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Cover image for StanceStream – Real-Time Multi-Agent AI Debates Powered by Redis
Elizabeth Stein
Elizabeth Stein

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StanceStream – Real-Time Multi-Agent AI Debates Powered by Redis

Redis AI Challenge: Real-Time AI Innovators

This is a submission for the Redis AI Challenge: Real-Time AI Innovators.

What I Built

StanceStream is a production-ready, multi-agent AI debate engine that turns political discussions into live, evolving simulations.
It uses all four Redis 8 data models in a single cohesive system to power intelligent agents with persistent personalities, memory-driven reasoning, stance evolution tracking, and real-time fact-checking.

This is not just a demo — it’s a fully operational real-time AI platform built for performance, scalability, and contest readiness.

Key Features:
-Real-time debates between intelligent AI agents with distinct political personalities
-Semantic caching engine achieving 70%+ hit rates with Redis Vector search
-Live stance evolution tracking using RedisTimeSeries
-Multi-agent intelligence with emotional states and coalition building
-Advanced fact-checking with multi-source verification and confidence scoring
-Business intelligence dashboard showing ROI and cost savings
-Concurrent debate support with topic isolation across all components
-Performance optimization via live metrics engine

Demo

Live app: https://stancestream.vercel.app/

Repo: https://github.com/forbiddenlink/stancestream

Highlights:
-4-mode navigation: Standard debate, Multi-debate viewer, Analytics dashboard, Business metrics
-Real-time stance charts: Track position changes during the debate
-Semantic Cache Dashboard: Live Redis Vector operations in action
-Agent battles: SenatorBot vs ReformerBot on 8+ political topics
-Topic synchronization: Ensures accurate stance tracking in multi-agent debates

How I Used Redis 8

*RedisJSON – Complex Data Structures
-Stores rich agent profiles with nested personality traits, evolving political stances, and emotional states
-Tracks cache metrics, contest scoring, and key moments with AI-generated summaries
-Maintains intelligence metrics for emotional trajectory analysis
*Redis Streams – Real-Time Messaging
-Broadcasts public debate messages via WebSocket to all connected clients
-Maintains private agent memories for strategic reasoning and coalition building
-Logs message history with pagination for replay and context management
*RedisTimeSeries – Time-Based Analytics
-Tracks stance evolution over time for each agent
-Measures emotional shifts that influence debate responses
-Records system performance metrics for live optimization
-Monitors debate momentum and trend patterns
*Redis Vector – Semantic Intelligence
-Runs semantic caching with an 85% similarity threshold, achieving 70%+ hit rates
-Powers the fact-checker, comparing claims against multiple knowledge bases using COSINE similarity
-Isolates topics to prevent cross-contamination between debates
-Uses OpenAI text-embedding-ada-002 (1536 dimensions) for high-quality embeddings

Advanced Redis Features
-Vector search with topic filtering to maintain cache accuracy
-Real-time Redis optimization with live performance tuning
-Concurrent connection management with automatic cleanup
-Enterprise-grade error handling and reconnection strategies
Memory-efficient operations using data retention policies

Production-Ready Architecture
-Centralized Redis connection manager with health monitoring
-WebSocket broadcasting to multiple clients for instant updates
-Background process orchestration for long-running simulations
-Comprehensive logging and live performance metrics

Why It’s Unique
StanceStream proves Redis is far more than a cache — it’s a complete real-time AI backbone.
By orchestrating multiple data models in a single architecture, it delivers intelligent, personality-driven debates with live analytics, semantic reasoning, and fact verification at scale.
This project demonstrates how Redis 8 can drive high-performance, multi-model AI applications in production environments, showcasing true real-time intelligence.

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