ai_seo_audit_team

🔍 AI SEO Audit Team

🎓 FREE Step-by-Step Tutorial

👉 Click here to follow our complete step-by-step tutorial and learn how to build this AI SEO Audit Team from scratch with detailed code walkthroughs, explanations, and best practices.

The AI SEO Audit Team is an autonomous, multi-agent workflow built with Google ADK. It takes a webpage URL, crawls the live page, researches real-time SERP competition, and produces a polished, prioritized SEO optimization report. The app uses Firecrawl via MCP (Model Context Protocol) for accurate page scraping and Google's Gemini 2.5 Flash for analysis and reporting.

Features

• End-to-End On-Page SEO Evaluation
• Automated crawl of any public URL (Firecrawl MCP)
• Structured audit of titles, headings, content depth, internal/external links, and technical signals
• Competitive SERP Intelligence
• Google Search research for the inferred primary keyword
• Analysis of top competitors, content formats, title patterns, and common questions
• Actionable Recommendations
• Prioritized optimization roadmap with rationale and expected impact
• Keyword strategy, schema opportunities, internal linking ideas, and measurement plan
• Clean Markdown report ready for stakeholders or ticket creation
• ADK Dev UI Integration
• Trace view of each agent step (crawl → SERP → report)
• Easy environment variable management through .env

Agent Workflow

All agents run sequentially using ADK’s SequentialAgent, passing state between stages via the shared session.

Requirements

System Requirements

• Python 3.10+ for Google ADK
• Node.js (for Firecrawl MCP server via npx)

Python Dependencies

Install the Python dependencies:

pip install -r requirements.txt

API Keys

You will need valid API keys:

• GOOGLE_API_KEY – Gemini (Google AI Studio) for LLM + Google Search
• FIRECRAWL_API_KEY – Firecrawl MCP server (get one here)

Set your environment variables (e.g., add to your shell profile or export in your terminal):

export GOOGLE_API_KEY=your_gemini_key
export FIRECRAWL_API_KEY=your_firecrawl_key

Alternatively, you can put these in a .env file if you prefer.

Running the App with ADK Dev UI

1. Activate your environment (optional but recommended):

```bash

cd advanced_ai_agents/multi_agent_apps/agent_teams/ai_seo_audit_team

```

1. Install dependencies (if not already):

```bash

pip install -r requirements.txt

```

1. Launch the ADK web UI from the project root:

```bash

adk web

```

1. In the UI:

• Select the ai_seo_audit_team app.
• Provide the target URL when prompted.
• Watch the agents execute in the Trace panel (Firecrawl → Google Search → Report).

Usage Tips

• Ensure the target URL is publicly accessible without auth requirements.
• The workflow is optimized for a single URL per run; start a new session for each distinct audit.
• The final report can be copied directly into docs, tickets, or shared with stakeholders.

Folder Structure

ai_seo_audit_team/
├── agent.py          # Multi-agent workflow definitions
├── requirements.txt  # Minimal dependencies
├── __init__.py       # Module initialization
└── README.md         # You are here

Next Steps & Extensibility

• Add automated evaluations via ADK Eval Sets for regression testing.
• Hook the Markdown report into Slack/email connectors or ticketing systems.
• Swap in alternative SERP providers (Serper, Tavily) if you prefer non-Google search APIs.
• Extend the workflow with additional agents (e.g., content brief generator, schema builder) using the shared session state.

Happy auditing! 🚀