Open deep research - AI-powered autonomous research workflow

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Open Deep Research AI Powered Autonomous Research Workflow Description This workflow automates deep research by leveraging AI driven search queries, web scraping, content analysis, and structured r...

Best for

Market Research automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

@n8n/n8n-nodes-langchain.chattrigger, @n8n/n8n-nodes-langchain.chainllm, @n8n/n8n-nodes-langchain.lmchatopenrouter, n8n-nodes-base.code, n8n-nodes-base.httprequest, @n8n/n8n-nodes-langchain.agent, n8n-nodes-base.splitinbatches, @n8n/n8n-nodes-langchain.memorybufferwindow

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Leonard.

Original n8n.io source

1.1 Workflow description

Title: Open deep research - AI-powered autonomous research workflow
Workflow name: Open deep research - AI-powered autonomous research workflow

Open Deep Research - AI-Powered Autonomous Research Workflow

Description

This workflow automates deep research by leveraging AI-driven search queries, web scraping, content analysis, and structured reporting. It enables autonomous research with iterative refinement, allowing users to collect, analyze, and summarize high-quality information efficiently.

How it works

🔹 User Input
- The user submits a research topic via a chat message.
🧠 AI Query Generation
- A Basic LLM generates up to four refined search queries to retrieve relevant information.
🔎 SERPAPI Google Search
- The workflow loops through each generated query and retrieves top search results using the SerpAPI API.
📄 Jina AI Web Scraping
- Extracts and summarizes webpage content from the URLs obtained via SerpAPI.
📊 AI-Powered Content Evaluation
- An AI Agent evaluates the relevance and credibility of the extracted content.
🔁 Iterative Search Refinement
- If the AI finds insufficient or low-quality information, it generates new search queries to improve results.
📜 Final Report Generation
- The AI compiles a structured markdown report, including sources with citations.

Set Up Instructions

🚀 Estimated setup time: ~10-15 minutes

✅ Required API Keys:
- SerpAPI → For Google Search results
- Jina AI → For text extraction
- OpenRouter → For AI-driven query generation and summarization
⚙️ n8n Components Used:
- AI Agents with memory buffering for iterative research
- Loops to process multiple search queries efficiently
- HTTP Requests for direct API interactions with SerpAPI and Jina AI
📝 Recommended Enhancements:
- Add sticky notes in n8n to explain each step for new users
- Implement Google Drive or Notion Integration to save reports automatically

🎯 Ideal for:
✔️ Researchers & Analysts - Automate background research
✔️ Journalists - Quickly gather reliable sources
✔️ Developers - Learn how to integrate multiple AI APIs into n8n
✔️ Students - Speed up literature reviews

🔗 Completely free and open-source! 🚀

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Chat Message Trigger

Type / Role: @n8n/n8n-nodes-langchain.chatTrigger - chatTrigger
Config choices: Version 1.1

Block 2 - Generate Search Queries using LLM

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.5

Block 3 - LLM Response Provider (OpenRouter)

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenRouter - lmChatOpenRouter
Config choices: Version 1

Block 4 - Parse and Chunk JSON Data

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 5 - Perform SerpAPI Search Request

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 6 - Perform Jina AI Analysis Request

Type / Role: n8n-nodes-base.httpRequest - httpRequest
Config choices: Version 4.2

Block 7 - Format SerpAPI Organic Results

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 8 - Extract Relevant Context via LLM

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 9 - Generate Comprehensive Research Report

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 1.7

Block 10 - Split Data for SerpAPI Batching

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 11 - Split Data for Jina AI Batching

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 12 - LLM Memory Buffer (Input Context)

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 13 - LLM Memory Buffer (Report Context)

Type / Role: @n8n/n8n-nodes-langchain.memoryBufferWindow - memoryBufferWindow
Config choices: Version 1.3

Block 14 - Fetch Wikipedia Information

Type / Role: @n8n/n8n-nodes-langchain.toolWikipedia - toolWikipedia
Config choices: Version 1

Block 15 - Sticky Note: SerpAPI Setup

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 16 - Sticky Note: Jina AI Setup

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 17 - Sticky Note: OpenRouter API Setup

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

3. Summary Table

Workflow	Open deep research - AI-powered autonomous research workflow
Complexity	advanced
Nodes	17
Categories	Market Research, AI RAG
Author	Leonard
Published	11 Feb 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/2883/2883.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Market Research workflows AI RAG workflows

Frequently asked questions

What does Open deep research - AI-powered autonomous research workflow do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI RAG use case.

Leonard

3 workflows

Nodes

@n8n/n8n-nodes-langchain.chattrigger @n8n/n8n-nodes-langchain.chainllm @n8n/n8n-nodes-langchain.lmchatopenrouter n8n-nodes-base.code n8n-nodes-base.httprequest @n8n/n8n-nodes-langchain.agent n8n-nodes-base.splitinbatches @n8n/n8n-nodes-langchain.memorybufferwindow

Complexity

advanced

Published 11 Feb 2025

Likes 0

View on n8n.io Download Workflow

Install path: /data/workflows/2883/2883.json

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Open deep research - AI-powered autonomous research workflow

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

Open Deep Research - AI-Powered Autonomous Research Workflow

Description

How it works

Set Up Instructions

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Chat Message Trigger

Block 2 - Generate Search Queries using LLM

Block 3 - LLM Response Provider (OpenRouter)

Block 4 - Parse and Chunk JSON Data

Block 5 - Perform SerpAPI Search Request

Block 6 - Perform Jina AI Analysis Request

Block 7 - Format SerpAPI Organic Results

Block 8 - Extract Relevant Context via LLM

Block 9 - Generate Comprehensive Research Report

Block 10 - Split Data for SerpAPI Batching

Block 11 - Split Data for Jina AI Batching

Block 12 - LLM Memory Buffer (Input Context)

Block 13 - LLM Memory Buffer (Report Context)

Block 14 - Fetch Wikipedia Information

Block 15 - Sticky Note: SerpAPI Setup

Block 16 - Sticky Note: Jina AI Setup

Block 17 - Sticky Note: OpenRouter API Setup

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions