A new paradigm for intelligent research: Deep-Research in-depth analysis

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Recently, there is a very popular project on GitHub, deep-research, which reproduces ChatGPT’s Deep Research function.
However, that project only supports running on the terminal, so is there a more convenient and faster way?
A web UI that can display the entire search process in real time. Take a look at the examples in the video. Pure front-end pages, API keys are stored locally.

“Deep-research” is an AI-driven research assistant developed by GitHub user dzhng. It combines search engines, web crawling, and large language models to perform iterative, in-depth research on any topic. The goal of the project is to provide the simplest implementation of an in-depth research agent, an agent that can optimize its research direction over time and delve into topics.

Main functions:

  • iterative study: Agents can continuously adjust and deepen research directions based on the information collected.
  • web scraping: Get the latest relevant information by grabbing network data.
  • large language models: Utilize advanced language models to understand and generate content relevant to research topics.

Project structure:

  • src/: Contains the main source code files.
  • .env.example: Environment variable example file.
  • Dockerfile: File used to build Docker images.
  • docker-compose.yml: Configuration file used to define and run multi-container Docker applications.
  • README.md: The main descriptive document of the project.

Usage method:

  1. environment settings: According to .env.example File to configure necessary environment variables.
  2. Install dependencies: Use npm install Install the required dependency packages.
  3. run the agent: Implementation npm start Start the agent and start the research process.

Example:

Assuming the research topic is “Impact of climate change on marine ecology”, the agent will:

  1. Use search engines to find the latest relevant research and news.
  2. Grab data from authoritative websites and get detailed information.
  3. Use large language models to analyze and summarize the collected data and generate reports.

Through the above steps, the agent can provide in-depth understanding and analysis of the specified topic, helping users efficiently obtain the information they need.

Github：https://github.com/dzhng/deep-research
Demo：https://deep-research.ataw.top/

Oil tubing:

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