Agentic Memory Operations

System Behavior	Evolution Operation
Automatic Merging of Highly Similar Memories	Identifies memories with content/semantic duplication exceeding thresholds, generates unified memory nodes, and establishes knowledge graph associations.
Dynamic Context Association Updates	Detects context drift, reconstructs semantic networks, and optimizes cross-scenario context anchors.
Intelligent Tag Unification	Analyze tag semantic overlap, create standardized tag systems, and build multi-level classification indices.
Autonomous Linking	Recognizes temporal patterns and constructs chronological memory chains.

Execution Flow:

1. Input sanitization

2. LLM-based semantic parsing

3. JSON schema validation

4. Error recovery mechanisms

Example Transformation:json

# Input
"Discovered new protein folding mechanism using AlphaFold2"

# Output
{
    "keywords": ["protein_folding", "AlphaFold2", "biochemical_discovery"],
    "context": "Scientific discovery in computational biology",
    "tags": ["AI_research", "biochemistry", "machine_learning"]
}

Evolution JSON Schema:

{
    "should_evolve": true,
    "evolution_type": ["merge", "context_update"],
    "affected_memories": ["mem_123", "mem_456"],
    "evolution_details": {
        "new_context": "Advanced protein folding techniques",
        "new_relationships": ["ML_models", "drug_discovery"]
    }
}