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token-compressor

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    • A
      license
      Not graded
      quality
      A
      maintenance
      Shrinks logs, JSON, docs and web pages before your AI reads them: offline, deterministic, no API key. Folds repetitive log lines (12,037 to 127 tokens on a 400-line log) while code, URLs and quoted strings come back byte-for-byte. Also fetches URLs as clean text, counts tokens exactly, and has optional local Ollama tools for image and text compression.
      289 PyPI
      25
      Academic Free v1.1
    • A
      license
      Not graded
      quality
      D
      maintenance
      Token compression for AI contexts, reducing token consumption by compressing conversation exchanges before they enter the LLM context window.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides production-grade context compression for LLM agent conversations with Q-protective ranking, epistemic markers, and semantic store, reducing token usage while preserving equivalence.
      3
      Business Source 1.1
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables local, offline prompt compression, smart asking when needed, and routing to the cheapest capable model to save tokens and costs for terminal AI agents.
      16 npm
      1
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to deterministically shrink multi-turn conversation history and prompts by stripping verbose boilerplate and filler tokens while preserving critical tool parameters and named entities. This lets users cut inference costs by roughly 40-60% without losing essential context, running either as a native MCP server over JSON-RPC stdio or as an importable zero-dependency Python module.
      7
      MIT
    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP-compatible clients and autonomous agents to dynamically compress long-horizon context by pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by 40-70%. It reduces token burn and latency through deterministic, zero-dependency processing.
      7
      -