always-on-memory-agent

Always-On Memory Agent Skill

A tested comparison, not a build. It covers an existing always-on memory agent sample and the managed Vertex AI Memory Bank, so your agent can help you run the sample, expect its rough edges, and pick the right option.

[!IMPORTANT] The sample is not ours. It lives in Google's repo at github.com/GoogleCloudPlatform/generative-ai/tree/main/gemini/agents/always-on-memory-agent, written by Shubham Saboo. This skill tests it and compares it, it does not reimplement it.

[!IMPORTANT] Model id gemini-3.1-flash-lite, GA, not new (announced March 2026, GA on Vertex May 2026). Vertex AI Memory Bank is GA. Neither is a new capability, the point is the honest comparison.

The sample in one look

• Four ADK Agents, an orchestrator routes to ingest, consolidate, query. Tool calling, not output_schema.
• Always-on, an aiohttp API on :8888, an inbox folder watcher, and a consolidation loop every 30 minutes.
• SQLite store with summary, entities, topics, connections, importance per memory.

Run it (from the sample repo):

pip install -r requirements.txt
export GOOGLE_API_KEY=your_ai_studio_key
python agent.py            # watches ./inbox, serves :8888
curl -X POST localhost:8888/ingest -d '{"text":"Sascha streams Nordlicht Friday"}'
curl "localhost:8888/query?q=when+is+the+stream"

Rough edges, measured when running it

• Retrieval is a recent-window load, not search. read_all_memories is SELECT * FROM memories ORDER BY created_at DESC LIMIT 50. There is no vector search in the sample. Past 50 memories, older ones silently drop out of the query context.

[!WARNING] Symptom, a query stops finding an older fact once the store grows past 50 memories. Cause, the LIMIT 50 recent-window load, there is no similarity retrieval to pull it back.

• Consolidation is capped at LIMIT 10. read_unconsolidated_memories reads at most 10, so a burst of more than ten new memories only partly consolidates per pass.
• Per ingest latency 2.4 to 4.7s. Every ingest routes orchestrator to sub agent to tool and back, measured on 3 files.
• One shared SQLite file, no per user isolation. Fine for a personal assistant, not multi user.
• Thin governance. A delete endpoint exists, no retention, audit, or scoped access. The repo frames it as a reference implementation.

The managed alternative, Vertex AI Memory Bank

Same three inputs, run for real during the episode. Memory Bank merged them into fewer, user-scoped memories and retrieved by vector similarity search in 0.7s, versus the sample loading its recent window.

[!IMPORTANT] Memory Bank retrieval is a scoped vector similarity search, and memories are stored first person per user_id. This is the core difference from the sample, the sample removed the vector search, Memory Bank keeps it.

scripts/memory_bank_quickstart.py, verified, point it at your own file and question.

gcloud auth application-default login
python scripts/memory_bank_quickstart.py --project YOUR_PROJECT --file notes.txt \
    --query "what do you know about the project?" --user alice

The real calls are client.agent_engines.create() (engine create was 4.3s in prep), generate_memories(direct_contents_source={"events": [...]}, scope=...), and retrieve_memories(similarity_search_params={"search_query": ...}).

When to use which

• Learn the mechanics, run something small and personal, or stay local and offline, the sample is a great teaching tool and it works.
• Real users, isolation, retention, and retrieval that scales past a recent window, use managed Memory Bank.

Supporting files

• scripts/memory_bank_quickstart.py, the verified Memory Bank create, generate, retrieve utility, parameterized.
• reference.md, the full verified head to head, both run on the same inputs.
• sample-files/, example multimodal inputs (a note, a chat screenshot, a voice memo) to feed either system.

Dependencies and Prerequisites

• Python 3.12 recommended. google-adk >= 2.4.0, google-genai >= 2.12.1 for the sample, google-cloud-aiplatform >= 1.161.0 for the Memory Bank quickstart.

[!WARNING] The macOS system python is 3.9 and both ADK and google-genai require >=3.10. On 3.9 pip serves a stale ADK 1.x and the API looks broken. Use uv venv --python 3.12.

Documentation Pages

You MUST fetch the matching page before writing code. These hosted docs are the source of truth for parameters, types, and edge cases.

• The sample, https://github.com/GoogleCloudPlatform/generative-ai/tree/main/gemini/agents/always-on-memory-agent
• Memory Bank with ADK, https://docs.cloud.google.com/gemini-enterprise-agent-platform/scale/memory-bank/adk-quickstart
• ADK memory concepts, https://google.github.io/adk-docs/sessions/memory/

From the episode

Video walkthrough, link to follow once the stream is up.