REPOGEO REPORT · LITE

octo-models/octo

Default branch main · commit 241fb351 · scanned 7/1/2026, 11:01:40 AM

GitHub: 1,690 stars · 269 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 octo-models/octo, 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

hightopics#1

Add specific topics to the repository

Why:

COPY-PASTE FIX

robotics, generalist-robot-policy, transformer, diffusion-policy, robot-learning, language-conditioned-robotics, goal-conditioned-robotics, finetuning, pre-trained-models

highreadme#2

Reposition the README's opening paragraph to clarify its specific domain

Why:

CURRENT

This repo contains code for training and finetuning Octo generalist robotic policies (GRPs).
Octo models are transformer-based diffusion policies, trained on a diverse mix of 800k robot trajectories.

COPY-PASTE FIX

Octo is a cutting-edge generalist robot policy (GRP) framework, providing pre-trained transformer-based diffusion policies for diverse robotic tasks. This repository offers code for training, finetuning, and deploying Octo models, enabling robot control via language commands or goal images, and supporting efficient adaptation to new robot setups.

mediumreadme#3

Add a 'Why Octo?' section to highlight differentiators

Why:

COPY-PASTE FIX

Add a new section, e.g., '## Why Octo?', explaining its modular attention structure, finetuning efficiency, and support for multiple inputs/outputs compared to other generalist robot policies.

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 octo-models/octo

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Perceiver IO

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Perceiver IO · recommended 2×
CLIP · recommended 1×
Robotics Transformer (RT-1/RT-2) · recommended 1×
RT-2 · recommended 1×
DETR · recommended 1×

CATEGORY QUERY
How can I implement a generalist robot policy for diverse tasks using pre-trained models?
you: not recommended
AI recommended (in order):
1. CLIP
2. Robotics Transformer (RT-1/RT-2)
3. RT-2
4. DETR
5. Perceiver IO
6. Florence-2
7. Stable Baselines3
8. Isaac Gym
9. Hugging Face Transformers library
10. ViT
11. BLIP
12. LLaVA
13. InstructBLIP
14. PaLM-E
AI recommended 14 alternatives but never named octo-models/octo. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What AI models allow robot control via language commands and goal images, supporting finetuning?
you: not recommended
AI recommended (in order):
1. RT-X (Robotics Transformer X)
2. OpenVLA (Open-Vocabulary Language-conditioned Agent)
3. CLIP (Contrastive Language-Image Pre-training)
4. Diffusion Policy
5. Perceiver IO
6. RoboCat
AI recommended 6 alternatives but never named octo-models/octo. 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 octo-models/octo?
pass
AI named octo-models/octo explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts octo-models/octo in production, what risks or prerequisites should they evaluate first?
pass
AI named octo-models/octo 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 octo-models/octo solve, and who is the primary audience?
pass
AI named octo-models/octo 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)

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octo-models/octo — 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