Deep Research Web UI: review, pricing, security and alternatives

What Deep Research Web UI does

Deep Research Web UI belongs to the deep research family: agents that search, read and cross-check dozens of sources to produce a sourced report. On our grid it ranks #15 of the 18 projects of this family listed in the directory.

You use it from: web app. It can run on your own machine or server.

Who is Deep Research Web UI for?

It targets analysts, students and teams who must cross-check many sources to produce a reasoned summary. Typical tasks people give it:

• write a sourced overview of a topic
• compare products or competitors
• prepare a technical or regulatory watch

It is a good candidate if you prefer doing everything in a browser, with no install and your data must stay on your machines.

Access and human control

According to its documentation, Deep Research Web UI can:

• call online services and APIs

We could not establish whether it asks for human approval before sensitive actions: test it first on an unimportant account or folder.

Deep Research Web UI pricing

The code is public but the repository declares no licence: reuse is not formally allowed. The real cost comes from model calls: each loop step sends the whole context back to the model, and a task of several dozen steps can cost far more than a single question. The LLM price calculator estimates it for the chosen model.

Getting started with Deep Research Web UI

1. Install it: the project ships as a Node.js package (install with npm, or run directly with npx). The exact command is in the repository README.
2. Create an API key with the model provider and put it in an environment variable, never in a shared or versioned file.
3. Give it a first playground with nothing at stake: a test folder, a secondary account or a throwaway project. Only open your real accounts once you have seen how it behaves.
4. Give it a short, checkable task, review the result and its action log, then raise the difficulty step by step.

Security: risk level

An agent's report may cite misread or invented sources: check the links before reusing a figure.

Points found for Deep Research Web UI:

• Outside content: it reads web pages or API responses, which may contain booby-trapped instructions (prompt injection).
• No licence: legally shaky for company use.

Three habits: least privilege (minimal rights, dedicated keys), human approval for irreversible actions, a sandbox for code execution. The AI agent security check reviews your setup in a few minutes.

Project activity and releases

Created on February 10, 2025, the AnotiaWang/deep-research-web-ui repository has 2,223 stars, 303 forks and 14 open issues, with 230 commits in total. It received changes this month.

Over the last 52 weeks: 67 commits, an average of 1.3 per week.

12 releases in total; 2 in the last twelve months. Latest release: v1.3.0, on September 28, 2026.

Deep Research Web UI vs other deep research

GitHub stars of the best-rated open-source projects in the deep research family. Stars measure attention, not quality: a heavily promoted project collects them fast, a specialised tool will have few.

Deep Research Web UI and other deep research at a glance

The 7 best-ranked agents in the same family, 7 of them open source. The score out of 100 only applies to open-source projects; for proprietary services we show the published entry price. A dash marks information the documentation does not state.

How we rated it

Score: 49/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 16/40: 2,223 stars on a log scale (40 points from about 200,000).
• Activity 23/30: last commit less than a month ago, 2 releases in six months, 67 commits in a year.
• Framework 10/30: licence, default human approval, self-hosting, model choice and documentation site.

The score does not say whether the agent will succeed at your tasks: it measures how solid the project is. The full grid is on the method page.