Product Manager

200 Reddit Threads Told Us Exactly What to Build Next

217 threads in 23 minMarketing & SEO5 min read

Key Takeaway

We scraped 200+ Reddit threads about multi-agent frameworks, categorized every complaint, and built our product roadmap directly from competitor pain points.

The Problem

User research is expensive when you do it right. Recruiting participants, scheduling interviews, synthesizing notes β€” a proper research sprint takes 3-4 weeks and $10K+ in time. Most early-stage startups skip it and guess.

The alternative: go where people already complain. Reddit is the world's largest unsolicited feedback database. r/MachineLearning, r/LangChain, r/AutoGPT, r/artificial, r/LocalLLaMA β€” thousands of posts from people describing exactly what's broken about the tools we compete with. Every thread is a user interview we didn't have to schedule.

The problem was volume. There were hundreds of relevant threads across a dozen subreddits. Reading them manually would take days. We needed automated collection and structured synthesis.

The Solution

An Apify actor to scrape the threads. An Mr.Chief skill to categorize every complaint by type, frequency, and severity. The output: a ranked pain point list that became the first draft of our product roadmap.

The Process

Step 1: Configure the Reddit scraper.

javascriptShow code
// reddit-research-scraper.js
const input = {
  startUrls: [
    "https://reddit.com/r/LangChain",
    "https://reddit.com/r/AutoGPT",
    "https://reddit.com/r/MachineLearning",
    "https://reddit.com/r/LocalLLaMA",
    "https://reddit.com/r/artificial"
  ],
  searchKeywords: [
    "multi-agent", "agent framework", "AutoGPT problems",
    "LangChain issues", "CrewAI bugs", "agent deployment",
    "AI agent production", "agent reliability"
  ],
  maxPostsPerSubreddit: 50,
  includeComments: true,
  maxCommentsPerPost: 30,
  sortBy: "relevance",
  timeFilter: "year",
  minScore: 5
};

Total collected: 217 threads, 4,300+ comments.

Step 2: Pain point categorization.

The agent analyzed every complaint and categorized it:

View details
Pain Point Categories Identified:

1. RELIABILITY
   - Agents hallucinate tool calls
   - Infinite loops with no timeout
   - Non-deterministic behavior in production
   - "It works 70% of the time in dev, 30% in prod"

2. OBSERVABILITY
   - No visibility into what agents are doing
   - Can't debug why an agent took a wrong turn
   - No logging that's actually readable
   - Token costs invisible until the bill arrives

3. DEPLOYMENT
   - "How do I actually run this in production?"
   - No persistent state between runs
   - Scaling a single agent is hard; scaling 10 is impossible
   - Infrastructure complexity is killing adoption

4. COST CONTROL
   - No spend limits per agent or per run
   - One runaway agent can rack up $400 in tokens overnight
   - No way to set fallback to cheaper models

5. HUMAN-IN-THE-LOOP
   - Hard to insert approval steps
   - No way to pause and resume
   - Can't hand off between agent and human mid-task

6. MULTI-AGENT COORDINATION
   - Agents don't know what other agents are doing
   - No shared memory between agents
   - Orchestration logic is just more code to debug

Step 3: Frequency and severity ranking.

Pain PointThread MentionsAvg UpvotesSeverity (AI-scored)Priority
No observability / can't debug8934CriticalP0
Reliability in production7641CriticalP0
Deployment complexity6828HighP1
Cost control / runaway spend5437HighP1
Human-in-the-loop workflows4122MediumP2
Multi-agent coordination3831HighP1
State persistence3319MediumP2

The combination of mention frequency and average upvotes on those threads gives a proxy for how broadly the pain is felt vs. how intensely individuals feel it.

Three Features Built Directly From Reddit

1. Agent Activity Feed (from: "no observability" β€” P0)

Real-time log of every tool call, model call, and decision point. Searchable. Filterable by agent. Every entry links to the full context that produced it.

2. Spend Guards (from: "runaway cost" β€” P1)

Per-agent token budgets with hard stops. Configurable: fail silently, fail loudly, or pause and notify. Optional: automatic fallback to a cheaper model when budget is 80% consumed.

3. Approval Gates (from: "human-in-the-loop" β€” P2)

Any agent step can be flagged as requiring human approval before execution. The agent pauses, sends a notification, waits for the approve/reject, and resumes with the decision in its context.

All three shipped within 90 days of the research run.

The Results

217

Threads collected

4,300+

Comments analyzed

47 unique

Pain points categorized

23 minutes

Time to complete research

3 (within 90 days)

Features shipped from Reddit data

~20

User interview sessions replaced

$8,000-12,000

Estimated research cost savings

The highest-ROI part: the pain points confirmed things we suspected but couldn't prioritize. The observability problem was the #1 complaint β€” and we'd been treating it as a nice-to-have. Reddit moved it to P0.


Your competitors' users are writing your product roadmap for free. You just need to go read the threads.

product researchRedditAI automationcompetitor analysis

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200 Reddit Threads Told Us Exactly What to Build Next β€” Mr.Chief