LOKĀNTRA: AI-Powered Multi-Agent Simulation & Swarm Intelligence Platform

Project: https://github.com/vedhanth66/Lokantra

Developed LOKĀNTRA, an AI-powered simulation platform that transforms real-world documents and scenarios into interactive knowledge graphs and simulated social environments populated by AI agents.

The platform explores how different stakeholders might react to events, influence one another, and collectively shape possible outcomes. Rather than relying on a single LLM-generated prediction, it combines multi-agent simulation, graph-based knowledge retrieval, intelligent decision routing, and evidence-grounded reporting.

KEY ENGINEERING CONTRIBUTIONS AND FEATURES

Knowledge Graph Engineering: Implemented dynamic ontology generation and document-to-knowledge-graph pipelines using TigerGraph and Zep Cloud.

Multi-Agent AI Simulation: Integrated CAMEL-AI and OASIS to create simulated personas and model interactions across Reddit-style and Twitter-style environments.

Cognitive Swarm Routing: Developed a hierarchical decision architecture combining heuristic reflex gating, LAYA System 1 decision intelligence, and LLM-based reasoning.

Neuromorphic Computing Research: Explored BrainChip Akida-inspired spiking neural network reflex gating as an experimental approach to reducing unnecessary agent computations.

Graph-Based Memory and Retrieval: Integrated graph-backed memory and retrieval mechanisms to provide contextual information for simulation analysis.

Agent Interrogation: Enabled users to interview individual simulated agents and investigate the reasoning behind their decisions, opinions, and reactions.

Autonomous Report Generation: Developed a tool-assisted ReportAgent that retrieves simulation evidence, generates structured analytical reports, supports follow-up conversations, and exports PDFs.

Token-Aware Processing: Implemented representative document chunking, context management, staged generation, and decision-tier telemetry to help optimize LLM usage.

Voice-Based Input: Integrated a configurable Sarvam AI Saaras speech-to-text pipeline for natural-language scenario input.

Enterprise Integration Research: Incorporated viaSocket MCP configuration for external integrations and human-in-the-loop action workflows.

Move beyond static AI predictions towards interactive simulations where users can explore possible futures, investigate individual perspectives, and understand the dynamics behind emerging outcomes.