AI-Powered SEO Research Agent with OpenAI & SerpApi

James Collins -

Search engines and AI are rapidly reshaping how businesses find opportunities online. Today’s “AI agents” – systems that autonomously browse and query the web on a user’s behalf – are already changing SEO best practices.

For example, industry data shows that ChatGPT’s user agents doubled their web-search activity in July 2025, fundamentally altering how sites need to be discovered and indexed . At the same time, language models alone cannot know the latest trends or live keyword data.

To bridge this gap, we built a SEO Research Agent: a chat-based assistant that combines OpenAI’s new function-calling with SerpApi’s Google search tools. It plans queries, gathers live SERP data, and synthesizes a full cited SEO report – giving marketers up-to-date insights into keywords, competitors, and news-driven content ideas.

Why Use an AI Agent for SEO Research

SEO research often means chasing fresh, authoritative information. For instance, effective keyword discovery relies on current autocomplete suggestions and competitor SERPs . Google Autocomplete is one of the most accurate keyword research tools for real-time ideas . Likewise, staying ahead in content strategy requires monitoring industry news. However, manually collecting this data is tedious. That’s where an agentic AI comes in.

By combining a language model’s reasoning with automated search tools, our agent can batch together keyword queries, competitor analysis, and news searches, then distill the results into a concise report. This “plan → execute → synthesize” workflow is an established pattern for multi-tool AI agents .

The model first plans all needed searches, the system executes them in parallel (using SerpApi to avoid captchas and proxy issues), and then the agent synthesizes the findings into a structured answer . In practice, this means our SEO agent automatically collects live SERP snippets, autocomplete suggestions, ranking positions, and recent headlines – all without extra work from the user.

How the SEO Research Agent Works

The agent is implemented in Python as a conversational assistant. It uses OpenAI’s API with function-calling tools. We’ve defined four main tools based on SerpApi endpoints:

  • search_web – Runs a Google organic search for a query, returning the top titles and snippets.
  • search_autocomplete – Calls Google Autocomplete to list related keyword suggestions (long-tail terms).
  • search_news – Queries Google News for top headlines and snippets on a topic.
  • check_rank – Finds the ranking position of a given domain for a specific keyword (scanning Google’s top 100 results).

These tools are exposed to the language model through a system prompt that enforces an iterative research loop. In essence:

  • Phase 1: the model writes a natural-language plan, describing which keywords and data it needs (for example, “we’ll start by gathering autocomplete suggestions for the brand name, then list competitors from the SERP, and finally check our rank on those terms.”).
  • Phase 2: the model emits a batch of structured tool calls in one JSON object. Each call has an ID, name, and arguments (e.g. {id: "c1", type: "function", function: { name: "search_autocomplete", arguments: {"query": "ai SEO tools"} }}), which the host executes in parallel. This approach dramatically reduces latency and ensures comprehensive coverage. Once all tool calls have run, their results (concise title:snippet lists, or ranking positions) are returned to the model as “tool” messages. The agent may iterate: if the first set of results suggests new queries (perhaps additional keywords or competitor sites), the model can plan a second round of tool calls. This loop continues until the model has gathered enough information.
  • Phase 3: the model generates the final SEO Report in Markdown. This report includes sections like Keyword Opportunities, SERP Insights, Domain Ranking, and News & Topical Opportunities, each formatted with bullets and embedded citations from the tool outputs. By following this explicit plan–execute–synthesize cycle, the agent provides a transparent, auditable research process .

Key Tools & Capabilities

The SEO agent’s power comes from integrating SerpApi’s search data:

  • Real-time SERP Data: By calling SerpApi’s Google Search API, the agent gets organic results, snippets, and related searches. As Nimbleway explains, a SERP API “scrapes and retrieves data from search engine results pages” and can return organic results, ads, snippets, URLs, knowledge graph data, and related searches . We use this to identify competitor domains, featured snippets, and keyword contexts. For example, search_web might reveal that “brightdata.com” and “apify.com” are top competitors for web scraping APIs, with specific snippet text that the report can cite.
  • Keyword Suggestions: The search_autocomplete tool taps Google Autocomplete for suggestions on each seed keyword or brand name. This yields dozens of long-tail and related keyword ideas. Experts note that starting with Google suggestions is a core keyword research technique . In fact, “Google Autocomplete is the most accurate keyword research tool” for uncovering current search queries . Our agent formats these suggestions into bullet lists for the Keyword Opportunities section.
  • News & Trends: The search_news function uses SerpApi’s Google News API to fetch recent headlines relevant to the topic. Monitoring news is crucial for timely SEO content: as RapidSeedBox points out, you can “keep up with industry news” by pulling data from Google News and spotting brand mentions in real time . In the report’s News & Topical Opportunities section, the agent highlights any breaking stories or trending angles related to the keywords (with source citations).
  • Rank Checking: Finally, the check_rank tool runs a Google search for a keyword and scans the results for the target domain. It reports the rank position (or notes if the domain is not in the top results). According to SEO guides, rank tracking is one of the most common uses of SERP APIs, since it automates the tedious task of checking positions . Our agent uses this to populate the Domain Ranking section: e.g. “example.com ranks #4 for ‘best coffee grinder’ and is not in the top 50 for ‘grinder maintenance tips’.”

Behind the scenes, the code parallelizes these API calls for speed. SerpApi handles proxy rotation and CAPTCHAs, so we get reliable, structured JSON results for each query. The agent then extracts just titles, snippets, or rank numbers, keeping the returned context concise for the model to digest. This combination of tools means the agent covers broad SEO research tasks (keyword ideation, competitor scan, ranking analysis, trend spotting) in one multi-step workflow.

Example SEO Report Output

After gathering data, the agent writes a cohesive report in Markdown. The report generally follows this structure:

  • Keyword Opportunities: A bullet list of long-tail keywords and related terms from autocomplete. For example: - "brand X tutorial", - "brand X vs competitors", etc. These come from the search_autocomplete results, and focus on phrases that real users are searching for.
  • SERP Insights: Highlights of the web search results. It might say, for example, “Competitor: apify.com – offers web scraping & automation tools .” (Here we might cite a relevant snippet from the search results). The agent points out high-ranking competitors, identifies whether featured snippets or rich cards appear, and notes any content gaps. As one AI-SEO guide explains, specialized agents can “analyze the data by themselves and provide...actionable insights” rather than just raw numbers . Our agent does the same for SERP analysis.
  • Domain Ranking: A list showing where the target domain ranks for each keyword. E.g. - example.com ranks #1 for "primary keyword" or “not in top 50” for weaker terms. These come from the check_rank outputs. The report may include all keywords checked (often 5–10 or more) to give a clear picture of current SEO standing.
  • News & Topical Opportunities: Any timely articles or news items. For instance: "New AI agents in search workflows" – recent news item (source). This section is drawn from the search_news results. The agent treats emerging trends as SEO opportunities, noting angles that content creators could exploit. (RapidSeedBox notes that pulling Google News is great for tracking brand mentions and breaking stories in real time .)
  • Recommendations: Based on the analysis, the agent may add suggested actions (e.g. target long-tail keywords, improve content around topics where competitors rank, etc.). These are generated by the model using the collected data. Everything is written in a clear, business-friendly tone with inline citations back to the search snippets.

The final report is easy to read and can even be published internally or shared. All key claims are traceable to the underlying SERP data (for example, competitor mentions are accompanied by “[Source]” linking to the snippet’s origin). This makes the process auditable and transparent – a direct benefit of using an agentic approach where each fact comes from a known search query .

Getting Started

The SEO Research Agent runs locally via Python. Setup is straightforward:

  1. Install requirements: Python 3.9+ and pip install openai serpapi.
  2. Set API keys: Provide your OPENAI_API_KEY and SERPAPI_API_KEY (SerpApi has a free tier with 250 searches/month for testing).
  3. Run the agent: You can use the CLI (python seo_agent.py -q "SEO analysis for example.com") or import the SEOResearchAgent class in your code. It supports interactive mode as well: just run without -q to chat.

For example, running:

python seo_agent.py -q "SEO report for vanta.com"

will prompt the agent to produce a full SEO report for the given domain. The conversation trace can be saved in JSON (--outfile trace.json) for debugging or auditing purposes.

Under the hood, the code follows the plan–tool–report loop described above. The system prompt guides the model to first plan the research (listing data to collect), then emit all tool calls together, then refine if needed, and finally output the Markdown report. This matches best practices for multi-tool AI agents and ensures the agent doesn’t stop at shallow answers. Instead, it iterates until it confidently has comprehensive data for all sections.

Conclusion

By combining OpenAI’s latest models with SerpApi’s real-time search APIs, the SEO Research Agent brings cutting-edge AI to everyday SEO workflows. It automates keyword brainstorming, competitor audits, rank tracking, and news monitoring – tasks that normally take hours of manual search and curation.