TheoremExplainAgent: review, pricing, security and alternatives

What TheoremExplainAgent does

TheoremExplainAgent belongs to the business agents family: agents specialised in one job: customer support, sales, finance, legal, HR, security. On our grid it ranks #44 of the 52 projects of this family listed in the directory.

You use it from: terminal (CLI). Usable models: GPT (OpenAI), Gemini (Google), and any OpenAI-compatible model. It can run on your own machine or server.

Who is TheoremExplainAgent for?

It targets a specific job (customer support, sales, finance, legal…) with workflows already set up. Typical tasks people give it:

• handle first-line customer requests
• prepare standard documents
• track cases and follow up

It is a good candidate if you are comfortable in a terminal and your data must stay on your machines.

Access and human control

According to its documentation, TheoremExplainAgent can:

• run commands in a terminal
• read and edit files
• 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.

TheoremExplainAgent pricing

The code is free, under the MIT licence.

Getting started with TheoremExplainAgent

1. Install it: the project ships as a Python package (install with pip or uv). 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

A business agent acts on the company's behalf: define what it may promise, refund or change, and keep a record of its decisions.

Points found for TheoremExplainAgent:

• Sensitive access: it can run commands in a terminal, with no established default human approval.
• Outside content: it reads web pages or API responses, which may contain booby-trapped instructions (prompt injection).
• Maintenance: no commit for 15 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 February 16, 2025, the TIGER-AI-Lab/TheoremExplainAgent repository has 1,513 stars, 203 forks and 9 open issues, with 41 commits in total. Last change 15 months ago.

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

The project publishes no numbered releases on GitHub: you install it from the main branch or a package manager.

TheoremExplainAgent vs other business agents

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

TheoremExplainAgent and other business agents 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: 39/100, computed only from public, verifiable data, with no subjective testing or partnership:

• Adoption 14/40: 1,513 stars on a log scale (40 points from about 200,000).
• Activity 0/30: last commit 15 months ago, 0 releases in six months, 0 commits in a year.
• Framework 25/30: 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.