Research & Report Builder
Submit a topic via webhook to trigger an automated research workflow that refines the scope, gathers data from Tavily.
What it does
Submit a topic via webhook to instantly trigger an automated research workflow that clarifies your scope, gathers data from Tavily, and delivers a structured Markdown report directly into your Notion database. You get a comprehensive, ready-to-use summary without manually searching or compiling sources yourself.
Step by step
- Configure the webhook node to accept POST requests with a JSON payload containing the initial research topic.
- Set up the Tavily search node with API key authentication and define specific search parameters like depth or max results.
- Map the final Markdown string from the LLM output to the Notion page content property using the appropriate field ID.
Where the LLM does the work
- Generate clarifying questions based on the initial topic to narrow down the research scope before searching.
- Synthesize the raw search results into a coherent, structured Markdown report with headings and bullet points.
- Refine the tone and style of the final report to ensure it meets specific professional or academic standards.
Watch out for
Tools that fit
The agent brief
You are helping me build the following AI agent workflow. ## Goal Research & Report Builder: Submit a topic via webhook to trigger an automated research workflow that refines the scope, gathers data from Tavily. ## Specification - What it does: Submit a topic via webhook to instantly trigger an automated research workflow that clarifies your scope, gathers data from Tavily, and delivers a structured Markdown report directly into your Notion database. You get a comprehensive, ready-to-use summary without manually searching or compiling sources yourself. - Trigger: Triggered by an event (Manual · on webhook request) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - n8n: Workflow orchestration platform - Tavily Search API: Web search and content extraction - Notion: Storing final research reports - OpenRouter Chat Model: AI agent reasoning and report generation ## Known pitfalls, handle each one explicitly in your implementation 1. Tavily API calls can fail if the topic is too broad so add error handling to retry with refined keywords. 2. LLM output formatting might break Markdown parsing so validate the structure before sending it to Notion. 3. Webhook payloads can be malformed by external clients so add validation logic to reject invalid JSON inputs. ## Reference implementation https://n8n.io/workflows/7160 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/automated-research-report-builder/ via usecasesforagents.com
Frequently asked questions
Can I use a different Automation Platform than n8n?
Yes. n8n is only the example automation platform in this recipe. The same flow works with Home Assistant. Swap the automation platform connection and keep the rest of the setup as written.
Want this running in your business?
Who it's for
Built for founders and execs who need research handled without hiring for it.
Freelancers and consultants can run this per-client with no changes beyond the source data.
Analysts get a ready-made pattern for turning raw numbers into a report someone will actually read.