Google's Interactions API Centralizes Gemini Management

The Interactions API is Google's new standard interface for building anything with Gemini models, replacing the older generateContent API as the recommended starting point for new projects. The core idea is consolidation: instead of using different patterns for simple text generation, image or video understanding, structured outputs, tool calls, and multi-step AI agents, developers now work through one unified endpoint, called an Interaction, that handles all of it.

An Interaction is essentially a complete record of one turn in a conversation or task. It captures everything that happened during that turn in order: what the model was "thinking," any tools it called and what those tools returned, and the model's final output. That full record is what makes the rest of the API's features possible.

Key Points

• Google's Interactions API is now Generally Available as of June 2026 and is the recommended way to build new projects on Gemini, though the older generateContent API remains supported
• The core change is a single, unified endpoint that handles standard model calls, multimodal inputs, and specialized agents like Deep Research through the same interface
• The API can now manage conversation history on Google's servers using a previous_interaction_id, removing the need for developers to resend full chat history on every request
• This server-side memory also improves caching efficiency, which Google says lowers token costs in multi-turn conversations
• Stored interactions are retained for 55 days on paid tiers and 1 day on the free tier by default, with configurable retention windows and manual deletion available

How It Works

The mechanic that matters most for anyone building AI-powered products is server-side conversation memory. In the past, continuing a multi-turn conversation meant the developer had to resend the entire chat history with every new request. The Interactions API lets developers instead pass a single previous_interaction_id, and Google's servers retrieve the relevant history automatically.

That said, this memory is selective. It only carries forward the actual conversation, meaning the inputs and outputs. Settings like which tools are available, system instructions, and generation settings such as temperature have to be re-specified on every new call, since those apply only to that specific interaction and don't carry over automatically.

The API also supports mixing model types within a single conversation. A developer could use Google's specialized Deep Research agent to gather information, then hand that off to a standard Gemini model to summarize or reformat the findings, all linked together through the same previous_interaction_id chain. One practical constraint worth knowing: if an earlier step in the conversation produces an image or other non-text output, every subsequent model in that chain has to be able to accept that format as input, which rules out looping back to a text-only or audio-only model partway through.

Why This Matters for Cost and Performance

Beyond convenience, Google says the server-side memory approach improves what's called cache hit rate, meaning the system can more efficiently reuse previously processed context instead of reprocessing it from scratch on every call. For any application running frequent multi-turn conversations, that translates directly into lower token costs and faster responses, since less of the conversation needs to be freshly computed each time.

The tradeoff is data retention. By default, Google stores every interaction to enable these features, keeping paid-tier data for 55 days and free-tier data for just 1 day. Developers who don't want this can opt out by setting store=false, though doing so disables both the previous_interaction_id shortcut and background execution for long-running tasks. Paid-tier users can also adjust the default retention window down to 7, 14, or 28 days inside Google AI Studio, or delete stored interactions manually at any time.

What's Available, and What's Still Missing

The Interactions API currently supports Google's full current model lineup, including Gemini 3.5 Flash, Gemini 3.1 Pro Preview, and the Gemma 4 open models, along with specialized agents like Deep Research and an early-access coding agent called Antigravity. It's accessible through Google's GenAI SDKs for Python and JavaScript.

A handful of features haven't made the jump from the older generateContent API yet, including custom safety settings, the Batch API, automatic function calling in Python, and video clipping metadata. Teams relying on any of those specific features will need to stay on generateContent for now, or plan around the gap, since Google has not given a firm timeline for closing it. Google has also confirmed that going forward, all new model capabilities and agentic features will launch on the Interactions API first, which signals where the platform's development priority sits even while the legacy API remains supported.

The Practical Takeaway

For marketing and growth teams evaluating AI adoption strategy, the relevant signal here isn't the technical plumbing itself, it's what it enables: cheaper, faster, more coherent multi-turn AI experiences, which matters directly for anything built on customer-facing chat, personalization, or research-assistant style tools. Teams already building on Gemini should treat this as the direction the platform is heading, since new capabilities will land here first.

It's also worth folding into any broader growth strategy conversation about vendor selection and total cost of AI tooling. Server-side caching efficiency is a real cost lever at scale, and data retention defaults are worth a compliance review before rolling any Gemini-based tool into a workflow that touches customer data.