REPOGEO REPORT · LITE

Svalorzen/AI-Toolbox

Default branch master · commit 05c935cc · scanned 6/10/2026, 5:03:18 PM

GitHub: 671 stars · 102 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)

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

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 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 Svalorzen/AI-Toolbox, 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
Update the README's main heading (H1) to be more descriptive
Why:
CURRENT
```
# AI-Toolbox
```
COPY-PASTE FIX
```
# AI-Toolbox: A C++ Library for Reinforcement Learning and Planning (with Python Bindings)
```

highreadme#2

Refine the README's introductory paragraph for immediate clarity

Why:

CURRENT

This C++ toolbox is aimed at representing and solving common AI problems, implementing an easy-to-use interface which should be hopefully extensible to many problems, while keeping code readable. Current development includes MDPs, POMDPs and related algorithms.

COPY-PASTE FIX

AI-Toolbox is a C++ framework specifically designed for representing and solving Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs), with comprehensive Python bindings. It implements an easy-to-use, extensible interface for reinforcement learning and planning algorithms, while keeping code readable.

mediumtopics#3

Add more specific topics to emphasize its framework/library nature

Why:

CURRENT

artificial-intelligence, c-plus-plus, markov-decision-processes, mdps, planning, pomdps, python, reinforcement-learning

COPY-PASTE FIX

artificial-intelligence, c-plus-plus, markov-decision-processes, mdps, planning, pomdps, python, reinforcement-learning, cpp-framework, rl-library, planning-algorithms

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 Svalorzen/AI-Toolbox

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

POMDPs.jl

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

POMDPs.jl · recommended 1×
MCTS.cpp · recommended 1×
OpenAI Gym · recommended 1×
MDPSolver · recommended 1×
RLPy · recommended 1×

CATEGORY QUERY
What C++ frameworks are available for solving Markov Decision Processes with Python integration?
you: not recommended
AI recommended (in order):
1. POMDPs.jl
2. MCTS.cpp
3. OpenAI Gym
4. MDPSolver
5. RLPy
AI recommended 5 alternatives but never named Svalorzen/AI-Toolbox. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a robust library to implement reinforcement learning and planning algorithms for AI problems.
you: not recommended
AI recommended (in order):
1. RLlib
2. Stable Baselines3
3. Tianshou
4. Acme
5. CleanRL
6. Dopamine
AI recommended 6 alternatives but never named Svalorzen/AI-Toolbox. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
warn
Suggestion:
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 Svalorzen/AI-Toolbox?
pass
AI named Svalorzen/AI-Toolbox explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts Svalorzen/AI-Toolbox in production, what risks or prerequisites should they evaluate first?
pass
AI named Svalorzen/AI-Toolbox 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 Svalorzen/AI-Toolbox solve, and who is the primary audience?
pass
AI named Svalorzen/AI-Toolbox explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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Svalorzen/AI-Toolbox — 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