REPOGEO REPORT · LITE

aiming-lab/SimpleMem

Default branch main · commit 60a48e83 · scanned 6/23/2026, 5:32:33 AM

GitHub: 3,529 stars · 365 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 aiming-lab/SimpleMem, 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

Rephrase the README's introductory paragraph to explicitly state SimpleMem's purpose as an LLM agent memory solution.

Why:

CURRENT

<small>Store, compress, and retrieve long-term memories with semantic lossless compression. Now with multimodal support for text, image, audio & video.</small>

COPY-PASTE FIX

SimpleMem provides an efficient, lifelong memory solution for LLM agents, enabling them to store, compress, and retrieve long-term memories with semantic lossless compression. It now supports multimodal data including text, image, audio, and video.

mediumreadme#2

Add a section comparing SimpleMem to common LLM memory solutions.

Why:

COPY-PASTE FIX

Add a new section (e.g., 'Why SimpleMem? (vs. Vector Databases)' or 'Comparison to Alternatives') with content like: 'While vector databases such as Pinecone, Weaviate, and Qdrant excel at raw vector storage and similarity search, SimpleMem is specifically engineered for the unique challenges of LLM agent memory. It focuses on semantic lossless compression, multimodal integration, and lifelong memory management, offering a more holistic and agent-centric approach beyond simple retrieval.'

lowhomepage#3
Populate the repository's homepage URL.
Why:
COPY-PASTE FIX
```
https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=SimpleMem
```

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 aiming-lab/SimpleMem

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×
ChromaDB · recommended 1×
Weaviate · recommended 1×
Qdrant · recommended 1×
Milvus · recommended 1×

CATEGORY QUERY
How can I implement efficient lifelong memory for my LLM agents, including multimodal data?
you: not recommended
AI recommended (in order):
1. ChromaDB
2. Weaviate
3. Pinecone
4. Qdrant
5. Milvus
6. FAISS
7. PostgreSQL
8. MongoDB
9. Elasticsearch
AI recommended 9 alternatives but never named aiming-lab/SimpleMem. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools help with semantic compression and retrieval for LLM agent knowledge bases?
you: not recommended
AI recommended (in order):
1. Pinecone
2. Weaviate (weaviate/weaviate)
3. Qdrant (qdrant/qdrant)
4. Chroma (chroma-core/chroma)
5. Faiss (facebookresearch/faiss)
6. Milvus (milvus-io/milvus)
7. Elasticsearch (elastic/elasticsearch)
AI recommended 7 alternatives but never named aiming-lab/SimpleMem. 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 aiming-lab/SimpleMem?
pass
AI named aiming-lab/SimpleMem explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts aiming-lab/SimpleMem in production, what risks or prerequisites should they evaluate first?
pass
AI named aiming-lab/SimpleMem 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 aiming-lab/SimpleMem solve, and who is the primary audience?
pass
AI named aiming-lab/SimpleMem 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)

[![RepoGEO](https://repogeo.com/badge/aiming-lab/SimpleMem.svg)](https://repogeo.com/en/r/aiming-lab/SimpleMem)

HTML

<a href="https://repogeo.com/en/r/aiming-lab/SimpleMem"><img src="https://repogeo.com/badge/aiming-lab/SimpleMem.svg" alt="RepoGEO" /></a>

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aiming-lab/SimpleMem — 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