onecrawler 0.1.5

An async python crawling framework for discovering URLs, extracting links, and scraping structured content.

Onecrawler

An async Python crawling framework for discovering URLs, extracting links, and scraping structured content.

Installation · Quick Start · Documentation

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Overview

Onecrawler helps you build maintainable crawling and extraction workflows without turning every project into a custom scraping script. It provides a shared configuration model, async execution, sitemap discovery, browser-backed link extraction, heuristic content extraction, and optional GenAI extraction for typed outputs.

Recommended workflow:

1. Use sitemaps first whenever possible.
2. Fall back to browser link extraction when sitemap coverage is missing or dynamic.
3. Scrape the final URL list with heuristic extraction by default.
4. Use GenAI extraction when you need structured output in a Pydantic schema.

async with LinkExtractor(settings) as link_engine:
    links = await link_engine.run("https://example.com")

async with Scraper(settings) as scraper_engine:
    records = await scraper_engine.run(links)

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Features

Capability	Details
Sitemap discovery	Resolves robots.txt, common sitemap paths, nested indexes, .xml.gz, feeds, and HTML fallback
Browser link extraction	Shallow and deep Playwright-backed discovery for JavaScript-rendered or sitemap-poor sites
URL filtering	Wildcard path filters with include_link_patterns
Content filtering	Composable post-extraction filters by date, keywords, file type, and cosine similarity with AND/OR/NOT logic
Async performance	Tunable concurrency, retries, timeouts, and crawl limits
Content extraction	Heuristic extraction with trafilatura for fast article-like content
GenAI extraction	Optional model-assisted extraction for strongly typed Pydantic outputs
Output formats	markdown, json, txt, xml, xmltei
Proxy support	Single proxy or rotating proxy pools for browser and sitemap workflows
Browser controls	Viewport, user agent, locale, timezone, storage state, and runtime settings

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When To Use What

Need	Use	Why
Fast URL discovery from a public site	UniversalSiteMap	Simplest, fastest, and least expensive way to collect URLs
Links from one listing page	Shallow LinkExtractor	Reads direct same-site links from the page
Recursive discovery through navigation	Deep LinkExtractor	Follows internal links until your configured limit
Bulk article or page text extraction	Heuristic Scraper	Deterministic and avoids model cost
Typed fields or semantic normalization	GenAI extraction	Produces schema-shaped output for downstream systems

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Installation

pip install onecrawler

Install Playwright browser binaries when you use browser-backed crawling or scraping:

python -m playwright install chromium

Install optional GenAI dependencies when you use model-assisted extraction:

pip install "onecrawler[genai]"

[!NOTE] GenAI extraction requires an API key from your chosen provider (OpenAI, Google) or a running Ollama instance. See GenAI Extraction for details.

For local development:

git clone https://github.com/sayedshaun/onecrawler.git
cd onecrawler
python -m pip install -e ".[dev]"
python -m playwright install chromium

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Docker Support

OneCrawler provides an optimized Docker image that includes all necessary browser dependencies. This is the recommended way to run the framework in production or CI/CD environments.

Build the Image

docker pull ghcr.io/sayedshaun/onecrawler:latest

[!TIP] You can rename it for convenience

docker tag ghcr.io/sayedshaun/onecrawler:latest onecrawler

Run a Script with Docker

docker run -it --rm -v $(pwd):/app onecrawler python your_script.py

[!NOTE] The script must be located at the root of the mounted volume.

---

Quick Start

from onecrawler import Crawler, Settings

async def main():
    settings = Settings(
        link_extraction_limit=10,
        concurrency=7
    )

    async with Crawler(settings) as engine:
        results = await engine.run("https://www.example.com/")

    with open("output.json", "w", encoding="utf-8") as f:
        json.dump(results, f, ensure_ascii=False, indent=4)


if __name__ == "__main__":
    import asyncio
    asyncio.run(main())

Separate Workflow

import json
from onecrawler import Settings, LinkExtractor, Scraper


async def main():
    settings = Settings(
        link_extraction_strategy="deep",
        link_extraction_limit=10,
        concurrency=7,
        scraping_strategy="heuristic",
        scraping_output_format="json",
        enable_human_behaviors=True,
    )

    async with LinkExtractor(settings) as link_engine:
        links = await link_engine.run("https://www.example.com/")

    async with Scraper(settings) as scraper_engine:
        results = await scraper_engine.run(links)

    with open("output.json", "w", encoding="utf-8") as f:
        json.dump(results, f, ensure_ascii=False, indent=4)


if __name__ == "__main__":
    import asyncio

    asyncio.run(main())

[!TIP] Always set link_extraction_limit when crawling broad sites. Without it, discovery can run indefinitely on large domains.

---

Browser Link Extraction

Use browser extraction when sitemaps are incomplete, unavailable, or unable to expose JavaScript-rendered links.

import asyncio
from onecrawler import Settings, LinkExtractor


async def main():
    settings = Settings(
        link_extraction_strategy="deep",
        link_extraction_limit=250,
        include_link_patterns=["/news/*"],
        concurrency=5,
    )

    async with LinkExtractor(settings) as engine:
        links = await engine.run("https://example.com/news")

    print(f"Collected {len(links)} links")


if __name__ == "__main__":
    asyncio.run(main())

[!TIP] Use include_link_patterns to keep discovery focused on relevant paths. For example, ["/blog/*", "/docs/*"] prevents the crawler from wandering into auth pages, admin routes, or unrelated sections.

[!NOTE] Deep extraction follows internal links recursively. Use shallow strategy when you only need links visible on a single listing page — it's significantly faster.

---

Content Filtering

Filter crawled results by date, keywords, file type, or semantic similarity. Filters are passed to Crawler.run() or Crawler.stream() and applied after content extraction.

import asyncio
from onecrawler import Crawler, Settings
from onecrawler.filters import by_date, by_keywords
from onecrawler.filters.chain import AND


async def main():
    settings = Settings(
        link_extraction_limit=50,
        concurrency=5,
    )

    # Keep only pages from 2025 that mention "python" or "async"
    content_filter = AND(
        by_date(start="2025-01-01", end="2025-12-31"),
        by_keywords(["python", "async"]),
    )

    async with Crawler(settings) as engine:
        results = await engine.run(
            "https://example.com/blog",
            filters=content_filter,
        )

    print(f"Matched {len(results)} pages")


if __name__ == "__main__":
    asyncio.run(main())

Available Filters

Filter	Import	Purpose
by_date(start, end)	onecrawler.filters	Keep items within a YYYY-MM-DD date range
by_keywords(keywords)	onecrawler.filters	Keep items whose text contains any keyword
by_files(types)	onecrawler.filters	Keep items by logical file type (pdf, image, docx, text)
by_extension(extensions)	onecrawler.filters	Keep items by URL file extension (.pdf, .jpg)
by_cosine_similarity(query, threshold)	onecrawler.filters	Keep items whose text is semantically similar to a query

Composing Filters

Use AND, OR, and NOT from onecrawler.filters.chain to combine filters:

from onecrawler.filters import by_date, by_keywords, by_files
from onecrawler.filters.chain import AND, OR, NOT

# Pages from 2025 that mention "python" but are not PDFs
f = AND(
    by_date(start="2025-01-01"),
    by_keywords(["python"]),
    NOT(by_files(["pdf"])),
)

# Pages that mention "AI" or are from 2025
f = OR(
    by_keywords(["AI"]),
    by_date(start="2025-01-01", end="2025-12-31"),
)

Streaming With Filters

Filters work with Crawler.stream() for real-time filtered output:

async with Crawler(settings) as engine:
    async for item in engine.stream(
        "https://example.com/news",
        filters=by_cosine_similarity("climate policy", threshold=0.3),
    ):
        print(item["title"])

[!TIP] Filters run after content extraction, so they work with any scraping strategy. Use by_cosine_similarity for topic-focused crawls and by_date to keep results fresh.

[!NOTE] by_date reads the filedate or date field from extracted content. Pages without a parseable date are excluded when a date filter is active.

---

GenAI Extraction With a Schema

Use GenAI extraction when you need a strongly typed response shape instead of plain content.

pip install "onecrawler[genai]"

import asyncio
from typing import Optional
from pydantic import BaseModel
from onecrawler import Settings, GenerativeAISettings, Scraper


class ArticleSummary(BaseModel):
    title: str
    author: Optional[str] = None
    published_at: Optional[str] = None
    summary: str
    topics: list[str]


async def main():
    settings = Settings(
        scraping_strategy="genai",
        scraping_output_format="json",
        genai=GenerativeAISettings(
            provider="openai",
            model_name="gpt-4o-mini",
            api_key="YOUR_API_KEY",
            output_schema=ArticleSummary,
        ),
        concurrency=2,
        request_timeout=30,
    )

    async with Scraper(settings) as scraper:
        result = await scraper.run("https://example.com/articles/story")

    print(result.model_dump() if hasattr(result, "model_dump") else result)


if __name__ == "__main__":
    asyncio.run(main())

[!TIP] Keep concurrency low (2–4) for GenAI extraction. Each page triggers a model call; high concurrency can exhaust rate limits quickly and inflate costs.

[!WARNING] Never hardcode your API key in source files. Use environment variables or a secrets manager instead:

import os
api_key=os.environ["OPENAI_API_KEY"]

Supported Providers

Provider	Requires	Models
OpenAI	api_key	GPT-4o, GPT-4o-mini, etc.
Google	api_key	Gemini models
Ollama	base_url (no key needed)	Any locally hosted model

Ollama Example

settings = Settings(
    scraping_strategy="genai",
    genai=GenerativeAISettings(
        provider="ollama",
        model_name="llama3:8b",
        base_url="http://localhost:11434/",
        output_schema=ArticleSummary,
    ),
)

[!NOTE] Ollama requires a running local instance. Install it from ollama.com and pull your model (ollama pull llama3:8b) before running.

---

Proxy Support

Attach one proxy or a rotating proxy pool directly to Settings.

from onecrawler import Settings, ProxySettings


settings = Settings(
    proxies=[
        ProxySettings(server="http://proxy-1.example:8080"),
        ProxySettings(
            server="http://proxy-2.example:8080",
            username="user",
            password="pass",
        ),
    ],
    proxy_rotation="round_robin",
)

Use proxy=ProxySettings(...) for a single proxy, or proxies=[...] with proxy_rotation for a pool.

[!TIP] round_robin rotation distributes requests evenly across your proxy pool. For rate-limited targets, pair this with a modest concurrency value and a request_delay to avoid triggering bans.

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Production Tips

[!IMPORTANT] Split URL discovery and scraping into separate pipeline steps. Collecting all URLs first gives you a checkpoint to resume from if scraping fails partway through — without re-running discovery.

[!TIP] Start with UniversalSiteMap before reaching for browser extraction. Sitemap-based discovery is faster, cheaper, and more complete on well-maintained sites. Fall back to LinkExtractor only when sitemaps are missing or stale.

[!TIP] Use heuristic scraping (scraping_strategy="heuristic") for bulk content extraction. Reserve GenAI extraction for cases where you genuinely need structured, schema-shaped output — it adds latency and cost at scale.

[!CAUTION] Respect robots.txt and a site's terms of service before crawling. Onecrawler does not enforce crawl policies automatically — you are responsible for staying within allowed access patterns.

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License

Released under the MIT License. See LICENSE for full terms.