# Public Conversation Monitor | Sprinklr Alternative (`khadinakbar/sprinklr-alternative`) Actor

Collect a current, source-linked public Reddit conversation snapshot for explicit brand or topic queries. Returns deduplicated posts with transparent signals and provenance. Built for bounded listening research; $0.01/conversation plus $0.00005 start.

- **URL**: https://apify.com/khadinakbar/sprinklr-alternative.md
- **Developed by:** [Khadin Akbar](https://apify.com/khadinakbar) (community)
- **Categories:** Social media, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $10.00 / 1,000 public conversation collecteds

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

## Public Conversation Monitor | Sprinklr Alternative

Collect a current, source-linked snapshot of public Reddit conversations for a brand, competitor, product, or topic. The Actor searches each explicit watch query, removes duplicate source posts while retaining every matching query, and returns one structured row per retained conversation.

Every row preserves the public source URL, subreddit, source-supplied timestamps and engagement counts, the owner-managed provider route, and the exact collection time. It also applies a small transparent `lexicon-v1` word list to label sentiment, intent, and urgency. Those labels are lightweight triage signals—not AI analysis, human judgment, customer-service tickets, or claims about a person's views.

This is a focused **public-conversation listening workflow alternative**, not a replacement for Sprinklr’s complete enterprise platform.

### Best fit

Use this Actor when a researcher, founder, product team, or communications lead needs a bounded public Reddit pulse in JSON for an explicit watchlist. It is useful when the next step is an internal review, spreadsheet, alert, or another API workflow that needs source URLs and clearly scoped provenance.

Choose Sprinklr or another full social-CXM suite when you need publishing, managed engagement, customer-care case handling, advertising, CRM/DAM/BI integrations, team governance, approval workflows, multi-channel dashboards, historical workspace reporting, global coverage, licensed sources, or monitored private/connected accounts.

### What you get

Each dataset row is one public Reddit post returned by the configured search route. The Actor does not collect private messages, bypass login walls, scrape connected accounts, or infer a customer identity.

| Field | Meaning |
| --- | --- |
| `title`, `text`, `sourceUrl`, `publishedAt` | Source-supplied conversation details and public source URL |
| `matchedQueries`, `subreddit`, `author` | Watch-query and available public community context |
| `engagement` | Source-supplied score, comment count, and upvote ratio when present |
| `sentiment`, `intent`, `urgency` | Transparent deterministic triage signals; not a human or AI decision |
| `provider`, `sourceRequestUrl`, `collectedAt` | Collection route and freshness provenance |

Null fields mean the public source did not supply a value. The Actor does not invent missing timestamps, authors, engagement, or source metadata.

### Input

```json
{
  "watchQueries": ["OpenAI", "Anthropic"],
  "timeframe": "week",
  "sort": "new",
  "maxItems": 50,
  "excludeKeywords": ["giveaway"],
  "responseFormat": "concise"
}
```

`maxItems` is a whole-run cap across every supplied watch query. It limits persisted rows and therefore bounds the main event charge. `new` is appropriate for a current pulse; use `top` or `comment_count` when manual review needs more established conversations.

### Pricing and cost boundary

The intended live configuration is Pay per event + platform usage: $0.00005 for Actor start and $0.01 for each normalized public conversation persisted in the dataset. A 50-conversation run has a maximum event cost of $0.50005, plus Apify platform usage.

No conversation event is charged for a rejected duplicate, excluded post, invalid source row, or empty result. The Actor uses owner-managed public-data routes; it never asks callers to paste a provider key, password, or social cookie. Verify the live Pricing tab and delayed run charge readback before using a price for production budgeting.

### Best results

Use distinct, literal watch queries that name the brand, product, or competitor you intend to review. Start with `timeframe: "week"`, `sort: "new"`, and a modest `maxItems` value, then inspect the source URLs before widening the window. Add `excludeKeywords` only for recurring noise you have already seen. Re-run the same bounded input on your chosen cadence to create your own comparable history; returned rows remain a current collection snapshot rather than a complete conversation archive.

### Outcome contract

Every terminal path writes both `OUTPUT` and `RUN_SUMMARY`. The machine-readable outcome values are:

```text
COMPLETE
PARTIAL
VALID_EMPTY
INVALID_INPUT
UPSTREAM_FAILED
CONFIG_ERROR
```

Use `RUN_SUMMARY` to see the collected rows, source-route detail, and next workflow step for the returned outcome.

### Example output

```json
{
  "id": "reddit:abc123",
  "platform": "reddit",
  "title": "Example discussion title",
  "text": "Example discussion title\n\nSource-supplied public post text.",
  "matchedQueries": ["OpenAI"],
  "subreddit": "MachineLearning",
  "author": "public_user",
  "publishedAt": "2026-08-31T12:00:00.000Z",
  "sourceUrl": "https://www.reddit.com/r/MachineLearning/comments/abc123/example/",
  "engagement": { "score": 42, "comments": 8, "upvoteRatio": 0.93 },
  "sentiment": "neutral",
  "sentimentScore": 0,
  "sentimentMethod": "lexicon-v1",
  "intent": "discussion",
  "urgency": "normal",
  "provider": "scrapecreators",
  "sourceRequestUrl": "https://api.scrapecreators.com/v1/reddit/search?query=OpenAI&sort=new&timeframe=week&trim=true",
  "collectedAt": "2026-08-31T12:05:00.000Z"
}
```

### API

Use the Actor endpoint with your own Apify token:

```bash
curl -X POST "https://api.apify.com/v2/acts/khadinakbar~sprinklr-alternative/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"watchQueries":["OpenAI"],"timeframe":"week","sort":"new","maxItems":25}'
```

### Prompt card

> Collect a current public Reddit conversation snapshot for these watch queries. Return up to 50 deduplicated source posts with their public URLs, subreddit, publication time, query labels, collection provenance, and deterministic signal fields. Keep the stated cost cap. Describe results as a bounded public-data sample and route broader social or customer-service workflows to their dedicated systems.

### Workflow scenario: from watchlist to review queue

A product lead supplies a brand and one competitor, chooses a one-week window, and requests newest posts. The Actor searches the explicit queries, merges repeated source URLs while retaining every query that matched, and stores source-linked rows. The lead can then filter the dataset by an explicit negative/complaint/high signal for manual review, while retaining the underlying source URL and method label instead of relying on an unexplained dashboard score.

### How this workflow compares with Sprinklr

| Decision point | This Actor | Sprinklr |
| --- | --- | --- |
| Bounded job | Current public Reddit post collection for an explicit watchlist | Broader social listening and unified customer-experience platform |
| Input | Up to five explicit queries and a bounded timeframe | Suite-level multi-channel configuration and workflows |
| Output | Source-linked JSON rows with deterministic signals and collection provenance | Broader monitoring, dashboards, workflows, analytics, and service outputs |
| Billing and same-job cost | Intended $0.01 per persisted conversation plus platform usage; equal-workload comparison is pending final-build evidence | Sprinklr markets an enterprise platform; an equal-workload allocation has not been recorded here |
| Effective efficiency | Final-build duration and usable-row evidence will be recorded before release; the workflow keeps its input and output contract small and inspectable | No authorized equal-workload timing observation is recorded here |
| Integrations | Standard Apify API, dataset readback, schedules, webhooks, and Actor chaining are the portable workflow surfaces; no named client integration is claimed yet | Sprinklr documents CRM, DAM, BI, and enterprise integration workflows |
| Automation | Repeat the same explicit input through an Apify schedule, webhook, API, or Actor chain after final-build verification | Sprinklr documents broader automated social and customer-service workflows |
| Better fit | An inspectable public-Reddit collection run feeding another system | Teams needing multi-channel listening, collaboration, publishing, engagement, governance, or care operations |

Sprinklr documents a platform that brings social listening, publishing, engagement, commerce, advertising, customer service, and advocacy together across 30+ digital and social channels. Those capabilities remain outside this Actor’s contract. Sprinklr is a trademark of its owner; this independent Actor is not affiliated with, associated with, or endorsed by Sprinklr.

### Focused workflow and next step

This Actor has one focused job: produce a current, source-linked Reddit conversation snapshot from a named watchlist. Use the dataset with your own review, alerting, or reporting workflow; schedule only an input whose watch queries and public-data scope you are authorized to monitor.

For a thread-level follow-up after you identify a specific source URL, use the [Reddit Posts & Comments Scraper](https://apify.com/khadinakbar/reddit-posts-comments-scraper) to collect the public post and comments under its own documented contract. That sibling workflow is a better fit for expanding one known thread than repeating a keyword-monitoring run.

### Agent and automation handoff

An AI agent can call this run-model Actor when the request is specifically for a current public Reddit listening snapshot. Use the `OUTPUT` record for the terminal outcome and dataset ID, then retrieve the default dataset to review source rows. Preserve `sourceUrl`, `matchedQueries`, `provider`, `sourceRequestUrl`, and `collectedAt` when passing the data onward, so the next workflow retains query and collection provenance. Stop or clarify when the requested job needs another platform, account access, a customer-service response, or a broader team workspace.

### Builder's note

I built the workflow around inspectability. A compact listening dataset is most useful when every returned row shows where it came from, why it matched, when it was collected, and how a simple triage label was produced. That is a better foundation for review than an unlabeled score—and it keeps broader multi-channel and operational needs easy to route to a product designed for them.

### Responsible use

Collect public sources you are authorized to monitor and respect platform terms, privacy expectations, and applicable law. Keep returned public usernames within an authorized review workflow, and include human review of source text and triage labels before material actions.

# Actor input Schema

## `watchQueries` (type: `array`):

One to five literal public-conversation search queries, such as OpenAI, "Acme Corp", or "Acme Corp" pricing. Each query is searched independently and retained rows name every matching query. Use search terms, not account passwords, cookies, private URLs, or a request to contact anyone.

## `timeframe` (type: `string`):

Reddit search window for each watch query. Choose day for fast-moving incidents, week for a normal pulse, month for broader review, year for a longer lookback, or all for provider-available history. Defaults to week; it does not create a recurring schedule.

## `sort` (type: `string`):

Ordering requested from the public Reddit search source. New is best for current monitoring, relevance finds close text matches, top surfaces high-scoring posts, and comment\_count surfaces discussion-heavy posts. Defaults to new; this is not a sentiment ranking.

## `maxItems` (type: `integer`):

Whole-run maximum number of normalized, deduplicated public conversations persisted, from 1 to 100. The actor stops before this cap, so conversation-event charges cannot exceed $0.01 multiplied by this number. It is not a per-query cap.

## `excludeKeywords` (type: `array`):

Optional case-insensitive literal terms that remove matching post titles or bodies, such as giveaway or hiring. Leave empty to retain all otherwise eligible conversations. These are not regular expressions, Boolean search syntax, or platform moderation rules.

## `responseFormat` (type: `string`):

Concise returns a bounded text excerpt for agent workflows; detailed returns the source-supplied title and body within a safe size limit. Both formats retain source URL, subreddit, timestamps, query labels, and deterministic signals. Defaults to concise; it does not generate an AI summary.

## Actor input object example

```json
{
  "watchQueries": [
    "OpenAI",
    "Anthropic"
  ],
  "timeframe": "week",
  "sort": "new",
  "maxItems": 50,
  "excludeKeywords": [
    "giveaway"
  ],
  "responseFormat": "concise"
}
```

# Actor output Schema

## `conversations` (type: `string`):

Deduplicated public Reddit conversations with watch-query labels, source URLs, and deterministic signals.

## `output` (type: `string`):

Compact terminal outcome, retained count, charge counters, and signal totals.

## `runSummary` (type: `string`):

Detailed source-route diagnostics, write counters, and local charge observations.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "watchQueries": [
        "OpenAI"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("khadinakbar/sprinklr-alternative").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 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);
});

// 📚 Want to learn more 📖? Go to → 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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "watchQueries": ["OpenAI"] }

# Run the Actor and wait for it to finish
run = client.actor("khadinakbar/sprinklr-alternative").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "watchQueries": [
    "OpenAI"
  ]
}' |
apify call khadinakbar/sprinklr-alternative --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,khadinakbar/sprinklr-alternative"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/gq6YcbgChKLmwNbYK/builds/Dv2mbZfZLn9Il9GBl/openapi.json