AI Debate Bot
Multi-Agent Structured Reasoning System
A comprehensive multi-agent LLM system capable of deep logical reasoning and real-time structured debate. Implements advanced conversational memory and dynamic persona adaptation.
The Problem
Existing LLM interfaces fail at complex reasoning tasks that require adversarial thinking. Single-prompt architectures hallucinate or agree blindly, making them unsuitable for robust analytical tasks.
Architecture
Implemented a multi-agent orchestration layer where distinct 'Persona Agents' debate a topic, monitored by a 'Judge Agent'. Redis handles real-time pub/sub for agent communication.
Decision Log
"Chose FastAPI over Flask for async native support, crucial for streaming LLM responses. LangChain was used for initial prototyping but heavily customized for the final production memory buffers."
Performance
Optimization
Reduced API costs by 40% using semantic caching (Redis + vector embeddings) to avoid redundant LLM calls on similar debate topics.
Scaling Logic
Stateless agent execution pods scaled horizontally via Kubernetes, with shared context maintained in a low-latency Redis cluster.
Challenges
Managing token context windows during long debates. Solved by implementing a summarizing compression agent that periodically compacts older context.
Final Impact
Currently used by 500+ researchers to stress-test logical arguments and uncover cognitive biases in AI models.