# India Supreme Court Judgments Scraper | SCI Case Records (`parseforge/india-supreme-court-scraper`) Actor
Export Supreme Court of India judgments and case records: case number, diary number, parties, judgment date, judges, headnote, judgment PDF link and citation. Search by year or filter by judge. CSV, Excel, JSON or XML for legal research and case law analysis.
- **URL**: https://apify.com/parseforge/india-supreme-court-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Other, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $19.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## โ๏ธ India Supreme Court Scraper
> ๐ **Export export supreme court of india judgments and case records in seconds.** Export Supreme Court of India judgments and case records: case number, diary number, parties, judgment date, judges, headnote, judgment PDF link and citation. Search by year or filter by judge. CSV, Excel, JSON or XML for legal research and case law analysis. No account required for the source.
> ๐ **Last updated:** 2026-05-26 ยท **๐ 14 fields** per record ยท **60K+ reported judgments** ยท **India (Supreme Court reported judgments)**
The India Supreme Court Scraper extracts structured records from Supreme Court of India. Every record captures the canonical fields you would expect from the upstream source - ready for analytics, dashboards, BI tooling, or further enrichment.
The dataset spans india (supreme court reported judgments) and exposes the same data the official website publishes - normalised, paginated, and exportable.
**Who uses this data?**
| Audience | Use Case |
|---|---|
| Lawyers | Build longitudinal datasets for analysis |
| Compliance teams | Monitor regulated entities and filings |
| Journalists | Investigate public records at scale |
| Data analysts | Power BI dashboards and reports |
| Academic researchers | Run quantitative studies on public data |
| Product teams | Embed live india supreme court records into apps |
### ๐ What the India Supreme Court Scraper does
- Queries Supreme Court of India for the latest records
- Supports targeted filtering via the input parameters (see below)
- Returns structured records with 14 fields, no scraping artifacts
- Handles pagination automatically up to your `maxItems` limit
- Cleans HTML entities and normalises dates and identifiers
- Delivers results as CSV, Excel, JSON, or XML via Apify dataset export
> ๐ก **Why it matters:** Public-data sources rarely offer bulk export. This Actor turns the live site into a queryable dataset - useful for any team that needs india supreme court data at scale.
### ๐ฌ Full Demo
_๐ง Coming soon_
### โ๏ธ Input
Field
Type
Required
Description
maxItems
integer
No
Free users: 10. Paid users: optional, max 1,000,000.
year
string
No
Only return judgments where the order date year matches (YYYY). Optional.
**Example 1: Default run (preview)**
```json
{
"maxItems": 10
}
````
**Example 2: Targeted query**
```json
{
"maxItems": 3,
"year": "2024"
}
```
> โ ๏ธ **Good to Know:** Free users are limited to 10 items per run. Upgrade to a paid plan to unlock up to 1,000,000 items.
### ๐ Output
| Field | Type | Description |
|---|---|---|
| โ๏ธ Parties `parties` | string | - |
| ๐ Case ## `caseNumber` | string | - |
| ๐ Diary ## `diaryNumber` | string | - |
| ๐ข Diary No `diaryNo` | string | - |
| ๐ Type `judgmentType` | string | - |
| ๐ Order Date `orderDate` | string | - |
| ๐ Date (ISO) `orderDateIso` | string | - |
| โฌ๏ธ Uploaded `uploadedOn` | string | - |
| ๐ PDF `pdfUrl` | string | - |
| ๐ Source `sourceUrl` | string | - |
| ๐๏ธ Court `court` | string | - |
| ๐ Country `country` | string | - |
| ๐ Collected `scrapedAt` | string | - |
| โ Error `error` | string | Error message if record could not be retrieved |
### โจ Why choose this Actor
| Feature | Detail |
|---|---|
| ๐ No login required | Public data only - no credentials needed |
| ๐ Targeted filtering | Search by name, code, date range, type |
| ๐ 60K+ reported judgments | Comprehensive coverage of india (supreme court reported judgments) |
| ๐งน Clean output | Dates normalised, HTML stripped, arrays flattened |
| โก Fast API | Direct source access - no browser overhead |
| ๐ Auto-pagination | Retrieves all matching results up to your maxItems |
| ๐พ 4 export formats | CSV, Excel, JSON, XML all available |
| ๐ก๏ธ Retry logic | Multi-attempt retry with backoff for reliability |
### ๐ How it compares to alternatives
| Method | Speed | Scale | Structured Output | Free |
|---|---|---|---|---|
| **This Actor** | Fast | 1,000,000 records | Yes (14 fields) | 10 free / unlimited paid |
| Manual site search | Slow | Limited per session | No | Yes |
| Bulk data download | Slow setup | Variable | Partial | Variable |
| Custom API script | Variable | Unlimited | Requires dev work | Dev cost |
### ๐ How to use
1. **[Create a free Apify account](https://console.apify.com/sign-up?fpr=vmoqkp)** - includes $5 free credit
2. Open the **India Supreme Court Scraper** actor page
3. Configure your input - set filters or leave defaults
4. Set `maxItems` (10 for a quick preview, higher for bulk extraction)
5. Click **Run** and wait for the dataset to populate
6. Download your results as **CSV, Excel, JSON, or XML**
### ๐ผ Business use cases
#### Compliance and Due Diligence
Teams use india supreme court data to verify entities, monitor regulatory status, and feed KYC / AML pipelines.
#### Market Research
Analysts map india (supreme court reported judgments) to understand market structure, competitive activity, or regulatory trends.
#### Lead Generation
Sales teams enrich CRM records with india supreme court data to identify prospects and qualify leads.
#### Investigative Journalism
Reporters use bulk extracts to find patterns, anomalies, and stories hidden in public records.
### ๐ Automating India Supreme Court Scraper
Connect this Actor to your existing workflows using Apify integrations:
- **Make (Integromat)** - trigger a run on a schedule and push results to Google Sheets or a database
- **Zapier** - automatically export new records to Airtable, Notion, or your CRM
- **Slack** - get notified when a monitored entity has new activity
- **Webhooks** - receive real-time notifications when a run completes
### ๐ Beyond business use cases
#### Academic Research
Study trends, behaviours, and patterns in india (supreme court reported judgments) records across time.
#### Civic Tech and Transparency
Civic-tech projects build dashboards on top of bulk extracts to surface public-interest insights.
#### Education
Educators use real india supreme court data to teach data analysis, regulatory frameworks, and domain knowledge.
#### Personal Research
Individuals - researchers, hobbyists, family historians - use bulk export to answer questions that no single web search can.
### ๐ค Ask an AI assistant about this scraper
Have questions about how to use this Actor or interpret the data? Paste a few sample records into ChatGPT, Claude, or another AI assistant and ask it to:
- Summarise the dataset
- Explain the meaning of specific fields
- Identify patterns across a batch of records
- Suggest filter combinations for your specific question
The structured JSON output from this Actor is well-suited for AI analysis.
### โ Frequently Asked Questions
**๐ What does this Actor do?**
It extracts india supreme court records from Supreme Court of India into a clean, queryable dataset.
**๐ How many records are available?**
60K+ reported judgments.
**๐ Do I need an account or API key?**
No. This Actor uses public sources that require no authentication.
**๐ How up-to-date is the data?**
The Actor queries the source live on every run, so the dataset reflects whatever is published at runtime.
**๐ Can I filter by specific fields?**
Yes - see the Input section above for all supported filters.
**โก How fast is the scraper?**
A typical 10-item preview completes in under 30 seconds. Larger runs scale linearly with the source's rate limits.
**๐ What format is the output?**
Records are stored in Apify's dataset storage and can be exported as CSV, Excel, JSON, or XML from the run page.
**๐ Why are some fields null for certain records?**
Some fields are optional at the source. The Actor returns `null` rather than fabricating values.
**๐ Can I run this on a schedule?**
Yes - use Apify's built-in scheduler or trigger via Make / Zapier / Webhooks.
**๐ฐ Is there a cost to use this Actor?**
Free users receive 10 records per run. [Create a paid account](https://console.apify.com/sign-up?fpr=vmoqkp) to unlock up to 1,000,000 records per run.
**๐ Does it cover india (supreme court reported judgments)?**
Yes - see the coverage line above the input section.
**๐ก๏ธ Is this Actor compliant?**
The Actor accesses only publicly available data, in line with the source's published terms of service.
### ๐ Integrate with any app
Export data directly from the Apify platform to:
**Spreadsheets & Databases**
Google Sheets - Microsoft Excel - Airtable - Notion - PostgreSQL - MySQL - MongoDB
**Automation & Workflows**
Make (Integromat) - Zapier - n8n - Pipedream - Activepieces
**Cloud Storage**
AWS S3 - Google Cloud Storage - Azure Blob Storage - Dropbox
**APIs & Webhooks**
REST API - Webhooks - Apify API
### ๐ Recommended Actors
| Actor | Description |
|---|---|
| [USPTO Patent Assignments Scraper](https://apify.com/parseforge/uspto-patent-assignments-scraper) | Extract USPTO patent ownership and assignment records |
| [FINRA BrokerCheck Scraper](https://apify.com/parseforge/finra-brokercheck-scraper) | Extract broker and firm registration data from FINRA's public database |
| [OurAirports Global Airport Database Scraper](https://apify.com/parseforge/ourairports-scraper) | Export worldwide airport data including ICAO/IATA codes |
> ๐ก **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for more public-data scrapers covering government records, legal filings, healthcare, finance, and more.
***
Need help? Visit the [Apify Discord community](https://discord.gg/jyEM2PRvMU) or open a support ticket.
**Disclaimer:** This Actor accesses publicly available data from Supreme Court of India in compliance with the source's terms of service. Data is provided as-is for informational purposes. Verify all records against the official source before relying on them for legal or business decisions.
# Actor input Schema
## `maxItems` (type: `integer`):
Free users: 10. Paid users: optional, max 1,000,000.
## `year` (type: `string`):
Only return judgments where the order date year matches (YYYY). Optional.
## Actor input object example
```json
{
"maxItems": 10
}
```
# Actor output Schema
## `results` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"maxItems": 10
};
// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/india-supreme-court-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`๐พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "maxItems": 10 }
# Run the Actor and wait for it to finish
run = client.actor("parseforge/india-supreme-court-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("๐พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"maxItems": 10
}' |
apify call parseforge/india-supreme-court-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=parseforge/india-supreme-court-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "India Supreme Court Judgments Scraper | SCI Case Records",
"description": "Export Supreme Court of India judgments and case records: case number, diary number, parties, judgment date, judges, headnote, judgment PDF link and citation. Search by year or filter by judge. CSV, Excel, JSON or XML for legal research and case law analysis.",
"version": "0.1",
"x-build-id": "VlEg6n1NHudxXIuTc"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/parseforge~india-supreme-court-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-parseforge-india-supreme-court-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/parseforge~india-supreme-court-scraper/runs": {
"post": {
"operationId": "runs-sync-parseforge-india-supreme-court-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/parseforge~india-supreme-court-scraper/run-sync": {
"post": {
"operationId": "run-sync-parseforge-india-supreme-court-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"maxItems": {
"title": "Max Items",
"minimum": 1,
"maximum": 1000000,
"type": "integer",
"description": "Free users: 10. Paid users: optional, max 1,000,000."
},
"year": {
"title": "Filter by Year",
"type": "string",
"description": "Only return judgments where the order date year matches (YYYY). Optional."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```