langchain-google-classroom 0.2.0

An integration package connecting Google Classroom and LangChain

langchain-google-classroom

An integration package connecting Google Classroom and LangChain.

Load courses, assignments, announcements, materials, student submissions, rubrics, topics, rosters, and file attachments as structured LangChain Document objects — ready for RAG pipelines, AI teaching assistants, and educational analytics.

Installation

pip install langchain-google-classroom

With optional parsers for PDF and DOCX attachments:

pip install "langchain-google-classroom[parsers]"

Quick Start

from langchain_google_classroom import GoogleClassroomLoader

# OAuth (opens browser on first run)
loader = GoogleClassroomLoader(
    course_ids=["123456789"],
)
docs = loader.load()

for doc in docs:
    print(f"[{doc.metadata['content_type']}] {doc.metadata.get('title', '')}")

Service Account

loader = GoogleClassroomLoader(
    service_account_file="service_account.json",
)

With Attachments and Vision LLM

from langchain_google_genai import ChatGoogleGenerativeAI

loader = GoogleClassroomLoader(
    course_ids=["123456789"],
    load_attachments=True,
    vision_model=ChatGoogleGenerativeAI(model="gemini-2.0-flash"),
)

Student Submissions, Topics, and Roster

loader = GoogleClassroomLoader(
    course_ids=["123456789"],
    load_submissions=True,
    load_topics=True,
    load_roster=True,
)

Features

• Full Classroom API coverage — assignments, announcements, materials, submissions, rubrics, topics, and roster
• Drive attachments — PDF, DOCX, CSV, text, and image parsing with Google Docs/Slides/Sheets export
• Vision LLM — embedded images described by Gemini, GPT-4V, or any vision-capable BaseChatModel
• YouTube and link attachments — metadata captured as structured documents
• Pluggable parsers — bring your own BaseBlobParser (PyMuPDF, Unstructured, etc.)
• File size guard — configurable max_file_size to skip oversized attachments
• Retry with backoff — exponential backoff with jitter on HTTP 429/500/503
• Flexible auth — service accounts, OAuth, cached tokens, or pre-built credentials
• Rich metadata — course info, timestamps, due dates, grades, links on every document
• Lazy and async loading — lazy_load() and alazy_load() for memory efficiency
• Pydantic v2 — fully typed BaseModel with model_dump(), JSON schema, and automatic scope injection

For full documentation and API reference, see the GitHub repository.