RRepoGEO

REPOGEO REPORT · LITE

adoresever/graph-memory

Default branch main · commit c926fa42 · scanned 6/15/2026, 10:56:55 PM

GitHub: 501 stars · 75 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 adoresever/graph-memory, 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 H1 subtitle for broader AI agent memory context

    Why:

    CURRENT
    Knowledge Graph Context Engine for OpenClaw
    COPY-PASTE FIX
    Knowledge Graph Library for AI Agent Memory & Context Management (OpenClaw Compatible)
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/adoresever/graph-memory
  • mediumtopics#3
    Expand repository topics with broader AI agent memory terms

    Why:

    CURRENT
    agent, claude-code, codex, graph, knowledge-graph, memory, openclaw, openclaw-plugin, opencode, sqlite
    COPY-PASTE FIX
    agent, claude-code, codex, graph, knowledge-graph, memory, openclaw, openclaw-plugin, opencode, sqlite, llm-agents, context-management, long-term-memory

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 adoresever/graph-memory
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Anthropic's Claude
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Anthropic's Claude · recommended 1×
  2. OpenAI's GPT-4 Turbo · recommended 1×
  3. LangChain · recommended 1×
  4. LlamaIndex · recommended 1×
  5. Pinecone · recommended 1×
  • CATEGORY QUERY
    How to manage and compress long conversation context for AI agents effectively?
    you: not recommended
    AI recommended (in order):
    1. Anthropic's Claude
    2. OpenAI's GPT-4 Turbo
    3. LangChain
    4. LlamaIndex
    5. Pinecone
    6. Weaviate
    7. Chroma
    8. text-embedding-ada-002
    9. Cohere Embed
    10. GPT-3.5 Turbo

    AI recommended 10 alternatives but never named adoresever/graph-memory. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool to build a persistent knowledge graph for AI agent memory and cross-session learning?
    you: not recommended
    AI recommended (in order):
    1. Neo4j
    2. TypeDB
    3. Amazon Neptune
    4. ArangoDB
    5. RDFox
    6. Datalog
    7. Soufflé
    8. PostgreSQL
    9. Apache AGE

    AI recommended 9 alternatives but never named adoresever/graph-memory. 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 adoresever/graph-memory?
    pass
    AI named adoresever/graph-memory explicitly

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

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

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

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adoresever/graph-memory — 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