The Problem

AI agents powered by LLMs face significant memory limitations:

No persistent memory across sessions
Context window limitations
Inefficient memory retrieval
Poor memory prioritization
Lack of memory explanation
Memory hallucinations

This results in agents that forget important context, repeat questions, and provide inconsistent responses.

How AttentionDB Solves It

AttentionDB provides a sophisticated memory management system for LLM agents:

1. Persistent Memory Architecture

Session Memory
- Per-conversation context
- Short-term memory buffer
- Active goals and tasks
Long-term Memory
- Cross-session knowledge
- User preferences
- Historical interactions
Shared Memory
- Common knowledge base
- Learned patterns
- Best practices

2. Intelligent Memory Management

AttentionDB uses multiple attention types to manage agent memory:

recency: Weight: 0.8
- Recent interactions
- Active context windows
- Current conversation flow
relevance: Weight: 0.7
- Topic similarity
- Task alignment
- User intent match
importance: Weight: 0.6
- Critical decisions
- User preferences
- Error corrections

3. Memory Operations

// Initialize agent memory
const agentMemory = await attentionDB.createAgentMemory({
  agentId: "agent_123",
  attention: {
    recency: 0.8,
    relevance: 0.7,
    importance: 0.6
  }
});

// Store memory
await agentMemory.store({
  type: "interaction",
  content: "User prefers dark mode",
  metadata: {
    confidence: 0.9,
    source: "explicit_statement"
  }
});

// Retrieve relevant context
const context = await agentMemory.recall({
  query: "user interface preferences",
  limit: 5
});

// Explain memory selection
const explanation = await agentMemory.explain({
  memoryId: "mem_456",
  format: "natural_language"
});

4. Key Features

Memory Injection

Dynamic context window management
Automatic memory prioritization
Relevance-based filtering

Memory Persistence

Cross-session state management
Durable storage backend
Backup and recovery

Memory Explanation

Transparent decision trails
Memory source attribution
Confidence scoring

Memory Optimization

Automatic summarization
Redundancy elimination
Memory compression

Real World Impact

Case Study: Customer Service AI

A major tech company implemented AttentionDB in their AI support agents:

Before: 40% context retention across sessions
After: 95% context retention, 70% more efficient
ROI: Reduced token usage by 50%

Success Metrics

95% context retention rate
70% reduction in repeated questions
50% lower token consumption
85% user satisfaction increase

Getting Started

# Install AttentionDB Agent Memory
npm install @attanix/agent-memory

# Initialize agent memory system
npx attanix init-agent --model=gpt-4

# Start memory tracking
npx attanix track-agent

Integration Example

import { AttentionDB } from '@attanix/agent-memory';
import { OpenAI } from 'openai';

// Initialize AttentionDB
const attentionDB = new AttentionDB({
  model: 'gpt-4',
  attention: {
    recency: 0.8,
    relevance: 0.7,
    importance: 0.6
  }
});

// Create agent with memory
const agent = await attentionDB.createAgent({
  name: 'support_agent',
  basePrompt: 'You are a helpful support agent...',
  memory: {
    sessionTTL: '24h',
    maxMemories: 1000
  }
});

// Handle conversation with memory
agent.on('message', async (msg) => {
  // Recall relevant memories
  const context = await agent.recall({
    query: msg.content,
    limit: 5
  });
  
  // Generate response with context
  const response = await agent.respond(msg, context);
  
  // Store new memory
  await agent.remember({
    type: 'interaction',
    content: {
      user: msg.content,
      agent: response
    }
  });
});

Best Practices

Configure memory TTL based on use case
Implement regular memory consolidation
Monitor memory usage and performance
Use explanation API for transparency

LLM Agent Memory: Persistent Context for AI Agents