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

{
  "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

{
  "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.