Metadata-Version: 2.4
Name: spidra
Version: 0.3.0
Summary: The official Python SDK for Spidra
Project-URL: Homepage, https://spidra.io
Project-URL: Repository, https://github.com/spidra-io/spidra-py
Project-URL: Documentation, https://docs.spidra.io
Author-email: Spidra <hello@spidra.io>
License: MIT
License-File: LICENSE
Keywords: ai,crawling,scraping,spidra,web scraping
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Requires-Dist: httpx>=0.27.0
Provides-Extra: dev
Requires-Dist: mypy>=1.10.0; extra == 'dev'
Requires-Dist: pytest-asyncio>=0.23.0; extra == 'dev'
Requires-Dist: pytest>=8.0.0; extra == 'dev'
Requires-Dist: respx>=0.21.0; extra == 'dev'
Description-Content-Type: text/markdown

# Spidra Python SDK

The official Python SDK for [Spidra](https://spidra.io) that allows you to scrape pages, run browser actions, batch-process URLs, and crawl entire sites. All results come back as structured data ready to feed into your LLM pipelines or store directly.

## Installation

```bash
pip install spidra
```

Get your API key at [app.spidra.io](https://app.spidra.io) under **Settings** > **API Keys**.

## Quick start

### Synchronous (simplest)

```python
from spidra import Spidra, ScrapeParams, ScrapeUrl

client = Spidra(api_key="spd_YOUR_API_KEY")

result = client.scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://news.ycombinator.com")],
    prompt="List the top 5 stories with title, points, and comment count",
    output="json",
))

print(result.content)
```

### In an async function

```python
import asyncio
from spidra import AsyncSpidra, ScrapeParams, ScrapeUrl

async def main():
    client = AsyncSpidra(api_key="spd_YOUR_API_KEY")

    result = await client.scrape(ScrapeParams(
        urls=[ScrapeUrl(url="https://news.ycombinator.com")],
        prompt="List the top 5 stories with title, points, and comment count",
        output="json",
    ))

    print(result.content)

asyncio.run(main())
```

### In a Jupyter Notebook

Jupyter already runs its own event loop, so `await` directly in a cell:

```python
from spidra import AsyncSpidra, ScrapeParams, ScrapeUrl

client = AsyncSpidra(api_key="spd_YOUR_API_KEY")

result = await client.scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://news.ycombinator.com")],
    prompt="List the top 5 stories with title, points, and comment count",
    output="json",
))

print(result.content)
```

> **Why?** Jupyter's event loop is already running. Using bare `await` in a cell works because Jupyter patches its loop to accept top-level awaits. Calling `asyncio.run()` would start a second loop, which Python does not allow.

## Two clients

| Client | When to use |
|--------|-------------|
| `Spidra` | Scripts, Django, Flask, CLI tools — anywhere you don't have an event loop |
| `AsyncSpidra` | FastAPI, async libraries, Jupyter notebooks |

Both have identical method signatures. All code examples below use `AsyncSpidra` with `await`. For sync usage, swap `AsyncSpidra` → `Spidra` and drop the `await`.

## Table of contents

- [Spidra Python SDK](#spidra-python-sdk)
  - [Installation](#installation)
  - [Quick start](#quick-start)
  - [Two clients](#two-clients)
  - [Table of contents](#table-of-contents)
  - [Scraping](#scraping)
    - [Basic scrape](#basic-scrape)
    - [Structured output with JSON schema](#structured-output-with-json-schema)
    - [Geo-targeted scraping](#geo-targeted-scraping)
    - [Authenticated pages](#authenticated-pages)
    - [Browser actions](#browser-actions)
    - [forEach: process every element on a page](#foreach-process-every-element-on-a-page)
      - [inline mode](#inline-mode)
      - [navigate mode](#navigate-mode)
      - [click mode](#click-mode)
      - [Pagination](#pagination)
      - [Per-element actions](#per-element-actions)
      - [itemPrompt vs top-level prompt](#itemprompt-vs-top-level-prompt)
    - [Manual job control](#manual-job-control)
    - [Poll options](#poll-options)
  - [Batch scraping](#batch-scraping)
  - [Crawling](#crawling)
  - [Logs](#logs)
  - [Usage statistics](#usage-statistics)
  - [Error handling](#error-handling)
  - [Debugging](#debugging)
  - [Context manager](#context-manager)

## Scraping

All scrape jobs run asynchronously on the server side. The `scrape()` method submits a job, polls until it finishes, and returns the result directly. If you need more control, use `start_scrape()` and `get_scrape()`.

Up to 3 URLs can be passed per request and they are processed in parallel.

### Basic scrape

```python
from spidra import AsyncSpidra, ScrapeParams, ScrapeUrl

client = AsyncSpidra(api_key="spd_YOUR_API_KEY")

result = await client.scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://example.com/pricing")],
    prompt="Extract all pricing plans with name, price, and included features",
    output="json",
))

print(result.content)
# { "plans": [{ "name": "Starter", "price": "$9/mo", "features": [...] }, ...] }
```

### Structured output with JSON schema

When you need a guaranteed shape, pass a `schema`. The API will enforce the structure and return `None` for any missing fields rather than hallucinating values.

```python
result = await client.scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://jobs.example.com/senior-engineer")],
    prompt="Extract the job listing details",
    output="json",
    schema={
        "type": "object",
        "required": ["title", "company", "remote"],
        "properties": {
            "title":      { "type": "string" },
            "company":    { "type": "string" },
            "remote":     { "type": ["boolean", "null"] },
            "salary_min": { "type": ["number", "null"] },
            "salary_max": { "type": ["number", "null"] },
            "skills":     { "type": "array", "items": { "type": "string" } },
        },
    },
))
```

### Geo-targeted scraping

Pass `use_proxy=True` and a `proxy_country` code to route the request through a specific country. Useful for geo-restricted content or localized pricing.

```python
result = await client.scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://www.amazon.de/gp/bestsellers")],
    prompt="List the top 10 products with name and price",
    use_proxy=True,
    proxy_country="de",
))
```

Supported country codes include: `us`, `gb`, `de`, `fr`, `jp`, `au`, `ca`, `br`, `in`, `nl`, `sg`, `es`, `it`, `mx`, and [40+ more](https://docs.spidra.io/features/stealth-mode#country-targeting). Use `"global"` or `"eu"` for regional routing.

### Authenticated pages

Pass cookies as a string to scrape pages that require a login session.

```python
result = await client.scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://app.example.com/dashboard")],
    prompt="Extract the monthly revenue and active user count",
    cookies="session=abc123; auth_token=xyz789",
))
```

### Browser actions

Actions let you interact with the page before the scrape runs. They execute in order, and the scrape happens after all actions complete.

```python
from spidra import BrowserAction

result = await client.scrape(ScrapeParams(
    urls=[
        ScrapeUrl(
            url="https://example.com/products",
            actions=[
                BrowserAction(type="click", selector="#accept-cookies"),
                BrowserAction(type="wait", duration=1000),
                BrowserAction(type="scroll", to="80%"),
            ],
        ),
    ],
    prompt="Extract all product names and prices",
))
```

**Available actions:**

| Action | Required fields | Description |
|--------|----------------|-------------|
| `click` | `selector` or `value` | Click a button, link, or any element |
| `type` | `selector`, `value` | Type text into an input or textarea |
| `check` | `selector` or `value` | Check a checkbox |
| `uncheck` | `selector` or `value` | Uncheck a checkbox |
| `wait` | `duration` (ms) | Pause execution for a set number of milliseconds |
| `scroll` | `to` (0–100%) | Scroll the page to a percentage of its height |
| `forEach` | `observe` | Loop over every matched element and process each one |

For `selector`, use a CSS selector or XPath. For `value`, use a plain English description and Spidra will locate the element using AI.

```python
# CSS selector
BrowserAction(type="click", selector="button[data-testid='submit']")

# Plain English
BrowserAction(type="click", value="Accept all cookies button")

# Type into a field
BrowserAction(type="type", selector="input[name='q']", value="wireless headphones")

# Wait for content to load
BrowserAction(type="wait", duration=2000)

# Scroll to bottom
BrowserAction(type="scroll", to="100%")
```

### forEach: process every element on a page

`forEach` finds a set of elements on the page and processes each one individually. It is the right tool when you need to collect data from a list of items, paginate through multiple pages, or click into each item's detail page.

> You don't need `forEach` if the data fits on a single page and is short — a plain `prompt` is simpler and works just as well.

**Use forEach when:**
- The list spans multiple pages and you need `pagination`
- You need to click into each item's detail page (`navigate` mode)
- You have 20+ items and want per-item AI extraction to stay consistent (`item_prompt`)

#### inline mode

Read each element's content directly without navigating. Best for product cards, search results, table rows.

```python
from spidra import BrowserAction

result = await client.scrape(ScrapeParams(
    urls=[
        ScrapeUrl(
            url="https://books.toscrape.com/catalogue/category/books/mystery_3/index.html",
            actions=[
                BrowserAction(
                    type="forEach",
                    observe="Find all book cards in the product grid",
                    mode="inline",
                    capture_selector="article.product_pod",
                    max_items=20,
                    item_prompt="Extract title, price, and star rating. Return as JSON: {title, price, star_rating}",
                ),
            ],
        ),
    ],
    prompt="Return a clean JSON array of all books",
    output="json",
))
```

#### navigate mode

Follow each element's link to its destination page and capture content there. Best for product listings where the full detail is only on the individual page.

```python
BrowserAction(
    type="forEach",
    observe="Find all book title links in the product grid",
    mode="navigate",
    capture_selector="article.product_page",
    max_items=10,
    wait_after_click=800,
    item_prompt="Extract title, price, star rating, and availability. Return as JSON.",
)
```

#### click mode

Click each element, capture the content that appears (a modal, drawer, or expanded section), then move on. Best for hotel room cards, FAQ accordions, or any UI where clicking reveals hidden content.

```python
BrowserAction(
    type="forEach",
    observe="Find all room type cards",
    mode="click",
    capture_selector="[role='dialog']",
    max_items=8,
    wait_after_click=1200,
    item_prompt="Extract room name, bed type, price per night, and amenities. Return as JSON.",
)
```

#### Pagination

After processing all elements on the current page, follow the next-page link and continue collecting.

```python
BrowserAction(
    type="forEach",
    observe="Find all book title links",
    mode="navigate",
    max_items=40,
    pagination={
        "next_selector": "li.next > a",
        "max_pages": 3,  # 3 additional pages beyond the first
    },
)
```

`max_items` applies across all pages combined. The loop stops when you hit `max_items`, run out of pages, or reach `max_pages`.

#### Per-element actions

Run additional browser actions on each item after navigating or clicking into it, before the content is captured.

```python
BrowserAction(
    type="forEach",
    observe="Find all book title links",
    mode="navigate",
    capture_selector="article.product_page",
    max_items=5,
    wait_after_click=1000,
    actions=[
        BrowserAction(type="scroll", to="50%"),
    ],
    item_prompt="Extract title, price, and full description. Return as JSON.",
)
```

#### item_prompt vs top-level prompt

Both are optional and serve different purposes.

| | `item_prompt` | `prompt` |
|--|--|--|
| When it runs | During scraping, once per item | After all items are collected |
| What it sees | One item's content | All items combined |
| Output location | Feeds into the top-level `prompt` | `result.content` |

### Manual job control

Use `start_scrape()` and `get_scrape()` when you want to manage polling yourself, or fire-and-forget and check back later.

```python
# Submit a job and get the job_id immediately
queued = await client.start_scrape(ScrapeParams(
    urls=[ScrapeUrl(url="https://example.com")],
    prompt="Extract the main headline",
))

# Check status at any point
status = await client.get_scrape(queued.job_id)

if status.status == "completed":
    print(status.result.content)
elif status.status == "failed":
    print(status.error)
```

Job statuses: `queued`, `waiting`, `active`, `completed`, `failed`.

### Poll options

`scrape()`, `batch_scrape()`, and `crawl()` accept `poll_interval` and `timeout` keyword arguments to control polling behaviour.

```python
result = await client.scrape(
    params,
    poll_interval=3.0,
    timeout=120.0,
)
```

## Batch scraping

Submit up to 50 URLs in a single request. All URLs are processed in parallel. Each URL is a plain string.

```python
from spidra import BatchScrapeParams

batch = await client.batch_scrape(BatchScrapeParams(
    urls=[
        "https://shop.example.com/product/1",
        "https://shop.example.com/product/2",
        "https://shop.example.com/product/3",
    ],
    prompt="Extract product name, price, and availability",
    output="json",
    use_proxy=True,
))

for item in batch.items:
    if item.status == "completed":
        print(item.url, item.result)
    elif item.status == "failed":
        print(item.url, item.error)
```

Item statuses: `pending`, `running`, `completed`, `failed`.

**Retry failed items:**

```python
queued = await client.start_batch_scrape(BatchScrapeParams(
    urls=["https://example.com/1", "https://example.com/2"],
    prompt="Extract the page title",
))

# Later, after checking status
result = await client.get_batch_scrape(queued.batch_id)
if result.failed_count > 0:
    await client.retry_batch_scrape(queued.batch_id)
```

**Cancel a running batch:**

```python
response = await client.cancel_batch_scrape(batch_id)
print(f"Cancelled {response.cancelled_items} items, refunded {response.credits_refunded} credits")
```

**List past batches:**

```python
response = await client.list_batch_scrapes(page=1, limit=20)

for job in response.jobs:
    print(job.uuid, job.status, f"{job.completed_count}/{job.total_urls}")
```

## Crawling

Given a starting URL, Spidra discovers pages automatically according to your instruction and extracts structured data from each one.

```python
from spidra import CrawlParams

job = await client.crawl(CrawlParams(
    base_url="https://competitor.com/blog",
    crawl_instruction="Find all blog posts published in 2024",
    transform_instruction="Extract the title, author, publish date, and a one-sentence summary",
    max_pages=30,
    use_proxy=True,
))

for page in job.result:
    print(page.url, page.data)
```

`transform_instruction` is optional. When omitted (and no `schema` is set), each page's `data` field contains the raw page markdown — no AI extraction is called and no token credits are charged for extraction.

**All `CrawlParams` options:**

| Parameter | Type | Description |
|-----------|------|-------------|
| `base_url` | `str` | **Required.** Starting URL for the crawl. |
| `crawl_instruction` | `str` | Which pages to discover. Defaults to `"Find all pages on the website"`. |
| `transform_instruction` | `str \| None` | What to extract from each page. Omit to get raw markdown with no AI charges. |
| `schema` | `dict \| None` | JSON Schema for structured per-page output. Root must be `type: object`. |
| `max_pages` | `int \| None` | Cap on pages crawled. |
| `max_depth` | `int \| None` | Max link depth from the base URL. `0` = base URL only. |
| `include_paths` | `list[str] \| None` | Only crawl pages whose path matches one of these patterns. |
| `exclude_paths` | `list[str] \| None` | Skip pages whose path matches any of these patterns. |
| `allow_subdomains` | `bool \| None` | Follow links to subdomains of the base domain. |
| `crawl_entire_domain` | `bool \| None` | Follow any link on the same root domain regardless of path. |
| `ignore_query_params` | `bool \| None` | Treat URLs differing only by query string as the same page. |
| `webhook_url` | `str \| None` | URL that receives POST notifications as the job progresses. |
| `use_proxy` | `bool \| None` | Route requests through a residential proxy. |
| `proxy_country` | `str \| None` | Two-letter country code for geo-targeted proxy routing. |
| `cookies` | `str \| None` | Cookie string for authenticated crawls. |
| `scrape_mode` | `"default" \| "fast" \| None` | `"fast"` skips JavaScript rendering for static pages. |

**Submit without waiting:**

```python
queued = await client.start_crawl(CrawlParams(
    base_url="https://example.com/docs",
    crawl_instruction="Find all documentation pages",
    max_pages=50,
))

# Check status later
status = await client.get_crawl(queued.job_id)
```

**Limit depth and scope:**

```python
job = await client.crawl(CrawlParams(
    base_url="https://example.com/blog",
    crawl_instruction="Find all blog posts",
    max_depth=2,
    include_paths=["/blog/"],
    exclude_paths=["/blog/tag/", "/blog/author/"],
    ignore_query_params=True,
))
```

**Get signed download URLs for all crawled pages:**

Each page includes `html` and `markdown` fields with S3-signed URLs that expire after 1 hour.

```python
response = await client.crawl_pages(job_id)

for page in response.pages:
    print(page.url, page.status)
    # Download raw HTML:     page.html
    # Download markdown:     page.markdown
```

**Re-extract with a new instruction:**

Runs a new AI transformation over an existing completed crawl without re-crawling any pages. Charges credits for the transformation only.

```python
queued = await client.crawl_extract(source_job_id, "Extract only the product SKUs and prices as a CSV")

# Poll the new job manually
result = await client.get_crawl(queued.job_id)
```

**Crawl history and stats:**

```python
response = await client.crawl_history(page=1, limit=10)
stats = await client.crawl_stats()
print(f"Total crawls: {stats.total}")
```

## Logs

Scrape logs are stored for every job that runs through the API.

```python
# List logs with optional filters
response = await client.scrape_logs(
    status="failed",
    search_term="amazon.com",
    start_date="2024-01-01",
    end_date="2024-12-31",
    page=1,
    limit=20,
)

for log in response.logs:
    print(log.urls[0].get("url"), log.status, log.credits_used)
```

**Get a single log with full extraction result:**

```python
log = await client.get_scrape_log("log-uuid")
print(log.result_data)  # the full AI output for that job
```

## Usage statistics

Returns credit and request usage broken down by day or week.

```python
# Range options: "7d" | "30d" | "weekly"
rows = await client.usage("30d")

for row in rows:
    print(row.date, row.requests, row.credits, row.tokens)
```

## Error handling

Every API error raises a typed exception. Catch the specific class you care about or fall back to the base `SpidraError`.

```python
from spidra import (
    AsyncSpidra,
    SpidraError,
    SpidraAuthenticationError,
    SpidraForbiddenError,
    SpidraRateLimitError,
    SpidraServerError,
    ScrapeParams,
    ScrapeUrl,
)

try:
    result = await client.scrape(ScrapeParams(
        urls=[ScrapeUrl(url="https://example.com")],
        prompt="...",
    ))
except SpidraAuthenticationError:
    # 401: No x-api-key header sent
    print("Check your API key")
except SpidraForbiddenError:
    # 403: Invalid API key, or monthly credit limit reached — check e.message to distinguish
    print("Access denied or out of credits")
except SpidraRateLimitError:
    # 429: Too many requests
    print("Rate limited, back off and retry")
except SpidraServerError:
    # 500: Something went wrong on Spidra's side
    print("Server error, try again")
except SpidraError as e:
    # Any other API error
    print(f"{e.status}: {e.message}")
```

All error classes expose `err.status` (HTTP status code) and `err.message`.

## Debugging

Enable debug logging to see every HTTP request, response, and retry attempt:

```python
import logging
logging.getLogger("spidra").setLevel(logging.DEBUG)
```

Sample output:

```
DEBUG:spidra:POST /scrape (attempt 1/4)
DEBUG:spidra:Response 200 in 1.23s
```

## Context manager

Use `AsyncSpidra` as an async context manager to ensure the HTTP connection pool is properly closed.

```python
async with AsyncSpidra(api_key="spd_YOUR_API_KEY") as client:
    result = await client.scrape(ScrapeParams(
        urls=[ScrapeUrl(url="https://example.com")],
        prompt="Extract the page title",
    ))
    print(result.content)
```

## Requirements

- Python 3.9 or later
- A Spidra API key ([sign up free](https://spidra.io))

## License

MIT
