Agents and automation thrive on internet context, but the real challenge is keeping that data reliable and up-to-date in production.
Enter Tavily + n8n: by combining Tavily’s real-time web search, extraction, and crawling tools with n8n’s no-code automation canvas, your workflows can suddenly ask the web smart questions, extract raw content, and feed structured answers directly into the rest of your system.
In this blog, we’ll explore why this setup matters, how it works, and walk through a hands-on workflow you can copy in minutes to automate use cases like job-hunting, competitor monitoring, or news tracking.
Why combine Tavily with n8n?
N8n gives you the ergonomics: a visual canvas, triggers, batching, and simple integration nodes for moving data between services. Tavily gives you the internet: agent-friendly search, full-page extraction, and structured outputs (clean Markdown, summaries, images) designed for LLMs and automations.
Together you get:
- Reliable, production-ready web data.
- Agent-friendly outputs that make LLM prompting deterministic and cheap.
- No-code orchestration so product and operations teams can iterate on processes fast.
But the real win? The web is no longer an afterthought; It becomes a first-class input to your automations.
The Automation Traps Teams Keep Falling Into — and Why They Still Matter
Teams building automations repeatedly run into the same traps:
- Results are noisy: pages yield long blobs and irrelevant sections.
- LLMs get garbage in, produce garbage out, and you pay the token bill.
- Scaling across domains means duplicating engineering effort.
Tavily abstracts the web as a clean, queryable tool. N8n lets non-engineers iterate rapidly. Together, they turn fragile, manual workflows into robust, automated processes.
What Tavily Brings to Your n8n Workflows
🔍 Search the web Optimized for relevancy and low latency. Perform real-time searches with precise controls like time ranges, domain filters, topic filters, and search depth. Get contextual results ranked for your AI applications.
📄 Extract structured data from URLs Supporting markdown, or cleaned text. Transform any webpage into LLM-ready data with automatic content cleaning, format conversion, and text extraction that preserves structure and meaning.
🕸️ Crawl entire domains at scale Optimized for intelligent URL discovery and results. Explore entire websites with smart crawling strategies, handle dynamic content, and efficiently surface all accessible pages.
✨ Agent-friendly outputs Receive summarized results, relevant snippets, and optional raw content — designed to plug directly into LLM prompts or n8n nodes that transform, store, or notify.
How to set up Tavily with n8n: Quick start
Follow this checklist to get a working connection in minutes:
- Log in to n8n (cloud or self-hosted).
- Create a new workflow — choose your trigger (Webhook, Schedule).
- Add an AI Agent node — this will orchestrate your workflow.
- Configure Tavily as a tool inside the AI Agent:
- Add your Tavily API key in the node credentials.
- Select the tool type: search, extract, or crawl.
- Map inputs: feed in your query or URLs (manually or from a previous node output).
- Send / Store / Act: push summaries to Email, Slack, Google Sheets, or your CRM directly from the AI Agent.
Deep-dive example — Automated job search (copy-and-run)
Automatically hunt for new “Software Engineering Intern” roles weekly, summarize them, and send a digest email with the top postings. This workflow is ideal for developers, recruiters, or students who want fresh job listings in their inbox every week.
Try it yourself and get hands-on experience with n8n and Tavily! ✨
Access the template here.
Workflow Overview
The workflow is built in n8n and integrates Tavily’s AI-powered search with OpenAI for formatting and Gmail for sending emails. Here’s the step-by-step process
1. Trigger
Node: Schedule Trigger
- Runs daily/weekly at a chosen time (e.g., 08:00 AM).
Ensures your workflow automatically executes without manual intervention.
2. AI Agent
Node: AI Agent
Start by setting up the AI Agent node with a prompt and a system message:
Prompt: Defines the high-level role of the agent and lets it know the workflow context.
You are an autonomous research agent tasked with monitoring job postings with Tavily API tool.
You will interact with the workflow in three stages:
System Message: Provides detailed instructions on how to process and format the job postings. It tells the agent exactly what fields to extract, how to structure each posting, and ensures only input data is used.
You are an information extraction and formatting specialist.
You have received job postings retrieved via the Tavily API tool. Your job is to:
Count how many individual job postings are included in the input. Each posting is a separate object with its own title, URL, company, location, description, and other metadata.
Then, for each job posting, extract and format:
Job Title
URL
Posting date (use ISO format: YYYY-MM-DD, if available)
A 2–3 sentence summary of the job description
1–2 key requirements or highlights
Company name
Company description (if provided)
Company website (if available)
Location (if provided)
Do not include any hallucinated or assumed data. Only format what's explicitly in the input.
Format each job posting like this:
Job Posting <number>
Job Title: <Job Title>
URL: <URL>
Posting date: <YYYY-MM-DD>
Job Description: <Brief description in 2–3 sentences>
Key Requirements:
<Point 1>
<Point 2>
Company: <Company Name>
Company Description: <Short description>
Company Website: <URL>
Location: <Location>
--- END JOB POSTING ---
Output only structured job postings in this format.
You must process all available job postings in the input. Do not stop early, do not skip entries, and do not truncate the output.
Use only the input below. Do not perform any web searches or add external information.
Tool: Tavily Search Tool
Query
- Think of this as the web search instructions for the AI agent.
- Example: "Roles posted this week for Software Engineering"
- Keep it concise — under 400 characters — to ensure accurate results.
Include Trusted Domains
- Prioritize results from reliable sources like:
- linkedin.com
- indeed.com
- glassdoor.com
Time Range
- Limit results to a specific period to get only recent postings.
- Example: past week (time_range: week)
Include Raw Content
- Fetch the full page content of each job posting, not just the content snippets.
- This allows the AI to extract detailed descriptions, requirements, and company info.
Optional: Advanced Search Depth
3. Bundle Results
Node: Edit Fields
- Collects all raw job postings into a single array.
- Prepares them for structured formatting.
4. Restructure Output with LLM
Node: OpenAI Chat Model (or any LLM)
- Purpose: Reformat the raw postings into a human-readable, structured format.
Example Formatting:
Job Posting <number>
1. Job Title: <Exact title>
2. URL: <Exact URL>
3. Posting Date: <YYYY-MM-DD or Not specified>
4. Job Description: <Exact text>
5. Requirements:
<Point 1>
<Point 2>
6. Company Name: <Company name>
7. Company Description: <Company description>
8. Company Website: <URL>
9. Location: <Exact location>
- Rules:
- Only use information explicitly present in the posting
- Do not hallucinate missing data
- Implementation: The LLM node processes all entries, producing clean, structured text ready for email.
5. Code Node for Structuring
Node: Code
- Flattens all AI outputs into a single array
- Extracts each field with regex
- Handles missing fields (Not specified)
- Returns structured objects ready for aggregation
Key logic:
const structuredPostings = postings.map((post, index) => ({
posting_number: index + 1,
job_title: post.match(/Job Title:\s*(.+)/)?.[1] || "Not specified",
url: post.match(/URL:\s*(.+)/)?.[1] || "Not specified",
posting_date: post.match(/Posting Date:\s*(.+)/)?.[1] || "Not specified",
job_description: post.match(/Job Description:\s*([\s\S]*?)(?:\nRequirements:|$)/)?.[1] || "Not specified",
requirements: post.match(/Requirements:\s*([\s\S]*?)(?:\n|$)/)?.[1]?.split('\n') || [],
company_name: post.match(/Company Name:\s*(.+)/)?.[1] || "Not specified",
company_description: post.match(/Company Description:\s*(.+)/)?.[1] || "Not specified",
company_website: post.match(/Company Website:\s*(.+)/)?.[1] || "Not specified",
location: post.match(/Location:\s*(.+)/)?.[1] || "Not specified"
}));
6. Aggregate Node
Node: Aggregate
- Combines all job postings into one item.
- Prepares a single output string for email delivery.
7. Gmail Node — Send Email
Node: Gmail
- Recipient
- Subject: "New Jobs for this week!"
- Body: Combines all job postings into one formatted email.
Uses JavaScript mapping to loop over the aggregated array:
{{
$json.data.map(post => {
return `Job Posting ${post.posting_number} - ${post.job_title}
🔗 URL: ${post.url}
🗓️ Posting Date: ${post.posting_date}
📝 Job Description:
${post.job_description}
📌 Requirements:
${post.requirements.length > 0
? post.requirements.map((point, i) => `${i + 1}. ${point}`).join('\n')
: 'No requirements available.'}
🏢 Company Name: ${post.company_name}
📝 Company Description: ${post.company_description}
🔗 Company Website: ${post.company_website}
📍 Location: ${post.location}
—`;
}).join('\n\n')
}}
Result: All job postings are sent in one cohesive email, readable and structured.
8. Optional Enhancements
- Send output to Slack, Google Sheets, or CRM in addition to Gmail.
- Schedule workflow to run daily, weekly, or monthly.
Outcome
- Weekly digest of the latest job postings.
- No manual data collection or formatting.
- Scalable to other roles, industries, or search queries.
Use cases that thrive with Tavily + n8n
- Job Search Automation: daily report for roles that match your filter, delivered to email or Slack.
- Competitive Intelligence: watch competitor blogs, product changelogs, and pricing pages; create tickets when changes are detected.
- Market Research: daily collection of new industry articles, distilled into short briefs.
- Content Curation: auto-collect relevant links, extract main content, push to a CMS or Notion database.
- Lead Enrichment: pull public info about leads and enrich CRM records automatically.
- Incident / Security Monitoring: detect sudden changes in documentation or public disclosures and notify the right team.
A short case study (imagined, but practical)
Imagine a small recruiting team that needs to surface remote internships for students. Previously they relied on manual keyword searches and dozens of bookmark folders. With Tavily + n8n they:
- Scheduled a daily job to search for targeted keywords.
- Automatically extracted only the job description.
- Created candidate-ready summary cards and posted them to a private Slack channel.
Result: the team cut their manual intake time by 80% and doubled the number of qualified leads surfaced each week.
The web as a first-class data source
N8n empowers automation. Tavily brings the web into that automation with the kind of structure agents and LLMs actually need. Together they let product teams, operations, and non-engineers build robust workflows that tap the live internet.
If you’re designing automations that need reliable web context — job monitors, content pipelines, competitor watchers , or lead enrichments — try wiring Tavily into an n8n flow.