Research Context Management With Incremental Knowledge Accumulation

via “incremental indexing and graph update with change detection”

A modular graph-based Retrieval-Augmented Generation (RAG) system

Unique: Implements change detection at the document level with selective re-extraction and graph merging, avoiding full re-indexing while maintaining graph consistency. Preserves entity IDs across updates, enabling stable references and reducing community reassignments.

vs others: More efficient than full re-indexing for large corpora with frequent updates, and more sophisticated than naive append-only approaches that don't handle entity deduplication or community optimization.

via “incremental codebase indexing and context updates for real-time pattern learning”

Code faster with whole-line & full-function code completions.

An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations. [#opensource](https://github.com/stanford-oval/storm/)