Local Deep Research: review, pricing, security and alternatives

What Local Deep Research does

Local Deep Research 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 #5 of the 18 projects of this family listed in the directory.

You use it from: web app, terminal (CLI), and API. Usable models: local models (Ollama, llama.cpp, vLLM…), Gemini (Google), Qwen (Alibaba), Mistral, and any OpenAI-compatible model. It can run on your own machine or server.

Who is Local Deep Research 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 are comfortable in a terminal, your data must stay on your machines, and you want the option of a local model, with no API fees.

Access and human control

According to its documentation, Local Deep Research can:

• call online services and APIs
• read and edit files

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

Local Deep Research pricing

The code is free, under the MIT licence.

Getting started with Local Deep Research

1. Install it: the project ships as a Python package (install with pip or uv). The exact command is in the repository README.
2. Pick the model: an API key from a provider, or a local model if your machine has enough graphics memory (the VRAM calculator tells you).
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 Local Deep Research:

• Outside content: it reads web pages or API responses, which may contain booby-trapped instructions (prompt injection).

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 9, 2025, the LearningCircuit/local-deep-research repository has 9,402 stars, 883 forks and 991 open issues, with 8,480 commits in total. It received changes this month.

Over the last 52 weeks: 4,986 commits, an average of 95.9 per week.

172 releases in total; 30 in the last twelve months. Latest release: v1.10.7, on August 28, 2026.

Local Deep Research 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.

Local Deep Research 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: 74/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 24/40: 9,402 stars on a log scale (40 points from about 200,000).
• Activity 30/30: last commit less than a month ago, 27 releases in six months, 4,986 commits in a year.
• Framework 20/30: 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.