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    • F
      license
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      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by letting the model issue constrained symbolic commands that execute server-side against in-memory handles, so only compact result stubs—rather than raw text—travel through the conversation. It also provides server-side memo storage with expandable handles, letting agents retain and retrieve large context across long sessions at a fraction of the token cost.
      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
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by executing symbolic search, filter, and aggregate commands server-side and returning compact handle stubs instead of full result sets. It also lets agents stash long context as server-side memos and expand them on demand, cutting token usage across multi-turn sessions.
      4 npm
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables an LLM to analyze documents far larger than its context window by issuing symbolic search, filter, aggregation, and recursive sub-query commands that execute server-side, returning compact handle stubs instead of full result sets. It also lets agents stash context server-side as expandable memos, cutting token usage across long sessions.
      4 npm
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables token-efficient analysis of documents far larger than an LLM's context window by executing type-checked symbolic S-expression commands over server-side, handle-backed results instead of raw text. It also lets agents stash context in server-side memos that can be expanded later, avoiding repeated roundtrips of large text blobs.
      4 npm
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables analysis of documents far larger than an LLM's context window by having the model emit constrained symbolic commands that a logic engine parses, type-checks, and executes over the full text, returning compact handle stubs instead of bulk data. Also lets agents stash context server-side as memos and expand them on demand, cutting token usage across long sessions.
      4 npm
      MIT

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