RRepoGEO

REPOGEO REPORT · LITE

mit-han-lab/streaming-llm

Default branch main · commit 2e504260 · scanned 6/27/2026, 6:33:05 PM

GitHub: 7,237 stars · 399 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
22 /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
1 / 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 mit-han-lab/streaming-llm, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a concise, tool-focused opening sentence to the README

    Why:

    CURRENT
    # Efficient Streaming Language Models with Attention Sinks
    COPY-PASTE FIX
    # Efficient Streaming Language Models with Attention Sinks

This repository provides the official implementation of StreamingLLM, a method and library for enabling Large Language Models to process infinite-length inputs efficiently.
  • mediumreadme#2
    Add a 'Key Features' or 'Why StreamingLLM?' section to the README

    Why:

    COPY-PASTE FIX
    Consider adding a new section like '## Key Features' or '## Why StreamingLLM?' that explicitly lists the benefits and use cases, such as:
- **Infinite Context Length:** Process inputs of arbitrary length without performance degradation.
- **Memory Efficiency:** Drastically reduce KV cache memory footprint.
- **Performance:** Maintain near full-context performance.
- **Easy Integration:** Seamlessly integrate with popular LLM frameworks like HuggingFace Transformers and TensorRT-LLM.

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 mit-han-lab/streaming-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. Hugging Face Transformers · recommended 2×
  3. FlashAttention-2 · recommended 1×
  4. FlashAttention · recommended 1×
  5. xformers · recommended 1×
  • CATEGORY QUERY
    How to efficiently handle very long input sequences for large language models?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2
    2. FlashAttention
    3. xformers
    4. PyTorch
    5. Hugging Face Transformers
    6. RoPE
    7. ALiBi
    8. Llama
    9. GPT-NeoX
    10. Falcon
    11. BLOOM
    12. MPT
    13. NTK-RoPE Scaling
    14. Linear RoPE Scaling
    15. Longformer
    16. BigBird
    17. Reformer
    18. PPLX-7B-Online
    19. GPT-4 Turbo
    20. Claude 2.1
    21. Pinecone
    22. Weaviate
    23. ChromaDB
    24. OpenAI's `text-embedding-ada-002`
    25. Hugging Face's `all-MiniLM-L6-v2`
    26. H-Transformer
    27. Transformer-XL
    28. XLNet

    AI recommended 28 alternatives but never named mit-han-lab/streaming-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for maintaining LLM performance with continuous, long-form dialogue?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Rasa
    5. SpaCy
    6. OpenAI API
    7. Hugging Face Transformers
    8. PyTorch
    9. TensorFlow
    10. Anthropic Claude API
    11. Google Gemini API

    AI recommended 11 alternatives but never named mit-han-lab/streaming-llm. 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 mit-han-lab/streaming-llm?
    pass
    AI did not name mit-han-lab/streaming-llm — likely talking about a different project

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

  • If a team adopts mit-han-lab/streaming-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mit-han-lab/streaming-llm 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 mit-han-lab/streaming-llm solve, and who is the primary audience?
    pass
    AI did not name mit-han-lab/streaming-llm — likely talking about a different project

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

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mit-han-lab/streaming-llm — 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