Claygent
Claygent is Clay's native AI agent, combining LLM reasoning with web scraping and search. You give it a research prompt (find the company's tech stack, identify the CMO's LinkedIn, summarize their latest blog post) and it autonomously navigates the web, extracts data, and returns structured results. Claygent is purpose-built for GTM enrichment pipelines, not general-purpose LLM tasks.
Official site ↗Why it ranks here
Claygent is the only AI research agent that lives inside a GTM workflow tool. You do not need to chain an LLM, a scraper, and a search API yourself; Claygent does it in one step. For teams already using Clay, it eliminates the integration tax.
Where it sits
Claygent is narrower than Claude or GPT: it excels at web research and data extraction but cannot handle nuanced reasoning, long-form drafting, or classification tasks unrelated to enrichment. It is a specialist, not a generalist.
How GTM Engineering teams use it
GTM engineers use Claygent for lead enrichment (given a domain, find employee count, tech stack, recent funding, LinkedIn profiles), competitive intelligence (scrape a competitor's pricing page), and intent signal capture (summarize a prospect's recent blog posts or press releases). Claygent runs inside Clay tables, so the output feeds directly into scoring, routing, or outreach steps.
In-depth notes
Claygent credits are separate from Clay credits; heavy usage adds cost. Latency is higher than a direct LLM call because it performs multi-step web research. Accuracy depends on the quality of your prompt and the target site structure. Claygent occasionally fails on JavaScript-heavy sites or paywalled content. Clay's support docs include prompt templates for common research tasks.
Best for
Automated lead enrichment, competitive research, tech stack detection, LinkedIn profile scraping.
Avoid if
You need reasoning or classification unrelated to web research, or you are not already using Clay.
The rest of LLM APIs & intelligence
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OpenRouter Strong Model gateway providing unified API access to dozens of LLMs with automatic routing. #7 
Amazon Bedrock Strong AWS-managed service providing API access to Claude, Llama, Mistral, and other models. #8 
Groq Situational Ultra-fast inference API for Llama, Mistral, and Gemma with sub-second latency. #9 
Together AI Situational Hosted inference for open-source models with fine-tuning and custom deployment. #10 
Cohere Niche Enterprise LLM with strong embeddings, reranking, and classification APIs. #13 
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Setup
Add a Claygent column in a Clay table, write a research prompt (find the tech stack for this domain), and map input columns as variables. Claygent autonomously searches the web and returns structured results without writing scraper code.
First thing to build
Build a lead enrichment pipeline that takes account domains and returns employee count, recent funding, and key hiring signals. Claygent will visit the company website, LinkedIn, and news sources, then summarize findings in your requested format.
What actually matters
- Write specific prompts with clear output format requests (return as JSON with keys: employee_count, funding_date, signal)
- Claygent credits are separate from Clay credits; monitor usage to avoid surprise costs
- Expect 10-30 second latency per row due to multi-step web research
- Use fallback columns for when Claygent cannot find data (paywalled sites, JavaScript-heavy pages)
Watch out
Claygent will fabricate data if the target site does not contain the requested information; validate a sample before scaling to thousands of rows.