TextLens API for Python

Add text analysis to your Python app in 5 lines. Readability grades (8 formulas), sentiment scoring, keyword extraction, and SEO analysis — no textstat, VADER, or spaCy to install and configure.

import requests

result = requests.post(
    'https://api.ckmtools.dev/v1/analyze',
    headers={'X-API-Key': 'your_key'},
    json={'text': 'Your content here...'}
).json()

print(result['readability']['consensus_grade'])  # Grade 8
print(result['sentiment']['label'])              # positive
print(result['keywords']['top_5'])               # [content, quality, ...]

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What you get

8 Readability Formulas

Flesch-Kincaid, Gunning Fog, SMOG, Coleman-Liau, ARI, Dale-Chall, Linsear Write, plus a consensus grade. No single Python library covers all eight.

Sentiment + Keywords

AFINN sentiment scoring for long-form content — label, score, and confidence. TF-IDF keyword extraction with relevance scores. SEO quality scoring.

Zero Library Dependencies

No NLTK corpus downloads (150MB+), no spaCy model files (12MB–560MB), no pip deps beyond requests. Works in serverless, CI/CD, and containers.

The response

One endpoint returns everything you need:

{
  "readability": {
    "fleschKincaidGrade": 8.2,
    "gunningFog": 10.1,
    "smog": 9.4,
    "colemanLiau": 11.3,
    "ari": 9.7,
    "daleChall": 7.8,
    "linsearWrite": 8.5,
    "consensusGrade": "Grade 9",
    "fleschReadingEase": { "score": 62.3, "interpretation": "Standard" }
  },
  "sentiment": {
    "label": "positive",
    "score": 4.2,
    "confidence": 0.71
  },
  "keywords": [
    { "word": "content", "score": 5.1, "count": 4, "density": 1.87 },
    { "word": "readability", "score": 4.8, "count": 3, "density": 1.40 }
  ],
  "seo": { "score": 74, "grade": "B" },
  "statistics": { "words": 342, "sentences": 18, "paragraphs": 5 },
  "meta": { "processing_time_ms": 14 }
}

Python patterns

requests — most common recommended

import requests
import os

class TextLensClient:
    BASE_URL = 'https://api.ckmtools.dev/v1/analyze'

    def __init__(self, api_key: str):
        self.session = requests.Session()
        self.session.headers.update({'X-API-Key': api_key})

    def analyze(self, text: str) -> dict:
        response = self.session.post(self.BASE_URL, json={'text': text})
        response.raise_for_status()
        return response.json()

client = TextLensClient(os.environ['TEXTLENS_KEY'])
result = client.analyze(article.body)

print(f"Grade: {result['readability']['consensusGrade']}")
print(f"Sentiment: {result['sentiment']['label']}")
print(f"SEO score: {result['seo']['score']}/100")

httpx — async client

import httpx
import asyncio
import os

async def analyze_text(text: str) -> dict:
    async with httpx.AsyncClient() as client:
        response = await client.post(
            'https://api.ckmtools.dev/v1/analyze',
            headers={'X-API-Key': os.environ['TEXTLENS_KEY']},
            json={'text': text}
        )
        response.raise_for_status()
        return response.json()

# Analyze multiple documents concurrently
async def batch_analyze(texts: list[str]) -> list[dict]:
    tasks = [analyze_text(t) for t in texts]
    return await asyncio.gather(*tasks)

results = asyncio.run(batch_analyze(articles))

urllib — stdlib, zero pip deps

import urllib.request
import json
import os

def analyze(text: str, api_key: str) -> dict:
    data = json.dumps({'text': text}).encode('utf-8')
    req = urllib.request.Request(
        'https://api.ckmtools.dev/v1/analyze',
        data=data,
        headers={
            'Content-Type': 'application/json',
            'X-API-Key': api_key,
        },
        method='POST'
    )
    with urllib.request.urlopen(req) as resp:
        return json.loads(resp.read())

result = analyze(document, os.environ['TEXTLENS_KEY'])
print(result['readability']['consensusGrade'])

Why not textstat + VADER + spaCy?

Three packages, three APIs, three version conflicts. textstat covers readability but skips sentiment. vaderSentiment covers sentiment but not readability. spaCy requires a model download (12MB to 560MB) before you can do anything. And none of them give you SEO scoring. TextLens API covers all of it in a single HTTP call — no corpus downloads, no model files, no dependency resolver headaches. The same endpoint works from Python, Ruby, Go, or any language with an HTTP client.