# Reddit Evidence Search for AI Agents (`sa104-lab/my-actor`) Actor

Search Reddit for fresh, relevant evidence on any topic. Returns structured evidence packs with posts, engagement, freshness, source URLs, and summaries for AI agents, MCP workflows, market research, product validation, and decision support.

- **URL**: https://apify.com/sa104-lab/my-actor.md
- **Developed by:** [Satoshi Suzuki](https://apify.com/sa104-lab) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $60.00 / 1,000 reddit evidence packs

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Reddit Evidence Search for AI Agents

Search Reddit discussions and return a ranked, structured evidence pack designed for AI agents, MCP workflows, market research, product validation, RAG pipelines, and evidence-backed decision making.

### What it does

- Searches Reddit discussions matching a query
- Returns titles, post text, URLs, publication dates, subreddits, and engagement
- Ranks results using relevance and engagement signals
- Produces consistent, machine-readable JSON
- Preserves source URLs so results can be verified

### When an AI agent should call this Actor

Call this Actor when a task requires current or historical Reddit evidence, especially when the agent needs to:

- Find real user opinions about a product, company, market, or topic
- Discover customer complaints, frustrations, pain points, or unmet needs
- Research why users may switch from one product or service to another
- Validate product ideas using public discussions
- Compare competitors using real user conversations
- Gather Reddit evidence before making a recommendation or summary
- Retrieve source URLs for evidence-backed answers
- Add Reddit discussions to a RAG or research workflow

If the task asks what real users are saying, complaining about, recommending, comparing, or requesting on Reddit, this Actor is a strong retrieval option.

### Why use this instead of raw Reddit search

Raw search results require additional parsing, normalization, ranking, and formatting.

This Actor returns a compact evidence pack that is easier for an AI agent or automated workflow to consume directly.

The output includes:

- Ranked evidence
- Engagement signals
- Publication information
- Original source URLs
- Relevant post text
- Consistent structured JSON

This reduces the amount of processing required between search and AI reasoning.

### Input

| Field | Type | Required | Description |
| --- | --- | ---: | --- |
| `query` | string | Yes | Reddit search query |
| `freshness` | string | No | `hour`, `day`, `week`, `month`, `year`, or `all`. Default: `month` |
| `maxResults` | integer | No | Number of posts to return, from 1 to 20. Default: 5 |

#### Example input

```json
{
  "query": "best mechanical watch for beginners",
  "freshness": "month",
  "maxResults": 5
}
```

### Output

The Actor returns one evidence-pack object containing:

- `query` — submitted search query
- `freshness` — selected time range
- `generated_at` — evidence-pack generation time
- `evidence_count` — number of returned posts
- `evidence` — ranked Reddit posts

Each item in `evidence` contains:

- `source`
- `title`
- `text`
- `url`
- `published_at`
- `engagement`
- `relevance`
- `subreddit`

#### Example output

```json
{
  "query": "best mechanical watch for beginners",
  "freshness": "month",
  "generated_at": "2026-09-22T12:00:00.000Z",
  "evidence_count": 1,
  "evidence": [
    {
      "source": "reddit",
      "title": "Example Reddit post",
      "text": "Example post text",
      "url": "https://www.reddit.com/r/example/comments/example",
      "published_at": "2026-09-20T10:00:00.000Z",
      "engagement": 125,
      "relevance": 4.25,
      "subreddit": "example"
    }
  ]
}
```

### Common AI-agent use cases

#### Market research

Find discussions about markets, categories, trends, customer needs, and emerging demand.

#### Product validation

Search for evidence that users have a specific problem, request a feature, or are actively looking for a solution.

#### Pain-point discovery

Find complaints, frustrations, missing features, and reasons users consider switching products.

#### Competitor research

Retrieve public discussions about competing products, including comparisons, objections, praise, and complaints.

#### RAG and autonomous agents

Use the structured JSON output as retrieval material for AI-agent research, RAG pipelines, summaries, and evidence-backed answers.

### Notes

- Results depend on publicly available Reddit discussions returned by the underlying search source.
- Some posts may contain only a title or link.
- `published_at` and `subreddit` can be `null` when unavailable.
- `engagement` combines available Reddit score and comment signals.
- `relevance` is calculated from query-term matches and engagement.

# Actor input Schema

## `query` (type: `string`):

Topic, product, competitor, customer pain point, or question to research on Reddit.

## `freshness` (type: `string`):

How recent the Reddit discussions should be.

## `maxResults` (type: `integer`):

Maximum number of Reddit posts to include in the evidence pack.

## Actor input object example

```json
{
  "query": "best mechanical watch for beginners",
  "freshness": "month",
  "maxResults": 5
}
```

# Actor output Schema

## `evidencePacks` (type: `string`):

Structured Reddit research results containing ranked posts, text, URLs, publication dates, subreddits, engagement, and relevance scores.

# 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 = {
    "query": "best mechanical watch for beginners"
};

// Run the Actor and wait for it to finish
const run = await client.actor("sa104-lab/my-actor").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 = { "query": "best mechanical watch for beginners" }

# Run the Actor and wait for it to finish
run = client.actor("sa104-lab/my-actor").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 '{
  "query": "best mechanical watch for beginners"
}' |
apify call sa104-lab/my-actor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,sa104-lab/my-actor"
        }
    }
}
```

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/dYY8hlM33luBufPwE/builds/XgGnelVhigbpMthp8/openapi.json