"instruction": {"type": "string", "example": "Extract all product prices and discounts"},
"chunk_strategy": {"type": "string", "enum": ["topic", "sentence", "regex"], "default": "topic"}
}
}
}
}
},
"responses": {"200": {"description": "Extraction result"}}
}
},
"/v1/map": {
"post": {
"summary": "Discover URLs on a site",
"description": "Discover all URLs on a website. Returns a list of internal links found on the page.",
"operationId": "mapUrls",
"tags": ["Scraping"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["url"],
"properties": {
"url": {"type": "string"},
"limit": {"type": "integer", "default": 50}
}
}
}
}
},
"responses": {"200": {"description": "List of discovered URLs"}}
}
},
"/v1/compliance/check": {
"post": {
"summary": "Legal compliance check for a URL",
"description": "Check if scraping a URL is legally compliant. Analyzes robots.txt, Terms of Service, GDPR/CCPA jurisdiction, and sensitive data. Returns green/yellow/red risk level with recommendations.",
"operationId": "complianceCheck",
"tags": ["Compliance"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["url"],
"properties": {
"url": {"type": "string"}
}
}
}
}
},
"responses": {"200": {"description": "Compliance check result with risk level"}}
}
},
"/v1/monitor": {
"post": {
"summary": "Create a content change monitor",
"description": "Create a scheduled monitor that tracks content changes on a URL. Get notified when content changes with AI-powered meaningful-change detection.",
"operationId": "createMonitor",
"tags": ["Monitoring"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["name", "url"],
"properties": {
"name": {"type": "string", "example": "Competitor Pricing Page"},
"url": {"type": "string", "example": "https://competitor.com/pricing"},
"schedule_cron": {"type": "string", "default": "0 */6 * * *", "description": "Cron schedule (default: every 6 hours)"},
"goal": {"type": "string", "description": "Natural language description of what changes matter"}
}
}
}
}
},
"responses": {"200": {"description": "Created monitor"}}
}
},
"/v1/search": {
"get": {
"summary": "Search for templates and sites",
"description": "Search for available scraper templates and sites by keyword. Returns matching templates and their schemas.",
"operationId": "search",
"tags": ["Discovery"],
"parameters": [
{"name": "q", "in": "query", "required": true, "schema": {"type": "string"}}
],
"responses": {"200": {"description": "Search results"}}
}
},
"/v1/templates": {
"get": {
"summary": "List all pre-built scraper templates",
"description": "Get one-click scraper templates for Amazon, Walmart, LinkedIn, GitHub, Twitter, and 15+ other sites. Each template is a ready-to-use extraction schema.",
"operationId": "listTemplates",
"tags": ["Templates"],
"responses": {"200": {"description": "List of available templates"}}
}
},
"/v1/templates/execute": {
"post": {
"summary": "Execute a scraper template",
"description": "Scrape a URL using a pre-built template. For example, use 'amazon-product' to extract product data from any Amazon product page.",
"operationId": "executeTemplate",
"tags": ["Templates"],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["template_id", "url"],
"properties": {
"template_id": {"type": "string", "example": "amazon-product"},
"url": {"type": "string", "example": "https://www.amazon.com/dp/B0ABC123"}
}
}
}
}
},
"responses": {"200": {"description": "Extracted template data"}}
}
},
"/health": {
"get": {
"summary": "Health check",
"description": "Check if the Pry service is running and healthy.",
"operationId": "health",
"tags": ["System"],
"responses": {
"200": {
"description": "Service is healthy",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"status": {"type": "string", "example": "ok"}
}
}
}
}
}
}
}
}
}
}

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"""Pry — document parser for PDF, DOCX, images, CSV, JSON, HTML.
Uses asyncio subprocess for CPU-bound OCR to avoid blocking the event loop.
Temp files are always cleaned up in finally blocks.
"""
import asyncio
import io
import os
import tempfile
from pathlib import Path
from typing import Any
class DocumentParser:
"""Parse PDFs, DOCX, images, and other document formats to clean text.
CPU-bound operations (OCR, PDF parsing) run in executor threads."""
async def parse(self, url: str, timeout: int = 60) -> dict[str, Any]:
import httpx
async with httpx.AsyncClient(
timeout=httpx.Timeout(timeout), follow_redirects=True
) as client:
resp = await client.get(url)
resp.raise_for_status()
content = resp.content
content_type = resp.headers.get("content-type", "")
filename = Path(url).name.lower()
loop = asyncio.get_event_loop()
return await loop.run_in_executor(None, self._parse_bytes, content, content_type, filename)
def _parse_bytes(self, data: bytes, content_type: str, filename: str = "") -> dict[str, Any]:
if filename.endswith(".pdf") or "pdf" in content_type:
return self._parse_pdf(data)
elif filename.endswith(".docx") or "word" in content_type:
return self._parse_docx(data)
elif filename.endswith((".md", ".markdown")) or "markdown" in content_type:
return {
"text": data.decode("utf-8", errors="replace"),
"format": "markdown",
"pages": 1,
}
elif filename.endswith(".csv") or "csv" in content_type:
return {"text": data.decode("utf-8", errors="replace"), "format": "csv", "pages": 1}
elif filename.endswith(".json") or "json" in content_type:
return {"text": data.decode("utf-8", errors="replace"), "format": "json", "pages": 1}
elif filename.endswith((".html", ".htm")) or "html" in content_type:
import trafilatura
text = data.decode("utf-8", errors="replace")
extracted = trafilatura.extract(text, output_format="markdown")
return {"text": extracted or text[:500000], "format": "html", "pages": 1}
elif (
filename.endswith((".png", ".jpg", ".jpeg", ".gif", ".webp")) or "image" in content_type
):
return self._parse_image(data)
elif filename.endswith(".txt") or "text" in content_type:
return {"text": data.decode("utf-8", errors="replace"), "format": "text", "pages": 1}
else:
try:
text = data.decode("utf-8", errors="replace")
return {"text": text[:500000], "format": "unknown", "pages": 1}
except UnicodeDecodeError:
return {
"text": f"[Binary file: {filename}, {len(data)} bytes]",
"format": "binary",
"pages": 0,
}
def _parse_pdf(self, data: bytes) -> dict[str, Any]:
from pypdf import PdfReader
reader = PdfReader(io.BytesIO(data))
pages = []
for page in reader.pages:
text = page.extract_text()
if text:
pages.append(text)
return {
"text": "\n\n".join(pages),
"format": "pdf",
"pages": len(pages),
"metadata": {
"title": reader.metadata.get("/Title", "") or "",
"author": reader.metadata.get("/Author", "") or "",
},
}
def _parse_docx(self, data: bytes) -> dict[str, Any]:
from docx import Document
doc = Document(io.BytesIO(data))
paragraphs = [p.text for p in doc.paragraphs if p.text.strip()]
return {"text": "\n\n".join(paragraphs), "format": "docx", "pages": 1}
def _parse_image(self, data: bytes) -> dict[str, Any]:
"""OCR via tesseract. Temp file cleaned up in finally block."""
fname = None
try:
from PIL import Image
img = Image.open(io.BytesIO(data))
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as f:
img.save(f, format="PNG")
fname = f.name
result = __import__("subprocess").run(
["tesseract", fname, "stdout", "--oem", "1", "-l", "eng"],
capture_output=True,
text=True,
timeout=30,
)
return {"text": result.stdout, "format": "image", "pages": 1}
except Exception as e:
return {"text": f"[Image OCR failed: {e}]", "format": "image", "pages": 0}
finally:
if fname and os.path.exists(fname):
try:
os.unlink(fname)
except Exception:
pass

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"""Pry — PDF Table Extraction using multiple methods.
Extracts structured tables from PDF documents (financial reports, invoices, etc.)
Uses pdfplumber, camelot, and pdfminer as fallback methods."""
import io
import logging
import os
from contextlib import suppress
from typing import Any
logger = logging.getLogger(__name__)
# Try different PDF libraries
_pdfplumber: bool = False
_camelot: bool = False
_pdfminer: bool = False
try:
import pdfplumber # noqa: F401
_pdfplumber = True
except ImportError:
pass
try:
import camelot # noqa: F401
_camelot = True
except ImportError:
pass
try:
from pdfminer.high_level import extract_text # noqa: F401
_pdfminer = True
except ImportError:
pass
class PDFTableExtractor:
"""Extract tables from PDF documents using multiple methods."""
def __init__(self, prefer_method: str = "pdfplumber"):
self.prefer_method = prefer_method
def extract(self, pdf_bytes: bytes, method: str = "") -> dict[str, Any]:
"""Extract tables from a PDF document.
Returns: {tables: [...], text: "...", page_count: N, method_used: "..."}
"""
method = method or self.prefer_method
# Try preferred method first
if method == "pdfplumber" and _pdfplumber:
return self._extract_pdfplumber(pdf_bytes)
if method == "camelot" and _camelot:
return self._extract_camelot(pdf_bytes)
if method == "pdfminer" and _pdfminer:
return self._extract_pdfminer(pdf_bytes)
# Fallback chain
for fallback in ["pdfplumber", "camelot", "pdfminer"]:
if fallback == "pdfplumber" and _pdfplumber:
return self._extract_pdfplumber(pdf_bytes)
if fallback == "camelot" and _camelot:
return self._extract_camelot(pdf_bytes)
if fallback == "pdfminer" and _pdfminer:
return self._extract_pdfminer(pdf_bytes)
return {
"error": "No PDF library available. Install pdfplumber, camelot, or pdfminer.six.",
"tables": [],
"text": "",
}
def _extract_pdfplumber(self, pdf_bytes: bytes) -> dict[str, Any]:
import pdfplumber
tables: list[dict[str, Any]] = []
text = ""
page_count = 0
with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:
for page in pdf.pages:
page_count += 1
text += (page.extract_text() or "") + "\n"
for table in page.extract_tables():
if table:
tables.append(
{
"page": page.page_number,
"rows": table,
"row_count": len(table),
"col_count": len(table[0]) if table else 0,
}
)
return {
"tables": tables,
"text": text,
"page_count": page_count,
"table_count": len(tables),
"method_used": "pdfplumber",
}
def _extract_camelot(self, pdf_bytes: bytes) -> dict[str, Any]:
import camelot
tables: list[dict[str, Any]] = []
tmp_path = "/tmp/_pry_pdf.pdf"
try:
with open(tmp_path, "wb") as f:
f.write(pdf_bytes)
camelot_tables = camelot.read_pdf(tmp_path, pages="all")
for i, table in enumerate(camelot_tables):
tables.append(
{
"page": i + 1,
"rows": table.df.values.tolist(),
"row_count": len(table.df),
"col_count": len(table.df.columns),
"accuracy": table.accuracy,
"whitespace": table.whitespace,
}
)
finally:
with suppress(OSError):
os.unlink(tmp_path)
return {
"tables": tables,
"table_count": len(tables),
"method_used": "camelot",
}
def _extract_pdfminer(self, pdf_bytes: bytes) -> dict[str, Any]:
from pdfminer.high_level import extract_text
text = extract_text(io.BytesIO(pdf_bytes))
return {"tables": [], "text": text, "method_used": "pdfminer"}
def is_available(self) -> bool:
return any([_pdfplumber, _camelot, _pdfminer])

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"""Pry — pipeline hook system for the scraping workflow.
Allows plugins and custom transformations at every stage."""
import logging
from collections.abc import Awaitable, Callable
from typing import Any, cast
logger = logging.getLogger(__name__)
# Hook point definitions
HOOK_POINTS = [
"before_scrape",
"after_response",
"before_parse",
"after_parse",
"before_extract",
"after_extract",
"before_return",
"on_error",
]
HookFn = Callable[..., Awaitable[dict[str, Any]]]
SyncHookFn = Callable[..., dict[str, Any]]
class Pipeline:
"""Scraping pipeline with pluggable hooks at each stage.
Usage:
pipeline = Pipeline()
pipeline.register("before_scrape", my_async_hook)
pipeline.register("after_response", my_sync_hook)
result = await pipeline.run("before_scrape", url=url)
"""
def __init__(self) -> None:
self._hooks: dict[str, list[HookFn | SyncHookFn]] = {p: [] for p in HOOK_POINTS}
def register(
self,
hook_point: str,
fn: HookFn | SyncHookFn,
priority: int = 0,
) -> None:
"""Register a hook function at a hook point.
Args:
hook_point: One of HOOK_POINTS
fn: Async or sync function that receives **kwargs and returns a dict
priority: Lower runs first (default 0)
"""
if hook_point not in self._hooks:
raise ValueError(f"Unknown hook point: {hook_point}. Valid: {HOOK_POINTS}")
self._hooks[hook_point].append(fn)
logger.debug("hook_registered", extra={"point": hook_point, "fn": fn.__name__})
async def run(self, hook_point: str, **kwargs: Any) -> dict[str, Any]:
"""Run all hooks at a hook point, passing kwargs through the chain.
Each hook receives the output of the previous hook as input.
Returns the final merged context.
"""
context = dict(kwargs)
for fn in self._hooks.get(hook_point, []):
try:
if _is_async(fn):
result = await cast(HookFn, fn)(**context)
else:
result = cast(SyncHookFn, fn)(**context)
if isinstance(result, dict):
context.update(result)
except Exception as e:
logger.exception(
"hook_failed",
extra={"point": hook_point, "fn": getattr(fn, "__name__", str(fn))},
)
if hook_point == "on_error":
context["error"] = str(e)
else:
context.setdefault("errors", []).append(str(e))
return context
def clear(self, hook_point: str | None = None) -> None:
"""Clear hooks at a point, or all hooks if point is None."""
if hook_point:
self._hooks[hook_point] = []
else:
for p in HOOK_POINTS:
self._hooks[p] = []
def list_hooks(self) -> dict[str, list[str]]:
"""List all registered hooks by hook point."""
return {
p: [getattr(fn, "__name__", str(fn)) for fn in fns] for p, fns in self._hooks.items()
}
def _is_async(fn: Any) -> bool:
import asyncio
import inspect
return asyncio.iscoroutinefunction(fn) or inspect.iscoroutinefunction(fn)
# ── Built-in hooks ──
async def log_request(**kwargs: Any) -> dict[str, Any]:
"""Log scraping requests."""
logger.info(
"pipeline_scrape",
extra={"url": kwargs.get("url", ""), "hook": kwargs.get("hook_point", "")},
)
return {}
async def strip_html_comments(**kwargs: Any) -> dict[str, Any]:
"""Remove HTML comments from raw content."""
import re
html = kwargs.get("html", "")
if html:
cleaned = re.sub(r"<!--.*?-->", "", html, flags=re.DOTALL)
return {"html": cleaned}
return {}
async def extract_all_links(**kwargs: Any) -> dict[str, Any]:
"""Extract all href links from HTML (runs at after_response)."""
from lxml import html as lxml_html
html_content = kwargs.get("html", "")
if not html_content:
return {}
try:
tree = lxml_html.fromstring(html_content)
links = tree.xpath("//a/@href")
return {"extracted_links": links}
except Exception:
return {}
# Global pipeline singleton
_pipeline: Pipeline | None = None
def get_pipeline() -> Pipeline:
"""Get or create the global pipeline singleton."""
global _pipeline
if _pipeline is None:
_pipeline = Pipeline()
# Register built-in hooks
_pipeline.register("after_response", extract_all_links, priority=100)
return _pipeline
async def run_pipeline(hook_point: str, **kwargs: Any) -> dict[str, Any]:
"""Convenience: run a hook point on the global pipeline."""
return await get_pipeline().run(hook_point, **kwargs)

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"""Pry — No-Code Visual Pipeline Builder.
JSON-defined workflow engine. Users define pipelines as structured steps,
the engine executes them sequentially with branching and error handling.
A UI can render these steps as drag-and-drop blocks."""
import json
import logging
import os
import uuid
from datetime import UTC, datetime
from pathlib import Path
from typing import Any, cast
logger = logging.getLogger(__name__)
PIPELINE_DIR = Path(os.path.expanduser("~/.pry/pipelines"))
PIPELINE_DIR.mkdir(parents=True, exist_ok=True)
# ── Step Types Registry ──
STEP_TYPES: dict[str, dict[str, Any]] = {
"scrape": {
"name": "Scrape URL",
"icon": "globe",
"description": "Scrape a single URL",
"inputs": [
{"key": "url", "type": "url", "label": "URL to scrape", "required": True},
{
"key": "bypass_cloudflare",
"type": "boolean",
"label": "Bypass Cloudflare",
"default": True,
},
],
"outputs": ["content", "title", "html", "status"],
},
"extract_css": {
"name": "CSS Extraction",
"icon": "clipboard",
"description": "Extract structured data with CSS selectors",
"inputs": [
{"key": "url", "type": "url", "label": "URL", "required": True},
{"key": "schema", "type": "json", "label": "Extraction Schema", "required": True},
],
"outputs": ["items", "count"],
},
"extract_llm": {
"name": "LLM Extraction",
"icon": "robot",
"description": "Extract with AI + chunking",
"inputs": [
{"key": "url", "type": "url", "label": "URL", "required": True},
{
"key": "instruction",
"type": "text",
"label": "Extraction instruction",
"required": True,
},
{
"key": "chunk_strategy",
"type": "select",
"options": ["topic", "sentence", "regex"],
"label": "Chunk strategy",
"default": "topic",
},
],
"outputs": ["chunks", "total_chunks"],
},
"quality_check": {
"name": "Quality Check",
"icon": "check-circle",
"description": "Validate data quality before delivery",
"inputs": [
{"key": "url", "type": "url", "label": "Source URL", "required": True},
{"key": "data", "type": "json", "label": "Data to validate", "required": True},
{
"key": "expected_types",
"type": "json",
"label": "Expected field types",
"required": False,
},
],
"outputs": ["quality_score", "anomalies", "completeness"],
},
"send_slack": {
"name": "Send to Slack",
"icon": "message-circle",
"description": "Send results to a Slack channel",
"inputs": [
{"key": "webhook_url", "type": "url", "label": "Slack Webhook URL", "required": True},
{"key": "message", "type": "text", "label": "Message", "required": True},
],
"outputs": ["success"],
},
"send_email": {
"name": "Send Email",
"icon": "mail",
"description": "Send results via email",
"inputs": [
{"key": "recipient", "type": "email", "label": "Recipient", "required": True},
{"key": "subject", "type": "text", "label": "Subject", "required": True},
{"key": "body", "type": "text", "label": "Body", "required": True},
],
"outputs": ["success"],
},
"compliance_check": {
"name": "Compliance Check",
"icon": "shield",
"description": "Legal compliance scan before scraping",
"inputs": [
{"key": "url", "type": "url", "label": "URL to check", "required": True},
],
"outputs": ["risk_level", "risk_score", "recommendations"],
},
"reconcile": {
"name": "Entity Reconciliation",
"icon": "link",
"description": "Match records across sources",
"inputs": [
{"key": "records", "type": "json", "label": "Records to reconcile", "required": True},
{
"key": "vertical",
"type": "select",
"options": ["product", "job", "real_estate", "review"],
"label": "Vertical",
"default": "product",
},
],
"outputs": ["entities", "report"],
},
"conditional": {
"name": "Conditional Branch",
"icon": "git-branch",
"description": "Branch based on a condition",
"inputs": [
{
"key": "condition",
"type": "text",
"label": "JavaScript condition expression",
"required": True,
},
],
"outputs": ["true", "false"],
},
"delay": {
"name": "Delay",
"icon": "clock",
"description": "Wait before next step",
"inputs": [
{
"key": "seconds",
"type": "number",
"label": "Seconds to wait",
"default": 5,
"required": True,
},
],
"outputs": ["waited"],
},
"transform": {
"name": "Transform Data",
"icon": "refresh-cw",
"description": "Apply JSON transformation to data",
"inputs": [
{"key": "data", "type": "json", "label": "Input data", "required": True},
{
"key": "transform",
"type": "json",
"label": "JQ-style transform rules",
"required": True,
},
],
"outputs": ["result"],
},
"export_training": {
"name": "Export Training Data",
"icon": "brain",
"description": "Export as AI training dataset",
"inputs": [
{"key": "records", "type": "json", "label": "Records to export", "required": True},
{
"key": "clean_room",
"type": "boolean",
"label": "Strip PII/copyright",
"default": True,
},
],
"outputs": ["dataset_id", "record_count"],
},
}
# ── Pipeline Engine ──
def validate_pipeline(pipeline: dict[str, Any]) -> list[str]:
"""Validate a pipeline definition. Returns list of errors."""
errors = []
steps = pipeline.get("steps", [])
if not steps:
errors.append("Pipeline must have at least one step")
step_ids = set()
for i, step in enumerate(steps):
step_id = step.get("id", f"step_{i}")
if step_id in step_ids:
errors.append(f"Duplicate step ID: {step_id}")
step_ids.add(step_id)
step_type = step.get("type")
if step_type not in STEP_TYPES:
errors.append(f"Step {i}: Unknown type '{step_type}'")
continue
step_def = STEP_TYPES[step_type]
for inp in step_def["inputs"]:
if inp.get("required") and inp["key"] not in step.get("inputs", {}):
errors.append(f"Step '{step_id}': Missing required input '{inp['key']}'")
return errors
async def run_pipeline(
pipeline: dict[str, Any], context: dict[str, Any] | None = None
) -> dict[str, Any]:
"""Execute a pipeline definition sequentially.
Args:
pipeline: Pipeline definition with steps array
context: Initial context variables
Returns execution results with step outputs.
"""
pipeline_id = pipeline.get("id") or uuid.uuid4().hex[:8]
steps = pipeline.get("steps", [])
ctx = dict(context or {})
results: list[dict[str, Any]] = []
failed = False
error: str | None = None
for i, step in enumerate(steps):
step_id = step.get("id", f"step_{i}")
step_type = step.get("type")
inputs = step.get("inputs", {})
# Resolve template variables in inputs
resolved_inputs = _resolve_templates(inputs, ctx)
logger.info(
"pipeline_step_start",
extra={"pipeline_id": pipeline_id, "step": step_id, "type": step_type},
)
step_result: dict[str, Any] = {
"step_id": step_id,
"type": step_type,
"status": "running",
"started_at": datetime.now(UTC).isoformat(),
}
try:
output = await _execute_step(step_type, resolved_inputs, ctx)
step_result["status"] = "success"
step_result["output"] = output
# Store outputs in context as step_id.output_key
if isinstance(output, dict):
for key, value in output.items():
ctx[f"{step_id}.{key}"] = value
except Exception as e:
step_result["status"] = "failed"
step_result["error"] = str(e)[:500]
logger.error(
"pipeline_step_failed",
extra={"pipeline_id": pipeline_id, "step": step_id, "error": str(e)},
)
failed = True
error = str(e)
if step.get("on_error") == "abort":
results.append(step_result)
break
step_result["finished_at"] = datetime.now(UTC).isoformat()
results.append(step_result)
return {
"pipeline_id": pipeline_id,
"pipeline_name": pipeline.get("name", "Unnamed"),
"total_steps": len(steps),
"completed_steps": len(results),
"successful_steps": sum(1 for r in results if r["status"] == "success"),
"failed_steps": sum(1 for r in results if r["status"] == "failed"),
"failed": failed,
"error": error,
"steps": results,
"context_keys": list(ctx.keys()),
}
async def _execute_step(
step_type: str, inputs: dict[str, Any], ctx: dict[str, Any]
) -> dict[str, Any]:
"""Execute a single pipeline step."""
if step_type == "scrape":
from scraper import PryScraper
s = PryScraper()
result = await s.scrape(
inputs.get("url", ""), {"bypass_cloudflare": inputs.get("bypass_cloudflare", True)}
)
return result
elif step_type == "extract_css":
from extraction import JsonCssExtractionStrategy
from scraper import PryScraper
s = PryScraper()
result = await s.scrape(inputs.get("url", ""))
html = result.get("raw_html", "")
if not html:
from client import get_client
client = await get_client()
resp = await client.get(inputs["url"], timeout=30, follow_redirects=True)
html = resp.text
strategy = JsonCssExtractionStrategy(inputs.get("schema", {}))
items = strategy.extract(html)
return {"items": items, "count": len(items)}
elif step_type == "quality_check":
from quality import run_quality_check
return await run_quality_check(
url=inputs.get("url", ""),
data=inputs.get("data", {}),
)
elif step_type == "compliance_check":
from compliance import run_compliance_check
return await run_compliance_check(inputs.get("url", ""))
elif step_type == "reconcile":
from reconciliation import reconcile
return await reconcile(
records=inputs.get("records", []),
vertical=inputs.get("vertical", "product"),
)
elif step_type == "send_slack":
from destinations import write_to_slack
result = await write_to_slack(
webhook_url=inputs.get("webhook_url", ""),
message=inputs.get("message", ""),
)
return result
elif step_type == "send_email":
from destinations import write_to_email
result = await write_to_email(
recipient=inputs.get("recipient", ""),
subject=inputs.get("subject", ""),
body=inputs.get("body", ""),
)
return result
elif step_type == "delay":
import asyncio
seconds = inputs.get("seconds", 5)
await asyncio.sleep(seconds)
return {"waited": seconds}
elif step_type == "transform":
data = inputs.get("data", {})
transform = inputs.get("transform", {})
result = _apply_transform(data, transform)
return {"result": result}
elif step_type == "export_training":
from training_data import export_training_dataset
result = export_training_dataset(
records=inputs.get("records", []),
clean_room=inputs.get("clean_room", True),
)
return result
else:
raise ValueError(f"Unknown step type: {step_type}")
def _resolve_templates(inputs: dict[str, Any], ctx: dict[str, Any]) -> dict[str, Any]:
"""Resolve {{ variable }} templates in input values."""
import re
resolved: dict[str, Any] = {}
for key, value in inputs.items():
if isinstance(value, str):
resolved[key] = re.sub(
r"\{\{(\w+(?:\.\w+)*)\}\}", lambda m: str(ctx.get(m.group(1), m.group(0))), value
)
elif isinstance(value, dict):
resolved[key] = _resolve_templates(value, ctx)
elif isinstance(value, list):
resolved[key] = [
_resolve_templates(v, ctx) if isinstance(v, dict) else v for v in value
]
else:
resolved[key] = value
return resolved
def _apply_transform(data: Any, transform: dict[str, Any]) -> Any:
"""Apply simple JQ-style transforms."""
if isinstance(transform, dict):
result: dict[str, Any] = {}
for key, rule in transform.items():
if isinstance(rule, str) and rule.startswith("$."):
# JSON path extraction
path = rule[2:].split(".")
value = data
for part in path:
if isinstance(value, dict):
value = value.get(part, None)
elif isinstance(value, list) and part.isdigit():
idx = int(part)
value = value[idx] if 0 <= idx < len(value) else None
else:
value = None
result[key] = value
elif isinstance(rule, str) and rule.startswith("$"):
result[key] = data
else:
result[key] = rule
return result
return data
# ── Pipeline CRUD ──
def save_pipeline(pipeline: dict[str, Any]) -> dict[str, Any]:
"""Save a pipeline definition."""
pipeline_id = pipeline.get("id") or uuid.uuid4().hex[:8]
pipeline["id"] = pipeline_id
pipeline["updated_at"] = datetime.now(UTC).isoformat()
path = PIPELINE_DIR / f"{pipeline_id}.json"
try:
path.write_text(json.dumps(pipeline, indent=2))
logger.info(
"pipeline_saved", extra={"pipeline_id": pipeline_id, "name": pipeline.get("name")}
)
return {"success": True, "pipeline_id": pipeline_id}
except OSError as e:
return {"success": False, "error": str(e)}
def get_pipeline(pipeline_id: str) -> dict[str, Any] | None:
"""Get a saved pipeline definition."""
path = PIPELINE_DIR / f"{pipeline_id}.json"
if not path.exists():
return None
try:
return cast("dict[str, Any]", json.loads(path.read_text()))
except (json.JSONDecodeError, OSError):
return None
def list_pipelines() -> list[dict[str, Any]]:
"""List all saved pipelines."""
pipelines = []
for path in sorted(PIPELINE_DIR.glob("*.json"), key=os.path.getmtime, reverse=True):
try:
data = json.loads(path.read_text())
pipelines.append(
{
"id": data.get("id"),
"name": data.get("name", "Unnamed"),
"description": data.get("description", ""),
"step_count": len(data.get("steps", [])),
"updated_at": data.get("updated_at"),
}
)
except (json.JSONDecodeError, OSError):
continue
return pipelines
def delete_pipeline(pipeline_id: str) -> bool:
"""Delete a saved pipeline."""
path = PIPELINE_DIR / f"{pipeline_id}.json"
if path.exists():
path.unlink()
return True
return False

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"""Pry — Proxy Manager with affiliate-signup flow.
Tries free proxies first (Tor, public rotating). When premium proxy is needed,
prompts user to sign up via affiliate links. Pre-wired to 5+ providers."""
import json
import logging
import os
import time
from dataclasses import dataclass
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
PROXY_DIR = Path(os.path.expanduser("~/.pry/proxies"))
PROXY_DIR.mkdir(parents=True, exist_ok=True)
# Free proxy sources (no signup required)
FREE_PROXY_SOURCES = [
{
"name": "Tor",
"type": "socks5",
"url": "socks5://127.0.0.1:9050",
"cost": "Free",
"speed": "Slow",
"reliability": "High",
},
{
"name": "Public Pool (round-robin)",
"type": "http",
"url": "rotating",
"cost": "Free",
"speed": "Variable",
"reliability": "Low",
},
{
"name": "Cloudflare WARP",
"type": "wireguard",
"url": "warp://1.1.1.1",
"cost": "Free",
"speed": "Fast",
"reliability": "High",
},
]
# Premium proxy providers with full affiliate details
PREMIUM_PROXY_PROVIDERS = [
{
"name": "Bright Data",
"tag": "brightdata",
"url": "https://brightdata.com/?ref=pry",
"signup_url": "https://brightdata.com/cp/start?aff_id=pry",
"api_endpoint": "https://api.brightdata.com",
"auth_format": "username:password",
"commission": "$2-$50 per signup, up to 10% recurring",
"cookie_days": 30,
"free_trial": "Yes, $5 credit",
"min_price": "$0.10/GB residential",
"speed": "Very Fast",
"reliability": "Very High",
"ips": "72M+ residential, 770K+ datacenter",
"geo_coverage": "195 countries",
"protocols": ["HTTP", "SOCKS5", "HTTPS"],
"note": "Industry leader. Best for high-volume scraping.",
},
{
"name": "Smartproxy",
"tag": "smartproxy",
"url": "https://smartproxy.com/?ref=pry",
"signup_url": "https://dashboard.smartproxy.com/registration?aff_id=pry",
"api_endpoint": "https://api.smartproxy.com",
"auth_format": "username:password",
"commission": "20% recurring lifetime",
"cookie_days": 60,
"free_trial": "Yes, 100MB",
"min_price": "$0.10/GB residential",
"speed": "Fast",
"reliability": "High",
"ips": "40M+ residential",
"geo_coverage": "195 countries",
"protocols": ["HTTP", "SOCKS5"],
"note": "Good balance of price and quality.",
},
{
"name": "Oxylabs",
"tag": "oxylabs",
"url": "https://oxylabs.io/?ref=pry",
"signup_url": "https://dashboard.oxylabs.io/?aff_id=pry",
"api_endpoint": "https://api.oxylabs.com",
"auth_format": "username:password",
"commission": "Partner program (10-30% recurring)",
"cookie_days": 30,
"free_trial": "Yes, 5K requests",
"min_price": "$1.20/GB residential",
"speed": "Very Fast",
"reliability": "Very High",
"ips": "100M+ residential",
"geo_coverage": "195 countries",
"protocols": ["HTTP", "SOCKS5", "HTTPS"],
"note": "Enterprise-grade. Best for big data scraping.",
},
{
"name": "IPRoyal",
"tag": "iproyal",
"url": "https://iproyal.com/?ref=pry",
"signup_url": "https://dashboard.iproyal.com/signup?aff=pry",
"api_endpoint": "https://api.iproyal.com",
"auth_format": "username:password",
"commission": "30% recurring lifetime",
"cookie_days": 60,
"free_trial": "No",
"min_price": "$0.04/GB residential",
"speed": "Medium",
"reliability": "High",
"ips": "300K+ residential",
"geo_coverage": "150+ countries",
"protocols": ["HTTP", "SOCKS5"],
"note": "Cheapest. Good for budget scraping.",
},
{
"name": "Webshare",
"tag": "webshare",
"url": "https://www.webshare.io/?ref=pry",
"signup_url": "https://proxy.webshare.io/register?aff=pry",
"api_endpoint": "https://proxy.webshare.io/api/v2",
"auth_format": "api_key",
"commission": "30% recurring lifetime",
"cookie_days": 60,
"free_trial": "Yes, 10 free proxies",
"min_price": "$0.03/proxy/month (datacenter)",
"speed": "Very Fast",
"reliability": "High",
"ips": "500K+ datacenter/residential",
"geo_coverage": "100+ countries",
"protocols": ["HTTP", "SOCKS5"],
"note": "Best free tier. Try before you buy.",
},
{
"name": "Proxy-Seller",
"tag": "proxyseller",
"url": "https://proxy-seller.com/?ref=pry",
"signup_url": "https://proxy-seller.com/?partner=pry",
"auth_format": "username:password",
"commission": "30% recurring",
"cookie_days": 60,
"free_trial": "No",
"min_price": "$1.40/proxy/month",
"speed": "Fast",
"reliability": "High",
"note": "Cheap private proxies.",
},
{
"name": "NetNut",
"tag": "netnut",
"url": "https://netnut.io/?ref=pry",
"signup_url": "https://netnut.io/?aff=pry",
"auth_format": "username:password",
"commission": "Partner program",
"cookie_days": 30,
"free_trial": "Yes, 7 days",
"min_price": "$0.20/GB",
"note": "Fast residential IPs from ISPs.",
},
{
"name": "ProxyMesh",
"tag": "proxymesh",
"url": "https://proxymesh.com/?ref=pry",
"signup_url": "https://proxymesh.com/?aff=pry",
"auth_format": "username:password",
"commission": "20% recurring",
"cookie_days": 30,
"free_trial": "No",
"min_price": "$0.80/proxy",
"note": "Simple rotating proxies.",
},
{
"name": "Decodo (Smartproxy)",
"tag": "decodo",
"url": "https://decodo.com/?ref=pry",
"signup_url": "https://decodo.com/signup?aff=pry",
"auth_format": "username:password",
"commission": "20% recurring",
"cookie_days": 60,
"note": "Budget option from Smartproxy.",
},
{
"name": "PacketStream",
"tag": "packetstream",
"url": "https://packetstream.io/?ref=pry",
"signup_url": "https://packetstream.io/signup?aff=pry",
"auth_format": "username:password",
"commission": "20% revenue share",
"cookie_days": 60,
"free_trial": "Pay-as-you-go",
"min_price": "$0.05/GB",
"note": "Bandwidth-sharing residential proxies.",
},
]
@dataclass
class ProxyConfig:
"""Proxy configuration."""
provider: str = "free"
proxy_url: str = ""
username: str = ""
password: str = ""
api_key: str = ""
proxy_type: str = "http"
geo: str = "auto"
rotation: str = "per_request"
auto_rotate: bool = True
class ProxyManager:
"""Manages proxy selection, free fallbacks, and premium signup flow."""
def __init__(self, data_dir: Path | None = None) -> None:
self.data_dir = data_dir or PROXY_DIR
self.data_dir.mkdir(parents=True, exist_ok=True)
self.config_file = self.data_dir / "active_config.json"
self.creds_file = self.data_dir / "credentials.json"
self.active_config = self._load_config()
self.credentials: dict[str, ProxyConfig] = self._load_credentials()
self.auto_configure_from_env()
def _load_config(self) -> ProxyConfig:
if self.config_file.exists():
try:
return ProxyConfig(**json.loads(self.config_file.read_text()))
except (json.JSONDecodeError, TypeError):
pass
return ProxyConfig()
def _load_credentials(self) -> dict[str, ProxyConfig]:
if self.creds_file.exists():
try:
data = json.loads(self.creds_file.read_text())
return {k: ProxyConfig(**v) for k, v in data.items()}
except (json.JSONDecodeError, TypeError):
pass
return {}
def _save_config(self) -> None:
try:
self.config_file.write_text(json.dumps(self.active_config.__dict__, indent=2))
except OSError as e:
logger.debug("proxy_config_save_failed", extra={"error": str(e)[:80]})
def _save_credentials(self) -> None:
try:
data = {k: v.__dict__ for k, v in self.credentials.items()}
self.creds_file.write_text(json.dumps(data, indent=2, default=str))
except OSError as e:
logger.debug("proxy_creds_save_failed", extra={"error": str(e)[:80]})
def get_proxy_url(self) -> str | None:
"""Get the active proxy URL. Returns None if no proxy configured."""
c = self.active_config
if c.provider == "free":
if c.proxy_url and c.proxy_url != "rotating":
return c.proxy_url
return None
if c.provider not in self.credentials:
return None
cred = self.credentials[c.provider]
if cred.api_key:
return f"http://{cred.api_key}@proxy.{cred.provider}.com:8000"
if cred.username and cred.password:
return f"{cred.proxy_type}://{cred.username}:{cred.password}@{cred.proxy_url}"
if cred.proxy_url:
return cred.proxy_url
return None
def test_proxy(
self, proxy_url: str, test_url: str = "https://httpbin.org/ip", timeout: int = 10
) -> dict[str, Any]:
"""Test a proxy and return its public IP, latency, and working status."""
import httpx
start = time.time()
try:
with httpx.Client(proxy=proxy_url, timeout=timeout) as c:
r = c.get(test_url)
elapsed = time.time() - start
return {
"working": r.is_success,
"latency": round(elapsed, 2),
"ip": r.text[:100] if r.is_success else "",
"status": r.status_code,
}
except Exception as e:
return {"working": False, "error": str(e)[:100]}
def needs_premium_proxy(self, last_error: str) -> bool:
"""Determine if we need a premium proxy based on the error type."""
indicators = [
"captcha",
"cloudflare",
"datadome",
"akamai",
"403",
"429",
"rate limit",
"blocked",
]
return any(i in last_error.lower() for i in indicators)
def get_signup_link(self, provider_tag: str = "") -> str:
"""Get the affiliate signup link for a provider. Records the click for revenue tracking."""
if not provider_tag:
provider_tag = "brightdata"
provider = next((p for p in PREMIUM_PROXY_PROVIDERS if p["tag"] == provider_tag), None)
if not provider:
provider = PREMIUM_PROXY_PROVIDERS[0]
try:
from referrals import ReferralTracker
tracker = ReferralTracker()
tracker.record_click(
provider["tag"],
provider["signup_url"],
source="proxy_manager",
user_id=os.getenv("USER", ""),
)
except Exception as e:
logger.debug("referral_track_failed", extra={"error": str(e)[:80]})
return provider["signup_url"]
def list_providers(self, free: bool = True, premium: bool = True) -> dict[str, Any]:
"""List all available proxy providers."""
result: dict[str, Any] = {"free": [], "premium": []}
if free:
result["free"] = FREE_PROXY_SOURCES
if premium:
result["premium"] = PREMIUM_PROXY_PROVIDERS
return result
def select_provider(self, provider_tag: str, credentials: dict | None = None) -> dict[str, Any]:
"""Select a premium provider. If credentials provided, save them."""
provider = next((p for p in PREMIUM_PROXY_PROVIDERS if p["tag"] == provider_tag), None)
if not provider:
return {"success": False, "error": f"Unknown provider: {provider_tag}"}
self.get_signup_link(provider_tag)
if credentials:
self.credentials[provider_tag] = ProxyConfig(
provider=provider_tag,
proxy_url=credentials.get("proxy_url", f"gate.{provider_tag}.com:8000"),
username=credentials.get("username", ""),
password=credentials.get("password", ""),
api_key=credentials.get("api_key", ""),
proxy_type=credentials.get("proxy_type", "http"),
)
self.active_config = self.credentials[provider_tag]
self._save_credentials()
self._save_config()
return {
"success": True,
"provider": provider_tag,
"config": self.active_config.__dict__,
}
return {
"success": False,
"needs_signup": True,
"signup_url": provider["signup_url"],
"message": (
f"Visit {provider['signup_url']} to sign up for {provider['name']}, "
"then call this endpoint again with credentials."
),
"provider": provider,
}
def auto_configure_from_env(self) -> bool:
"""Check environment variables for proxy credentials and auto-configure."""
for provider in PREMIUM_PROXY_PROVIDERS:
tag = provider["tag"]
env_prefix = f"PRY_PROXY_{tag.upper()}_"
url = os.getenv(env_prefix + "URL")
user = os.getenv(env_prefix + "USERNAME")
pwd = os.getenv(env_prefix + "PASSWORD")
api_key = os.getenv(env_prefix + "API_KEY")
if url or (user and pwd) or api_key:
self.credentials[tag] = ProxyConfig(
provider=tag,
proxy_url=url or f"gate.{tag}.com:8000",
username=user or "",
password=pwd or "",
api_key=api_key or "",
)
if not self.active_config.provider or self.active_config.provider == "free":
self.active_config = self.credentials[tag]
self._save_credentials()
self._save_config()
return True
return False
def get_recommendation(self, last_error: str = "") -> dict[str, Any]:
"""Get a recommendation for which proxy provider to use.
Returns: signup URL, estimated cost, why recommended.
"""
if not self.needs_premium_proxy(last_error):
return {"needs_premium": False, "message": "Free fallbacks sufficient"}
provider = PREMIUM_PROXY_PROVIDERS[0]
return {
"needs_premium": True,
"reason": f"Site appears blocked: '{last_error[:80]}'",
"recommended_provider": provider["name"],
"signup_url": self.get_signup_link(provider["tag"]),
"estimated_cost": provider["min_price"],
"free_trial": provider.get("free_trial", "Unknown"),
"all_providers": PREMIUM_PROXY_PROVIDERS[:3],
}
def record_referral_click(self, provider_tag: str, user_id: str = "") -> str:
"""Record a referral click for a specific provider. Returns the click_id."""
provider = next((p for p in PREMIUM_PROXY_PROVIDERS if p["tag"] == provider_tag), None)
if not provider:
return ""
try:
from referrals import ReferralTracker
tracker = ReferralTracker()
return tracker.record_click(
provider["tag"],
provider["signup_url"],
source="proxy_signup",
user_id=user_id,
)
except Exception as e:
logger.debug("referral_record_failed", extra={"error": str(e)[:80]})
return ""
def get_recent_clicks(self, days_back: int = 30) -> list[dict[str, Any]]:
"""Get recent proxy referral clicks for revenue tracking."""
try:
from referrals import ReferralTracker
tracker = ReferralTracker()
cutoff = datetime.now(UTC).timestamp() - (days_back * 86400)
return [
c
for c in tracker.clicks
if c.get("source", "").startswith("proxy")
and _parse_ts(c.get("timestamp", "")) >= cutoff
]
except Exception as e:
logger.debug("recent_clicks_failed", extra={"error": str(e)[:80]})
return []
def _parse_ts(ts: str) -> float:
"""Parse an ISO timestamp to epoch seconds. Returns 0 on error."""
if not ts:
return 0.0
try:
return datetime.fromisoformat(ts).timestamp()
except (ValueError, TypeError):
return 0.0

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"""Pry Python SDK — simple client library.
Usage:
from pry_sdk import PryCrawl
mc = PryCrawl("http://localhost:8002")
result = await mc.scrape("https://example.com")
"""
import asyncio
import logging
from typing import Any
import httpx
logger = logging.getLogger(__name__)
_LOOP: asyncio.AbstractEventLoop | None = None
def _get_or_create_loop() -> asyncio.AbstractEventLoop:
"""Get the running event loop or create a persistent one for sync use."""
global _LOOP
try:
return asyncio.get_running_loop()
except RuntimeError:
if _LOOP is None or _LOOP.is_closed():
_LOOP = asyncio.new_event_loop()
asyncio.set_event_loop(_LOOP)
return _LOOP
class PryCrawl:
"""Python SDK for Pry API."""
def __init__(
self, base_url: str = "http://localhost:8002", api_key: str | None = None, timeout: int = 60
):
self.base_url = base_url.rstrip("/")
self.timeout = timeout
self._headers = {"Content-Type": "application/json"}
if api_key:
self._headers["Authorization"] = f"Bearer {api_key}"
async def _post(self, path: str, data: dict[str, Any]) -> dict[str, Any]:
async with httpx.AsyncClient(timeout=self.timeout, headers=self._headers) as client:
resp = await client.post(f"{self.base_url}{path}", json=data)
resp.raise_for_status()
result: dict[str, Any] = resp.json()
return result
async def scrape(self, url: str, **options: Any) -> dict[str, Any]:
"""Scrape a single URL to markdown."""
return await self._post("/v1/scrape", {"url": url, **options})
async def scrape_json(self, url: str, schema: dict[str, str], **options: Any) -> dict[str, Any]:
"""Scrape with structured JSON extraction."""
return await self._post(
"/v1/scrape", {"url": url, "formats": ["json"], "jsonSchema": schema, **options}
)
async def crawl(self, url: str, max_pages: int = 10, **options: Any) -> dict[str, Any]:
"""Crawl multiple pages from a URL."""
return await self._post("/v1/crawl", {"url": url, "maxPages": max_pages, **options})
async def map(self, url: str, limit: int = 50) -> dict[str, Any]:
"""Discover URLs on a site."""
return await self._post("/v1/map", {"url": url, "limit": limit})
async def parse(self, url: str) -> dict[str, Any]:
"""Parse a document (PDF, DOCX, image)."""
return await self._post("/v1/parse", {"url": url})
async def automate(self, steps: list[dict[str, Any]], **options: Any) -> dict[str, Any]:
"""Run browser automation steps."""
return await self._post("/v1/automate", {"steps": steps, **options})
async def screenshot(self, url: str) -> dict[str, Any]:
"""Take a screenshot of a URL."""
return await self._post("/v1/screenshot", {"url": url})
async def health(self) -> dict[str, Any]:
"""Check service health."""
async with httpx.AsyncClient(timeout=5) as client:
resp = await client.get(f"{self.base_url}/health")
result: dict[str, Any] = resp.json()
return result
class PryCrawlSync:
"""Synchronous wrapper for Pry SDK."""
def __init__(
self, base_url: str = "http://localhost:8002", api_key: str | None = None, timeout: int = 60
):
self._client = PryCrawl(base_url, api_key, timeout)
def _run(self, coro: Any) -> Any:
loop = _get_or_create_loop()
return loop.run_until_complete(coro)
def scrape(self, url: str, **options: Any) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.scrape(url, **options))
return result
def scrape_json(self, url: str, schema: dict[str, str], **options: Any) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.scrape_json(url, schema, **options))
return result
def crawl(self, url: str, max_pages: int = 10, **options: Any) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.crawl(url, max_pages, **options))
return result
def map(self, url: str, limit: int = 50) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.map(url, limit))
return result
def parse(self, url: str) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.parse(url))
return result
def automate(self, steps: list[dict[str, Any]], **options: Any) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.automate(steps, **options))
return result
def screenshot(self, url: str) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.screenshot(url))
return result
def health(self) -> dict[str, Any]:
result: dict[str, Any] = self._run(self._client.health())
return result

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"""Pry — WebSocket streaming, scheduler, batch-file, recorder, transforms.
New capabilities that make Pry unbeatable."""
import csv
import io
import json
from datetime import UTC, datetime
from typing import Any
class StreamManager:
"""Manages WebSocket connections for real-time data streaming."""
def __init__(self):
self._connections: dict[str, set] = {}
def register(self, job_id: str, websocket):
if job_id not in self._connections:
self._connections[job_id] = set()
self._connections[job_id].add(websocket)
def unregister(self, job_id: str, websocket):
self._connections.get(job_id, set()).discard(websocket)
async def broadcast(self, job_id: str, data: dict):
for ws in self._connections.get(job_id, set()).copy():
try:
await ws.send_json(data)
except Exception:
self._connections.get(job_id, set()).discard(ws)
streams = StreamManager()
class BatchProcessor:
"""Process URLs from a file with a template selector."""
async def from_file(
self, filepath: str, template: dict, timeout: int = 30, max_urls: int = 1000
) -> list[dict]:
from scraper import PryScraper
s = PryScraper()
# Read URLs from file (one per line)
with open(filepath) as f:
urls = [line.strip() for line in f if line.strip() and not line.startswith("#")]
urls = urls[:max_urls]
from extractor import SchemaExtractor
ex = SchemaExtractor()
results = []
for i, url in enumerate(urls):
try:
result = await s.scrape(url, {"timeout": timeout})
if result.get("status") == "ok":
extracted = ex._pattern_extract(result.get("content", ""), template)
results.append({"url": url, "status": "ok", "data": extracted})
else:
results.append({"url": url, "status": "error", "error": result.get("error")})
except Exception as e:
results.append({"url": url, "status": "error", "error": str(e)})
# Progress update every 50 URLs
if (i + 1) % 50 == 0:
await streams.broadcast(
"batch", {"progress": f"{i + 1}/{len(urls)}", "results": len(results)}
)
return results
class Recorder:
"""Record browser interactions and export as automation scripts."""
def __init__(self):
self._recordings: dict[str, list[dict]] = {}
def start(self, session_id: str):
self._recordings[session_id] = []
def record(self, session_id: str, action: str, selector: str = "", value: str = ""):
if session_id not in self._recordings:
self._recordings[session_id] = []
self._recordings[session_id].append(
{
"action": action,
"selector": selector,
"value": value,
"timestamp": datetime.now(UTC).isoformat(),
}
)
def export(self, session_id: str, fmt: str = "json") -> Any:
steps = self._recordings.get(session_id, [])
if fmt == "json":
return json.dumps(steps, indent=2)
elif fmt == "yaml":
lines = ["steps:"]
for s in steps:
lines.append(f" - action: {s['action']}")
if s.get("selector"):
lines.append(f' selector: "{s["selector"]}"')
if s.get("value"):
lines.append(f' value: "{s["value"]}"')
return "\n".join(lines)
elif fmt == "pry":
# Generate pry.yml compatible output
return {"steps": steps}
return steps
def clear(self, session_id: str):
self._recordings.pop(session_id, None)
recorder = Recorder()
class TransformEngine:
"""Transform scraped data into multiple output formats."""
@staticmethod
def to_sql(data: dict, table: str = "scraped_data") -> str:
"""Convert scraped data to SQL INSERT statement."""
columns = list(data.keys())
values = []
for v in data.values():
if isinstance(v, str):
escaped = v.replace("'", "''")
values.append(f"'{escaped}'")
elif v is None:
values.append("NULL")
else:
values.append(str(v))
cols = ", ".join(columns)
vals = ", ".join(values)
return f"INSERT INTO {table} ({cols}) VALUES ({vals});"
@staticmethod
def to_csv(data: list[dict]) -> str:
"""Convert list of dicts to CSV string."""
if not data:
return ""
buf = io.StringIO()
w = csv.DictWriter(buf, fieldnames=data[0].keys())
w.writeheader()
w.writerows(data)
return buf.getvalue()
@staticmethod
def to_html_table(data: list[dict]) -> str:
"""Convert list of dicts to HTML table."""
if not data:
return "<table></table>"
cols = data[0].keys()
rows = "\n".join(
f" <tr>{''.join(f'<td>{v}</td>' for v in row.values())}</tr>" for row in data
)
return f"<table>\n <tr>{''.join(f'<th>{c}</th>' for c in cols)}</tr>\n{rows}\n</table>"
@staticmethod
def to_markdown_table(data: list[dict]) -> str:
if not data:
return ""
cols = list(data[0].keys())
header = "| " + " | ".join(cols) + " |"
sep = "| " + " | ".join(["---"] * len(cols)) + " |"
rows = "\n".join("| " + " | ".join(str(v) for v in row.values()) + " |" for row in data)
return f"{header}\n{sep}\n{rows}"

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"""Pry — One-file config system.
Define all your scraping jobs in a pry.yml file, run them with `pry run`.
Example pry.yml:
```yaml
jobs:
- name: product_prices
url: https://store.com/products
schedule: every 1h
extract:
title: "h1.product-name"
price: ".price"
stock: ".stock-status"
output: csv
webhook: slack://C012345
```
"""
import json
import os
import yaml
class Pryfile:
"""Parse and execute pry.yml job definitions."""
def __init__(self, path: str = "pry.yml"):
self.path = path
self.jobs = []
self._load()
def _load(self):
if not os.path.exists(self.path):
# Try pry.yaml, Pryfile, pryfile.yml
for alt in ["pry.yaml", "Pryfile", "pryfile.yml", "pryfile.yaml"]:
if os.path.exists(alt):
self.path = alt
break
else:
return # No config file found — that's OK for CLI usage
with open(self.path) as f:
data = yaml.safe_load(f)
self.jobs = data.get("jobs", []) if data else []
self.global_settings = {k: v for k, v in (data or {}).items() if k != "jobs"}
async def run_all(self, scraper=None) -> list[dict]:
"""Execute all jobs defined in pry.yml. Returns results."""
from scraper import PryScraper
s = scraper or PryScraper()
results = []
for job in self.jobs:
try:
result = await self._run_job(job, s)
results.append(result)
except Exception as e:
results.append({"name": job.get("name", "unknown"), "error": str(e)})
return results
async def _run_job(self, job: dict, scraper) -> dict:
name = job.get("name", "unnamed")
url = job.get("url", "")
if not url:
return {"name": name, "error": "No URL specified"}
scrape_result = await scraper.scrape(
url,
{
"timeout": job.get("timeout", 30),
"bypass_cloudflare": job.get("bypass_cloudflare", True),
},
)
result = {
"name": name,
"url": url,
"status": scrape_result.get("status"),
"method": scrape_result.get("method", "unknown"),
"content_length": len(scrape_result.get("content", "")),
}
# Extract structured fields if defined
extract_schema = job.get("extract", {})
if extract_schema:
from extractor import SchemaExtractor
ex = SchemaExtractor()
extracted = ex._pattern_extract(scrape_result.get("content", ""), extract_schema)
result["extracted"] = extracted
# Transform output if specified
output_format = job.get("output", self.global_settings.get("output", "json"))
if output_format == "csv" and "extracted" in result:
import csv
import io
buf = io.StringIO()
w = csv.DictWriter(buf, fieldnames=result["extracted"].keys())
w.writeheader()
w.writerow(result["extracted"])
result["output"] = buf.getvalue()
elif output_format == "json":
result["output"] = json.dumps(result.get("extracted", {}), indent=2)
return result
def list_jobs(self) -> list[dict]:
"""List all configured jobs without executing them."""
return [
{
"name": j.get("name"),
"url": j.get("url"),
"schedule": j.get("schedule", "manual"),
"output": j.get("output", "json"),
}
for j in self.jobs
]

36
pulsemcp.json Normal file
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@ -0,0 +1,36 @@
{
"$schema": "https://pulsemcp.com/schema.json",
"name": "pry",
"display_name": "Pry — Web Scraping & Browser Automation",
"description": "Scrape, crawl, extract, and automate any website. Self-hosted, x402 pay-per-call, MCP-compatible.",
"version": "3.0.0",
"license": "MIT",
"author": "Rug Munch Media LLC",
"homepage": "https://pry.dev",
"repository": "https://github.com/cryptorugmuncher/pry",
"categories": ["Web Scraping", "Browser Automation", "Data Extraction"],
"install": {
"type": "pip",
"package": "pry",
"command": "python3 -m mcp_production"
},
"transports": {
"stdio": {
"command": "python3 -m mcp_production"
},
"sse": {
"url": "https://mcp.pry.dev/sse"
}
},
"tools": [
"pry_scrape",
"pry_crawl",
"pry_extract",
"pry_template",
"pry_search_templates",
"pry_enrich",
"pry_x402_pricing",
"pry_referrals"
],
"tags": ["scraping", "automation", "extraction", "x402", "self-hosted"]
}

102
pyproject.toml Normal file
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[build-system]
requires = ["setuptools>=69.0"]
build-backend = "setuptools.build_meta"
[project]
name = "pry"
version = "3.0.0"
description = "Free web scraping + browser automation API — self-hosted, no API keys needed"
readme = "README.md"
requires-python = ">=3.11"
license = { text = "Proprietary" }
authors = [{ name = "Rug Munch Media LLC" }]
dependencies = [
"fastapi>=0.115.0",
"uvicorn[standard]>=0.32.0",
"trafilatura>=2.0.0",
"readability-lxml>=0.8.1",
"lxml>=5.3.0",
"httpx>=0.28.0",
"markdownify>=0.14.0",
"pydantic>=2.0.0",
"pydantic-settings>=2.0.0",
"playwright>=1.50.0",
"redis>=5.0.0",
"pypdf>=5.0.0",
"python-docx>=1.1.0",
"tiktoken>=0.8.0",
"pillow>=10.0.0",
"click>=8.0.0",
"pyyaml>=6.0",
"pandas>=2.0.0",
"anyio>=4.0.0",
"croniter>=2.0.0",
]
[project.optional-dependencies]
dev = [
"pytest>=8.0",
"pytest-asyncio>=0.24.0",
"pytest-cov>=5.0.0",
"ruff>=0.7.0",
"mypy>=1.12.0",
"pre-commit>=4.0.0",
]
[project.scripts]
pry = "cli:main"
[tool.setuptools.packages.find]
include = ["pry*"]
[tool.ruff]
target-version = "py311"
line-length = 100
[tool.ruff.lint]
select = ["E", "F", "I", "N", "W", "B", "A", "C4", "SIM", "UP", "RUF"]
ignore = ["E501", "N815", "B008", "A002", "RUF006"]
[tool.ruff.format]
quote-style = "double"
indent-style = "space"
[tool.mypy]
python_version = "3.11"
strict = true
ignore_missing_imports = true
exclude = [
"build/",
"dist/",
".git/",
"__pycache__/",
]
warn_unused_ignores = false
[[tool.mypy.overrides]]
module = [
"trafilatura",
"trafilatura.*",
"readability",
"readability.*",
"playwright",
"playwright.*",
"pypdf",
"pypdf.*",
"docx",
"docx.*",
"PIL",
"PIL.*",
"markdownify",
"markdownify.*",
"pandas",
"pandas.*",
"yaml",
"yaml.*",
"numpy",
"numpy.*",
]
ignore_missing_imports = true
[tool.pytest.ini_options]
asyncio_mode = "auto"
testpaths = ["tests"]

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