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Lobstr.io
lobstrio-sdk
Python SDK for the Lobstr.io API — web scraping automation platform

PyPI Python Tests License Last commit Issues Stars Forks Downloads Ruff mypy


  • Sync + async clients with the same API surface
  • Typed dataclass models for all responses
  • Lazy auto-pagination
  • Automatic token resolution from CLI config or environment

Installation

pip install lobstrio-sdk

Requires Python 3.10+. The only runtime dependency is httpx.

Authentication

The client resolves your API token in this order:

  1. ExplicitLobstrClient(token="your-token")
  2. Environment variableLOBSTR_TOKEN
  3. CLI config file~/.config/lobstr/config.toml (same file used by lobstr CLI)

If you already have the CLI set up, the SDK works with no configuration:

from lobstrio import LobstrClient

client = LobstrClient()  # token auto-resolved
user = client.me()
print(user.email)

Quick Start

from lobstrio import LobstrClient

with LobstrClient() as client:
    # Account info
    user = client.me()
    balance = client.balance()
    print(f"{user.email}{balance.credits} credits")

    # List crawlers
    for crawler in client.crawlers.list():
        print(f"{crawler.name} ({crawler.id})")

    # Create a squid, add tasks, run it
    squid = client.squids.create("google-maps-scraper", name="My Scrape")
    client.squids.update(squid.id, params={"language": "English (United States)"})
    client.tasks.add(squid=squid.id, tasks=[{"url": "https://maps.google.com/..."}])
    run = client.runs.start(squid=squid.id)

    # Wait for completion with progress callback
    #   (or use client.runs.call(squid=squid.id) to start + wait in one call)
    final = client.runs.wait(run.id, callback=lambda s: print(f"{s.percent_done}%"))

    # Download results
    client.runs.download(run.id, "results.csv")

Resources

All API operations are organized under resource namespaces on the client.

User
user = client.me()           # User profile
balance = client.balance()   # Account balance (credits, subscription)
Crawlers — browse scraper templates
crawlers = client.crawlers.list()              # All crawlers
crawler = client.crawlers.get("crawler-id")    # Single crawler
params = client.crawlers.params("crawler-id")  # Parameter schema
attrs = client.crawlers.attributes("crawler-id")  # Result columns

Models: Crawler, CrawlerAttribute, CrawlerParams

Squids — manage scraper instances
# List & iterate
squids = client.squids.list(limit=50, page=1)
for squid in client.squids.iter():     # auto-paginate all squids
    print(squid.name)

# CRUD
squid = client.squids.create("crawler-id", name="My Project")
squid = client.squids.get("squid-id")
squid = client.squids.update("squid-id", name="Renamed", concurrency=2,
                              params={"language": "English"})
client.squids.empty("squid-id")        # remove all tasks
client.squids.delete("squid-id")

Model: Squid (id, name, crawler, is_active, concurrency, params, created_at, ...)

Tasks — manage input URLs and keywords
# List & iterate
tasks = client.tasks.list(squid="squid-id")
for task in client.tasks.iter(squid="squid-id"):
    print(task.id)

# Add tasks
result = client.tasks.add(
    squid="squid-id",
    tasks=[
        {"url": "https://maps.google.com/maps?cid=123"},
        {"url": "https://maps.google.com/maps?cid=456"},
    ],
)
print(f"Added {len(result.tasks)}, {result.duplicated_count} duplicates")

# Upload from CSV/TSV
resp = client.tasks.upload(squid="squid-id", file="tasks.csv")
status = client.tasks.upload_status(resp["id"])

# Get & delete
task = client.tasks.get("task-hash")
client.tasks.delete("task-hash")

Models: Task, TaskStatus, AddTasksResult, UploadStatus, UploadMeta

Runs — start, monitor, and download
# Start a run
run = client.runs.start(squid="squid-id")

# List runs
runs = client.runs.list(squid="squid-id")
for run in client.runs.iter(squid="squid-id"):
    print(run.id, run.status)

# Monitor
run = client.runs.get("run-id")
stats = client.runs.stats("run-id")
print(f"{stats.percent_done}% done, {stats.total_results} results")

# Wait for completion (blocking, with optional progress callback)
final = client.runs.wait("run-id", poll_interval=5.0,
                          callback=lambda s: print(f"{s.percent_done}%"))

# Start and wait in one call (mirrors start() + wait())
final = client.runs.call(squid="squid-id", poll_interval=5.0,
                         callback=lambda s: print(f"{s.percent_done}%"))

# Bound the wait: RunTimeout (a TimeoutError subclass) if it overruns.
# The run keeps going server-side — re-attach later with wait()/get().
from lobstrio import RunTimeout
try:
    final = client.runs.wait("run-id", timeout=600)   # also on call()
except RunTimeout:
    ...

# Download results
url = client.runs.download_url("run-id")   # signed S3 URL
client.runs.download("run-id", "output.csv")  # download to file

# Abort
client.runs.abort("run-id")

# Tasks within a run
tasks = client.runs.tasks("run-id")

Models: Run, RunStats

Results — fetch scraped data
results = client.results.list(squid="squid-id", page_size=100)

# Auto-paginate all results
for row in client.results.iter(squid="squid-id"):
    print(row)  # dict

Results are returned as plain dict objects (the schema depends on the crawler).

Accounts — manage connected platform accounts
accounts = client.accounts.list()
account = client.accounts.get("account-id")
types = client.accounts.types()     # available account types

# Sync account with cookies
resp = client.accounts.sync(type="google", cookies={"SID": "...", "HSID": "..."})
status = client.accounts.sync_status(resp["id"])

# Update limits
client.accounts.update("account-id", type="google", params={"daily_limit": 100})

# Delete
client.accounts.delete("account-id")

Models: Account, AccountType, SyncStatus

Delivery — configure result delivery
# Email
client.delivery.email("squid-id", email="you@example.com")
client.delivery.test_email(email="you@example.com")

# Google Sheets
client.delivery.google_sheet("squid-id", url="https://docs.google.com/spreadsheets/d/...", append=True)
client.delivery.test_google_sheet(url="https://docs.google.com/spreadsheets/d/...")

# Webhook
client.delivery.webhook("squid-id", url="https://your-server.com/hook",
                         on_done=True, on_error=True)
client.delivery.test_webhook(url="https://your-server.com/hook")

# S3
client.delivery.s3("squid-id", bucket="my-bucket", target_path="scrapes/",
                    aws_access_key="...", aws_secret_key="...")
client.delivery.test_s3(bucket="my-bucket")

# SFTP
client.delivery.sftp("squid-id", host="ftp.example.com", username="user",
                      password="pass", directory="/uploads")
client.delivery.test_sftp(host="ftp.example.com", username="user",
                           password="pass", directory="/uploads")

Models: EmailDelivery, GoogleSheetDelivery, S3Delivery, WebhookDelivery, SFTPDelivery

Async Client

The async client mirrors the sync API exactly, using async/await:

from lobstrio import AsyncLobstrClient

async def main():
    async with AsyncLobstrClient() as client:
        user = await client.me()
        print(user.email)

        crawlers = await client.crawlers.list()
        for c in crawlers:
            print(c.name)

        squid = await client.squids.create("crawler-id", name="Async Scrape")
        await client.tasks.add(squid=squid.id, tasks=[{"url": "..."}])
        run = await client.runs.start(squid=squid.id)
        final = await client.runs.wait(run.id)
        await client.runs.download(run.id, "results.csv")

All resource methods (client.crawlers.*, client.squids.*, etc.) work identically — just add await.

Pagination

Resources that return lists support two patterns:

Single page (.list()) — returns one page of results:

page1 = client.squids.list(limit=10, page=1)
page2 = client.squids.list(limit=10, page=2)

Auto-pagination (.iter()) — lazy iterator that fetches pages on demand:

for squid in client.squids.iter(limit=50):
    print(squid.name)  # automatically fetches next pages

The async client provides AsyncPageIterator for use with async for.

Error Handling

All API errors raise typed exceptions with status_code, message, and body:

from lobstrio import LobstrClient, AuthError, NotFoundError, RateLimitError, APIError

try:
    client.squids.get("nonexistent")
except NotFoundError as e:
    print(f"Not found: {e.message}")
except AuthError:
    print("Invalid or expired token")
except RateLimitError as e:
    print(f"Rate limited, retry after {e.retry_after}s")
except APIError as e:
    print(f"API error [{e.status_code}]: {e.message}")
Exception HTTP Status When
AuthError 401 Invalid or missing token
NotFoundError 404 Resource doesn't exist
RateLimitError 429 Too many requests (has retry_after)
APIError 4xx/5xx All other API errors

CLI vs SDK

CLI (pip install lobstrio) SDK (pip install lobstrio-sdk)
Use case Terminal workflows, quick scrapes, cron jobs Scripts, pipelines, applications
Interface Shell commands Python API
Output Rich tables, progress bars, CSV files Typed dataclass models
Async No Yes (AsyncLobstrClient)
Pagination Manual (--page, --limit) Auto (client.squids.iter())

For terminal workflows, see lobstrio — the companion CLI tool.

FAQ

Where do I get an API token?

Go to Dashboard → API to find your token. It's always available there, pre-generated.

Do I need the CLI installed for the SDK to work?

No. The SDK is standalone. However, if you have the CLI configured (lobstr config set-token), the SDK will automatically pick up the token from ~/.config/lobstr/config.toml — no code changes needed.

How do I handle rate limiting?

Catch RateLimitError and use its retry_after attribute:

from lobstrio import RateLimitError
import time

try:
    results = client.results.list(squid="squid-id")
except RateLimitError as e:
    time.sleep(float(e.retry_after or 5))
    results = client.results.list(squid="squid-id")
Can I use the async client with Django/FastAPI?

Yes. Use AsyncLobstrClient in any async context:

from lobstrio import AsyncLobstrClient

async def scrape_view(request):
    async with AsyncLobstrClient() as client:
        results = await client.results.list(squid="squid-id")
        return results

Development

# Clone and install
git clone https://github.com/lobstrio/lobstrio-sdk.git
cd lobstrio-sdk
pip install -e ".[dev]"

# Run unit tests
pytest

# Run live tests (requires API token)
pytest tests/test_live.py -v

# Lint & type check
ruff check src/ tests/
mypy src/lobstrio/

Releasing

Publishing to PyPI is triggered by a GitHub Release, not by merging to master. Merging alone does not publish. To cut a release:

  1. Bump the version in src/lobstrio/_version.py (e.g. 0.4.1).
  2. Add a matching entry to CHANGELOG.md.
  3. Merge to master (via PR; CI must be green).
  4. Create a GitHub Release tagged vX.Y.Z on master:
    gh release create v0.4.1 --target master --title "v0.4.1" --notes ""

Creating the release fires .github/workflows/publish.yml (on: release: published), which builds with uv and publishes to PyPI using the PYPI_TOKEN repo secret. Confirm it went out: pip index versions lobstrio-sdk (or check the PyPI page). Note: a PyPI version cannot be overwritten — bump the version for every release.

Contributing

Contributions are welcome! See CONTRIBUTING.md for development setup, code style, and versioning guidelines.

Changelog

See CHANGELOG.md for release history.

License

Apache 2.0

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