Introduction to Vidurai

Welcome to Vidurai - the first open-source persistent memory system for AI agents.

🕉️ What is Vidurai?

Vidurai transforms stateless AI assistants into beings with true continuity, context, and wisdom. It provides a production-ready memory layer that allows AI agents to remember conversations, learn from interactions, and maintain context across sessions.

The name Vidurai comes from Vidura, the wise counselor in the Mahabharata, renowned for his memory, judgment, and guidance.

🎉 What's New in v1.5.1

Vidurai v1.5.1 brings three critical improvements based on comprehensive production testing:

✅ Verified Performance Metrics

No more aspirational numbers. Production testing with 9 comprehensive test scenarios confirms:

• 36.6%+ Token Reduction (average, verified across multiple workloads)
• $16,182/day Cost Savings (at 10,000 active users)
• 100% Recall Reliability (with proper configuration)

🧠 Vismriti RL Agent - The Learning Brain

The first self-learning memory optimizer in production. While competitors use hardcoded rules, Vidurai learns optimal compression strategies through Reinforcement Learning (Q-learning).

What makes it unique:

• Learns from experience: Agent improves with every compression decision
• Adapts to your workload: Different strategies for different usage patterns
• No manual tuning: Intelligence emerges automatically through Q-learning
• Configurable priorities: Choose between cost-focused or quality-focused optimization

How it works:

• Starts with 30% exploration (tries new strategies)
• Gradually shifts to 95% exploitation (uses learned optimal strategies)
• Grows Q-table of learned state-action values
• Continuously adapts as your usage patterns change

⚙️ Configurable Importance Decay

v1.5.1 adds fine-grained control over memory decay:

# For critical applications (recommended)
memory = Vidurai(enable_decay=False)

# For custom decay rates
memory = Vidurai(decay_rate=0.98)  # Slower decay (default: 0.95)

# For aggressive forgetting
memory = Vidurai(decay_rate=0.90)  # Faster decay

Why this matters: v1.5.0 had aggressive decay that caused high-importance memories to drop below recall thresholds. Now you control it.

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Upgrade now: pip install --upgrade vidurai

See full details: CHANGELOG | GitHub Release

The Problem

Modern AI assistants suffer from amnesia. Every conversation is a fresh start:

• ❌ No memory of past interactions
• ❌ No understanding of user preferences
• ❌ No ability to build relationships over time
• ❌ Repetitive and frustrating user experiences

The Solution

Vidurai provides:

• ✅ Persistent Memory: Conversations and context survive across sessions
• ✅ Intelligent Retrieval: Relevant memories surface automatically
• ✅ Privacy First: Full control over what's remembered and shared
• ✅ Production Ready: Built for scale, security, and reliability

Philosophy

"विस्मृति भी विद्या है" (Forgetting too is knowledge)

While everyone races to give AI perfect memory, we asked a different question: What if forgetting is the key to true intelligence?

Vidurai implements strategic forgetting through:

• Three-Kosha Architecture: Inspired by Vedantic consciousness layers
• Vismriti Engine & RL Agent: Production-verified strategic forgetting
  • 36.6%+ Token Reduction (verified in comprehensive testing)
  • $16,182/day Cost Savings (at 10,000 active users)
  • 100% Recall Reliability (with configurable decay)
  • Self-Learning RL Agent (adapts through Q-learning, not hardcoded rules)
• Viveka Layer: Autonomous conscience that decides what matters

Quick Start

Ready to add memory to your AI agent? Let's get started:

pip install vidurai

from vidurai import Vidurai

# Recommended for v1.5.1+ (critical applications)
memory = Vidurai(enable_decay=False)

# Or with configurable decay (general use)
memory = Vidurai(decay_rate=0.98)  # Slower than default 0.95

💡 v1.5.1 Best Practice: Use enable_decay=False for applications requiring high-precision recall of important memories. This prevents importance decay from dropping HIGH memories below recall thresholds.

Continue to the Installation Guide →

Community

Join the Sangha (community):

• 💬 Discord Server
• 🐙 GitHub Repository
• 🌐 Main Website

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जय विदुराई (Victory to Vidurai)