REPOGEO REPORT · LITE

memodb-io/memobase

Default branch main · commit 358c16bb · scanned 6/25/2026, 5:57:49 AM

GitHub: 2,767 stars · 219 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)

3 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 memodb-io/memobase, 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 core value proposition for LLM user memory at the top of README

Why:

CURRENT

The current README excerpt places the core description after badges and a "News" section.

COPY-PASTE FIX

Move the sentence "Memobase is a **user profile-based memory system** designed to bring long-term user memory to your LLM applications." to be the very first paragraph immediately following the `<h1>Memobase</h1>` title, before any badges or news.

mediumtopics#2

Add more specific LLM personalization and agent memory topics

Why:

CURRENT

ai-companion, ai-memory, chatgpt, llm-application, llm-memory, long-term-memory, memory, rag, retrieval, user-memory

COPY-PASTE FIX

ai-companion, ai-memory, chatgpt, llm-application, llm-memory, long-term-memory, memory, rag, retrieval, user-memory, llm-personalization, chatbot-memory, agent-memory, user-profiles

lowcomparison#3

Add a comparison section to differentiate from common alternatives

Why:

COPY-PASTE FIX

Add a new section to the README, e.g., "## Memobase vs. Other Memory Solutions" or "## Why Memobase?", that explicitly compares its "user profile-based long-term memory" approach to generic vector databases (like Pinecone, Weaviate) and LLM orchestration frameworks (like LangChain, LlamaIndex), highlighting its unique focus on user profiles and personalized experiences.

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 memodb-io/memobase

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Pinecone

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Pinecone · recommended 2×
LangChain · recommended 1×
Weaviate · recommended 1×
Chroma · recommended 1×
Qdrant · recommended 1×

CATEGORY QUERY
How can I implement long-term, user-specific memory for my AI chatbot applications?
you: not recommended
AI recommended (in order):
1. LangChain
2. Pinecone
3. Weaviate
4. Chroma
5. Qdrant
6. Redis
7. RediSearch
8. PostgreSQL
9. pgvector
10. MongoDB
11. Firebase Firestore
12. Realtime Database
13. SQLAlchemy
14. SQLite
15. MySQL
AI recommended 15 alternatives but never named memodb-io/memobase. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help LLMs remember user interactions and personalize future responses?
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. Pinecone
4. Weaviate (weaviate/weaviate)
5. Redis (redis/redis)
6. Faiss (facebookresearch/faiss)
AI recommended 6 alternatives but never named memodb-io/memobase. 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 memodb-io/memobase?
pass
AI named memodb-io/memobase explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts memodb-io/memobase in production, what risks or prerequisites should they evaluate first?
pass
AI named memodb-io/memobase 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 memodb-io/memobase solve, and who is the primary audience?
pass
AI named memodb-io/memobase 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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HTML

<a href="https://repogeo.com/en/r/memodb-io/memobase"><img src="https://repogeo.com/badge/memodb-io/memobase.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