open-compress/claw-compactor

• highreadme#1

  Reposition the README H1 to explicitly state LLM token compression

  Why:

  CURRENT

  # Claw Compactor
  
  ### 14-Stage Fusion Pipeline for LLM Token Compression

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  # Claw Compactor: 14-Stage Fusion Pipeline for LLM Token Compression

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• mediumabout#2

  Enhance the repository's 'about' description for clarity

  Why:

  CURRENT

  14-stage Fusion Pipeline for LLM token compression — reversible compression, AST-aware code analysis, intelligent content routing. Zero LLM inference cost. MIT licensed.

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  Claw Compactor is a 14-stage Fusion Pipeline for LLM token compression, designed to drastically reduce LLM inference costs and optimize context windows. It features reversible compression, AST-aware code analysis, and intelligent content routing. MIT licensed.

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• lowreadme#3

  Add a 'How Claw Compactor Compares' section to the README

  Why:

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  ## How Claw Compactor Compares
  
  Claw Compactor is an LLM token compression engine, not an LLM itself (like GPT-3.5 or Llama 2) or an embedding/reranking API (like Sentence-transformers or Cohere Rerank). Instead, Claw Compactor works *with* any LLM to reduce input token count, lower inference costs, and expand effective context windows, making your existing LLM infrastructure more efficient.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I reduce LLM inference costs by compressing input tokens?

  you: not recommended
  AI recommended (in order):

  1. gpt-3.5-turbo
  2. Llama 2 7B
  3. Hugging Face sentence-transformers library (sbert-org/sentence-transformers)
  4. Cohere Rerank API
  5. Anthropic Claude 3 Opus/Sonnet/Haiku
  6. Google Gemini 1.5 Pro
  7. OpenAI GPT-4 Turbo
  8. Hugging Face transformers library (huggingface/transformers)

  AI recommended 8 alternatives but never named open-compress/claw-compactor. This is the gap to close.

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• CATEGORY QUERY

  Looking for a Python library to optimize LLM context windows with AST-aware compression.

  you: not recommended
  AI recommended (in order):

  1. Guidance (microsoft/guidance)
  2. Tree-sitter (tree-sitter/tree-sitter)
  3. ast module
  4. libcst (Instagram/LibCST)
  5. LangChain (langchain-ai/langchain)

  AI recommended 5 alternatives but never named open-compress/claw-compactor. This is the gap to close.

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• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of open-compress/claw-compactor?

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  ✓AI named open-compress/claw-compactor explicitly

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

• If a team adopts open-compress/claw-compactor in production, what risks or prerequisites should they evaluate first?

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  ✓AI named open-compress/claw-compactor 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 open-compress/claw-compactor solve, and who is the primary audience?

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  ✓AI named open-compress/claw-compactor explicitly

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