Gemini Memory API: What Carries Between Requests

A Gemini memory API question has three separate answers: personalization in the Gemini app, conversation state carried between API requests, and durable information your application stores. Google’s Interactions API can continue a conversation using a previous interaction ID; that does not make the conversation a permanent project database.

For work that must outlast a particular conversation, fenbs keeps explicit project decisions and progress that supported connected assistants can read. A developer still has to decide which records an application needs, who may access them and how those records reach a model.

Which kind of memory are you asking about?

Google documents Gemini app personalization↗ (opens in a new tab) separately from its developer interfaces. Do not expect a user’s personal app memories to appear just because your application calls a Gemini model. Supply the authorized information through the API workflow your application actually uses.

For the consumer experience, see the Gemini memory feature; this article concerns the developer boundary. If API is unfamiliar, it means a defined way for software to send a request and receive an answer. Our API documentation guide explains how to read that contract.

How does a later request receive earlier turns?

Google’s Interactions API overview↗ (opens in a new tab) describes server-side conversation state. A later call can pass the completed interaction’s ID in previous_interaction_id so the service retrieves earlier conversation turns. Stateless operation instead requires the relevant conversation history to be supplied explicitly.

An interaction ID is a reference your software must associate with the correct conversation. Treat that association as part of the product. For example, a support assistant must resume the customer’s conversation rather than whichever interaction happened to finish most recently on the server.

The same documentation says tools, system instructions and generation settings are specific to each interaction and must be supplied again when needed. Continuing a conversation therefore does not excuse your application from sending the instructions and tools required for the next turn.

A useful debugging question is simple: what exact input and conversation reference did this request receive? An answer that omits yesterday’s decision may reflect a missing reference, a missing project record or a reasoning error. Those are different failures and need different fixes.

Does keeping history give you permanent memory?

Stored API interactions have retention and deletion behavior. Google’s overview explains the store control and links storage to available stateful features. Check that page for the current retention rules rather than treating an interaction ID as an indefinite archive.

Even while history remains available, it records a conversation rather than a cleaned, approved account of the work. Say a designer proposes a blue package, later rejects it and finally approves green. A transcript preserves the discussion; a project record should clearly identify the approved green version.

• Store the current decision, its scope and the person who approved it.
• Keep a source reference so someone can inspect the evidence behind a summary.
• Distinguish finished work from untested changes and open questions.
• Replace outdated current summaries while preserving the history needed to explain changes.

This is an application design recommendation, not a promise that the API will perform those steps. Assign responsibility for maintaining the record before you depend on it. A small, reviewed summary is often easier to check than an entire conversation.

Is caching another name for remembering?

No. Google’s context caching documentation↗ (opens in a new tab) describes reusing repeated input for efficiency. Support depends on the API interface and model. A cache hit does not tell you whether the material is current, approved or appropriate for the person asking.

Imagine repeatedly analyzing an unchanged catalog. Reusing that input can make sense. If the catalog changes, your application still needs a way to select the current version. A cache is not the place to decide whether an old delivery estimate still applies.

Likewise, a model’s context window is the working input it can handle, not the lifetime of your project record. Google’s token guide↗ (opens in a new tab) explains how to measure input. Tokens are the pieces of text and other content the model processes; count them when building a request, rather than assuming every stored conversation fits.

What should your application save between sessions?

Save information that changes the next action. In a hypothetical product launch, that includes the approved package, the unresolved accessibility review and the next person who must make a choice. Keep source documents separately and supply the relevant parts when needed.

Give each saved fact a clear scope. A preference for short replies may apply across conversations. Approval of a particular package applies to one project. Mixing those two makes it easier for an unrelated conversation to receive instructions that do not belong there.

Decide what happens when the request record expires or a different assistant takes over. Can the next session locate the approved decision without recovering the entire transcript? That is the practical question behind a reliable AI handoff.

How to do this with fenbs

1. Create a free account and add the project whose work must survive a session ending.
2. Add current tasks and record the choices a person approved in Decisions and rules. Keep guesses and unanswered questions visibly unresolved.
3. Connect a supported client such as Gemini CLI through Integrations. MCP is a standard way for an assistant to use an outside tool; approve the connection through browser sign-in.
4. Ask the assistant to read Rules, Where we left off and the relevant task before starting. Before it stops, have it save completed work, checks, uncertainties and the next step.

fenbs does not automatically capture requests from your custom Gemini API application or import its conversations. It is not the right fit when you need an embedded memory engine for your own customer-facing product. Its fit here is the shared work record used by people and supported connected assistants.

How can you check that continuity really works?

Test two boundaries independently. First continue a conversation and ask about a harmless detail from its earlier turn. Then start a genuinely new session and supply only the project record; ask for the approved decision and remaining work. The second check tells you whether the durable record is sufficient.

Include a correction in the test. Change an approved project detail through your intended review process, then verify that the next request receives the replacement. Record a failure as missing input, stale input or an incorrect answer so you can fix the responsible layer.

Different providers expose different conversation interfaces; the Claude API guide covers that neighboring product. Reusable Gemini setups are covered in Gemini Gems. For the wider distinction between working context and lasting records, explore the AI memory hub.