Provides intelligent code context and analysis through semantic compression, AST parsing, and multi-language support. Offers 60-80% token reduction while enabling AI assistants to understand codebases through local analysis, OpenAI-enhanced insights, and GitHub repository integration.
Provides structured, token-efficient code context for AI assistants by indexing codebases with AST-level analysis, supporting 17+ languages, enabling semantic search, call-site tracking, and PR attribution.
Transforms codebases into a knowledge graph for AI agents, enabling semantic search, impact analysis, and persistent session memory with up to 94% token savings.
Turn any codebase into an AI-readable neural map — with proof. Every claim linked to code anchors (line + SHA-256 hash), every context window optimized with greedy token budgeting, every session protected by drift detection. Tree-sitter indexing across 11 languages, cross-session learning, AI enrichment, and 28 MCP tools. Zero config — just connect and your AI agent remembers everything.
Make any LLM a codebase expert instantly. Provides deep code intelligence through semantic search, architecture mapping, security analysis, and smart context that fits perfectly in token windows.
Provides AI coding agents with persistent architectural memory of codebases, enabling impact analysis, test generation, and code generation with reduced token usage.
