How to Automate Keyword Research With OpenClaw in 2026

Why Automate Keyword Research

Manual keyword research follows a predictable pattern. You start with seed keywords, expand them using tools, organize them into clusters, classify search intent, identify gaps versus competitors, and prioritize based on volume and difficulty. Every step is systematic. Every step is repeatable. That makes it a perfect candidate for automation.

The problem is not that manual research is inaccurate. An experienced SEO professional doing keyword research by hand will produce excellent results. The problem is time. A thorough keyword research cycle for a mid-size website takes 6 to 8 hours. For an agency managing 10 clients, that is 60 to 80 hours per month spent on a task that follows the same process every time.

OpenClaw changes the equation. By connecting its keyword-research skill to an LLM and configuring your data sources, you can run a complete research cycle in 15 to 45 minutes. You spend your time reviewing and refining the output rather than building spreadsheets from scratch. If you are new to OpenClaw, read our complete OpenClaw SEO guide first for the full setup context.

What You Can Automate

Not every part of keyword research should be automated. Here is a realistic breakdown of what OpenClaw handles well and where human judgment still matters.

Prerequisites

Before starting, you need OpenClaw installed and running with an LLM connected. If you have not done this yet, follow the setup instructions in our complete guide. For keyword research specifically, you need:

Install the keyword-research skill if you have not already:

openclaw skills install keyword-research
openclaw skills install competitor-watch
openclaw config set keyword-research.llm claude-sonnet-4-6
openclaw config set keyword-research.output_format csv

If you want volume data, connect a data API. DataForSEO is the most popular choice because of its per-request pricing model:

# Optional: Connect volume data provider
openclaw config set keyword-research.volume_api dataforseo
openclaw config set keyword-research.volume_api_key your-api-key
openclaw config set keyword-research.volume_api_location "United States"

Step 1: Seed Keyword Expansion

Seed expansion is where OpenClaw turns a handful of starting keywords into hundreds or thousands of variations. The skill uses multiple data sources to maximize coverage: Google autocomplete, People Also Ask boxes, related searches, and the LLM itself for semantic variations.

Running Your First Expansion

Start by messaging OpenClaw with your seed keywords. You can send this through Telegram, Slack, or whichever messaging platform you configured:

/keyword-research expand
Seeds: ai seo tools, seo automation, ai content optimization
Depth: deep
Sources: autocomplete, paa, related, semantic
Location: United States
Language: English

The depth parameter controls how aggressively the skill expands. Use "shallow" for a quick 100 to 200 keyword list. Use "deep" for a comprehensive 800 to 2,000 keyword list. Deep expansion takes longer and costs more in API calls, but catches long-tail variations that shallow mode misses.

How Expansion Works Under the Hood

The skill runs through several passes. First, it queries Google autocomplete for every letter of the alphabet appended to each seed keyword (a technique sometimes called the "alphabet soup" method). Then it scrapes People Also Ask questions from the SERPs for each seed. Next, it collects related searches from the bottom of the SERP. Finally, it uses the connected LLM to generate semantic variations, synonyms, and related concepts that the SERP data might miss.

Each source contributes differently. Autocomplete catches what people actually type. People Also Ask reveals questions and informational intent. Related searches surface adjacent topics. LLM-generated variations fill gaps in coverage, particularly for newer topics where SERP data is thin.

Configuring Expansion Depth

# Fine-tune expansion parameters
openclaw config set keyword-research.autocomplete_depth 3
# 1 = seed only, 2 = seed + top variations, 3 = full alphabet

openclaw config set keyword-research.paa_depth 2
# 1 = first level PAA, 2 = follow-up PAA questions

openclaw config set keyword-research.semantic_variations 50
# Number of LLM-generated variations per seed

openclaw config set keyword-research.dedup_threshold 0.85
# Similarity threshold for removing near-duplicates

The dedup_threshold setting is important. Set it too high (0.95+) and you keep near-duplicate keywords that clutter your list. Set it too low (below 0.75) and you lose legitimate variations. The default of 0.85 works well for most niches.

Step 2: Clustering Automation

Once you have an expanded keyword list, the next step is grouping semantically related keywords into clusters. Each cluster represents a single page or piece of content. This is where the LLM really earns its keep -- semantic clustering is something AI does remarkably well.

Running Automated Clustering

/keyword-research cluster
Input: latest_expansion_results
Method: semantic
Min_cluster_size: 3
Max_cluster_size: 25
Naming: auto

The method parameter accepts "semantic" (LLM-based grouping), "serp" (groups keywords that share top-ranking URLs), or "hybrid" (combines both). Semantic clustering is faster and cheaper. SERP-based clustering is more accurate for understanding Google's view of keyword relationships but requires scraping the SERPs for every keyword, which takes longer and costs more.

Understanding the Output

OpenClaw produces a structured output with cluster names, member keywords, and confidence scores. A typical cluster looks like this:

Cluster: "AI SEO Tools Comparison"
Confidence: 0.92
Keywords (14):
  - ai seo tools
  - best ai seo tools 2026
  - ai seo software comparison
  - top ai tools for seo
  - ai powered seo platforms
  - seo tools with ai features
  - ai seo tool reviews
  - best ai tools for search optimization
  - ai seo tool pricing
  - free ai seo tools
  - ai seo tools for small business
  - enterprise ai seo solutions
  - ai seo tools vs traditional seo tools
  - which ai seo tool is best

The confidence score tells you how tightly related the keywords in the cluster are. Scores above 0.85 indicate a strong, cohesive cluster. Scores between 0.70 and 0.85 may need manual review -- some keywords might fit better in a different cluster. Scores below 0.70 suggest the cluster should be split.

Hybrid Clustering for Maximum Accuracy

For the most accurate results, use hybrid clustering. This runs semantic clustering first, then validates the groups against actual SERP overlap. Keywords that the LLM grouped together but that do not share any top-10 results get flagged for review.

/keyword-research cluster
Input: latest_expansion_results
Method: hybrid
Serp_check_top: 10
Overlap_threshold: 0.3
Flag_mismatches: true

Hybrid clustering costs more because it scrapes SERPs, but the accuracy improvement is worth it for important projects. For routine monthly refreshes, semantic-only clustering is sufficient.

Step 3: Search Intent Classification

Every keyword carries an intent: informational, navigational, commercial investigation, or transactional. Getting intent right determines the type of content you create. OpenClaw classifies intent by analyzing both the keyword text and the actual SERP features present for each keyword.

Running Intent Classification

/keyword-research classify-intent
Input: latest_cluster_results
Method: serp_signals
Categories: informational, navigational, commercial, transactional
Confidence_threshold: 0.75

The serp_signals method checks what appears on the actual SERP for each keyword. Shopping results suggest transactional intent. Knowledge panels suggest informational. Ads at the top suggest commercial. This method is more accurate than text-only classification because it reflects how Google actually interprets the query.

What the Classification Reveals

Intent classification transforms a flat keyword list into a content strategy blueprint. Here is what each intent type tells you about the content you should create:

You can feed the classified output directly into content planning. Our Search Intent Classifier tool can validate the automated classifications if you want a second opinion.

Step 4: Competitor Keyword Gap Analysis

Gap analysis identifies keywords your competitors rank for that you do not. It is one of the highest-value keyword research activities because it surfaces proven opportunities -- if a competitor ranks for a keyword, there is demonstrated search demand and the topic is clearly relevant to your space.

Running Gap Analysis

/keyword-research competitor-gaps
Your_domain: yoursite.com
Competitors: competitor1.com, competitor2.com, competitor3.com
Min_competitor_position: 20
Max_your_position: 100
Include_not_ranking: true
Output: gaps_with_clusters

The include_not_ranking flag is important. Setting it to true means OpenClaw will also find keywords where you have zero presence, not just keywords where you rank lower. These complete gaps often represent the biggest opportunities.

How OpenClaw Finds Gaps

Without a paid data API, OpenClaw uses SERP scraping to approximate gap analysis. It takes your competitors' top-performing pages, extracts the keywords those pages target (from titles, headings, meta descriptions, and content), and checks whether your site appears in the top 100 results for those terms. This approach is slower and less comprehensive than using an Ahrefs or SEMrush API, but it works without any paid subscription.

With a connected API, gap analysis is faster and more thorough. OpenClaw pulls the full keyword profile for each competitor and cross-references it against your rankings in seconds rather than minutes.

# Connect Ahrefs API for faster, more accurate gap analysis
openclaw config set keyword-research.gap_api ahrefs
openclaw config set keyword-research.gap_api_key your-ahrefs-api-key

# Or use SEMrush
openclaw config set keyword-research.gap_api semrush
openclaw config set keyword-research.gap_api_key your-semrush-api-key

Interpreting Gap Results

OpenClaw categorizes gaps into three priority levels based on competitor ranking strength and the number of competitors ranking for each keyword:

• High priority: Multiple competitors rank in the top 10, and you are not ranking at all. These keywords are proven to drive traffic in your niche.
• Medium priority: One or two competitors rank in positions 11 to 20. There is opportunity but the keyword may be more competitive or less relevant.
• Low priority: Competitors rank but outside the top 20. The keyword might be tangential to your core business.

The gap results feed directly into the prioritization step, where you combine gap data with volume estimates and difficulty signals to build your final keyword target list.

Step 5: Keyword Prioritization

With expanded, clustered, intent-classified, and gap-analyzed keywords, the final step is prioritization. This is where you decide which keywords to target first. OpenClaw can automate the scoring, but the strategic decisions about business alignment still need human input.

Automated Scoring

/keyword-research prioritize
Input: latest_gap_results + latest_cluster_results
Scoring_factors:
  - volume_estimate: weight 0.25
  - competitor_gap: weight 0.30
  - intent_match: weight 0.20
  - difficulty_estimate: weight 0.15
  - cluster_size: weight 0.10
Business_focus: commercial, transactional
Top_n: 100

The scoring weights are configurable. The defaults above prioritize competitor gaps (if competitors rank for it, it is probably worth targeting) and volume. Adjust weights based on your strategy. If you are focused on bottom-of-funnel conversions, increase the intent_match weight. If you are building topical authority, increase cluster_size weight to favor topics where you can create multiple pieces of content.

Output: Your Prioritized Keyword Map

The final output is a prioritized keyword map in CSV format. Each row includes the keyword, its cluster name, search intent, estimated volume range, difficulty estimate, competitor gap score, overall priority score, and a suggested content type. This map becomes the foundation for your keyword strategy.

# Example output (top 5 rows)
keyword,cluster,intent,volume_range,difficulty,gap_score,priority,content_type
ai seo tools comparison,AI SEO Tools,commercial,2400-3600,medium,0.95,94,comparison-post
best ai seo software 2026,AI SEO Tools,commercial,1900-2800,medium,0.88,91,roundup-post
how to automate seo,SEO Automation,informational,1200-1800,low,0.82,87,how-to-guide
ai content optimization guide,Content Optimization,informational,800-1200,low,0.79,83,guide
seo automation tools pricing,SEO Automation,transactional,600-900,medium,0.91,81,comparison-post

Adding Business Context

The automated scoring gets you 80% of the way there. The remaining 20% requires business context that OpenClaw cannot infer. Review the top 100 keywords and ask yourself: Does this keyword align with our services? Would someone searching this term be a potential customer? Can we create genuinely useful content for this query? Remove keywords that score high on SEO metrics but low on business relevance.

Putting It All Together: The Full Workflow

Here is the complete keyword research workflow as a single automated sequence. You can run this as a scheduled job or trigger it manually:

/keyword-research full-pipeline
Seeds: [your seed keywords]
Competitors: [competitor domains]
Your_domain: yoursite.com
Expansion_depth: deep
Clustering_method: hybrid
Intent_method: serp_signals
Scoring_weights:
  volume: 0.25
  gap: 0.30
  intent: 0.20
  difficulty: 0.15
  cluster_size: 0.10
Output_format: csv
Notify_on_complete: true

This single command runs the entire pipeline: expansion, deduplication, clustering, intent classification, competitor gap analysis, and prioritization. When it finishes, OpenClaw sends you a message with the CSV file attached and a summary of the top findings.

Scheduling Monthly Research Cycles

# Schedule a full research cycle on the 1st of every month
openclaw schedule create \
  --name "monthly-keyword-research" \
  --cron "0 8 1 * *" \
  --command "/keyword-research full-pipeline" \
  --config monthly-kw-config.yaml \
  --notify telegram

Between full cycles, set up a weekly lightweight check for new autocomplete suggestions and PAA questions. This catches emerging keywords before your competitors notice them:

# Weekly new keyword check every Monday at 7 AM
openclaw schedule create \
  --name "weekly-keyword-check" \
  --cron "0 7 * * 1" \
  --command "/keyword-research expand --depth shallow --diff-only" \
  --notify telegram

Manual vs Automated Keyword Research

Here is a direct comparison of the manual process versus the OpenClaw-automated process for a typical mid-size website.

The takeaway is clear: automated research beats manual research on speed, coverage, cost, and scalability. Manual research wins on strategic depth and clustering precision. The optimal approach is automated research for data gathering plus human review for strategic filtering.

If you want the best of both worlds without managing the setup yourself, our keyword strategy service combines AI-powered research with expert strategic analysis. We use automation for the heavy lifting and human expertise for the decisions that matter.

Tips and Best Practices

Start Small and Validate

Run your first automated research cycle on a topic you know well. Compare the output against what you would have produced manually. This calibrates your expectations and helps you tune the configuration before running it on unfamiliar topics.

Use Multiple Seed Sets

Do not rely on a single set of seed keywords. Run separate expansions for different angles: product terms, problem terms, competitor brand terms, and industry terms. Merge the results for the most comprehensive coverage.

Review Cluster Boundaries

The most common issue with automated clustering is cluster boundaries -- where one cluster ends and another begins. Spend your review time on keywords that sit at the edges of clusters rather than reviewing entire clusters from scratch.

Track Changes Over Time

Enable the diff_only flag on scheduled runs to see what changed since the last cycle. New autocomplete suggestions, new PAA questions, and new competitor keywords are more actionable than reviewing the full keyword list every month.

Set Budget Caps

Keyword research with deep expansion and hybrid clustering can consume significant API tokens. Set a per-run budget cap to prevent unexpected costs. A typical full pipeline run stays under $3 with Claude, but misconfigured expansion depth can push it to $10 or more.

Frequently Asked Questions