REPOGEO REPORT · LITE

agiresearch/AIOS

Default branch main · commit 5de61c9a · scanned 5/15/2026, 8:11:57 AM

GitHub: 5,681 stars · 789 forks

AI VISIBILITY SCORE

28 /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

2 / 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 agiresearch/AIOS, 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 relevant topics to the repository

Why:

COPY-PASTE FIX

llm-agents, ai-agents, agent-operating-system, llm-orchestration, multi-agent-system, resource-management, agent-sdk, llm-framework

highreadme#2

Refine README opening to emphasize 'Operating System' differentiator

Why:

CURRENT

AIOS is the AI Agent Operating System, which embeds large language model (LLM) into the operating system and facilitates the development and deployment of LLM-based AI Agents. AIOS is designed to address problems (e.g., scheduling, context switch, memory management, storage management, tool management, Agent SDK management, etc.) during the development and deployment of LLM-based agents, towards a better AIOS-Agent ecosystem for agent developers and agent users. AIOS includes the AIOS Kernel (this AIOS repository) and the AIOS SDK (the Cerebrum repository). AIOS supports both Web UI and Terminal UI.

COPY-PASTE FIX

AIOS is the **AI Agent Operating System**, a foundational platform designed to manage and orchestrate large language model (LLM) agents at a system level, unlike traditional frameworks or libraries. It addresses critical challenges such as scheduling, context switching, memory, storage, and tool management, providing a robust ecosystem for developing and deploying sophisticated LLM-based agents. AIOS includes the AIOS Kernel (this repository) and the AIOS SDK (the Cerebrum repository), supporting both Web UI and Terminal UI.

mediumreadme#3

Clarify the project's license in the README

Why:

COPY-PASTE FIX

Add a 'License' section to the README, stating: 'This project is licensed under [Specify the actual license(s) here, e.g., 'a custom license combining elements of X and Y']. Please refer to the `LICENSE` file for full details.'

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 agiresearch/AIOS

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
LlamaIndex · recommended 2×
Haystack · recommended 2×
OpenAI Assistants API · recommended 2×
AutoGen · recommended 1×

CATEGORY QUERY
What tools help manage resources and orchestrate multiple LLM agents efficiently?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. AutoGen
5. CrewAI
6. OpenAI Assistants API
7. Kubernetes
AI recommended 7 alternatives but never named agiresearch/AIOS. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a comprehensive framework for building, deploying, and managing large language model agents.
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. Microsoft Semantic Kernel
5. AutoGPT
6. OpenAI Assistants API
7. Guidance
AI recommended 7 alternatives but never named agiresearch/AIOS. 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 agiresearch/AIOS?
pass
AI did not name agiresearch/AIOS — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts agiresearch/AIOS in production, what risks or prerequisites should they evaluate first?
pass
AI named agiresearch/AIOS 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 agiresearch/AIOS solve, and who is the primary audience?
pass
AI named agiresearch/AIOS 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 agiresearch/AIOS. 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/agiresearch/AIOS.svg)](https://repogeo.com/en/r/agiresearch/AIOS)

HTML

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

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agiresearch/AIOS — 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