DeepScientist

GitHub | 中文文档 | English Docs | Paper | Website

15-minute local setup · One repo per quest · Visible research progress · Human takeover anytime

Built-in runners: Codex, Claude Code, Kimi Code, OpenCode

Quick Start • Launch Your First Project • Product Tour • Codex Setup • Claude Setup • Kimi Setup • OpenCode Setup

Maintainers: BenchStore YAML Guide

May 12 update: v1.6.0 is available with Claude Code, OpenCode, Kimi Code, BenchStore, and science evidence workflows.

Unlike one-shot AI Scientist or autoresearch-style systems, DeepScientist is a local-first autonomous research studio that keeps the full loop moving on your machine, from baselines and experiment rounds to paper-ready outputs, with a 10-minute setup. Powered by Findings Memory, Bayesian optimization, and the Research Map, it keeps turning each new result into the next starting point and goes deep through broader exploration and, when needed, thousands of experiment validations.

If you want the technical deep dive behind DeepScientist, watch the Video.

---

https://github.com/user-attachments/assets/3c7abb44-2b25-4477-a011-10a3154d6d76

Still Spending Your Time On Research Grunt Work?

What drains researchers is often not the lack of ideas. It is the endless cycle of low-leverage work:

• new papers keep coming, but only a small fraction turns into an actionable next-step research plan
• baseline repos fail on environment, dependency, data, and script issues before real work even starts
• experiment results get scattered across terminals, scripts, notes, and chats, making later review painful
• writing, figures, and analysis live in separate tools, so turning them into a coherent paper takes far too long

This is the problem DeepScientist is built to solve:

turn fragmented, repetitive, easy-to-lose research work into a local AI workspace that can keep moving, keep accumulating, and keep getting stronger over time

DeepScientist Is Not Just Another "Research Chatbot"

It is not a tool that summarizes papers, throws you a few ideas, and leaves the dirty work to you.

It is much closer to a real long-running AI research partner:

What common AI tools often look like	What DeepScientist does instead
Great at chatting, but context disappears quickly	Turns tasks, files, branches, artifacts, and memory into durable state
Good at suggesting ideas, but weak at sustained execution	Pushes papers, baselines, experiments, and writing inside one workspace
Strong automation, but feels like a black box	Lets you inspect the process through the web workspace, Canvas, files, and terminal
Hard to take over once it goes off track	Lets you pause, take over, edit plans, change code, and continue at any time
Each run ends when the run ends	Preserves failed paths, winning paths, and reproduction lessons for the next round

About

DeepScientist is not a one-shot agent demo. It is a system built for long-horizon research work.

What Can It Actually Help You Get Done?

1. Start a real project from a paper or a research question

• feed it a core paper, a GitHub repository, or a natural-language research objective
• it turns those inputs into an executable quest instead of a chat that loses state after a few turns

2. Reproduce baselines and keep the reproduction reusable

• restore repositories, prepare environments, handle dependencies, and track the critical failures
• preserve what broke, what got fixed, and which steps are trustworthy for future rounds

3. Run experiments continuously instead of stopping after one pass

• propose the next hypothesis from existing results
• branch, ablate, compare, and record conclusions
• keep failed routes as assets instead of deleting them

4. Turn results into materials you can actually ship

• organize findings, conclusions, and analysis
• produce figures, reports, and paper drafts
• support local PDF and LaTeX compilation workflows

5. Follow the same research effort from multiple surfaces

• the web workspace in your browser
• the TUI workflow on a remote server
• external connector surfaces for collaboration and progress updates

The current docs already cover these collaboration channels:

• Weixin
• QQ
• Telegram
• WhatsApp
• Feishu
• Lingzhu / Rokid

Why Is It Easier To Keep Using?

What retains users is not a flashy demo. It is a system that becomes more useful the longer you work with it.

DeepScientist tends to stick for four reasons:

Local-first by default

• code, experiments, drafts, and project state stay on your own machine or server by default
• this is especially valuable for unpublished ideas, sensitive experiment history, and longer-running research loops

One repo per quest

• every quest is a real Git repository
• branches, worktrees, files, and artifacts naturally express research structure

The process is not a black box

• it does not only give you an output
• you can inspect what it read, what it changed, what it kept, and what it plans to do next

Human collaboration is built in

• DeepScientist can move autonomously
• you can also step in, edit, redirect, and hand control back whenever you want

Why Try It Now?

Because this is not just a concept. It is a real system with public docs, a public paper, and a public install path.

• 2026/03/24: DeepScientist officially released v1.5
• 2026/02/01: the paper went live on OpenReview for ICLR 2026
• npm install path is already available: @researai/deepscientist
• both Chinese and English docs are available, along with Web, TUI, and connector entry points

Product Preview

Architecture Overview

Example Outputs

Example paper output 1 Paper-facing deliverables can be preserved directly inside the quest instead of being split across external tools.	Example paper output 2 DeepScientist can carry work through writing, review, figure polish, and export workflows.

Workspace Preview

Start Research Kick off a quest from a paper, repository, or natural-language goal.	Canvas Inspect branches, baselines, and accumulated research structure as a visible map.	Studio + Details Review metrics, traces, and project state without leaving the same workspace.

Progress Reporting

Projects surface after long-running work

Who Will Love DeepScientist Most?

• graduate students and engineers who want to reproduce papers and push beyond existing baselines
• labs or research teams running long experiment loops, ablations, and structured result analysis
• people who want code, experiments, notes, and writing to live in one workspace
• users who do not want to hand unpublished ideas and intermediate results directly to a pure cloud workflow
• people who want to run work on servers while following progress from web, TUI, or messaging surfaces

The Core Philosophy Behind DeepScientist

We believe a system that is actually suitable for research should at least satisfy these principles:

• one quest, one repository, instead of letting everything dissolve after a short conversation
• branches and worktrees should express research routes naturally instead of being forced into chat history
• failed paths should be preserved, summarized, and reused instead of overwritten
• human researchers should always retain takeover power instead of being locked outside the loop
• the research process should be reviewable, inspectable, and auditable instead of relying on "the model says it did it"

If that sounds like the way you want to work, DeepScientist is worth trying now.

🚀 Get Started In 30 Seconds

If you want to try it right now, choose one of these two paths: run the npm commands yourself, or ask the coding tool you already use to install it for you.

Platform note: DeepScientist fully supports Linux and macOS. Native Windows support is currently experimental (strongly recommend WSL2).

Option 1: Manual Install With npm

Use this path when you already know which runner you want and prefer to control the install, login, and launch commands yourself.

DeepScientist ships four built-in runners:

• codex: use this when codex already works directly on your machine
• claude: use this when claude already works directly on your machine
• kimi: use this when kimi already works directly on your machine
• opencode: use this when opencode already works directly on your machine

If one of these CLIs already works for you, DeepScientist can usually meet you there instead of asking you to rebuild your whole setup first.

Think of the startup choice like this: bring one runner that already works, and DeepScientist gives you a persistent local research workspace around it.

If you just want the safest recommendation, start with Codex first.

🎯 Recommended first run: codex

npm install -g @researai/deepscientist
codex login
ds --here

If Claude Code already works directly in your shell, use this lane:

npm install -g @researai/deepscientist
claude --version
ds doctor --runner claude
ds --here --runner claude

If Kimi Code already works directly in your shell, use this lane:

npm install -g @researai/deepscientist
kimi --version
ds doctor --runner kimi
ds --here --runner kimi

If OpenCode already works directly in your shell, use this lane:

npm install -g @researai/deepscientist
opencode --version
ds doctor --runner opencode
ds --here --runner opencode

If you want to connect Gemini or Ollama, first use the runner-specific docs instead of guessing DeepScientist fields:

• Gemini: prefer OpenCode Setup
• Ollama: choose Codex, Claude Code, or OpenCode with Local Model Backends Guide

To stop the managed local daemon and all currently running agents:

ds --stop

🛠 Prefer installing from a Git checkout instead of npm? Use the repo path directly:

git clone https://github.com/ResearAI/DeepScientist.git
cd DeepScientist
bash install.sh
ds

Option 2: Let A Coding Tool Install It

Use this path when you already work inside Codex, Claude Code, OpenCode, Cursor, or another coding agent. There are only two steps:

1. Launch the coding tool in a directory where you are comfortable installing DeepScien