MindMesh AI - 7 AI Agents Debate Your Decisions in Real-Time

Published: 1 month ago (December 12, 2025 at 10:21 PM EST)

4 min read

Source: Dev.to

🎥 Video Demo

🎯 What Problem Does It Solve?

Making complex life decisions—career changes, buying a house, starting a business—is hard.
The biggest obstacle is confirmation bias: we gravitate toward information that confirms our existing beliefs, miss risks, and overlook alternative perspectives.

MindMesh AI tackles this by simulating a team of 7 specialized AI agents that:

Analyze your question from multiple angles simultaneously
Debate each other in real time
Detect biases and verify facts
Deliver a balanced, evidence‑based recommendation

Think of it as having a research team, devil’s advocate, fact‑checker, and strategic advisor—all working together in about 5 seconds.

💡 Why I Built This

I was stuck between staying in a stable job or pursuing AI/ML full‑time. Friends, articles, and pro/con lists still left me uncertain. The idea struck me: What if multiple AI agents could debate my decision, each from a different perspective?

The resulting system gives me (and anyone else) a multi‑perspective analysis in seconds, breaking the echo‑chamber effect.

✨ What Makes It Special

1. Parallel Agent Processing ⚡

All 7 agents run simultaneously using Google Gemini’s speed and async processing.
Result: ~5‑second comprehensive analysis vs. 35+ seconds sequentially—a 7× speed boost.

2. Real‑Time Agent Debate 🎭

WebSocket connections stream each agent’s response as it finishes:

📊 Research Agent – drops statistics
💡 Pro Advocate – builds the case for
😈 Con Advocate – identifies risks
🎯 Bias Checker – calls out weak reasoning
✅ Fact Checker – verifies claims
🎓 Synthesizer – delivers the verdict

You watch the debate unfold live.

3. Intelligence Transparency 🔍

Every agent’s reasoning is visible:

Data that influenced the recommendation
Strongest arguments
Detected biases
Verified facts
Confidence level (X/10)

No black box.

4. Production‑Ready Features 🚀

History System – revisit past analyses
Smart Follow‑ups – AI suggests relevant next questions
Export Analysis – download as Markdown
Confidence Visualization – see recommendation strength
Mobile Responsive – works on all devices

🛠️ How It Works

Tech Stack

Backend: Python, FastAPI, WebSockets, async/await
Frontend: React 18, Vite, Tailwind CSS
AI: Google Gemini API (1.5‑flash for speed, 1.5‑pro for depth)
Real‑time: WebSocket streaming

Architecture

User Question
    ↓
WebSocket Connection
    ↓
PHASE 1: Parallel Analysis
├─ Research Agent (data & statistics)
├─ Pro Advocate (arguments FOR)
└─ Con Advocate (arguments AGAINST)
    ↓ (all run simultaneously)
PHASE 2: Quality Control
├─ Bias Checker (analyzes Phase 1)
└─ Fact Checker (verifies claims)
    ↓
PHASE 3: Synthesis
└─ Synthesizer (final recommendation)
    ↓
Structured Output + Confidence Score

Agent Specializations

📊 Research Agent – gathers statistics, trends, market data; provides an objective foundation.
💡 Pro Advocate – optimistic, builds the strongest case for the decision.
😈 Con Advocate – cautious, highlights potential problems and risks.
🎯 Bias Checker – critical thinker that spots logical fallacies and weak reasoning.
✅ Fact Checker – verifies claims for accuracy, flags unverified statements.
🎓 Synthesizer – weighs all perspectives and delivers a structured recommendation with a confidence score.
🧠 Orchestrator – (behind the scenes) coordinates workflow and manages agent communication.

🚀 Try It Live

Live Demo:
GitHub Repo:

No login required—just visit and ask a question.

💭 Example Questions

“Should I switch careers to AI/ML engineering?”
“Is buying a house in 2025 a good financial decision?”
“Should I start a SaaS business or get a job?”
“Is remote work better than office work?”

🎨 User Experience Highlights

Beautiful Dark Theme UI – gradient backgrounds, smooth animations, color‑coded agent cards.
Real‑Time Feedback – live status updates (e.g., “🚀 Activating agent swarm…”), processing time display.
Smart Interactions – one‑click example questions, history sidebar, export button, follow‑up suggestions.

📊 Technical Achievements

Performance

5‑second analysis (7 agents in parallel)
7× faster than sequential processing
Real‑time streaming via WebSockets
Async/await for non‑blocking operations

Code Quality

Modular architecture with separate agent classes
Graceful error handling with helpful messages
Type safety using Pydantic models
Well‑documented, maintainable codebase

Scalability

Stateless agents – easy to add more
WebSocket pooling – supports multiple concurrent users
API‑first design – ready for mobile apps
Environment‑based configs – simple deployment

🎯 Challenge Requirements Met

✅ Software side project – built from scratch with Python & React
✅ Web application – live at
✅ My own code – 100 % original implementation
✅ Easy testing – no login, instant access
✅ Live demo – deployed on Vercel
✅ GitHub repo – open source at
✅ 1‑minute pitch video – embedded above

What the App Does

MindMesh AI takes any decision‑making question, runs it through 7 specialized agents in parallel, and returns a balanced recommendation in about 5 seconds.

Why I Built It

To overcome my own confirmation‑bias problem when deciding on a career path and to help others avoid echo chambers in decision‑making.

What Makes It Unique

Multi‑agent debate system – first of its kind for decision intelligence
Real‑time streaming – watch agents think and debate live
Full transparency – see every agent’s reasoning, not just the final answer
7× faster – parallel processing vs. sequential AI responses