REPOGEO REPORT · LITE

MineDojo/Voyager

Default branch main · commit 55e45a88 · scanned 6/28/2026, 5:32:45 AM

GitHub: 7,011 stars · 680 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

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 MineDojo/Voyager, 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 README's opening to clarify project type

Why:

CURRENT

We introduce Voyager, the first LLM-powered embodied lifelong learning agent in Minecraft that continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention.

COPY-PASTE FIX

Voyager is a novel framework for LLM-powered embodied lifelong learning agents, distinct from traditional reinforcement learning environments or libraries. It enables autonomous skill acquisition and code generation within open-ended simulated environments like Minecraft, without human intervention.

mediumtopics#2

Add more specific topics for core functionality

Why:

CURRENT

embodied-learning, large-language-models, minecraft, open-ended-learning

COPY-PASTE FIX

embodied-learning, large-language-models, minecraft, open-ended-learning, skill-acquisition, code-generation, llm-agent-framework

lowreadme#3

Add a 'Comparison with Alternatives' section to README

Why:

COPY-PASTE FIX

## Comparison with Alternatives

Unlike general-purpose reinforcement learning environments (e.g., OpenAI Gym, Gymnasium, Unity ML-Agents) or libraries (e.g., Stable Baselines3, Ray RLlib), Voyager is specifically designed as an LLM-powered agent framework for autonomous skill acquisition and code generation in open-ended environments. It focuses on lifelong learning, iterative prompting, and building an executable skill library, rather than providing a generic environment interface or a suite of RL 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 MineDojo/Voyager

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Unity ML-Agents

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Unity ML-Agents · recommended 2×
OpenAI Gym · recommended 2×
Stable Baselines3 · recommended 2×
Farama Foundation Gymnasium · recommended 1×
DeepMind Lab · recommended 1×

CATEGORY QUERY
How can I create an AI agent that autonomously learns and explores within a simulated environment?
you: not recommended
AI recommended (in order):
1. Unity ML-Agents
2. OpenAI Gym
3. Farama Foundation Gymnasium
4. Stable Baselines3
5. DeepMind Lab
6. MetaWorld
7. Minigrid
8. Isaac Gym
9. Isaac Sim
10. PyBullet
AI recommended 10 alternatives but never named MineDojo/Voyager. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What platforms enable an AI agent to acquire skills and generate code from environmental feedback?
you: not recommended
AI recommended (in order):
1. OpenAI Gym
2. Gymnasium
3. Stable Baselines3
4. Ray RLlib
5. AI Habitat
6. Acme
7. Project Malmo
8. Unity ML-Agents
9. OpenSpiel
AI recommended 9 alternatives but never named MineDojo/Voyager. 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 MineDojo/Voyager?
pass
AI named MineDojo/Voyager explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts MineDojo/Voyager in production, what risks or prerequisites should they evaluate first?
pass
AI named MineDojo/Voyager 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 MineDojo/Voyager solve, and who is the primary audience?
pass
AI named MineDojo/Voyager explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of MineDojo/Voyager. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

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

HTML

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

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