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    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables token-efficient analysis of very large documents by letting an LLM issue constrained symbolic commands while results and context are stored server-side as compact handles and memos. This avoids loading full documents or large intermediate outputs into the model context.
      4 npm
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables token-efficient analysis of documents far larger than an LLM context window by executing validated symbolic commands server-side and returning compact handles or memos instead of full text.
      4 npm
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables token-efficient analysis of very large documents by executing constrained symbolic REPL commands against server-side handles, memory pads, and recursive queries rather than passing entire documents through the model context.
      4 npm
      MIT
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables MCP clients to analyze documents far larger than their context window by having the model emit validated symbolic S-expression commands that are parsed and executed server-side against the loaded content. In-memory handles, recursive sub-queries, and server-side memos keep token usage low while preserving full data for on-demand expansion.
      4 npm
      -
    • F
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window without vector databases or chunking, by having the model emit constrained symbolic S-expression commands that a logic engine parses, validates, and executes over the source text. Handle-based in-memory storage and server-side memo pads keep results and stashed context as compact stubs, cutting token usage dramatically while supporting recursive sub-queries over chunks.
      4 npm
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by letting the model emit validated symbolic commands that execute server-side, returning compact handles rather than full result sets. It also lets agents stash context as server-side memos that can be expanded later, cutting token usage across long sessions without vector databases or chunking heuristics.
      4 npm
      MIT