RRepoGEO

REPOGEO REPORT · LITE

EverMind-AI/EverOS

Default branch main · commit e37205f5 · scanned 5/22/2026, 3:03:07 AM

GitHub: 5,437 stars · 580 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
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 EverMind-AI/EverOS, 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 the README's core message to emphasize long-term memory as the primary problem solved

    Why:

    CURRENT
    **EverOS** is a unified home for applying, building, and evaluating long-term memory in self-evolving agents.
    COPY-PASTE FIX
    EverOS is the essential long-term memory operating system for self-evolving AI agents, solving the challenge of persistent, context-aware recall for ongoing conversations and learning. It provides a unified home for applying, building, and evaluating memory systems.
  • mediumreadme#2
    Add a 'Why EverOS?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why EverOS for Agent Memory? \n Unlike general agent frameworks or vector databases, EverOS provides a dedicated operating system for long-term memory, offering advanced evaluation, architecture methods, and use cases specifically designed for persistent, self-evolving agent recall.'
  • lowtopics#3
    Add more specific topics related to persistent and conversational memory

    Why:

    CURRENT
    agent-memory, agentic-ai, ai, chats, clawdbot, clawdbot-skill, llm, long-term-memory, mcp, memory, memory-management, python3, rag, skills
    COPY-PASTE FIX
    agent-memory, agentic-ai, ai, chats, clawdbot, clawdbot-skill, llm, long-term-memory, mcp, memory, memory-management, python3, rag, skills, persistent-memory, conversational-ai-memory, agent-memory-management

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 EverMind-AI/EverOS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Pinecone · recommended 1×
  4. Chroma · recommended 1×
  5. Redis · recommended 1×
  • CATEGORY QUERY
    How can I give my AI agents persistent memory for ongoing conversations and learning?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Chroma
    3. Redis
    4. PostgreSQL with pgvector
    5. LangChain
    6. LlamaIndex

    AI recommended 6 alternatives but never named EverMind-AI/EverOS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help manage and evaluate long-term retrieval augmented generation for AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Weights & Biases (W&B)
    4. Arize AI
    5. Haystack
    6. Ragas
    7. MLflow

    AI recommended 7 alternatives but never named EverMind-AI/EverOS. 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 EverMind-AI/EverOS?
    pass
    AI named EverMind-AI/EverOS explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts EverMind-AI/EverOS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EverMind-AI/EverOS 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 EverMind-AI/EverOS solve, and who is the primary audience?
    pass
    AI named EverMind-AI/EverOS 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 EverMind-AI/EverOS. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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EverMind-AI/EverOS — RepoGEO report