Sanbi ADK Agent 🔍

AI brand-visibility audits as an agent. Ask "How visible is sight360.com for LASIK surgery?" and get a competitive leaderboard of who ChatGPT and Gemini actually recommend — built on Google's Agent Development Kit, Vertex AI, and the Model Context Protocol.

Google for Startups AI Agents Challenge — Track 3: Refactor for Google Cloud Marketplace & Gemini Enterprise. This repo is a refactor of sanbi.ai's production audit engine (live SaaS, FastAPI + Supabase + Railway) onto Google Cloud-native agent infrastructure.

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What it does

Brands are losing discoverability as search shifts to AI assistants. Sanbi answers the new question: "When someone asks an AI for a recommendation in my category, do I show up?" — and then acts on the answer.

The agent runs a 5-step measure → act pipeline:

1. generate_audit_prompts — researches the brand with Gemini + Google Search grounding (Vertex AI), extracts identity (industry, audience, competitors), and generates realistic branded + unbranded buyer queries.
2. query_engines — fires every query at multiple AI engines in parallel (OpenAI + Vertex Gemini with grounded search), capturing raw responses and citations.
3. grade_responses — LLM-grades each response (visibility, rank, sentiment, competitors mentioned), computes weighted visibility scores, and builds a competitive leaderboard + gap analysis + executive summary.
4. find_growth_opportunities — classifies every cited source through Sanbi's deterministic platform taxonomy (reddit / forum / Q&A / youtube / reviews / blog / wiki…), ranks them with a replyability-weighted score, and verifies the URLs are real — AI engines hallucinate citations, and we prove which ones (HEAD checks, YouTube oEmbed, Reddit OAuth).
5. draft_growth_actions — generates a different growth motion per surface: authentic reply drafts for forums/reddit, expert answers for Q&A, comment + video briefs for YouTube, review-acquisition plays for review platforms, counter-content briefs for blogs. You can't blog your way into a forum thread — the agent routes work to the right channel automatically.

A coordinator agent routes the conversation between two specialists — an audit agent (measure, tools 1–3) and a growth agent (act, tools 4–5) — that share audit data through ADK session state. Raw multi-KB engine responses live in session state, never in the model's context window; only compact summaries flow through the agents.

Architecture

                        ┌────────────────────────────────┐
  user ── ADK web UI ──▶│  sanbi_coordinator (root agent)  │   model: gemini-2.5-flash
                        │  routes; no tools of its own     │
                        └─────┬────────────────┬─────────┘
                   transfer│                  │transfer
              ┌───────────▼───────┐  ┌───────▼──────────────┐
              │  audit_agent       │  │  growth_agent        │
              │  MEASURE           │  │  ACT                 │
              │  1. generate_audit │  │  4. find_growth_     │
              │     _prompts       │  │     opportunities    │
              │  2. query_engines  │  │     (classify→rank   │
              │  3. grade_responses│  │      →verify URLs)   │
              │                    │  │  5. draft_growth_    │
              │                    │  │     actions          │
              └─────────┬─────────┘  └──────────┬─────────┘
                        │ shared ADK session state │
                        │ ("audit:<id>" entries)   │
                        ┌─────────────▼─────────────┐
                        │        sanbi_core/        │──▶ Vertex Gemini + Google Search grounding
                        └─────────────┬─────────────┘──▶ OpenAI ∥ Vertex Gemini (parallel)
                                      │
                        ┌─────────────▼─────────────┐
  any MCP client ──────▶│  MCP server (FastMCP)     │
  (Claude, Gemini CLI)  │  run_visibility_audit     │──▶ full pipeline incl. growth inbox
                        └─────────────────────────┘
                                  deployed on Cloud Run

• sanbi_core/ — the engine, ported from production: planning (brand research + prompt generation), execution (multi-engine querying), analysis (grading + leaderboard), platforms (deterministic citation-source taxonomy), verifier (anti-hallucination URL checks), growth (opportunity scoring + platform playbooks).
• agents/sanbi_audit/ — the ADK multi-agent system: coordinator + audit/growth specialists.
• mcp_server/ — the same audit exposed as a Model Context Protocol tool, so any MCP-capable agent can embed Sanbi audits.

ADK design notes

• Multi-agent delegation — the coordinator owns no tools; it transfers to audit_agent or growth_agent based on intent, and the specialists hand off to each other (audit → growth) when the user wants the full pipeline.
• Session state, not context stuffing — tools receive ADK's ToolContext and persist audits in tool_context.state under audit:<id> keys. Raw engine responses (5–15k chars each) never enter the model's window. State lives in whatever SessionService the runner provides — in-memory in the dev UI, swappable to a persistent service in production without touching tool code.
• Demo-robust tool contracts — every post-planning tool takes audit_id: str = "" and falls back to the session's active_audit_id, so a model that forgets to thread the id still lands on the right audit.
• Agent evaluation — a starter evalset lives in agents/sanbi_audit/evalsets/ (generated by scripts/make_evalset.py from ADK's own schema models):

adk eval agents/sanbi_audit agents/sanbi_audit/evalsets/routing.evalset.json \
    --config_file_path agents/sanbi_audit/evalsets/test_config.json

Quickstart

# 1. Clone + install
git clone https://github.com/AdityaDREXEL/sanbi-adk-agent && cd sanbi-adk-agent
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 2. Configure
cp .env.example .env          # fill in GOOGLE_CLOUD_PROJECT + OPENAI_API_KEY
gcloud auth application-default login

# 3. Verify clients
python scripts/smoke_test.py

# 4. Run the agent (ADK dev UI at http://localhost:8000)
adk web agents

Then chat: "Audit sight360.com for LASIK surgery in Philadelphia — then find where AI engines cite from, verify which sources are real, and draft growth actions for the top opportunities."

Run the MCP server

# stdio (Claude Desktop / MCP Inspector)
python -m mcp_server.server

# HTTP (Cloud Run style)
MCP_TRANSPORT=http MCP_PORT=8081 python -m mcp_server.server

Deploy to Cloud Run

gcloud run deploy sanbi-adk-agent \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --set-env-vars GOOGLE_GENAI_USE_VERTEXAI=TRUE,GOOGLE_CLOUD_PROJECT=$PROJECT,GOOGLE_CLOUD_LOCATION=us-central1 \
  --set-secrets OPENAI_API_KEY=openai-api-key:latest

Tests

244 offline tests cover the scoring formula, LLM-output coercion (None/string ranks, fenced JSON), citation extraction + Google-redirect filtering, leaderboard aggregation, engine-failure isolation, the platform classifier taxonomy, URL-verification verdicts (incl. the Reddit-OAuth and YouTube-oEmbed edge cases), growth-opportunity scoring to the decimal, the agents' session-state flow, and the MCP tool contract. All LLM/HTTP calls are mocked — the suite runs with zero credentials and zero API spend.

pip install -r requirements-dev.txt
pytest

Tech

Layer	Tech
Agent framework	Google Agent Development Kit (ADK)
LLM	Gemini 2.5 Flash via Vertex AI (agent brain, research, grading, grounded search)
Audited engines	OpenAI + Vertex Gemini
Protocol	Model Context Protocol (MCP)
Runtime	Cloud Run (containerized, serverless)
Grounding	Vertex AI Google Search tool

Marketplace & Gemini Enterprise roadmap

This refactor is step 1 of bringing Sanbi to Google Cloud Marketplace:

• Marketplace listing — containerized Cloud Run service with usage-based billing hooks.
• Gemini Enterprise / Agentspace — register the agent so enterprise marketing teams can invoke audits from their Google Workspace.
• AlloyDB — replace production Supabase persistence for audit history & trend tracking.
• Identity Platform — multi-tenant auth for agency use.
• Scheduled audits — Cloud Scheduler → Cloud Run jobs for weekly visibility tracking (production Sanbi already does this on Railway; the port is mechanical).

Relationship to production

sanbi.ai runs this same pipeline in production across 4 engines (OpenAI, Gemini, Perplexity, Claude) with batch execution, Supabase persistence, citation-growth mining (1,000+ community opportunities per brand), and Stripe billing. This repo extracts the core audit loop, re-routes all Gemini traffic through Vertex AI, and rebuilds the interface as an ADK agent + MCP tool — the agent-native form factor of the product.

License

MIT