RRepoGEO

REPOGEO REPORT · LITE

AVIDS2/memorix

Default branch main · commit fd133ecd · scanned 6/15/2026, 4:02:14 AM

GitHub: 522 stars · 39 forks

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 AVIDS2/memorix, 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 and expand the core value proposition immediately after the H1

    Why:

    CURRENT
    <h1 align="center">Memorix</h1>

<p align="center">
  <strong>Open-source cross-agent memory layer for coding agents.</strong><br>
  Tiered MCP support across Cursor, Claude Code, Codex, Windsurf, Gemini CLI, GitHub Copilot, Kiro, OpenCode, Antigravity, Trae, and other MCP-compatible clients.
</p>
    COPY-PASTE FIX
    # Memorix

Memorix is an open-source, local-first memory control plane specifically designed for coding agents. It provides a cross-agent memory layer, enabling seamless context sharing and synchronization across various AI coding assistants like Cursor, Claude Code, Codex, Windsurf, Gemini CLI, GitHub Copilot, Kiro, OpenCode, Antigravity, and Trae, via the Model Context Protocol (MCP).
  • mediumhomepage#2
    Add a dedicated project homepage URL

    Why:

    COPY-PASTE FIX
    Add a URL to a dedicated project homepage (e.g., `https://memorix.dev` or `https://avids2.github.io/memorix`) in the repository's 'About' section.
  • mediumreadme#3
    Add a section to explicitly differentiate Memorix from other AI tools

    Why:

    COPY-PASTE FIX
    ## How Memorix Differs

Memorix is a specialized memory layer for *coding agents*, not a general-purpose AI framework (like LangChain or LlamaIndex), a standalone vector database (like Pinecone or Weaviate), or a version control system (like Git). Its core focus is on cross-agent context synchronization and persistent memory specifically for AI-driven development workflows.

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 AVIDS2/memorix
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
git/git
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. git/git · recommended 2×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. Pinecone · recommended 1×
  5. weaviate/weaviate · recommended 1×
  • CATEGORY QUERY
    How can I share context and memory across different AI coding assistants?
    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. Chroma (chroma-core/chroma)
    6. Qdrant (qdrant/qdrant)
    7. Git (git/git)
    8. Dropbox
    9. Google Drive

    AI recommended 9 alternatives but never named AVIDS2/memorix. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help synchronize coding agent memory for collaborative development workflows?
    you: not recommended
    AI recommended (in order):
    1. Git (git/git)
    2. LFS (git-lfs/git-lfs)
    3. GitHub
    4. GitLab
    5. Bitbucket
    6. DVC (iterative/dvc)
    7. MLflow (mlflow/mlflow)
    8. Weights & Biases (wandb/wandb)
    9. Ray Serve (ray-project/ray)
    10. Kubernetes (kubernetes/kubernetes)
    11. Redis (redis/redis)
    12. NFS
    13. EFS
    14. Azure Files
    15. Google Filestore

    AI recommended 15 alternatives but never named AVIDS2/memorix. 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 AVIDS2/memorix?
    pass
    AI named AVIDS2/memorix explicitly

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

  • If a team adopts AVIDS2/memorix in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AVIDS2/memorix 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 AVIDS2/memorix solve, and who is the primary audience?
    pass
    AI named AVIDS2/memorix explicitly

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

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AVIDS2/memorix — 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