Polybius: Multi-Domain Cognitive Agent Framework

Polybius is an adaptive, multi-domain intelligent agent framework designed for contextual awareness, predictive reasoning, and real-time collaboration between agents. It combines memory, multi-agent coordination, and explainable AI to deliver reliable, self-optimizing performance.

Core Features

• Contextual Memory Matrix – Stores and recalls layered situational data, preserving both facts and relationships over time.
• Multi-Agent Collaboration Hub – Enables specialized agents to share insights and coordinate actions in real time.
• Scenario Simulation Engine – Runs “what-if” simulations to forecast outcomes before executing tasks.
• Adaptive Skill Loader – Dynamically attaches or removes tools (translation, data mining, generative media, etc.) as needed.
• Behavioral Evolution Model – Improves decision-making using reinforcement learning and live feedback.
• Explainable AI Layer – Generates transparent, human-readable reasoning for predictions and actions.

Installation

npm install
npm test                   # Run test suite
npm run dev                # Development mode  
npm start                  # Production mode

Quick Start

import { ContextualMemoryMatrix } from './src/core/memory';
import { CollaborationHub } from './src/core/collaboration';
import { ScenarioSimulationEngine } from './src/core/simulation';

const memory = new ContextualMemoryMatrix();
const hub = new CollaborationHub();
const simulator = new ScenarioSimulationEngine();

// Store contextual experience
await memory.store('mission', {task: 'Data mining operation'}, 0.85, ['analytics', 'ai']);

// Enable agent collaboration
hub.registerAgent('analysisAgent', {capabilities: ['data-processing', 'trend-detection']});
hub.registerAgent('reportAgent', {capabilities: ['summary-generation', 'visualization']});
hub.connectAgents('analysisAgent', 'reportAgent');

// Simulate an outcome
const outcome = await simulator.runScenario('Market prediction for Q4');
console.log(`Predicted trend: ${outcome.trend}, Confidence: ${outcome.confidence}`);

Configuration

{
  "name": "PolybiusCore",
  "personality": {
    "systemPrompt": "Multi-domain AI strategist with adaptive learning capabilities",
    "emotionalRange": {
      "creativity": 0.75,
      "analytical": 0.95,
      "empathy": 0.55,
      "humor": 0.35,
      "enthusiasm": 0.85
    },
    "tools": [
      {"name": "data_mining", "enabled": true},
      {"name": "scenario_simulation", "enabled": true},
      {"name": "sentiment_analysis", "enabled": true}
    ],
    "learningRate": 0.4,
    "memoryCapacity": 2000
  }
}

API Reference

Contextual Memory

await memory.store(type, data, importance, tags);
const recallData = await memory.recall('query', limit);
const insights = await memory.generateInsights();

Multi-Agent Collaboration

hub.registerAgent(name, config);
hub.connectAgents(agentA, agentB);
hub.broadcastMessage('analysisAgent', {topic: 'status-update'});

Scenario Simulation

const result = await simulator.runScenario(description);
await simulator.trainModel(historicalData);
const recommendations = await simulator.getRecommendations(currentState);

Examples

• examples/basic-agent.json – Minimal setup for single-agent tasks
• examples/collaborative-agents.json – Multi-agent coordination
• examples/predictive-strategist.json – High analytical reasoning with forecasting

Testing

The test suite covers:

• Contextual memory with relationship mapping
• Multi-agent coordination and communication
• Scenario simulation accuracy
• Adaptive skill loading behavior
• Explainable AI reasoning output
• No external API dependencies

Architecture

• Core: Contextual memory, multi-agent hub, simulation engine
• Tools: Data mining, sentiment analysis, scenario forecasting
• Config: Personality modeling with adaptive behaviors
• Tests: Comprehensive internal coverage with no external calls