Agentic Design

Patterns
📄

Contextual Unstructured Memory(CUM)

Explicit, modality-general memory system storing information across heterogeneous inputs for multi-agent agentic AI systems

Complexity: highMemory Management

🎯 30-Second Overview

Pattern: Explicit, modality-general memory system storing heterogeneous information without rigid schemas

Why: Flexible content storage, cross-modal discovery, adaptive organization, emergent content relationships

Key Insight: Multimodal Embeddings + Schema-Free Storage + Contextual Metadata → Adaptive cross-modal memory

⚡ Quick Implementation

1Vector Embeddings:Generate multimodal embeddings (CLIP, SentenceTransformers)
2Flexible Storage:Schema-free storage with metadata preservation
3Cross-Modal Index:Content-based indexing across modalities
4Semantic Retrieval:Similarity search with contextual filtering
5Agent Coordination:Shared multimodal memory access patterns
Example: multimodal_embeddings → schema_free_storage → cross_modal_indexing → semantic_retrieval → agent_coordination

📋 Do's & Don'ts

✅Use multimodal embeddings (CLIP, ALIGN) for cross-modal similarity
✅Preserve rich contextual metadata alongside content
✅Implement hybrid storage: vectors + original content + metadata
✅Enable emergent organization through usage patterns
✅Support schema evolution and dynamic content types
❌Force rigid schemas on inherently flexible content
❌Ignore modality-specific preprocessing and optimization
❌Store without contextual information about creation/usage
❌Use single-modal embeddings for multimodal content
❌Neglect computational costs of similarity search at scale

🚦 When to Use

Use When

  • • Multimodal content across text, images, audio, video
  • • Dynamic content types requiring schema flexibility
  • • Cross-modal discovery and content association
  • • Creative and exploratory multi-agent systems
  • • Heterogeneous data integration from multiple sources

Avoid When

  • • Highly structured data with stable schemas
  • • Performance-critical applications requiring fast exact queries
  • • Simple text-only or single-modality systems
  • • Regulatory compliance requiring strict data validation
  • • Resource-constrained environments with limited embedding capacity

📊 Key Metrics

Cross-Modal Retrieval Accuracy
% relevant content found across modalities
Storage Flexibility
New content types integrated without schema changes
Semantic Similarity Quality
Embedding space coherence across modalities
Content Discovery Rate
Serendipitous cross-modal connections found
Agent Collaboration Quality
Multi-agent content enhancement success
Memory Evolution Rate
Adaptive organization improvement over time

💡 Top Use Cases

Creative Content Systems: Text writers, visual designers, audio producers, video editors sharing unstructured creative assets (78% more flexible vs structured databases)
Research & Discovery Platforms: Scientists storing papers, images, data, code, notes without predefined schemas (cross-modal pattern recognition)
Educational Content Creation: Learning material development across text, visuals, audio, interactive elements (adaptive content organization)
Customer Support Systems: Handling tickets with text, images, voice recordings, screen captures (flexible problem-solving knowledge base)
Digital Asset Management: Marketing teams managing campaigns across multiple content types and formats (emergent content relationships)

Contextual Unstructured Memory

Modality-general memory system for heterogeneous inputs

Memory Network Graph

text
image
audio
video
code
structured
mixed

Memory Chunks (0)

No memory chunks stored yet

System Metrics

Total Chunks:0
Total Size:0.0 KB
Compression Ratio:100%
Avg Retrieval:0ms
Cross-Modal Links:0
Memory Usage:0%

Processing Agents

Universal Encoderidle
Tasks: 0 | Accuracy: 95%
Semantic Retrieveridle
Tasks: 0 | Accuracy: 92%
Cross-Modal Synthesizeridle
Tasks: 0 | Accuracy: 88%
Context Analyzeridle
Tasks: 0 | Accuracy: 90%
Memory Compressoridle
Tasks: 0 | Accuracy: 93%

Operation Log

Waiting for operations...

Contextual Unstructured Memory Algorithm

Core Principle: Modality-agnostic memory system that stores and retrieves heterogeneous information through unified embeddings and cross-modal associations.

Key Mechanisms: Universal encoding for all modalities, semantic embedding generation, similarity-based retrieval, automatic association discovery, and adaptive compression.

Modality Support: Text, images, audio, video, code, structured data, and mixed-modality content with seamless cross-modal querying.

Benefits: Unified memory interface, efficient heterogeneous storage, context-aware retrieval, and emergent cross-modal relationships.

References & Further Reading

Deepen your understanding with these curated resources

Multimodal Cognition & Memory

Perceptual Symbol Systems: A Theory of Grounded Cognition (Barsalou, 1999)

Embodied Cognition and Multimodal Memory (Wilson, 2002)

The Role of Context in Memory: Encoding and Retrieval (Smith, 1979)

Multimodal Memory Systems in Human Cognition (Paivio, 2007)

Multimodal AI & Foundation Models

Learning Transferable Visual Models From Natural Language (CLIP, Radford et al., 2021)

Scaling Up Visual and Vision-Language Representation Learning (ALIGN, Jia et al., 2021)

Multimodal Foundation Models: From Specialists to General-Purpose Assistants (Li et al., 2024)

Cross-Modal Representation Learning: A Survey (Liang et al., 2022)

Vector Databases & Schema-Free Storage

Pinecone: Vector Database for ML Applications

Weaviate: Vector Database with Schema Flexibility

Chroma: AI-native Open-source Embedding Database

MongoDB Atlas Vector Search: Flexible Document + Vector Storage

Content-Based Retrieval & RAG

Sentence-BERT: Sentence Embeddings using Siamese Networks (Reimers & Gurevych, 2019)

Retrieval-Augmented Generation for Knowledge-Intensive NLP (Lewis et al., 2020)

Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., 2020)

MultiModal-RAG: Retrieval Augmented Generation for Multimodal Content (Zhang et al., 2024)

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