dzhng/deep-research
deep-research
An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.
Usage guide
deep-research is an open-source project around agent, gpt, o3-mini with 19,220 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in TypeScript, useful for judging integration effort in a similar stack.
- GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- GitHub is the main evaluation surface; review the README, issues, and recent commits first.
Best for
- Evaluating deep-research for TypeScript AI workflows.
- Comparing a GitHub project with 19,220 stars and current repository activity.
Pros
- deep-research has visible GitHub traction with 19,220 stars. Topics: agent, ai, gpt.
- The GitHub repository is the primary evaluation surface.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the MIT terms fit your use case.
Production readiness
deep-research should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
MIT is reported by GitHub; review the repository license before redistribution or commercial use.
deep-research architecture preview
deep-research's main path starts at the entry surface, runs through Agent orchestration runtime, combines OpenAI, Runtime context, External tool adapters, and returns Assistant response / action result.
Entry
CLI / terminal entry
deep-research is primarily entered through a developer command or terminal workflow.
npm install
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
OpenAI
Model calls are likely routed through OpenAI based on README and topic signals.
OpenAI
Context
Runtime context
Runtime state, user input, repository files, or configuration provide context for each task.
context signal
Tools
External tool adapters
Tool adapters let the runtime act outside the model through External tool adapters.
tool signal
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- Node.js and the package manager used by the project
- Docker Engine with enough disk space for images and volumes
- A clean working directory for the first test run
Check the runtime environment
deep-research has Docker in the setup path. Confirm Docker Engine works and reserve enough disk space for images and volumes.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/dzhng/deep-research.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ npm installAdoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
An AI-powered research assistant that performs iterative, deep researc
This is one of the documented reasons to evaluate deep-research before choosing a stack.
Focus area: agent
This is one of the documented reasons to evaluate deep-research before choosing a stack.
AI Agents project comparison
Compare deep-research with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official deep-research setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Check exposed ports, mounted volumes, and environment variables before running the container in a shared environment.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented deep-research example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is deep-research?
deep-research is an open-source ai agents project. An AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models. The goal of this repo is to provide the simplest implementation of a deep research agent - e.g. an agent that can refine its research direction overtime and deep dive into a topic.
How do I install deep-research?
Start with the official README. The first detected setup step is: git clone https://github.com/dzhng/deep-research.git.
Is deep-research beginner-friendly?
If you already know the TypeScript ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can deep-research be used commercially?
GitHub detected the MIT repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does deep-research need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt deep-research?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.