RRepoGEO

REPOGEO REPORT · LITE

snap-research/locomo

Default branch main · commit 3eb6f2c5 · scanned 6/3/2026, 8:23:22 AM

GitHub: 917 stars · 91 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 snap-research/locomo, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    LoCoMo is a high-quality evaluation benchmark and dataset for very long-term conversational memory of LLM agents, supporting question-answering, event-summarization, and multimodal dialog generation tasks.
  • hightopics#2
    Add relevant GitHub topics

    Why:

    COPY-PASTE FIX
    llm-agents, conversational-ai, long-term-memory, benchmark, dataset, multimodal-dialog, question-answering, event-summarization, acl-2024
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line to the README, for example: "This project is licensed under a custom research license. Please refer to the LICENSE file for full details regarding usage and distribution."

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 snap-research/locomo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NarrativeQA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NarrativeQA · recommended 1×
  2. QMSum · recommended 1×
  3. ConvAI3 · recommended 1×
  4. Multi-Session Chat (MSC) Dataset · recommended 1×
  5. Personalized Dialogue Dataset (PDD) · recommended 1×
  • CATEGORY QUERY
    What are good benchmarks for evaluating very long-term conversational memory in LLM agents?
    you: not recommended
    AI recommended (in order):
    1. NarrativeQA
    2. QMSum
    3. ConvAI3
    4. Multi-Session Chat (MSC) Dataset
    5. Personalized Dialogue Dataset (PDD)
    6. WebArena

    AI recommended 6 alternatives but never named snap-research/locomo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find datasets for multimodal dialog generation or long-term conversational QA?
    you: not recommended
    AI recommended (in order):
    1. MultiWOZ
    2. DailyDialog
    3. VisDial
    4. Audio Visual Scene-Aware Dialog (AVSD)
    5. CoQA
    6. MMDialog
    7. Image-Chat

    AI recommended 7 alternatives but never named snap-research/locomo. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 snap-research/locomo?
    pass
    AI named snap-research/locomo explicitly

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

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

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

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snap-research/locomo — 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