Virtual Vakil: A Multi-Agent Reinforcement Learning System for Comprehensive Legal Intelligence and Judicial Reform
Virtual Vakil presents a multi-agent reinforcement learning system designed for comprehensive legal intelligence in the Indian judicial context. The system employs 15 specialised AI agents, each trained for distinct legal functions — from case law research to courtroom argument simulation and document drafting.
Agent Hierarchy
Tier 1 — Core Intelligence
Chanakya
Strategic Legal Advisor
Nyaydhish
AI Judge / Case Evaluator
Vad-Vivad
Courtroom Argument Simulator
Tier 2 — Research & Drafting
Vidhi-Vetta
Statutory Interpretation
Munshi
Document Drafting
Pustakalya
Legal Research Library
Tier 3 — Operations & Monitoring
Rakshak
Case Monitoring & Alerts
Sahaayak
Client Communication
Gidh
Regulatory Change Tracker
Research Highlights
First multi-agent RL system specifically designed for the Indian legal framework, incorporating IPC, CrPC, BNS, BNSS, and IT Act expertise.
15 specialised agents with role-specific reward functions — each optimised for a distinct legal task rather than general-purpose conversation.
Hierarchical coordination protocol enabling agents to collaborate on complex legal scenarios (e.g., Chanakya delegates research to Pustakalya, receives analysis from Nyaydhish).
Evaluation framework with domain-expert legal practitioners as annotators — not crowdsourced labels.
Deployment architecture optimised for WhatsApp delivery, enabling real-time legal assistance at scale.
Read the latest research
Our 2026 paper on VIM-1 details the quantized model architecture and privacy-first design.
Read VIM-1 Paper (2026)