RRepoGEO

REPOGEO REPORT · LITE

abacusai/Long-Context

Default branch main · commit c2a90d1f · scanned 6/16/2026, 9:38:24 AM

GitHub: 604 stars · 46 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 abacusai/Long-Context, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, context-window, context-extension, rope, positional-encoding, llm-evaluation, deep-learning-research, abacusai
  • highreadme#2
    Refine README opening to clarify project type

    Why:

    CURRENT
    The choice of how to encode positional information for transformers has been one of the key components of LLM architectures. An area that has been interesting to us and others in the community recently is whether LLMs can be extended to longer contexts.
    COPY-PASTE FIX
    This repository presents our research, code, and evaluation tooling for extending the context length capabilities of large language models (LLMs). We share experimental results, training scripts, and model weights from our work on adapting Llama models for significantly longer context windows, addressing a key challenge in LLM architectures.
  • mediumabout#3
    Refine About description to emphasize 'research repository'

    Why:

    CURRENT
    This repository contains code and tooling for the Abacus.AI LLM Context Expansion project. Also included are evaluation scripts and benchmark tasks that evaluate a model’s information retrieval capabilities with context expansion. We also include key experimental results and instructions for reproducing and building on them.
    COPY-PASTE FIX
    This open-source research repository from Abacus.AI provides code, tooling, and experimental results for LLM context expansion, including evaluation scripts, benchmark tasks, and instructions for reproducing and building on our findings.

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 abacusai/Long-Context
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Gemini 1.5 Pro
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Gemini 1.5 Pro · recommended 2×
  2. Llama 2 · recommended 2×
  3. Pinecone · recommended 2×
  4. langchain-ai/langchain · recommended 2×
  5. run-llama/llama_index · recommended 2×
  • CATEGORY QUERY
    How can I extend the context window of large language models for longer inputs?
    you: not recommended
    AI recommended (in order):
    1. Anthropic Claude
    2. Google Gemini 1.5 Pro
    3. OpenAI GPT-4 Turbo
    4. LongRoPE
    5. Llama 2
    6. Perplexity AI
    7. pplx-70b-online
    8. pplx-8x7b-online
    9. Pinecone
    10. Weaviate
    11. Milvus
    12. OpenAI's `text-embedding-ada-002`
    13. Hugging Face's `sentence-transformers`
    14. GPT-3.5 Turbo
    15. Llama 2
    16. ALiBi
    17. YaRN
    18. Mistral

    AI recommended 18 alternatives but never named abacusai/Long-Context. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What techniques improve LLM performance when processing very long documents or conversations?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. Weaviate (weaviate/weaviate)
    5. Pinecone
    6. OpenAI API (GPT-3.5 Turbo, GPT-4)
    7. Anthropic Claude (Claude 3 Opus/Sonnet/Haiku)
    8. Hugging Face Transformers library (huggingface/transformers)
    9. Anthropic Claude 3 (Opus, Sonnet, Haiku)
    10. Google Gemini 1.5 Pro
    11. Mistral Large
    12. Hugging Face Transformers library (huggingface/transformers)
    13. OpenAI Fine-tuning API
    14. Google Cloud Vertex AI
    15. LangChain (langchain-ai/langchain)
    16. LlamaIndex (run-llama/llama_index)

    AI recommended 16 alternatives but never named abacusai/Long-Context. 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 abacusai/Long-Context?
    pass
    AI named abacusai/Long-Context explicitly

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

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

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

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abacusai/Long-Context — 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