Read grades, attendance, homework, timetables and messages from Librus Synergia in Python. This independent, asynchronous client handles login, session recovery, pagination and request limits, returning typed Python objects rather than HTML.
Use it in personal scripts, notification services or application backends. One service can manage multiple logins while keeping their sessions and data separate. It is not an official Librus product.
Requirements: Python 3.13 or newer. Disk workflows require a trusted local
POSIX filesystem on Linux/macOS, or a fixed local NTFS volume on Windows with
private ACLs. Windows installations include pywin32 and tzdata automatically.
Network paths, reparse-point paths and unsafe storage permissions fail closed.
See platform requirements.
Status: 1.9.0 gives every ordinary grade its stable Librus ID (grade_id)
and links a corrected grade to its corrections, as the all view brackets them
(GradeRecords.corrections, grade corrections).
1.8.4 reads multi-line formative assessment text and repeated
correction lines in grade tooltips, checks the new-schema grade table in every grade view and reports
missing calendar-consumption consent as ConsentRequiredError.
1.8.3 reads behaviour grade symbols, filled descriptive
grade tables, the "Aktualnie brak ogłoszeń" page and absences excused
through e-Usprawiedliwienia. 1.8.2 keeps the modern mailbox session across a cancelled read
or a reached limit and sends its own version in the User-Agent. 1.8.1 recovers a session Synergia no longer knows (it answers
with a "Brak dostępu" page, not a login redirect), reads the lucky number
from the page header and summarizes a page that fails as a whole in
diagnostics. 1.8.0 reports where a failure
happened (code locations and a redacted page structure, see
failure diagnostics), reads gateway descriptive grades for schools on the new descriptive schema
(source-informed, first populated read live on one school) and keeps the 1.7.1 point
grades and multi-line grade comments. It keeps the 1.6.4 linear-time message body
rendering and the 1.6.2 completed-lesson and grade layouts, including behaviour
points, which are validated and not reported as grades.
Class free days, typed capability outcomes and the live-check freshness watchdog
remain available. See verification.
The stable 1.x API does not guarantee every school layout. School features depend
on what each account can access. See the compatibility policy.
See limitations below.
Install the exact release from PyPI after its gated publication completes:
python -m pip install librus-python-api==1.9.0Pin the exact version qualified by your application. Before publication, use a checkout or locally built wheel instead. From a checkout, install into a virtual environment:
python3 -m venv .venv
. .venv/bin/activate
python -m pip install .To install a locally built wheel instead:
python -m pip install ./dist/librus_python_api-1.9.0-py3-none-any.whlNo console script or background process is installed: import the library in your
own program. python -m librus_python_api.inspect_page only parses a saved page
offline for a bug report.
Provide your login and password through your application's secret management. The example reads environment variables; the library itself does not discover environment variables or credential files.
You also need an application context key. Generate it once, store it alongside your other application secrets, and reuse it across runs:
python -c 'import secrets; print(secrets.token_hex(32))'Set LIBRUS_LOGIN, LIBRUS_PASSWORD and LIBRUS_CONTEXT_KEY in your environment.
The last variable is the 64-character hex output from that command. Do not use
your password as this key. Then run:
import asyncio
import os
from datetime import date
from librus_python_api import AccountCredentials, HomeworkRangeRequest, LibrusService
async def main() -> None:
accounts = {
"school": AccountCredentials(
login=os.environ["LIBRUS_LOGIN"],
password=os.environ["LIBRUS_PASSWORD"],
)
}
context_key = bytes.fromhex(os.environ["LIBRUS_CONTEXT_KEY"])
async with LibrusService(accounts, context_key=context_key) as service:
client = service.account("school")
profile = await client.student_information()
print(profile)
today = date.today()
homework = await client.homework_range(
HomeworkRangeRequest(today.replace(day=1), today)
)
for item in homework.items:
print(item.subject, item.topic, item.due_on)
asyncio.run(main())"school" is your local account alias, not a student ID. Login occurs on the first
request. The async context manager closes sessions and outstanding work when it
exits. Results are immutable dataclasses; personal fields are omitted from their
repr, so access named attributes when displaying data intentionally.
Inside the service context above:
from datetime import timedelta
# School-provided final grades, grouped into typed subject records.
grades = await client.final_grades()
for subject in grades.items:
print(subject.subject, subject.annual.raw)
# A week always starts on Monday.
today = date.today()
monday = today - timedelta(days=today.weekday())
timetable = await client.timetable(monday)
# Inclusive date window. Neither attendance nor grades computes a GPA.
attendance = await client.attendance_window(today.replace(day=1), today)
# Permit reuse of this account's cached result for up to 60 seconds.
announcements = await client.announcements(max_age_seconds=60)
# Earlier school years, separate from the message archive (1.3.0).
archive = await client.school_year_archive()
for year in archive.years:
print(year.school_year, year.subjects)Collections expose named record tuples, for example homework.items, rather than
name-keyed dictionaries. Dates are Python date values where established by the
upstream contract. Displayed detail values remain strings; missing or unknown
values are not replaced with guessed zeros. Full signatures, result fields and
examples are in the API reference.
Add more aliases to accounts, then use service.account(alias) for each login.
Reuse one service so concurrent calls share its request budget and connection
limits. A parent login and a student login are separate contexts even when they
refer to the same student. Separate processes need application-level coordination
if they share an upstream traffic allowance.
from librus_python_api import RequestBudget
from librus_python_api.exceptions import LibrusError, ViewDisabledError
budget = RequestBudget(max_requests=20, timeout_seconds=60)
try:
batch = await client.messages(limit=25, max_pages=2, budget=budget)
for message in batch.items:
print(message.subject)
# Request another batch with cursor=batch.next_cursor when it is not None.
except ViewDisabledError:
print("This school has disabled the requested view.")
except LibrusError as error:
print(f"Request failed: {error.kind.value}")A budget covers login, queueing and all pages of an operation. Defaults allow 10 requests/second, a burst of 20 and four simultaneous requests across the service, with one at a time per login. Fresh reads are the default. Unsupported layouts raise typed errors rather than silently returning incomplete data. Cursors detect changes; they are not snapshots.
- Message content: opening received content can mark it read. Pass
allow_mark_read=Trueonly when your application permits that effect. - Sending: prepare a single-use send attempt and obtain approval in your
application. An
UNKNOWNresult must not trigger an automatic resend. - Attachments: stream bytes with explicit limits, or use the optional
files.publish_attachment()helper to save atomically into an existing directory. - Notifications: optional
NotificationStoreandNotificationWorkflowprovide durable checkpoints, pending delivery and explicit acknowledgement. - Persistent sends: optional
PersistenceStorerecords confirmations, claims and uncertain outcomes across restarts. Stores create or validate private caller-selected directories; core reads do not create files. Use explicitfiles.prepare_attachment_directory(path)to provision a private download directory without writing platform-specific ACL code.
The legacy and modern messaging backends have distinct references and permissions; select one explicitly. See the API reference for complete workflows.
LibrusService now requires context_key, exactly 32 secret random bytes.
client.context.identifier is an HMAC-SHA256 pseudonym bound to that key, the
alias, login and configured origins. Password changes preserve it. Different
application keys produce different identifiers; the identifier is not a login
credential or permission token. The separate context.alias is still plain text.
Back up and reuse the key with persistent state. Losing or rotating it changes all context identifiers. Do not treat an empty history under a different key as permission to resend a message. Version 0.7 uses storage and notification archive format 3 and refuses older formats without modifying them. Keep 0.6 stores and their pending/UNKNOWN records for reconciliation; there is no automatic migration. See the upgrade guide before reusing a persistent application.
Loguru is no longer a dependency. For optional diagnostics, pass
diagnostic_sink=librus_python_api.diagnostics.logging_sink after importing that
function. Configure handlers with Python's standard logging module. You can
also pass your own callable; events contain allowlisted timing/outcome fields,
not credentials, account aliases or response bodies. The old loguru_sink was
removed in 0.7.
| Area | Available |
|---|---|
| School data | Profile, grades, school-year archive, attendance, timetable, announcements, agenda, homework and completed lessons |
| Communication | Legacy/modern message lists and content, recipient discovery, bounded attachment streams and explicit sending |
| Application workflows | Shared multi-account limits, caching, notification checkpoints, optional durable send/notification stores |
Completed lessons may be disabled by the school. Behaviour notes are not implemented. Some recipient, archive and receipt layouts remain unqualified; backend acceptance is not proof of delivery. Tests cover supported contracts, not every school or role. Detailed coverage is in the verification log; open work is tracked in issues.
Errors carry only their kind. To see where a failure happened without any page
data, use the helpers in librus_python_api.diagnostics (see
API.md):
from librus_python_api.diagnostics import describe_failure, failure_frames
from librus_python_api.exceptions import LibrusError
try:
await client.grades()
except LibrusError as error:
print(
error.kind, failure_frames(error)
) # ('grade_records:_numeric:100', 'markup:tooltip_fields:113')
print(describe_failure(error)) # masked HTML structure around the failureTo check a saved page offline (a page source saved with Ctrl+U, or a copied
table), run python -m librus_python_api.inspect_page grades page.html (or
final-grades, or profile for the information page /informacja). Save the
page source itself: in Safari use File > Save As with format Page Source; in
Chrome on macOS open it with Option+Cmd+U, then Cmd+S. A source view copied
into TextEdit is a text document, which inspect_page rejects. It prints counts, or the failure's code locations and masked
structure, and makes no request. Pass a diagnostic_sink to LibrusService
to receive one DiagnosticEvent per operation, with frames on failure.
None of these print page text, values, logins or exception messages.
Use a repository checkout for tests and development tools; they are deliberately excluded from published source archives. See CONTRIBUTING.md for setup and offline checks. Report bugs through GitHub Issues and security concerns according to SECURITY.md. Do not attach credentials or raw school data to public reports.
MIT licensed. See LICENSE.