REPOGEO REPORT · LITE
microsoft/LLMLingua
Default branch main · commit e0e9d99b · scanned 6/27/2026, 1:31:35 AM
GitHub: 6,360 stars · 392 forks
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.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
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 microsoft/LLMLingua, 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.
- hightopics#1Add specific topics to improve categorization
Why:
CURRENT(none)
COPY-PASTE FIXllm-inference, prompt-compression, kv-cache, large-language-models, llm-acceleration, token-reduction, nlp, deep-learning
- highreadme#2Add a concise, keyword-rich introductory sentence to the README
Why:
CURRENTThe README currently jumps from the main title/links to news and then detailed sections.
COPY-PASTE FIXAdd this sentence immediately after the main title/links section: "LLMLingua is a cutting-edge framework designed to significantly speed up LLM inference and improve key information perception by compressing both prompts and KV-Cache, achieving up to 20x compression with minimal performance loss."
- mediumreadme#3Add a 'Why LLMLingua?' section to differentiate from general LLM tools
Why:
COPY-PASTE FIX## Why LLMLingua? While many tools focus on general LLM orchestration or tokenization, LLMLingua stands out by offering a unique, fine-grained approach to *prompt and KV-cache compression*. Unlike simple tokenizers (e.g., `tiktoken`) that count tokens, or frameworks (e.g., `LangChain`, `LlamaIndex`) that manage LLM interactions, LLMLingua actively reduces the *number of tokens* sent to the LLM and stored in KV-cache, preserving critical information to accelerate inference and reduce costs significantly.
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.
- openai/tiktoken · recommended 1×
- langchain-ai/langchain · recommended 1×
- run-llama/llama_index · recommended 1×
- huggingface/transformers · recommended 1×
- BerriAI/litellm · recommended 1×
- CATEGORY QUERYHow can I reduce the token count of prompts to accelerate large language model inference?you: not recommendedAI recommended (in order):
- tiktoken (openai/tiktoken)
- LangChain (langchain-ai/langchain)
- LlamaIndex (run-llama/llama_index)
- transformers (huggingface/transformers)
- LiteLLM (BerriAI/litellm)
- Guidance (microsoft/guidance)
AI recommended 6 alternatives but never named microsoft/LLMLingua. This is the gap to close.
Show full AI answer
- CATEGORY QUERYSeeking a library to compress KV-cache for faster LLM inference with long contexts.you: not recommendedAI recommended (in order):
- H2O (Heavy Hitter Oracle)
- StreamingLLM
- LLM-int8()
- bitsandbytes
- SparseGPT
- SpQR
- LongLoRA
- FlashAttention-2
AI recommended 8 alternatives but never named microsoft/LLMLingua. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
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 microsoft/LLMLingua?passAI named microsoft/LLMLingua explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts microsoft/LLMLingua in production, what risks or prerequisites should they evaluate first?passAI named microsoft/LLMLingua 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 microsoft/LLMLingua solve, and who is the primary audience?passAI named microsoft/LLMLingua explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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Live preview[](https://repogeo.com/en/r/microsoft/LLMLingua)<a href="https://repogeo.com/en/r/microsoft/LLMLingua"><img src="https://repogeo.com/badge/microsoft/LLMLingua.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
microsoft/LLMLingua — 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