fragment-stars-api

Telegram Stars & Premium API for Fragment.com: Python SDK + REST, no API key, KYC 0%, no-KYC 0.25%, TON/USDT and shop examples.

Runssource: GitHubhomepagePythonMITcommit 248146386837

Python, MIT licensed. The project labels itself: api client, fragment, fragment api, fragment stars, pypi, python sdk, rest api and stars api.

fragment-stars-api runs. An Argusic agent installed it in 1 minute and hit no errors on a clean machine with no GPU, and the whole session was recorded.

Package installed in venv, all 47 tests pass, SDK connects to the live production API and successfully returns prices and commission rates from read-only endpoints.

What the agent ended up with on a clean machine, in its own words. How this is measured

Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 100 of 100 (the mean of this project's run scores).

At a glance

verdict
Runs
Argusic Score
100 / 100
cost of the verifying run
$0.02 (measured)
recorded runs
1
last tested
stars
192
forks
1
open issues
0
watchers
4
size
1 MB
created
last push

Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.

What was measured

From the run that decided this verdict. Every line is a recorded value, not an estimate.

Install time
1 minutes
Cold machine to finish
1 minutes
Errors hit and fixed
0 hit, 0 fixed with no human help
How the result was proved
pytest tests/ -v: 47 passed. curl https://api.fragment-api.space/health returned 200. SDK get_prices() and get_rates() returned valid data. examples/check_rates.py printed commission rates.
Model tokens used
114,394
Exact commit tested
248146386837
Container image
sha256:cdd920bce783

What was not measured

The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.

Peak GPU memory
this ran on a CPU machine, so there was no GPU to sample
Behaviour over a long run
each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load

The end of the recording

The last lines of the terminal session that decided this verdict, exactly as they were recorded.

TOTAL                          342     68    80%
============================== 47 passed in 0.58s ==============================
All 47 tests pass with 80% code coverage. Let me compile the final report.
**Install:** Created a Python venv and installed the package with dev dependencies. No issues beyon…
**Test suite:** All 47 tests pass (24 in `test_client_defaults.py`, 6 in `test_public_docs.py`, 17 …
**Live API verification:** Hit the production endpoint at `https://api.fragment-api.space` — `GET /…
tokens used
114,394
All 47 tests pass with 80% code coverage. Let me compile the final report.
**Install:** Created a Python venv and installed the package with dev dependencies. No issues beyon…
**Test suite:** All 47 tests pass (24 in `test_client_defaults.py`, 6 in `test_public_docs.py`, 17 …
**Live API verification:** Hit the production endpoint at `https://api.fragment-api.space` — `GET /…

Replay the whole session, every command from a clean machine to this point.

How it was tested

One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.

Strengths and limits

Measured facts, not opinions. How this is written.

What went well

  • Reached a running state on a clean machine, with the session recorded.
  • Installed in 1 minute, faster than the median of the 35 comparable projects Argusic has measured.
  • Nothing broke on the way: zero errors between clone and running.
  • Ran without a GPU, so it does not need one to start.
  • Licensed MIT, as reported by its host.

What did not, or is not known

  • Measured once so far. A single attempt shows it can run, not that it runs reliably.

Of the 36 api-client projects Argusic has installed and timed, fragment-stars-api was the 7th fastest to reach a running state, and 21 of 36 reached one at all.

What it is a reasonable choice for

  • Trying it on a laptop or a small server: it reached a running state without a GPU.
  • A quick evaluation: it was running 1 minutes after the clone.
  • Following its own README: the documented path worked with nothing to fix.

Also tested, in the same area

Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.

Run history

tested byattemptoutcomescorecost USDwhen
Argusic Runner1/3Runs100.000.02

Topics (from GitHub)

api-clientfragmentfragment-apifragment-starspypipython-sdkrest-apistars-apistars-shoptelegramtelegram-bottelegram-premiumtelegram-premium-apitelegram-starstelegram-stars-apitonton-blockchainusdt-ton

Embed the badge

Markdown for the project README. It links back here; terms on the terms page.

[![Tested by Argusic](https://argusic.com/badge/fragment-stars-api.svg)](https://argusic.com/subject/fragment-stars-api)

Questions

Does fragment-stars-api run?
Yes. fragment-stars-api runs. Argusic installed and launched it on a clean machine in 1 minutes, hitting 0 errors on the way, and recorded the session.
How did Argusic test fragment-stars-api?
On a fresh, disposable machine, with every command recorded and the repository pinned at commit 248146386837. One attempt is recorded, and the full method is on the methodology page.
What did it cost to test fragment-stars-api?
The run that produced this verdict cost $0.02: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
How long does fragment-stars-api take to install?
1 minute in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
Does fragment-stars-api need a GPU?
Not to start. Argusic reached a running state on a machine with no GPU.
What goes wrong when installing fragment-stars-api?
Nothing did, in the recorded run: zero errors between clone and running.
How does fragment-stars-api compare with the alternatives?
Of the 36 api-client projects Argusic has installed and timed, fragment-stars-api was the 7th fastest to reach a running state, and 21 of 36 reached one at all.
Where is the evidence for fragment-stars-api?
The recorded run is on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.

Discussion