This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK . Finding the perfect, thoughtful gift shouldn't feel like a chore. Whether it's for a birthday, anniversary, or holiday, we all experience gift-buying paralysis: Generic suggestions : "Just buy them a mug or a generic gift card." Budget anxiety : Falling in love with an idea only to find out it costs 3x what you planned to spend. Missing the subtle nuances : Forgetting that someone dislikes clutter, lives in a tiny apartment, or prefers practical experiences over physical objects. To solve this, I built GiftAdvisor . It is an intelligent, consumer-friendly gift recommendation system built with Google Agent Development Kit (ADK) , Gemini ( gemini-3.1-flash-lite ) , and deployed seamlessly to Google Cloud Run . Live Demo & Links Live Cloud Run App : https://gift-advisor-1008832068452.us-central1.run.app GitHub Repository : https://github.com/inusha-thathsara/Multi-Agent-Gift-Idea-Generator-with-Google-ADK What I Built GiftAdvisor transforms unstructured descriptions of a person into tailored, ranked, and strictly budget-compliant gift recommendations. Instead of dumping everything into a single monolithic prompt, GiftAdvisor splits the cognitive load across three specialized AI agents orchestrated via Google ADK: Profile Analyzer Agent : Understands the human behind the prompt (lifestyle, hobbies, aesthetic preferences, and explicit anti-preferences ). Idea Finder Agent : Brainstorms creative, thoughtful candidate gifts across multiple categories with estimated market prices. Budget Filter Agent : Audits estimated prices, filters out anything exceeding the user's hard budget limit, swaps in budget-friendly alternatives, and delivers a ranked curation. Key Highlights & Features Pure Multi-Agent Pipeline : Built using Google ADK's LlmAgent , SequentialAgent , and InMemorySessionService . Zero-Overhead Scale-to-Zero : Deployed to Google Cloud Run with min-instances=0 (scales to zero when idle for $0.00 base cost). Modern Glassmorphism UI : Intuitive dark-mode consumer interface with 1-click preset profiles, interactive budget slider, and live pipeline stage tracking. Comprehensive Export System : Export recommendations with 1 click to Markdown ( .md ), JSON ( .json ), Clipboard, or Print / Save as PDF. Cloud Run Embed 1. Profile Analyzer Agent ( ProfileAnalyzerAgent ) Role : Empathy & Persona Architect. What it does : Ingests raw user inputs (e.g., "My 29yo sister loves specialty pour-over coffee and houseplants, but lives in a small apartment" ). It extracts core interests, lifestyle dimensions, emotional tone, and most importantly, anti-preferences (e.g., no large items, avoid generic mugs ). ADK Output Key : recipient_profile profile_analyzer_agent = LlmAgent ( name = " ProfileAnalyzerAgent " , model = model_name , instruction = """ You are an expert gift persona analyzer. Analyze the recipient ' s description, occasion, and relationship. Extract key traits, hobbies, lifestyle context, and explicit anti-preferences (what to avoid). Save your structured analysis to session state key ' recipient_profile ' . """ , output_key = " recipient_profile " , ) 2. Idea Finder Agent ( IdeaFinderAgent ) Role : Creative Ideation Specialist. What it does : Reads {recipient_profile} from the session state and ideates 6–10 candidate ideas across diverse categories (e.g., Experiential, Practical Everyday, Consumable / Artisan, Sentimental ). It attaches realistic estimated market prices to every item. ADK Output Key : candidate_gift_ideas idea_finder_agent = LlmAgent ( name = " IdeaFinderAgent " , model = model_name , instruction = """ You are a creative gift brainstormer. Given the recipient profile: {recipient_profile}
Brainstorm 6 to 10 distinct, creative gift ideas across multiple categories.
For each idea, provide a realistic estimated market price.
Save your candidate ideas to session state key ' candidate_gift_ideas ' . """ , output_key = " candidate_gift_ideas " , ) 3. Budget Filter Agent ( BudgetFilterAgent ) Role : Financial Auditor & Final Curator. What it does : Reads {candidate_gift_ideas} , {budget_limit} , and {currency} . It validates each candidate against the budget ceiling. Any item that exceeds the budget is logged in an Elimination Audit and replaced with a budget-friendly alternative. The agent then organizes recommendations into budget tiers ( Splurge, Sweet Spot, Budget Friendly ) with specific buying advice. ADK Output Key : final_gift_recommendations budget_filter_agent = LlmAgent ( name = " BudgetFilterAgent " , model = model_name , instruction = """ You are a meticulous gift budget auditor and curator.
Budget Limit: {budget_limit} {currency}
Candidate Ideas:
{candidate_gift_ideas}
1. Audit each idea against the budget ceiling.
2. Eliminate items that exceed the limit and suggest budget-friendly alternatives.
3. Present the Top 3-5 Recommended Gifts formatted into budget tiers with rationale.
Save the final report to session state key ' final_gift_recommendations ' . """ , output_key = " final_gift_recommendations " , ) 4. Orchestration with SequentialAgent Google ADK makes chaining agents intuitive using SequentialAgent . State flows from one agent's output_key directly into the next agent's prompt template variables: gift_advisor_pipeline = SequentialAgent ( name = " GiftAdvisorPipeline " , sub_agents = [ profile_analyzer_agent , idea_finder_agent , budget_filter_agent , ], ) Implementation & Architecture Backend Tech Stack Framework : Python 3.12, FastAPI, Uvicorn Agent Framework : google-adk (Agent Development Kit v2.7.0) Model : gemini-3.1-flash-lite (via google-genai ) Deployment : Google Cloud Run (Containerized via Docker) Cloud Run Production Optimization To keep running costs near $0.00 while maintaining rapid startup times: min-instances = 0 : Cloud Run spins down to zero instances when no traffic is being served. memory = 512MiB & cpu = 1 vCPU : Lightweight footprint optimized for async FastAPI and Google ADK orchestration. gemini-3.1-flash-lite : Ultra-fast latency with minimal token consumption. Key Learnings Separation of Concerns Prevents Hallucination : When asking a single LLM prompt to analyze personality, brainstorm 10 items, and filter by budget simultaneously, it often ignores budget limits or produces bland suggestions. By decoupling Analysis -> Ideation -> Budget Auditing into separate ADK agents, each agent performs its task with significantly higher precision. Session State is the Superpower of ADK : Using InMemorySessionService and prompt variable injection ( {recipient_profile} , {candidate_gift_ideas} ) made passing structured context between agents clean, traceable, and modular. Cloud Run + Gemini is a Perfect Match : Deploying containerized Python agent applications to Cloud Run gives you an instant HTTPS public API with scale-to-zero economics. No idle server bills, automatic TLS certificates, and global scaling out of the box. Conclusion & What's Next Building GiftAdvisor with Google ADK demonstrated how accessible and clean multi-agent orchestration has become in Python. Future Ideas Live Search Tool Integration : Connecting Google Search grounding or SerpAPI to pull real-time e-commerce links and stock availability. Group Gift Mode : Splitting a high-ticket budget across multiple contributors with automated per-person share calculations.