RRepoGEO

REPOGEO REPORT · LITE

Memento-Teams/Memento

Default branch main · commit 42fbbcac · scanned 6/19/2026, 8:57:51 AM

GitHub: 2,467 stars · 284 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 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
  • highreadme#1
    Reposition README opening to explicitly state AI/ML research project

    Why:

    CURRENT
    > A memory-based, continual-learning framework that helps LLM agents improve from experience **without** updating model weights.
    COPY-PASTE FIX
    > Memento is an open-source AI/ML research project presenting a memory-based, continual-learning framework that helps LLM agents improve from experience **without** updating model weights.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-agents, continual-learning, memory-augmented-learning, case-based-reasoning, large-language-models, ai, machine-learning, deep-learning, research
  • mediumreadme#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## 🆚 Comparison to Alternatives' or '## 💡 Why Memento?', explaining how Memento differs from and complements frameworks like LangChain, LlamaIndex, and AutoGen, specifically highlighting its focus on continual learning and memory-based improvement without fine-tuning LLMs.

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
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Chroma · recommended 1×
  4. Pinecone · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How can I make my LLM agents learn continually from experience without expensive model fine-tuning?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. Chroma
    3. Pinecone
    4. Weaviate
    5. LlamaIndex
    6. Neo4j
    7. Grakn
    8. TerminusDB
    9. LangChain's Knowledge Graph agents

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

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable LLM agents to improve performance using memory and past experiences?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. Haystack
    5. DSPy
    6. AgentVerse

    AI recommended 6 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?

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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