# 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

```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**:

```json
{
    "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"]
    }
}
```


---

# Agent Instructions: Querying This Documentation

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```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
