LLM-Council - Mobile App (Pre-release Annoucement)

Stop Asking One AI. Build an LLM Council for Complex Decision-Making 🚀

Standard LLM workflows usually follow a single path: you input a prompt, and a single model provides a single response. For simple tasks, this is efficient. However, for nuanced strategic planning, risk analysis, or complex architectural choices, relying on a single AI perspective introduces bias and blind spots.
To solve this, I built LLM Council—an open-source multi-agent debate framework that orchestrates specialized AI personas to stress-test ideas before synthesizing a finalized, objective strategy.
Here is a breakdown of the architecture, the technology stack, and how this agentic workflow can be utilized for advanced problem-solving.

🏗️ The Architecture: Multi-Agent Debate & Synthesis

Instead of routing a query to a standard chatbot, this project implements a specialized mixture-of-agents (MoA) orchestration pattern.

[Complex Problem Input]
│
▼
┌───────────────────┐ ┌───────────────────┐
│ Persona A (Risk) │◄────►│ Persona B (Tech) │ (Multi-Agent Debate)
└─────────┬─────────┘ └─────────┬─────────┘
│ │
└────────────┬─────────────┘
│
▼
┌─────────────────┐
│ Evaluator LLM │ (Synthesizes arguments)
└────────┬────────┘
│
▼
[Optimized Strategy Output]

Dynamic Persona Initialization: The framework analyzes the user's dilemma and dynamically provisions specialized AI agents with conflicting, complementary perspectives (e.g., a highly aggressive growth hacker vs. a conservative legal/risk compliance officer).
Autonomous Cross-Examination: The agents debate the core problem asynchronously. They challenge each other’s assumptions, identify edge cases, and call out flaws in reasoning.
The Evaluator Pattern: A separate, unbiased Evaluator LLM parses the entire debate transcript. It discards conversational noise, extracts verified insights, and outputs a highly structured, risk-mitigated decision roadmap.

🛠️ The Open-Source Tech Stack & Android v1 Build

The system is fully open-source, modular, and optimized for mobile performance.

Android v1 Deployment: You can test the application directly on your device by downloading from the given link below.

🗺️ Next on the Roadmap: Integrating Agentic RAG

The current baseline system uses the pre-trained weights of the underlying LLMs to fuel the debate. To make it highly viable for rapidly shifting markets, the system requires live data inputs.
I have set a community milestone on X (Twitter): If the launch post hits 100 likes, I will immediately integrate Agentic RAG using Perplexity or Tavily AI APIs. This will enable the individual agents to perform autonomous web searches during the debate stage, validating their arguments with real-time web data and factual documentation.