How to Use Vellum and OpenSEO for Your SEO Engine

Most teams know they need search traffic, but the traditional SEO workflow is broken.

You open an expensive dashboard, spend an hour filtering columns, export a spreadsheet, clean up the data, and copy five numbers into a doc before writing a single word. By the time you sit down to outline, your mental bandwidth was burned on data triage instead of creating content that wins.

Connecting OpenSEO to a personal AI assistant like Vellum inverts that dynamic completely.

Instead of treating search data like an external destination you have to visit, your assistant accesses live keyword metrics, competitor gaps, and SERP rankings directly inside your workspace. You bring the business positioning and editorial direction; your assistant pulls the live search ground truth and handles the execution.

Here is how to set up and run an agentic SEO and Generative Engine Optimization (GEO) engine without the dashboard tax.

Why Use OpenSEO & Vellum Assistant?

Pairing live search data with a personal AI assistant solves the two biggest bottlenecks in modern SEO: context loss and manual data triage.

Your business context: A standalone LLM or chat window treats every prompt as a blank slate. Vellum maintains persistent memory across conversations, so it already understands your product, your target audience, and your existing content pillars. When OpenSEO feeds live keyword and ranking data into that context, the assistant evaluates opportunities based on what your business actually does, not generic search theory.
No more manual data sorting: Traditional SEO requires toggling between browser tabs, exporting CSVs, filtering spreadsheets, and copying figures into docs. With OpenSEO connected directly to Vellum via MCP, search intelligence lives inside your writing environment. You spot a competitor gap, analyze the SERP, and outline a counter-piece in one continuous thread.
Turn data into action: Dashboards only tell you what happened in the past. An assistant with live SEO tools turns data into immediate output: checking the live top 10 results, dissecting where incumbent pages are shallow, generating an outline that fills the gap, and tracking rank movements after you publish.

A 60-Second Example

You: Competitor X just took the #1 spot for "best ai assistants 2026." What are they winning on that we missed?

Vellum Assistant: Let me pull their organic footprint and live SERP data through OpenSEO. Looking at the top 10 results, they're winning on three comparison keywords you don't rank for: local model execution, offline privacy, and pricing per seat. All three have low keyword difficulty (<25) and high search volume (>800/mo).

You: Outline a new section for our breakdown covering those exact three angles, and cite the current ranking consensus.

Vellum Assistant: Outline drafted. Here is where the incumbent pages are weak and the exact gap we can exploit.

The first answer pulls live search ground truth. The second is an editorial move executed in seconds.

How to Set Up Vellum + OpenSEO

Connecting OpenSEO to Vellum takes two minutes:

In Vellum, add the OpenSEO plugin from the plugin catalog (https://www.vellum.ai/plugins/openseo) or connect via MCP (https://openseo.so/docs/mcp).
Follow the OAuth prompt to sign in to your OpenSEO account and approve the connection.
Your assistant immediately gets access to all OpenSEO tools: keyword research, competitor analysis, live SERP lookups, backlink data, and rank tracking.

No custom scripts, no API keys to manually wire up. Once connected, your assistant uses real search data automatically when you plan, draft, and track content.

Why Connect SEO Data to an Assistant?

Without a live data source, AI models invent search volumes that sound plausible but are completely fabricated. Connecting OpenSEO gives your assistant real DataForSEO numbers directly in chat.

Traditional SEO platforms like Ahrefs or Semrush charge $130 or more per month for a single seat, largely to pay for proprietary dashboard interfaces. OpenSEO is $10 a month with $10 of usage credits included, and signing up includes $0.50 of trial credits.

Because an assistant only queries data when you need it, a typical keyword lookup or live SERP check costs about five cents. A lean team pays a fraction of enterprise SaaS pricing while getting search intelligence embedded directly where content is created.

3 Core Workflows for Agentic Search

Once OpenSEO is connected, three workflows replace the manual spreadsheet process.

1. Conversational Competitor Gap Mining

Finding competitor gaps manually takes hours of toggling filters, excluding branded terms, and cross-referencing published articles.

With an assistant connected to OpenSEO, you do it in one prompt:

"Look up competitor.com's organic footprint via OpenSEO. Filter out their branded terms, and show me the top 5 high-intent keywords with search volume over 500 and difficulty under 35 where we don't currently rank."

The assistant queries OpenSEO, pulls the live ranking data, filters out irrelevant queries, and returns actionable targets with real volume and difficulty scores.

From there, move straight into drafting:

"Take keyword #2 and generate an outline targeting that exact search intent. What specific questions are competitors failing to answer?"

The cycle between spotting an opportunity and structuring a draft drops from two hours to five minutes.

2. Live SERP and GEO Analysis

Traditional SEO stops at keyword volume. Generative Engine Optimization (GEO) requires understanding how search engines and AI models (Claude, ChatGPT, Perplexity, Google AI Overviews) synthesize answers on a topic.

Before drafting any major piece, have your assistant inspect the live ranking landscape:

"Pull the live top 10 Google results for [target query] via OpenSEO. Analyze the top ranking pages: What structure are they using? What specific questions or buyer requirements do they completely ignore? Where is the consensus weak?"

OpenSEO crawls the live SERP and returns the actual pages winning today. Your assistant breaks down the patterns:

If all ten ranking articles are superficial listicles that omit pricing or benchmarks, your editorial wedge is transparent pricing teardowns and reproducible tests.
If incumbent pages miss recent feature updates, that freshness hook becomes your opening thesis.

Instead of writing generic summaries and hoping search engines notice, live SERP data identifies the analytical void on page one so you can write the definitive piece that fills it.

3. Background Rank Tracking Without the Dashboard Tax

Tracking keyword movements usually requires logging into external dashboards to stare at ranking charts.

With OpenSEO in Vellum, rank tracking happens at the point of publication:

"Add these 6 keywords to our OpenSEO rank tracker. Flag any movement greater than 3 positions."

Rank checks run in the background. If an article climbs to position #3 or drops below the fold, your assistant catches it during routine checks and flags the delta directly in your feed. The feedback loop lives right where the work happens.

Guiding Principles for Prompting Your SEO Assistant

Push back on every recommendation: Your assistant will generate dozens of ideas. Make it show its reasoning, the trade-offs, and the primary metric that matters.
Describe your positioning clearly: Tell your assistant who your product is for, who it isn't for, and why customers pick you. Two sentences of clear positioning steers keyword research better than complex dashboard filters.
Name the deliverable, not the steps: Ask for what done looks like: "Give me the top 3 high-volume search queries for this topic that have low competition", not a step-by-step tutorial.
Keep judgment with the human: Search data and initial outlines are fast for an agent to pull, but unique perspective, honest benchmarks, and voice belong to you.

FAQ

What is OpenSEO?

OpenSEO is an open-source, agent-native SEO platform and Model Context Protocol (MCP) server. It connects AI assistants directly to live search data, including keyword search volume, difficulty, competitor gap analysis, backlink profiles, site audits, and live SERP rankings. Instead of trapping data in proprietary dashboards, OpenSEO provides real search data through tools and guided skills so assistants and content teams can research and execute directly in chat or code editors.

What is Vellum?

Vellum is an open-source personal AI assistant that lives on your computer. It has its own identity, persistent memory that carries across conversations, and its own email address. Unlike passive chat windows, Vellum works proactively in the background, connects to tools and services via plugins and MCP, and runs across Mac, iOS, Android, Slack, Telegram, and the web.

Why connect OpenSEO to an AI assistant instead of using a dashboard?

The web app is useful for high-level overviews, but connecting OpenSEO over MCP lets your assistant use search data as live context while drafting, planning, and editing. Instead of copying numbers from a dashboard into a doc, your assistant checks keyword volume and live SERP structure mid-conversation.

How much does this setup cost compared to legacy SEO suites?

Ahrefs and Semrush charge $130 or more per month for a single seat. OpenSEO is $10/month with $10 of usage credits included (and a free trial with $0.50 of credits). A typical keyword or SERP lookup costs around five cents, meaning small teams pay a fraction of enterprise SaaS pricing for real DataForSEO search data.

What is the difference between SEO and GEO in this workflow?

Traditional SEO focuses on keyword volume, metadata, and ranking algorithms. GEO (Generative Engine Optimization) focuses on long tail queries, getting cited, and referenced by AI models like Claude, ChatGPT, and Google AI Overviews. Using OpenSEO's live SERP tools allows your assistant to analyze what sources AI engines currently cite for a topic and structure your content to become the authoritative reference.

Does pulling search data slow down content drafting?

No, it does the opposite. By querying OpenSEO directly via chat prompts, competitive gap analysis and SERP structure reviews take seconds rather than the hours typically spent exporting CSVs and formatting spreadsheets.