AI agent developers are racing to release conversational assistants, but they face a persistent issue: lack of memory. Without any recall, these bots treat each chat as brand-new. They wind up asking the same questions over and over, ignore personal preferences and come across as impersonal. That gap frustrates users and challenges developers who want smoother, more intuitive interactions.
Early efforts tried feeding entire conversations back into a large language model’s context window. That tactic drove costs up and slowed responses. Overloaded with irrelevant details, the model could wander off-topic—what some call “lost in the middle”—or suffer from “context rot,” where older points vanish amid fresh input.
Google Cloud has launched a public preview of Memory Bank inside Vertex AI Agent Engine. This managed service tackles the memory gap by capturing key moments and details from past sessions. With Memory Bank, conversational agents can build on what they’ve learned about a user, making each chat feel more coherent and personalized.
For example, a healthcare agent might recall a patient’s allergy history and past symptoms to offer more accurate guidance in a follow-up session.
Memory Bank also lets developers define custom memory scopes and retention policies. Teams can specify which details to forget after a set period or mask sensitive data automatically. All memory storage remains encrypted and subject to access controls in Google Cloud, meeting compliance standards like HIPAA and SOC 2. By handling security and data governance under the hood, Memory Bank reduces the burden on teams that must maintain strict privacy rules. This approach helps agents work in critical industries—from finance to healthcare—without exposing personal information.
Memory Bank solves memory with:
- Personalized exchanges that keep track of user likes, milestones and earlier discussions.
- Sessions that resume smoothly, whether chats stretch across hours, days or weeks.
- Better context by loading relevant history so replies stay focused and meaningful.
- Fewer repeated inputs, which makes conversations feel more natural for both sides.
Memory Bank runs through a multi-stage cycle using Google’s Gemini models, following a topic-based research approach accepted by ACL 2025.
- It sifts through stored dialogues in Agent Engine Sessions to pull out key facts, preferences and events. This background work runs behind the scenes, sparing developers from building custom extraction pipelines.
- It labels each detail under scopes—like user ID—so data such as “I prefer sunny days” stays organized. As fresh information arrives, Memory Bank merges updates, flags contradictions and keeps memories current.
- It pulls back relevant memories at the start of a new session. Retrieval can be a straightforward list of facts or an embedding-based similarity search that spots the most relevant details for the current discussion.
Memory Bank plugs into Google’s Agent Development Kit (ADK) and taps into Agent Engine Sessions for session storage. Developers set up an agent in ADK, activate sessions to record dialogue, then switch on Memory Bank to carry context across chats.
Two main paths:
- Use Google’s ADK for a ready-built integration with Vertex AI.
- Build your own agent framework and call the Memory Bank API directly, working with systems such as LangGraph or CrewAI.
Teams new to Google Cloud can sign up in express mode using a Gmail account. This delivers an API key and gives free-tier access to experiment. Once the agent works under those quotas, you can shift the setup into a full Google Cloud project for live use.
A personal beauty companion agent demonstrates another application. It tracks a user’s evolving skin profile and past product feedback, then offers personalized suggestions at each visit. That ability to adapt as conditions shift highlights Memory Bank’s flexible, user-centered design.
The ACL 2025 paper behind Memory Bank outlines a topic-based grouping technique. By clustering related details, it reduces noise in retrieval and boosts accuracy, enabling models to recall only the most relevant memories for each conversation.
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