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

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

Quick Start

`typescript
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

`json
{
"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

$3

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

$3

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

$3

`typescript
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