RRepoGEO

REPOGEO REPORT · LITE

OSU-NLP-Group/HippoRAG

Default branch main · commit d437bfb1 · scanned 5/30/2026, 4:32:06 AM

GitHub: 3,553 stars · 362 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 OSU-NLP-Group/HippoRAG, 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
    rag, llm, knowledge-graph, memory, continual-learning, multi-hop-retrieval, nlp, neurips-2024, page-rank, long-term-memory
  • highabout#2
    Refine the 'About' description for clarity on core problems solved

    Why:

    CURRENT
    [NeurIPS'24] HippoRAG is a novel RAG framework inspired by human long-term memory that enables LLMs to continuously integrate knowledge across external documents. RAG + Knowledge Graphs + Personalized PageRank.
    COPY-PASTE FIX
    [NeurIPS'24] HippoRAG is a novel RAG framework for LLMs that enhances continual learning and multi-hop retrieval by integrating knowledge across external documents, inspired by human long-term memory. It leverages RAG, Knowledge Graphs, and Personalized PageRank.
  • mediumcomparison#3
    Add a comparison section to differentiate from general RAG frameworks

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Why HippoRAG? (vs. LangChain, LlamaIndex, etc.)' that explicitly compares HippoRAG's specialized capabilities (e.g., continual learning, multi-hop retrieval, memory framework) against general RAG frameworks, explaining when HippoRAG is a better fit for advanced use cases.

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 OSU-NLP-Group/HippoRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
langchain-ai/langchain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. langchain-ai/langchain · recommended 1×
  2. run-llama/llama_index · recommended 1×
  3. Pinecone · recommended 1×
  4. weaviate/weaviate · recommended 1×
  5. qdrant/qdrant · recommended 1×
  • CATEGORY QUERY
    How to build an LLM system that continuously learns and integrates new information?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Pinecone
    4. Weaviate (weaviate/weaviate)
    5. Qdrant (qdrant/qdrant)
    6. OpenAI's `text-embedding-ada-002`
    7. Hugging Face Transformers (huggingface/transformers)
    8. MLflow (mlflow/mlflow)
    9. Weights & Biases (wandb/wandb)
    10. Apache Kafka (apache/kafka)
    11. RabbitMQ (rabbitmq/rabbitmq-server)
    12. Airflow (apache/airflow)
    13. Prefect (PrefectHQ/prefect)

    AI recommended 13 alternatives but never named OSU-NLP-Group/HippoRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tool for improving multi-hop retrieval and complex context understanding in RAG applications efficiently?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Weaviate
    5. Neo4j
    6. Milvus
    7. Qdrant

    AI recommended 7 alternatives but never named OSU-NLP-Group/HippoRAG. 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 OSU-NLP-Group/HippoRAG?
    pass
    AI named OSU-NLP-Group/HippoRAG explicitly

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

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

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

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OSU-NLP-Group/HippoRAG — 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