Enables working with large documents of any size by intelligently segmenting them and using TF-IDF search to retrieve only relevant fragments, preventing context window saturation. Provides 31 domain-agnostic tools for document ingestion, semantic analysis, epistemological validation, and extraction verification across formats like PDF, EPUB, and HTML.
Enables file-based knowledge management with ranked keyword and semantic hybrid search, allowing AI agents to learn from documents and recall relevant knowledge as a persistent memory tool.
An implementation of the Recursive Language Models architecture that enables AI agents to process massive documents by programmatically decomposing them into sub-queries. It allows for cost-effective and accurate reasoning across millions of tokens by treating long-form data as an external environment for root and worker models.
Enables AI agents with long-term memory and retrieval-augmented generation (RAG) capabilities, allowing them to recall past conversations, search local files, and learn user preferences.
Provides recursive language model capabilities to AI assistants, enabling efficient exploration of large contexts through iterative Python code execution.
Provides AI agents with multi-format document indexing, hybrid dense and sparse search with reranking, and relational SQL querying over extracted tables.
