Provides coding agents with a mental map of codebases via progressive disclosure, enabling efficient exploration of project structure and entity relationships.
Generates comprehensive documentation (architecture overview, dependency graph, API surface, and README) for any codebase locally without external APIs.
Provides AI coding agents with durable architecture memory for repositories by generating structured project maps of responsibilities, relationships, and risks.
Provides intelligent summarization capabilities through a clean, extensible architecture. Mainly built for solving AI agents issues on big repositories, where large files can eat up the context window.
Intelligently analyzes codebases to enhance LLM prompts with relevant context, featuring adaptive context management and task detection to produce higher quality AI responses.
Automatically generates and maintains living documentation for codebases by creating hierarchical hivemind.md files and flowchart diagrams at every directory level, enabling AI navigation and real-time or retroactive documentation of code structure, requirements, and dependencies.