Google Always On Memory Agent Ends AI Forgetfulness - Learn How

Google has released the Always On Memory Agent, an open-source project that gives artificial intelligence a permanent, structured memory.

This new tool was developed by Google Senior AI Product Manager Shubham Saboo. It allows AI agents to remember past conversations and project details. The tool does this without the need for expensive or complex vector databases. By using the new Gemini 3.1 Flash-Lite model, this project makes persistent AI memory cheap. It also makes it accessible for small businesses and independent developers by storing information in a simple SQLite database.

Key Takeaways:

Ditching the Vector Database: The Always On Memory Agent replaces traditional vector databases with a lightweight SQLite architecture. This reduces memory usage from 150 MB per session to just 5 MB per 1,000 sessions.

Cost-Effective Intelligence: The system is built for Gemini 3.1 Flash-Lite, which costs only $0.25 per million input tokens. It is roughly eight times cheaper than earlier high-end models. This is achieved while maintaining a massive one-million-token context window.
Persistent Agent Loops: Built on the Google Agent Development Kit (ADK), the tool enables agents to do “background consolidation.” This process allows them to refine and organize their own memories while the user is away.

The Goldfish Problem: Why Artificial Intelligence Used to Forget?

For years, talking to an artificial intelligence (AI) felt like “talking to a goldfish”. You could have a great conversation, but the moment the session ended or the text got too long, the AI would lose its place.

This is not a bug; it is a limit of how current AI “brains,” called Transformers, actually work.

When a Transformer reads a sentence, it has to compare every word to every other word to understand the meaning. This process is very expensive. If you have a sentence with 10 words, the AI does 100 comparisons. But if you give it a whole book with 100,000 words, it has to do 10 billion comparisons.

As Richardson Gunde noted, this mathematical explosion means that AI assistants like ChatGPT or Claude eventually start to lose their focus. To save money and computer power, they “forget” the beginning of the chat once the context window is full. This is the “Goldfish Memory” problem.

Developers have tried to fix this by using ‘Vector Databases.’ These databases act like a giant digital library where the AI can look things up. But, these libraries are hard to build. They are expensive to keep running. Often, they are too complex for a small team to handle.

The Computational Cost of Context Length

So, why this memory wall is so hard to climb?

One must look at the mathematical burden of the attention mechanism used in modern large language models (LLMs). The computational requirements grow at an exponential rate. The relationship between the number of tokens (n) and the required comparisons can be simplified as:

Cost \propto n^2

This square-growth pattern means that doubling the amount of information the AI needs to remember actually doubles its memory needs. It quadruples the work the computer has to do.

Google’s Always On Memory Agent solves this by taking the “important” bits of a conversation into a permanent file. This way, the AI doesn’t have to re-read everything every time you ask a question.

A New Filing System: Ditching Vector DBs for SQLite

The biggest technical breakthrough in the Google Always On Memory Agent GitHub project is its simplicity. Instead of using a specialized “Vector Database,” it uses a standard SQLite database. A Vector Database shows words as long strings of numbers called embeddings. SQLite is a very common, very small filing system used in almost every smartphone and web browser.

Shubham Saboo, the lead developer behind the project, chose this path to make AI more affordable. By letting the LLM manage the memory directly, the system does not need a “clunky” retrieval stack. The agent takes in text, images, and audio, and then it “decides” what is worth saving. It writes these facts into simple tables that any computer can read.

Comparing Memory Storage Systems

Feature	Traditional Vector Database	Always On Memory Agent (SQLite)
Primary Tool	Pinecone, Milvus, Chroma	SQLite (Open Source)
Storage Weight	~150 MB per session	~5 MB per 1,000 sessions
Cost	High (Cloud hosting required)	Negligible (Runs on local files)
Logic	Mathematical similarity	LLM-driven structured recall
User Control	Low (Black box)	High (Editable tables)

This design is a “radical simplification”.

For a small business, this means you can build an AI that remembers your customers’ names and past orders. All this without having to pay for a massive cloud database every month. The agent acts like a personal assistant. It takes notes in a notebook rather than a scientist trying to map every word in a 3D space.

Gemini 3.1 Flash-Lite: The Brain Behind the Memory

While the SQLite database acts as the “notebook,” the agent needs a fast and cheap “brain” to read and write those notes.

Google has built this agent to work perfectly with Gemini 3.1 Flash-Lite. This model is the newest addition to the Gemini 3 family. It is designed specifically for “high-volume agentic tasks.” This means it is great at doing lots of small jobs very quickly and for very little money.

Flash-Lite is a massive upgrade over older models. It has a one-million-token context window. It’s like being capable of holding an entire library in its short-term memory while it works. It is also incredibly fast, producing 363 tokens per second. This speed allows the agent to organize its memory in the “background” without making the user wait.

The Economic Breakdown of Gemini Models

Model	Input Cost (per 1M tokens)	Output Cost (per 1M tokens)	Key Strength
Gemini 3.1 Pro	$2.00	$18.00	Complex Reasoning
Gemini 3 Flash	$0.30	$2.50	Balanced Speed
Gemini 3.1 Flash-Lite	$0.25	$1.50	High-Volume Work

The low price of Flash-Lite is the “secret sauce” of the Always On Memory Agent. Because it only costs $0.25 to read a million tokens, the agent can afford to “re-read” its own notes to organize them better. This process is called “Consolidation.” Just like humans dream at night to organize their thoughts, this agent can be set to run every 30 minutes. It cleans up its files, deletes duplicates, and highlights the most important things it learned.

The Google Agent Development Kit (ADK): Building the Future

The Always On Memory Agent is more than just a single piece of code. It is part of the Google Always On Memory Agent Github ecosystem.

This ecosystem is called the Agent Development Kit (ADK).

The ADK is a set of tools that makes building AI agents feel like building a regular app. It was released in early 2025 and has become the gold standard for developers who want to build “Production-Ready” agents.

One of the coolest parts of the ADK is that it treats memory as a “first-class concern.” Instead of forcing a developer to figure out how the AI should remember things, the ADK provides built-in interfaces. It separates data into two main buckets:

ADK Memory Service Layers

SessionService: This stores things the AI needs for right now, like the current conversation. When you close the chat, this data is deleted to keep things private and clean.
MemoryService: This is where the Always On Memory Agent lives. It stores long-term facts that should never be forgotten, like your favorite coding language or your company’s brand guidelines.

By using this kit, developers can build “A2A” (Agent-to-Agent) workflows. This means you could have one agent that manages your emails and another that manages your calendar. Because they both use the same ADK memory system, they can share information. If the email agent sees you have a new meeting, it can “write” that into the shared memory. The calendar agent will then see it and update your schedule.

Community Buzz: What Reddit is Saying

The release of the Always On Memory Agent has caused quite a stir on social media. Developers on the Google Always On Memory Agent Reddit threads are debating the impact. Is it a “game changer” or just a clever version of an old technique called RAG (Retrieval-Augmented Generation)?

One user on r/LocalLLaMA, GuiBiancarelli, noted that “the code is very simple” and could easily be adapted to run on local models instead of just Google’s cloud.

Others are more skeptical. A top commenter mentioned that having an agent “always on” might be too expensive for a single person running AI on their own home computer, because it requires a model to be running 24/7 just to organize the files.

Explore the thread here:

Google released "Always On Memory Agent" on GitHub – any utility for local models?
byu/makingnoise inLocalLLaMA

However, for small businesses (SMBs), the reaction has been overwhelmingly positive. Many see this as a way to avoid the “labor of manually creating a vector db”. In the past, if a small company wanted an AI that remembered its clients, it had to hire an expensive engineer to set up complex cloud databases. Now, they can just download the Saboo project from GitHub and start building.

Moving from “Chat” to “Action”: The Power of Persistence

The real value of an agent that remembers isn’t just better conversation; it is the ability to take action. When an AI has “Antigravity Permanent Memory,” it stops feeling like a tool and starts feeling like a teammate. It learns “how you build” through real patterns. If you always name your files a certain way or follow a specific logic in your spreadsheets, the agent notices.

This leads to a “compounding effect.” Every time you use the agent, it gets smarter. It doesn’t just store text; it stores your “habits”. This is especially useful in high-stakes environments like customer support or industrial safety.

Examples of Action-Oriented Memory

Retail Strategy: A retail agent can use Google Search and Maps to analyze potential store locations. It has persistent memory, which allows it to remember the details of 50 different neighborhoods. It can also compare them in a final report without getting confused.
Computer Use Reliability: Google is also testing “Computer Use” agents that can move your mouse and click buttons. These agents are much more reliable when they have a persistent memory of your screen layout. If a button moves, the agent “remembers” what it was looking for and finds it again.
Code Assist: For developers, Gemini Code Assist on GitHub can now remember coding standards across an entire team. When a senior developer corrects a junior developer’s code in a “Pull Request,” the AI agent learns. It adopts that correction as a new rule for the whole company.

Risks and Governance: When Agents Start ‘Dreaming’

One of the more complex parts of the Always On Memory Agent is the idea of “Background Consolidation”. This is when the agent works while you are asleep. It looks at all the memories it collected during the day and tries to make sense of them. While this is very powerful, it also raises concerns about “Governance and Compliance”.

If an agent is allowed to rewrite its own memory, what happens if it makes a mistake? If the agent “decides” that a customer prefers a certain product based on a misunderstood joke, that mistake becomes part of its permanent record. Developers call this “Context Drift.” To prevent this, Google has built safeguards into the ADK:

Human-in-the-Loop: The agent can be set to “ask for permission” before it saves a major new memory.

Audit Trails: Every time the agent changes a memory, it leaves a “breadcrumb.” This allows a human to see why the agent made that choice.
Data Isolation: Memories are tied to a specific “User ID.” This ensures the AI never accidentally shares one person’s private details with another user.

Action Points: How to Start Using the Memory Agent

If you are a developer or a business owner, you can start using this technology today. Because it is Open Source, the barrier to entry is very low. Here is a step-by-step guide to getting started:

Visit the GitHub: Go to the official Google Cloud Platform GitHub and find the always-on-memory-agent folder – access here
Get an API Key: You will need a key for Gemini 3.1 Flash-Lite. You can get this for free or at a low cost through Google AI Studio.
Clone and Install: Use the git clone command to bring the code to your computer. The project supports .env files, which makes it easy to set up your keys without sharing them with the world.

Set Up the ADK: Install the Google Agent Development Kit using the command pip install google-adk. This will give you the tools to run the agent in your own “Virtual Environment”.
Run the Dashboard: The project comes with a “Streamlit Dashboard.” This is a simple website that runs on your computer. It lets you see what the agent is “thinking”. You can also see what is stored in its SQLite memory tables.
Start Small: Don’t try to give the agent your whole company’s history on day one. Start by having it remember your own preferences for a single task, like organizing your emails or drafting research notes.

FAQs on Google Always On Memory Agent

1. What is the Always On Memory Agent by Google?

It is an open-source tool from Google that gives AI agents “long-term memory” using a simple SQLite database instead of complex cloud systems.

2. Is Google Always On Memory Agent free to use?

The code itself is free under the MIT license. You only pay for the “tokens” you use when the Gemini model reads or writes information.

3. Why is Google Always On Memory Agent called “Always On”?

Because it can run in the background to organize its own memories even when you aren’t actively chatting with it.

4. Does Google Always On Memory Agent work with local models?

The official version uses Google’s Gemini API. But the open-source community is already finding ways to make it compatible with local models like Llama 3 or Qwen.

5. How much space does Google Always On Memory Agent take?

Very little. It uses about 5 MB to store the memory of 1,000 different chat sessions.

6. What is the difference between Google Always On Memory Agent and a regular chatbot?

A regular chatbot forgets everything when you close the window. This agent remembers you and your projects forever.

7. Is my data safe when I use Google Always On Memory Agent?

Yes. The memory is stored in your own project or computer, and Google uses strict rules to keep different users’ memories separate.

8. Do I need to be a pro coder to set up Google Always On Memory Agent?

Knowing some Python is helpful. But, the project is designed for “Download-and-use”. The setup time is under three minutes.

9. Can Google Always On Memory Agent read my PDFs and images?

Yes. It uses Gemini’s multimodal powers to “see” images and “read” documents, then turns that info into text memories.

10. What is “Google Gemini Flash-Lite”?

It is Google’s most cost-efficient AI model. It is designed to be very fast and very cheap for tasks that need a lot of data processing.

11. What is the “Goldfish Memory” problem?

It is the tendency for AI to forget the beginning of a conversation because it is too expensive to keep all those words in its “active” brain.

12. How does SQLite help?

It acts like a standard notebook where the AI can write down facts. It’s much simpler and cheaper than the “Vector Databases” used by big tech companies.

13. Can I use Google Always On Memory Agent for my business?

Absolutely. The MIT license specifically allows for commercial use, making it great for customer support or project management.

14. What is the ADK?

The Agent Development Kit is a toolkit from Google that makes it easy to build, test, and deploy AI agents. These can actually do things like send emails or search the web.

15. Where can I find more help?

You can check the README file on the project’s GitHub page or join discussions on Reddit’s AI communities.

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