RRepoGEO

REPOGEO REPORT · LITE

xiaowu0162/LongMemEval

Default branch main · commit 9e0b455f · scanned 6/1/2026, 11:18:07 PM

GitHub: 817 stars · 62 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 xiaowu0162/LongMemEval, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, benchmark, long-term-memory, conversational-ai, nlp, evaluation, chat-assistants, iclr-2025
  • highreadme#2
    Clarify README's opening to emphasize 'benchmark' and 'LLM evaluation'

    Why:

    CURRENT
    We introduce LongMemEval, a comprehensive, challenging, and scalable benchmark for testing the long-term memory of chat assistants.
    COPY-PASTE FIX
    LongMemEval is a comprehensive, challenging, and scalable benchmark specifically designed for rigorously evaluating the long-term interactive memory capabilities of Large Language Models (LLMs) and chat assistants. It is not a chatbot development framework or tool, but an evaluation suite.
  • mediumhomepage#3
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://xiaowu0162.github.io/long-mem-eval/

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 xiaowu0162/LongMemEval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ConvLab-3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ConvLab-3 · recommended 1×
  2. Multi-Session Chat (MSC) Dataset · recommended 1×
  3. ParlAI · recommended 1×
  4. Topical-Chat · recommended 1×
  5. DSTC (Dialogue System Technology Challenges) Tracks · recommended 1×
  • CATEGORY QUERY
    What are the best benchmarks for evaluating large language model long-term conversational memory?
    you: not recommended
    AI recommended (in order):
    1. ConvLab-3
    2. Multi-Session Chat (MSC) Dataset
    3. ParlAI
    4. Topical-Chat
    5. DSTC (Dialogue System Technology Challenges) Tracks
    6. Personalization in Dialogue (PiD) Dataset

    AI recommended 6 alternatives but never named xiaowu0162/LongMemEval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I test a chatbot's ability to retain information across many user interactions?
    you: not recommended
    AI recommended (in order):
    1. Rasa X (RasaHQ/rasa-x)
    2. Botpress (botpress/botpress)
    3. Rasa (RasaHQ/rasa)
    4. Botium (botium/botium-core)
    5. pytest (pytest-dev/pytest)
    6. Jest (facebook/jest)
    7. Microsoft Bot Framework Emulator (microsoft/botframework-emulator)
    8. JUnit (junit-team/junit5)

    AI recommended 8 alternatives but never named xiaowu0162/LongMemEval. 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 xiaowu0162/LongMemEval?
    pass
    AI named xiaowu0162/LongMemEval explicitly

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

  • If a team adopts xiaowu0162/LongMemEval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named xiaowu0162/LongMemEval 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 xiaowu0162/LongMemEval solve, and who is the primary audience?
    pass
    AI named xiaowu0162/LongMemEval 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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xiaowu0162/LongMemEval — 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