MindSearch: review, pricing, security and alternatives

What MindSearch does

MindSearch 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 #17 of the 18 projects of this family listed in the directory.

Unlike a ready-made agent, a framework provides the parts to build one: tool calling, step sequencing, memory, coordination of several agents. It is aimed at teams that want a custom agent.

You use it from: web app and API. Usable models: GPT (OpenAI) and local models (Ollama, llama.cpp, vLLM…). It can run on your own machine or server.

Who is MindSearch 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, your data must stay on your machines, and you want the option of a local model, with no API fees.

Access and human control

A framework has no access of its own: the tools you plug in define what the agent can do. Start with read-only tools.

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

MindSearch pricing

The code is free, under the Apache-2.0 licence.

Getting started with MindSearch

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. 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. Start with the minimal example from the docs, then add one tool at a time and check what the agent does with it.
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 MindSearch:

• Maintenance: no commit for 16 months, flaws are no longer fixed.

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 July 28, 2024, the InternLM/MindSearch repository has 6,946 stars, 692 forks and 48 open issues, with 56 commits in total. Last change 16 months ago.

Over the last 52 weeks: 0 commits, an average of 0 per week.

1 releases in total; 0 in the last twelve months. Latest release: v0.1.0, on November 5, 2024.

MindSearch 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.

MindSearch 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: 42/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 22/40: 6,946 stars on a log scale (40 points from about 200,000).
• Activity 0/30: last commit 16 months ago, 0 releases in six months, 0 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.