RRepoGEO

REPOGEO REPORT · LITE

ECNU-SII/MIA

Default branch main · commit e442be26 · scanned 6/6/2026, 5:52:51 PM

GitHub: 786 stars · 85 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 ECNU-SII/MIA, 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 the README's core purpose statement to the top

    Why:

    CURRENT
    <h1 align="center"><br>Memory Intelligence Agent</h1>

<div align="center">

[](https://arxiv.org/pdf/2604.04503) [](https://huggingface.co/LightningCreeper/MIA) [](https://huggingface.co/datasets/LightningCreeper/MIA) [](https://www.python.org/downloads/release/python-3100/) [](https://opensource.org/licenses/MIT) [](https://sii-group.com/fr-FR/sii-sud-ouest)

[[English](./README.md)] [[Chinese](./README_CN.md)]
</div>

<p align="center"><i>An Agent Memory Framework Designed For Deep Research Agents</i></p>
    COPY-PASTE FIX
    <h1 align="center"><br>MIA: Memory Intelligence Agent</h1>

<p align="center"><i>An Agent Memory Framework Designed For Deep Research Agents</i></p>

<div align="center">

[](https://arxiv.org/pdf/2604.04503) [](https://huggingface.co/LightningCreeper/MIA) [](https://huggingface.co/datasets/LightningCreeper/MIA) [](https://www.python.org/downloads/release/python-3100/) [](https://opensource.org/licenses/MIT) [](https://sii-group.com/fr-FR/sii-sud-ouest)

[[English](./README.md)] [[Chinese](./README_CN.md)]
</div>
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ai-agents, agent-memory, llm-agents, deep-learning, research-agents, long-context, memory-framework, openclaw
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the full text of the MIT License, as suggested by the broken link in the README excerpt.

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 ECNU-SII/MIA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Hugging Face Transformers · recommended 1×
  5. FAISS · recommended 1×
  • CATEGORY QUERY
    How to build AI agents that retain information and learn from long-form text?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Hugging Face Transformers
    4. FAISS
    5. Pinecone
    6. Weaviate
    7. Qdrant
    8. Haystack
    9. OpenAI API
    10. GPT-4
    11. GPT-3.5 Turbo
    12. Rasa

    AI recommended 12 alternatives but never named ECNU-SII/MIA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to enhance deep research agent memory and reasoning capabilities.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. AutoGPT
    5. DSPy
    6. MemGPT

    AI recommended 6 alternatives but never named ECNU-SII/MIA. 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 ECNU-SII/MIA?
    pass
    AI named ECNU-SII/MIA explicitly

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

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

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

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ECNU-SII/MIA — 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