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name: gemini-memory-lifecycle description: strategies for the agent memory lifecycle (Extraction, Consolidation, Retrieval). Use this to implement long-term learning and personalization beyond a single session.

Gemini Memory Lifecycle Strategies

Goal

Transform transient conversation data into persistent, high-value "Memories" that allow the agent to learn about the user over time, creating a personalized experience.

The Core Lifecycle (ETL for Agents)

1. Extraction (Signal vs. Noise)

• Concept: Use an LLM to scan the raw session logs and extract only "meaningful" information, discarding pleasantries and filler.
• Method: Define "Topic Definitions" (e.g., User Preferences, Goals, Facts). If the data doesn't fit a topic, do not create a memory.
• Technique:
  • Schema-Based: Extract specific fields (e.g., {"food_preference": "vegan"}).
  • Natural Language: Extract atomic statements (e.g., "The user prefers window seats").

2. Consolidation (The Gardener)

• Concept: "Self-editing" the knowledge base. New information must be merged with old information to prevent contradictions and duplicates.
• Operations:
  • Create: If the insight is novel.
  • Update: If the insight refines existing knowledge (e.g., "User likes spicy food" -> "User likes mildly spicy food").
  • Delete/Invalidate: If the new info contradicts old info, remove the stale memory.

3. Retrieval (Finding Context)

• Concept: Fetching the right memory at the right time.
• Scoring Dimensions: Do not rely on Semantic Similarity alone. Use a Blended Score:
  1. Relevance: Vector similarity to the current query.
  2. Recency: Favor newer memories over older ones.
  3. Importance: Weight memories based on their significance (defined at generation time).

Memory Types

Declarative Memory ("Knowing What")

• Definition: Facts, figures, and user details (e.g., "My anniversary is October 26th").
• Storage: Best stored in Vector Databases (for semantic search) or Knowledge Graphs (for relationship mapping).

Procedural Memory ("Knowing How")

• Definition: Strategies and workflows. The agent remembers how it successfully solved a problem in the past.
• Application: Used to inject a "playbook" of successful steps into the prompt for complex tasks.

Best Practices

• Asynchronous Generation: Memory generation is expensive. Always run the Extraction and Consolidation steps in the background after the agent has responded to the user. Never block the user interface.
• Provenance: Track the "Source Reliability" of every memory. A memory derived from explicit user input ("I am vegan") is higher trust than one inferred from conversation ("User asked about salads").