# Google Cloud’s Always-On Memory Agent Replaces RAG and Embeddings With Continuous LLM Consolidation on Gemini 3.1 Flash-Lite > Source: > Published: 2026-07-18 07:57:51+00:00 Most AI agents forget. They process a request, answer it, then drop the context. Google Cloud’s [ generative-ai repository now ships a sample that tackles this directly. It is the Always-On Memory Agent](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/gemini/agents/always-on-memory-agent), a reference implementation that treats memory as a running process. **Always-On Memory Agent** Fundamentally, the project is a lightweight background agent that never stops. It runs 24/7 as a continuous process, not a one-shot call. It is built with **Google ADK** (Agent Development Kit) and **Gemini 3.1 Flash-Lite**. Notably, it uses no vector database and no embeddings. Instead, an LLM reads, thinks, and writes structured memory into **SQLite**. The model choice targets low latency and low cost for continuous background work. **How It Works: Ingest, Consolidate, Query** Architecturally, an orchestrator routes every request to one of three specialist sub-agents. Each sub-agent owns its own tools for reading or writing the memory store. First, the **IngestAgent** handles incoming content. It uses Gemini’s multimodal capabilities to extract a summary, entities, topics, and an importance score. That structured record then lands in the `memories` table. Next, the **ConsolidateAgent** runs on a timer, every 30 minutes by default. Like sleep cycles, it reviews unconsolidated memories and finds connections between them. Then it writes a synthesized summary, one key insight, and those connections to the database. Consequently, the agent builds new understanding while idle, with no prompt. Finally, the **QueryAgent** answers questions. It reads all memories and consolidation insights, then synthesizes a response. Importantly, it cites the memory IDs it used as sources. **Supported Inputs** Beyond text, the IngestAgent accepts 27 file types across five categories. Simply drop any supported file into the `./inbox` folder for automatic pickup. | Category | Extensions | |---|---| | Text | `.txt` , `.md` , `.json` , `.csv` , `.log` , `.xml` , `.yaml` , `.yml` | | Images | `.png` , `.jpg` , `.jpeg` , `.gif` , `.webp` , `.bmp` , `.svg` | | Audio | `.mp3` , `.wav` , `.ogg` , `.flac` , `.m4a` , `.aac` | | Video | `.mp4` , `.webm` , `.mov` , `.avi` , `.mkv` | | Documents | `.pdf` | **How It Compares to RAG, Summaries, and Knowledge Graphs** To clarify the difference, it frames three common memory approaches. Each solves part of the problem, yet leaves a gap. | Approach | How it stores | Active processing | Main limitation | |---|---|---|---| | Vector DB + RAG | Embeddings in a vector store | None | Passive; embeds once, retrieves later | | Conversation summary | Compressed text | None | Loses detail; no cross-reference | | Knowledge graphs | Nodes and edges | Manual upkeep | Expensive to build and maintain | | Always-On Memory Agent | Structured rows in SQLite | Continuous consolidation | Query reads up to 50 recent memories | Unlike RAG, this agent processes memory actively, not only on retrieval. **Use Cases With Examples** Practically, the pattern fits any workload needing durable, evolving context. **Consider three examples.** - A **research assistant** ingests PDFs, meeting audio, and screenshots all week. Later, it links a cost target to a reliability problem on its own. - A **personal knowledge base** absorbs notes, articles, and images continuously. Over time, consolidation surfaces themes you never explicitly connected. - A **support agent** stores past tickets as structured memories. Then it answers new questions with cited references to earlier cases. **Getting Started** With the design clear, setup stays minimal for early-level engineers. Install dependencies, set your key, then start the process. ``` pip install -r requirements.txt export GOOGLE_API_KEY="your-gemini-api-key" python agent.py ``` Once running, the agent watches `./inbox` , consolidates every 30 minutes, and serves an HTTP API on port 8888. Therefore, you can also feed it over HTTP. ``` # Ingest text curl -X POST http://localhost:8888/ingest \ -H "Content-Type: application/json" \ -d '{"text": "AI agents are the future", "source": "article"}' # Ask a question curl "http://localhost:8888/query?q=what+do+you+know" ``` Additionally, the API exposes `/status` , `/memories` , `/consolidate` , `/delete` , and `/clear` . An optional Streamlit dashboard adds ingest, query, browse, and delete controls. CLI flags change the watch folder, port, and consolidation interval. ``` python agent.py --watch ./docs --port 9000 --consolidate-every 15 ``` **Key Takeaways** **No vector DB, no embeddings**— an LLM reads, thinks, and writes structured memory into SQLite.** Runs 24/7**on Google ADK + Gemini 3.1 Flash-Lite as a lightweight background process.** Three sub-agents**under one orchestrator: Ingest, Consolidate, and Query.** Consolidates every 30 minutes**— links related memories and writes new insights while idle.** Ingests 27 file types**across text, images, audio, video, and PDFs, dropped into`./inbox` . Check out the ** FULL CODES here. **Also, feel free to follow us on **and don’t forget to join our**[Twitter](https://x.com/intent/follow?screen_name=marktechpost) **and Subscribe to** [150k+ML SubReddit](https://www.reddit.com/r/machinelearningnews/)**. 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