Token-optimized semantic code search with automatic context expansion for AI coding assistants, enabling efficient discovery of code relationships and reducing token usage.
Adds semantic code search to AI coding agents, enabling natural language queries across entire codebases to retrieve relevant code chunks, saving tokens and providing deep context.
Enables AI assistants to search through structured databases and unstructured content (documents, videos, files) using natural language queries with semantic understanding.
Transforms codebases into a knowledge graph for AI agents, enabling semantic search, impact analysis, and persistent session memory with up to 94% token savings.
Provides context-aware skill selection for AI agents, reducing token usage by 85-98% and improving accuracy through semantic retrieval, session memory, and feedback learning.