REPOGEO REPORT · LITE

memohai/Memoh

Default branch main · commit 665ded3b · scanned 5/26/2026, 5:27:11 PM

GitHub: 1,745 stars · 160 forks

AI VISIBILITY SCORE

33 /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

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 memohai/Memoh, 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 paragraph to explicitly state purpose and differentiate

Why:

CURRENT

Memoh(/ˈmemoʊ/) is an always-on, containerized AI agent orchestrator. Create multiple AI bots, each running in its own isolated container with persistent memory, and interact with them across Telegram, Discord and so on. Bots can execute commands, edit files, browse the web, call external tools via MCP, and remember everything — like giving each bot its own computer and brain.

COPY-PASTE FIX

Memoh(/ˈmemoʊ/) is a self-hosted, always-on AI agent platform and orchestrator, designed for creating and managing multiple AI bots in isolated containers with persistent memory. Unlike note-taking apps or knowledge bases, Memoh focuses on enabling AI agents to interact across platforms like Telegram and Discord, execute commands, and remember everything — like giving each bot its own computer and brain.

mediumtopics#2

Refine repository topics to remove ambiguity and add specificity

Why:

CURRENT

agent, ai, ai-companion, ai-memory, openclaw, personal-assistant

COPY-PASTE FIX

agent, ai, ai-companion, ai-memory, openclaw, ai-agents, ai-platform, containerized-ai

mediumcomparison#3

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

Add a new section to the README titled 'Comparison' or 'How Memoh Differs'. In this section, explain how Memoh provides a complete, self-hosted AI agent platform *on top of* or *distinct from* frameworks like LangChain/LlamaIndex or infrastructure like Kubernetes/Cloud Run, which are often used as components rather than full solutions.

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 memohai/Memoh

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Kubernetes

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Kubernetes · recommended 1×
LangChain · recommended 1×
LlamaIndex · recommended 1×
Pinecone · recommended 1×
Weaviate · recommended 1×

CATEGORY QUERY
How can I self-host an always-on AI agent platform for multiple bots with persistent memory?
you: not recommended
AI recommended (in order):
1. Kubernetes
2. LangChain
3. LlamaIndex
4. Pinecone
5. Weaviate
6. Milvus
7. PostgreSQL
8. Docker Compose
9. Redis
10. AutoGen
11. CrewAI
12. Qdrant
13. Chroma
14. FastAPI
15. Flask
16. MongoDB
17. FAISS
18. Node.js
19. NestJS
20. Express.js
21. langchain.js
AI recommended 21 alternatives but never named memohai/Memoh. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What's a good way to deploy containerized AI bots that integrate with messaging apps?
you: not recommended
AI recommended (in order):
1. Google Cloud Run
2. AWS Fargate
3. Amazon ECS
4. Amazon EKS
5. Azure Container Apps
6. Heroku Container Registry & Runtime
7. Kubernetes (kubernetes/kubernetes)
8. Google Kubernetes Engine (GKE)
9. Amazon Elastic Kubernetes Service (EKS)
10. Azure Kubernetes Service (AKS)
11. Railway
AI recommended 11 alternatives but never named memohai/Memoh. 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 memohai/Memoh?
pass
AI did not name memohai/Memoh — 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 memohai/Memoh in production, what risks or prerequisites should they evaluate first?
pass
AI named memohai/Memoh 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 memohai/Memoh solve, and who is the primary audience?
pass
AI named memohai/Memoh 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 memohai/Memoh. 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/memohai/Memoh.svg)](https://repogeo.com/en/r/memohai/Memoh)

HTML

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

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memohai/Memoh — 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