ainfo 0.2.1

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ainfo

gather structured information from any website - ready for LLMs

Architecture

The project separates concerns into distinct modules:

• fetching – obtain raw data from a source
• parsing – transform raw data into a structured form
• extraction – pull relevant information from the parsed data
• output – handle presentation of the extracted results

Usage

Command line

Install the project and run the CLI against a URL:

pip install -e .
ainfo run https://example.com

The command fetches the page, parses its content and prints the page text. Specify one or more built-in extractors with --extract to pull extra information. For example, to collect contact details and hyperlinks:

ainfo run https://example.com --extract contacts --extract links

Available extractors include:

• contacts – emails, phone numbers, addresses and social profiles
• links – all hyperlinks on the page
• headings – text of headings (h1–h6)

Use --json to emit machine-readable JSON instead of the default human-friendly format. The JSON keys mirror the selected extractors, with text always included. Retrieve the JSON schema for contact details with ainfo.output.json_schema.

For use within an existing asyncio application, the package exposes an async_fetch_data coroutine:

import asyncio
from ainfo import async_fetch_data

async def main():
    html = await async_fetch_data("https://example.com")
    print(html[:60])

asyncio.run(main())

To delegate information extraction or summarisation to an LLM, provide an OpenRouter API key via the OPENROUTER_API_KEY environment variable and pass --use-llm or --summarize:

export OPENROUTER_API_KEY=your_key
ainfo run https://example.com --use-llm --summarize

If the target site relies on client-side JavaScript, enable rendering with a headless browser:

ainfo run https://example.com --render-js

To crawl multiple pages starting from a URL and optionally run extractors on each page:

ainfo crawl https://example.com --depth 2 --extract contacts

The crawler visits pages breadth-first up to the specified depth and prints results for every page encountered. Pass --json to output the aggregated results as JSON instead.

Both commands accept --render-js to execute JavaScript before scraping, which uses Playwright. Installing the browser drivers may require running playwright install.

Utilities chunk_text and stream_chunks are available to break large pages into manageable pieces when sending content to LLMs.

Programmatic API

Most components can also be used directly from Python. Fetch and parse a page, then run the extractors yourself:

from ainfo.extractors import AVAILABLE_EXTRACTORS

from ainfo import fetch_data, parse_data, extract_information, extract_custom

html = fetch_data("https://example.com")
doc = parse_data(html, url="https://example.com")

# Contact details via built-in extractor
contacts = AVAILABLE_EXTRACTORS["contacts"](doc)

# All links
links = AVAILABLE_EXTRACTORS["links"](doc)

# Any additional data via regular expressions
extra = extract_custom(doc, {"prices": r"\$\d+(?:\.\d{2})?"})
print(contacts.emails, extra["prices"])

Serialise results with to_json or inspect the JSON schema with json_schema(ContactDetails).

Workflow examples

Save contact details to JSON

pip install -e .
ainfo run https://example.com --json > contacts.json

Summarize a large page with chunk_text

from ainfo import fetch_data, parse_data, chunk_text
from some_llm import summarize  # pseudo-code

html = fetch_data("https://example.com")
doc = parse_data(html, url="https://example.com")

parts = [summarize(chunk) for chunk in chunk_text(doc.text_content(), 1000)]
print(" ".join(parts))

Stream chunks on the fly

Fetch and chunk a page directly by URL or pass in raw text:

from ainfo import stream_chunks

for chunk in stream_chunks("https://example.com", size=1000):
    handle(chunk)  # send to LLM or other processor

Environment configuration

Copy .env.example to .env and fill in OPENROUTER_API_KEY, OPENROUTER_MODEL, and OPENROUTER_BASE_URL to enable LLM-powered features.

Limitations

• The built-in extract_information targets contact and social media details. Use extract_custom for other patterns or implement your own domain-specific extractors.