Pangram Labs Quickstart Guide

Usage

Install pangram

pip install pangram-sdk

Add your API key

Either export it as an environment variable:

export PANGRAM_API_KEY=<your API key>

Or pass it directly to the constructor:

from pangram import Pangram

my_api_key = ''  # Fill this in with your API key.
pangram_client = Pangram(api_key=my_api_key)

Make a request

Main prediction

Returns detailed analysis with AI-assistance detection and segment-level metrics The SDK submits to Pangram’s async inference API and waits for the completed result.

from pangram import Pangram

pangram_client = Pangram()
result = pangram_client.predict(text)
stage = result['stage']  # "STAGE_SUCCESS" after predict() completes.

# Analysis with AI-assistance detection.
fraction_ai = result['fraction_ai']
fraction_ai_assisted = result['fraction_ai_assisted']
fraction_human = result['fraction_human']
num_ai_segments = result['num_ai_segments']
# Access individual window classifications
for window in result['windows']:
    label = window['label']
    ai_assistance_score = window['ai_assistance_score']
    confidence = window['confidence']

Check for Plagiarism

The plagiarism detection API helps you identify potential plagiarism by comparing text against a vast database of online content:

from pangram import Pangram

pangram_client = Pangram()

text = "Text to check for plagiarism"
result = pangram_client.check_plagiarism(text)

if result['plagiarism_detected']:
    print(f"Plagiarism detected! {result['percent_plagiarized']}% of the text may be plagiarized.")
    for content in result['plagiarized_content']:
        print(f"Found match at {content['source_url']}")
        print(f"Matched text: {content['matched_text']}")

The plagiarism detection response includes

Whether plagiarism was detected
List of plagiarized content with source URLs
Total number of sentences checked
List of plagiarized sentences
Percentage of text that was plagiarized