Context Engineering & Memory Systems

Purpose

Optimize AI agent context management and build persistent memory systems.

Context Engineering

Principles

1. •Relevance: Include only contextually relevant information
2. •Hierarchy: Organize context by importance
3. •Freshness: Prioritize recent information
4. •Compression: Summarize long contexts

Techniques

Context Window Management

• •Sliding window for conversations
• •Summarization for long documents
• •Chunking for large codebases
• •Priority scoring for relevance

Context Injection

• •System prompts
• •Few-shot examples
• •Retrieved documents
• •Conversation history

Memory Systems

Short-Term Memory

• •Current conversation context
• •Active task information
• •Recent actions and results

Long-Term Memory

• •Persistent knowledge storage
• •Learned patterns and solutions
• •Historical decisions

Implementation

File-Based Memory

.opencode/memory/
├── cmz-knowledge.md      # Solutions and patterns
├── cmz-issues.md         # Known issues and resolutions
└── cmz-evolution.md      # Change history

Vector Memory (Future)

• •Embeddings for semantic search
• •Similarity matching
• •Automatic retrieval

Context Engineering Patterns

Pattern 1: Progressive Disclosure

Start with minimal context, expand as needed.

Pattern 2: Selective Retrieval

Retrieve only relevant information from memory.

Pattern 3: Hierarchical Summarization

Maintain summaries at different levels of detail.

Pattern 4: Temporal Weighting

Weight recent information more heavily.

Best Practices

1. •Regular Cleanup: Remove outdated context
2. •Explicit Storage: Store important learnings explicitly
3. •Retrieval Testing: Test memory retrieval accuracy
4. •Context Monitoring: Monitor context size and relevance

Integration

Works with CMZ self-learning protocol for continuous improvement.

Usage

Apply when:

• •Managing large codebases
• •Long-running conversations
• •Building agent memory
• •Optimizing context usage