REPOGEO REPORT · LITE

microsoft/ToRA

Default branch main · commit 213c1c99 · scanned 6/24/2026, 6:06:45 PM

GitHub: 1,117 stars · 79 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface microsoft/ToRA, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's initial description to highlight practical utility

Why:

CURRENT

<p align="center">Repo for "<a href="https://arxiv.org/abs/2309.17452" target="_blank">ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving</a>" [ICLR'2024]</p>

COPY-PASTE FIX

<p align="center">This repository provides the official implementation and models for ToRA, a series of Tool-integrated Reasoning LLM Agents designed to solve challenging mathematical reasoning problems by interacting with tools [ICLR'24]. Use ToRA to build or enhance LLM agents for complex mathematical problem-solving.</p>

mediumtopics#2

Expand the topics list with more specific keywords

Why:

CURRENT

autonomous-agents, language-model, llm, mathematical-reasoning, tool-learning

COPY-PASTE FIX

autonomous-agents, language-model, llm, mathematical-reasoning, tool-learning, llm-agents, mathematical-problem-solving, reasoning-agents

lowcomparison#3

Add a 'Why ToRA?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

## 🚀 Why ToRA? (or ToRA vs. General Frameworks)

ToRA specializes in tool-integrated reasoning specifically for complex mathematical problem-solving, offering pre-trained agents and a focused approach unlike general-purpose LLM agent frameworks such as LangChain or LlamaIndex. While those frameworks provide broad tools for agent construction, ToRA delivers a fine-tuned solution optimized for numerical and logical reasoning tasks.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface microsoft/ToRA

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenAI GPT-4 (or GPT-4o)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

OpenAI GPT-4 (or GPT-4o) · recommended 1×
sympy/sympy · recommended 1×
NumPy/SciPy · recommended 1×
Wolfram Alpha API (or Wolfram Mathematica) · recommended 1×
LangChain (or LlamaIndex) · recommended 1×

CATEGORY QUERY
How can I build an LLM agent for advanced mathematical problem-solving?
you: not recommended
AI recommended (in order):
1. OpenAI GPT-4 (or GPT-4o)
2. SymPy (sympy/sympy)
3. NumPy/SciPy
4. Wolfram Alpha API (or Wolfram Mathematica)
5. LangChain (or LlamaIndex)
6. Jupyter Notebook (or IPython)
7. Lean (or Coq)
AI recommended 7 alternatives but never named microsoft/ToRA. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a framework for LLM agents to perform complex reasoning using external tools.
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. AutoGPT
5. CrewAI
AI recommended 5 alternatives but never named microsoft/ToRA. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of microsoft/ToRA?
pass
AI named microsoft/ToRA explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts microsoft/ToRA in production, what risks or prerequisites should they evaluate first?
pass
AI named microsoft/ToRA explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo microsoft/ToRA solve, and who is the primary audience?
pass
AI named microsoft/ToRA explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/microsoft/ToRA.svg)](https://repogeo.com/en/r/microsoft/ToRA)

HTML

<a href="https://repogeo.com/en/r/microsoft/ToRA"><img src="https://repogeo.com/badge/microsoft/ToRA.svg" alt="RepoGEO" /></a>

Pro

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microsoft/ToRA — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite