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🏗️
Contextual Structured Memory(CSM)
Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise querying for multi-agent agentic AI systems
Complexity: highMemory Management
🎯 30-Second Overview
Pattern: Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise queries
Why: Precise querying, formal logic reasoning, data integrity, consistent structured knowledge access
Key Insight: Schemas + Knowledge Graphs + Structured Queries + Symbolic Logic → Precise reasoning & consistency
⚡ Quick Implementation
1Define Schemas:Create predefined memory structures & formats
2Build Graph:Implement knowledge graph with typed relationships
3Enable Queries:Support precise SQL-like structured queries
4Add Reasoning:Implement symbolic logic & inference rules
5Share Structure:Enable cross-agent structured knowledge access
Example: schema_design → knowledge_graph → structured_queries → symbolic_reasoning → cross_agent_sharing
📋 Do's & Don'ts
✅Use standardized schemas (JSON Schema, RDF, OWL) for consistency
✅Implement precise query capabilities with structured filters
✅Enable symbolic reasoning with formal logic rules
✅Maintain referential integrity across knowledge graphs
✅Version control schemas and support backward compatibility
❌Over-structure flexible data that benefits from loose schemas
❌Create overly complex schemas that are hard to understand
❌Ignore schema evolution and migration strategies
❌Use structured memory for highly dynamic unstructured content
❌Forget to validate data integrity when updating structures
🚦 When to Use
Use When
- • Complex domain relationships need modeling
- • Precise queries and reasoning required
- • Formal logic and symbolic reasoning needed
- • Data integrity and consistency critical
- • Multi-agent knowledge sharing important
Avoid When
- • Simple key-value storage needs
- • Highly dynamic unstructured content
- • Rapid prototyping with changing requirements
- • Natural language processing tasks
- • Real-time streaming data scenarios
📊 Key Metrics
Schema Coverage
% domain concepts properly modeled
Query Precision
Accuracy of structured query results
Reasoning Correctness
% valid logical inferences
Data Integrity
Referential consistency score
Schema Utilization
% schema fields actively used
Cross-Agent Consistency
Shared structure alignment rate
💡 Top Use Cases
Financial Analysis: Company financials, ratios, regulations in structured schemas (SQL-like queries: "SELECT companies WHERE pe_ratio < 25 AND sector = 'technology'")
Legal Systems: Laws, cases, precedents with formal relationship modeling (complex legal reasoning and precedent matching)
Scientific Databases: Research data with standardized ontologies and precise queries (materials science, drug discovery)
Enterprise Resource Planning: Business entities, processes, relationships in formal schemas (inventory, supply chain optimization)
Healthcare Records: Patient data, treatments, outcomes with medical ontologies (clinical decision support systems)
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.
🏗️
Contextual Structured Memory(CSM)
Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise querying for multi-agent agentic AI systems
Complexity: highMemory Management
🎯 30-Second Overview
Pattern: Memory organized in predefined, interpretable formats supporting symbolic reasoning and precise queries
Why: Precise querying, formal logic reasoning, data integrity, consistent structured knowledge access
Key Insight: Schemas + Knowledge Graphs + Structured Queries + Symbolic Logic → Precise reasoning & consistency
⚡ Quick Implementation
1Define Schemas:Create predefined memory structures & formats
2Build Graph:Implement knowledge graph with typed relationships
3Enable Queries:Support precise SQL-like structured queries
4Add Reasoning:Implement symbolic logic & inference rules
5Share Structure:Enable cross-agent structured knowledge access
Example: schema_design → knowledge_graph → structured_queries → symbolic_reasoning → cross_agent_sharing
📋 Do's & Don'ts
✅Use standardized schemas (JSON Schema, RDF, OWL) for consistency
✅Implement precise query capabilities with structured filters
✅Enable symbolic reasoning with formal logic rules
✅Maintain referential integrity across knowledge graphs
✅Version control schemas and support backward compatibility
❌Over-structure flexible data that benefits from loose schemas
❌Create overly complex schemas that are hard to understand
❌Ignore schema evolution and migration strategies
❌Use structured memory for highly dynamic unstructured content
❌Forget to validate data integrity when updating structures
🚦 When to Use
Use When
- • Complex domain relationships need modeling
- • Precise queries and reasoning required
- • Formal logic and symbolic reasoning needed
- • Data integrity and consistency critical
- • Multi-agent knowledge sharing important
Avoid When
- • Simple key-value storage needs
- • Highly dynamic unstructured content
- • Rapid prototyping with changing requirements
- • Natural language processing tasks
- • Real-time streaming data scenarios
📊 Key Metrics
Schema Coverage
% domain concepts properly modeled
Query Precision
Accuracy of structured query results
Reasoning Correctness
% valid logical inferences
Data Integrity
Referential consistency score
Schema Utilization
% schema fields actively used
Cross-Agent Consistency
Shared structure alignment rate
💡 Top Use Cases
Financial Analysis: Company financials, ratios, regulations in structured schemas (SQL-like queries: "SELECT companies WHERE pe_ratio < 25 AND sector = 'technology'")
Legal Systems: Laws, cases, precedents with formal relationship modeling (complex legal reasoning and precedent matching)
Scientific Databases: Research data with standardized ontologies and precise queries (materials science, drug discovery)
Enterprise Resource Planning: Business entities, processes, relationships in formal schemas (inventory, supply chain optimization)
Healthcare Records: Patient data, treatments, outcomes with medical ontologies (clinical decision support systems)
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Contribute to this collection
Know a great resource? Submit a pull request to add it.