RRepoGEO

REPOGEO REPORT · LITE

Memento-Teams/Memento

Default branch main · commit 42fbbcac · scanned 5/9/2026, 1:42:41 PM

GitHub: 2,420 stars · 286 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 Memento-Teams/Memento, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-agents, continual-learning, memory-augmented-learning, case-based-reasoning, large-language-models, ai-framework
  • highreadme#2
    Strengthen README's opening sentence to clarify LLM agent focus

    Why:

    CURRENT
    > A memory-based, continual-learning framework that helps LLM agents improve from experience **without** updating model weights.
    COPY-PASTE FIX
    > Memento is a memory-based, continual-learning framework specifically designed for LLM agents, enabling them to improve from experience **without** updating model weights.
  • mediumreadme#3
    Add a positioning statement relative to common LLM agent frameworks

    Why:

    COPY-PASTE FIX
    Unlike general-purpose LLM frameworks such as LangChain or LlamaIndex, Memento uniquely focuses on enabling continual learning for LLM agents through memory and experience, bypassing the need for model weight updates.

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 Memento-Teams/Memento
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 1×
  2. weaviate/weaviate · recommended 1×
  3. chroma-core/chroma · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I make my LLM agents learn continually from new experiences without model retraining?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Chroma (chroma-core/chroma)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. OpenAI API
    7. Argilla (argilla-io/argilla)
    8. Humanloop
    9. Weights & Biases (wandb/wandb)

    AI recommended 9 alternatives but never named Memento-Teams/Memento. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to enhance LLM agent performance using memory and experience, not weight updates.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. MemGPT
    4. Haystack
    5. DSPy
    6. AutoGPT
    7. BabyAGI

    AI recommended 7 alternatives but never named Memento-Teams/Memento. 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 Memento-Teams/Memento?
    pass
    AI named Memento-Teams/Memento explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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Memento-Teams/Memento — 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