Memory Management Best Practices

Optimize agent memory

Learn advanced techniques for structuring, organizing, and optimizing agent memory for maximum performance and recall accuracy.

Memory Architecture Principles

Hierarchical Structure

Organize memories in hierarchical categories with clear relationships between concepts and contexts.

Semantic Clustering

Group related memories together using semantic similarity to improve retrieval efficiency and context awareness.

Temporal Indexing

Include temporal information to help agents understand the chronological context of memories and information evolution.

Priority Weighting

Assign importance scores to memories based on frequency of access, relevance, and business impact.

Memory Content Optimization

1. Content Structure

Optimal Memory Format:

{
  "content": "Clear, concise description of the knowledge or fact",
  "context": "When and where this information applies",
  "metadata": {
    "category": "primary_classification",
    "subcategory": "specific_type",
    "keywords": ["key", "terms", "for", "search"],
    "confidence": 0.95,
    "source": "origin_of_information",
    "last_verified": "2024-01-15",
    "access_frequency": 42,
    "business_impact": "high",
    "related_memories": ["id1", "id2", "id3"]
  }
}

2. Content Guidelines

Be Specific and Actionable

Instead of "Customer service is important," write "Respond to customer inquiries within 2 hours during business hours (9 AM - 6 PM EST) to maintain satisfaction scores above 85%."

Include Context and Conditions

Specify when, where, and under what conditions the memory applies. This helps agents make appropriate decisions based on context.

Use Consistent Terminology

Maintain consistent vocabulary across all memories to improve semantic search accuracy and reduce confusion.

Retrieval Optimization

Search Strategy

Multi-Stage Retrieval Process:

Broad Search: Cast a wide net with general terms
Semantic Filtering: Use vector similarity to find relevant matches
Context Ranking: Rank results by contextual relevance
Confidence Scoring: Apply confidence thresholds
Final Selection: Choose top N most relevant memories

Query Enhancement

Query Expansion

Automatically expand queries with synonyms and related terms:

Original: "refund policy"
Expanded: "refund policy return money back guarantee cancellation"

Context Injection

Include conversation context in search queries:

Query: "shipping" + "customer complaint" + "delayed order"

Memory Lifecycle Management

Automated Maintenance

Regular Cleanup Schedule

Daily: Remove duplicate memories and update access frequencies
Weekly: Verify memory accuracy and update confidence scores
Monthly: Archive rarely accessed memories and optimize indexes
Quarterly: Review and update memory categories and relationships

Quality Metrics

Retrieval Accuracy: % of relevant results returned

Response Time: Average search latency

Memory Utilization: % of memories accessed regularly

Confidence Distribution: Average confidence scores

Advanced Techniques

Memory Graphs

Create knowledge graphs that show relationships between memories for enhanced context understanding.

Customer Service Policy
         ↓
    ┌────────────────────┐
    ↓                    ↓
Refund Process    Response Times
    ↓                    ↓
Digital Products   Email: 2 hours
Physical Products  Phone: immediate
    ↓                    ↓
30 days           Chat: 5 minutes
60 days

Contextual Memory Layers

Layer Structure:

Core Knowledge: Fundamental facts and policies (never expires)
Contextual Information: Situation-specific guidance (periodic review)
Temporal Data: Time-sensitive information (auto-expires)
Personal Context: User-specific memories (privacy-controlled)

Performance Optimization

Indexing Strategies

Create composite indexes for common query patterns
Use partial indexes for frequently accessed categories
Implement caching for popular memories
Optimize vector similarity search parameters

Memory Compression

Compress rarely accessed memories
Use summary representations for large documents
Implement tiered storage (hot/warm/cold)
Archive historical memories with reduced precision

Common Pitfalls to Avoid

Information Overload

Don't store every piece of information. Focus on actionable, relevant knowledge that agents actually need.

Inconsistent Metadata

Maintain consistent metadata schemas across all memories to ensure reliable search and categorization.

Stale Information

Regularly update and verify memories to prevent agents from using outdated or incorrect information.

Poor Query Design

Design search queries that are specific enough to be useful but broad enough to capture relevant variations.

Memory Management Best Practices

Optimize agent memory

Memory Architecture Principles

Hierarchical Structure

Semantic Clustering

Temporal Indexing

Priority Weighting

Memory Content Optimization

1. Content Structure

Optimal Memory Format:

2. Content Guidelines

Be Specific and Actionable

Include Context and Conditions

Use Consistent Terminology

Retrieval Optimization

Search Strategy

Multi-Stage Retrieval Process:

Query Enhancement

Query Expansion

Context Injection

Memory Lifecycle Management

Automated Maintenance

Regular Cleanup Schedule

Quality Metrics

Advanced Techniques

Memory Graphs

Contextual Memory Layers

Layer Structure:

Performance Optimization

Indexing Strategies

Memory Compression

Common Pitfalls to Avoid

Information Overload

Inconsistent Metadata

Stale Information

Poor Query Design

Next Steps

Apply These Practices

Performance Optimization