Architect A Personalized Multi-Agent System with Long-Term Memory

In support of our mission to accelerate the developer journey on Google Cloud, we built Dev Signal — a multi-agent system designed to transform raw community signals into reliable technical guidance by automating the path from discovery to expert creation.

In the or want to explore the code directly, you can find the complete project implementation in our to personalize these capabilities. We will implement a Root Orchestrator that manages three specialist agents: the Reddit Scanner, GCP Expert, and Blog Drafter, to provide a seamless flow from trend discovery to expert content creation. We will also integrate a long-term memory layer that enables the agent to learn from your feedback and persist your stylistic preferences across different conversations. This ensures that Dev Signal doesn't just process data, but actually learns to match your professional voice over time.

Infrastructure and Model Setup

First, we initialize the environment and the shared Gemini model.

Paste this code in dev_signal_agent/agent.py:

from google.adk.agents import Agent

from google.adk.apps import App

from google.adk.models import Gemini

from google.adk.tools import google_search, AgentTool, load_memory_tool, preload_memory_tool

from google.adk.tools.tool_context import ToolContext

from google.genai import types

from dev_signal_agent.app_utils.env import init_environment

from dev_signal_agent.tools.mcp_config import (

    get_reddit_mcp_toolset,

    get_dk_mcp_toolset,

    get_nano_banana_mcp_toolset

)



PROJECT_ID, MODEL_LOC, SERVICE_LOC, SECRETS = init_environment()



shared_model = Gemini(

    model="gemini-3-flash-preview",

    vertexai=True,

    project=PROJECT_ID,

    location=MODEL_LOC,

    retry_options=types.HttpRetryOptions(attempts=3),

)

Memory Ingestion Logic

We want Dev Signal to do more than just follow instructions — we want it to learn from you. By capturing your preferences, such as specific technical interests on Reddit or a preferred blogging style, the agent can personalize its output for future use. To achieve this, we use the , we will show you how to test the agent locally to verify these components on your workstation, before transitioning to a full production deployment on Google Cloud Run in Part 4. Can't wait? The full implementation is already available for you to explore on or dive into the official for the helpful review and feedback on this article.

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