| 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:
- Relevance: Vector similarity to the current query.
- Recency: Favor newer memories over older ones.
- 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").
