Open Deep Research

One-Minute Overview#

Open Deep Research is an AI research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. It's ideal for researchers, analysts, and students who need to thoroughly understand complex subjects. You should use it because it automates the tedious research process and generates comprehensive reports with complete citations.

Core Value: Automates the deep research process, providing comprehensive and structured information analysis

Quick Start#

Installation Difficulty: Medium - Requires API key configuration and environment setup

# Clone the repository
git clone https://github.com/dzhng/deep-research
cd deep-research
npm install

# Create environment file
echo "FIRECRAWL_KEY=your_firecrawl_key" >> .env.local
echo "OPENAI_KEY=your_openai_key" >> .env.local

Is this suitable for me?

• ✅ Academic Research: Automatically collects and organizes relevant literature information
• ✅ Market Research: Quickly gathers industry information and generates analysis reports
• ❌ Real-time News Tracking: Better suited for news aggregation tools than deep research
• ❌ Simple Fact Checking: May be overly complex for basic queries

Core Capabilities#

1. Iterative Research - Achieving deep understanding through cyclical exploration#

• Each search result generates new research directions, forming a recursive exploration chain Actual Value: Discovers important information and connection points that traditional single searches might miss

2. Intelligent Query Generation - Optimizing search strategies based on goals and findings#

• Uses LLMs to generate more targeted search queries Actual Value: Improves search efficiency and reduces irrelevant information

3. Depth & Breadth Control - Customizing research scope and depth#

• Configurable breadth parameter (recommended: 3-10) and depth parameter (recommended: 1-5) Actual Value: Adjust research scope based on time and needs, balancing comprehensiveness with efficiency

4. Smart Follow-up Questions - Deeply understanding research needs#

• Generates follow-up questions at the start to clarify research direction Actual Value: Ensures research results precisely match user's actual needs

5. Comprehensive Report Generation - Structuring research findings#

• Produces Markdown reports with complete citations and sources Actual Value: Provides ready-to-use research results with credible sources

6. Concurrent Processing - Efficiently utilizing computational resources#

• Handles multiple searches and result analysis simultaneously Actual Value: Reduces research waiting time and improves overall efficiency

Tech Stack & Integration#

Development Languages: TypeScript/JavaScript Major Dependencies: Firecrawl API (for web search and content extraction), OpenAI API (for o3 mini model), Node.js runtime Integration Method: Command-line tool

Ecosystem & Extensions#

• Python Implementation: Community provides Python version (https://github.com/Finance-LLMs/deep-research-python)
• Custom Endpoint Support: Configurable custom API endpoints supporting OpenRouter, Gemini and other compatible services
• Local LLM Support: Can be configured to use locally running language models

Maintenance Status#

• Development Activity: Actively maintained, with recent addition of DeepSeek R1 model support
• Recent Updates: Continuously updated to maintain compatibility with latest AI models
• Community Response: Good, with cross-language community implementations

Commercial & Licensing#

License: MIT

• ✅ Commercial Use: Allowed
• ✅ Modification: Allowed
• ⚠️ Restrictions: Must include original license and copyright notice

Documentation & Learning Resources#

• Documentation Quality: Comprehensive
• Official Documentation: https://github.com/dzhng/deep-research (in README)
• Example Code: Includes Node.js and Docker setup examples