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lyrenth-agents

Ten agents that read the web and finish the job.

Give one a few URLs and it hands back the thing you actually wanted: a comparison table, a brief on a company, the answer to a documentation question with the heading it came from, a sheet you open in Excel. Every claim carries the number of the page it came from, and every page that could not be read is named with the reason.

The reading goes through Lyrenth, which serves each page as clean text instead of HTML, so the pages arrive without the menus, banners, scripts and markup.

export LYRENTH_API_KEY=...        # free key: https://lyrenth.com/signup

uvx lyrenth-agents compare \
  https://en.wikipedia.org/wiki/PostgreSQL \
  https://en.wikipedia.org/wiki/MySQL

With that key alone, the agent reads the pages and prints the finished prompt with its numbered sources, ready to paste into whichever assistant you already use. Point it at a model and it answers on its own:

export LLM_BASE_URL=https://api.example.com/v1   # any OpenAI-compatible endpoint
export LLM_MODEL=your-model-name
export LLM_API_KEY=...

The ten

Command Give it Get back
compare Two or more product pages One table of what is actually different
brief A company's own pages What they do, who they sell to, and what their pages never answer
answers -q "..." The documentation that should hold the answer The answer with the page and heading behind every line
research -q "..." The pages you trust Plain prose with a number on every claim
summarise A long article What it claims, what that rests on, what it leaves out
extract Several pages of the same kind One row per page, a column for every shared field
terms A terms page or a privacy policy What you agree to, what data is taken, what happens in a dispute
people A team page and a careers page Who the pages name, and what the company is hiring for
integrations The pages that say what a product connects to The tools, grouped, and how firmly each one is claimed
digest The last few days of articles What happened, what is new, where the sources disagree

lyrenth-agents --list prints the same list with an example command for each one. Every agent also has a page at lyrenth.com/agents showing a real run.

A real run

Against the live API on 21 September 2026, unedited. The progress lines go to stderr, the answer to stdout.

$ uvx lyrenth-agents compare https://en.wikipedia.org/wiki/PostgreSQL https://en.wikipedia.org/wiki/MySQL
recipe   compare: Product comparison
read [1] PostgreSQL - Wikipedia  30,000 tokens  https://en.wikipedia.org/wiki/PostgreSQL
read [2] MySQL - Wikipedia  30,000 tokens  https://en.wikipedia.org/wiki/MySQL
context  60,000 tokens from 2 sources (raw HTML would be 412,333)

| Feature | PostgreSQL | MySQL |
| :--- | :--- | :--- |
| Software Type | Free and open-source object relational database management system [1] | free and open-source relational database management system (RDBMS) [2] |
| License | PostgreSQL License (free and open-source, permissive) [1] | GPLv2 or proprietary [2] |
| Developer | PostgreSQL Global Development Group [1] | Oracle Corporation [2] |
| Initial Release | 8 July 1996; 30 years ago (1996-07-08) [1] | 23 May 1995; 31 years ago (1995-05-23) [2] |
| Stable Release | 18.6 / 13 August 2026 [1] | 26.7.0 / 28 July 2026 [2] |
| Written In | C (and C++ for the LLVM dependency) [1] | C, C++ [2] |
...

Two encyclopedia articles that weigh 412,333 tokens as raw HTML arrived as 60,000 tokens of text, and the answer cites which of the two every cell came from.

What it does with the pages

  1. Reads text, not markup. Each page arrives as an AIDocument: the content, its canonical URL, when it was read, and what it costs in tokens.
  2. Shares the budget between the pages. Every page gets an equal share and a short page gives its leftover back to the long ones, so a comparison never comes back with one product filled in and the other column empty.
  3. Reads each page once. A mobile copy, a tracking parameter or an old redirect resolves to the same canonical URL and is dropped as a duplicate.
  4. Keeps provenance. Title, canonical URL and read time go into the prompt with the source number, which is what makes the citations checkable.
  5. Fails out loud. A page that could not be read is listed with the reason, and a citation pointing at a source number that does not exist is flagged.

Agents with more than one step

Some jobs are not one question. An agent can be written as a short list of steps that pass their work along, so it can read, then think about what it read, then do something with the result.

uvx lyrenth-agents company-pack \
  https://www.mozilla.org/en-US/about/ \
  https://www.mozilla.org/en-US/about/manifesto/ \
  https://www.mozilla.org/en-US/careers/
recipe   company-pack: Company pack
step     brief: 2,967 characters
step     pack: 1,563 characters
step     file: would write company-pack.md (1,563 bytes, 18 lines)
read [1] Learn about Mozilla  1,402 tokens  https://www.mozilla.org/en-US/about/
read [2] The Mozilla Manifesto  2,051 tokens  https://www.mozilla.org/en-US/about/manifesto/
read [3] Mozilla Careers  3,211 tokens  https://www.mozilla.org/en-US/careers/
context  6,664 tokens from 3 sources (raw HTML would be 50,413)

not done yet: would write company-pack.md (1,563 bytes, 18 lines)
add --yes to the same command to do it.

Three things to notice, because they are the rules the whole thing is built on:

  1. The pages are read once. The second step works from what the first one wrote. It is not handed the pages again, and it does not pay to read them again. It is handed the numbered list of sources, so the citations it carries forward still point at the right page.
  2. Nothing happened. A step that acts on the world says what it would do and stops. --yes performs it. A command you pasted from a website never writes, sends or posts on its own.
  3. The model never chose the destination. The path came from the agent and can be changed by you (--set file.path=meeting/mozilla.md), never by the text on a page that was read. A page can contain any sentence at all, including one addressed to your model.

Write your own

A recipe is data: the instructions for the model and the shape of the output. Register one and it runs on the same engine.

from lyrenth_agents import Recipe, get, register, run

register(
    Recipe(
        slug="changelog",
        name="Release notes",
        category="content",
        tagline="Turn release pages into one list of what changed.",
        what_you_give="The release or changelog pages, newest first.",
        what_you_get="One list of changes, newest first, with the page each came from.",
        system_prompt="You read release pages and list what changed...",
        output_hint="One list, newest first, with a source number on every line.",
        example_urls=["https://example.com/releases"],
    )
)

result = run(get("changelog"), ["https://example.com/releases"])
print(result.answer or result.prompt)

Options

Flag What it does
--list The recipes, with an example command for each
-q, --question What to ask of the pages (answers and research need one)
--sources-only Print the numbered sources and stop
--json The whole result as JSON, including the prompt
--budget Token budget for all the pages together
--fresh Ask Lyrenth for a fresh fetch instead of the stored page
--model-url, --model The model endpoint, instead of the environment

Licence

MIT. See LICENSE.

Release files for lyrenth-agents 0.2.0

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