$ loading_
Give AI coding agents persistent local project memory and semantic search.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MemoryStar" yet — see the docs or source repo.
Search MemoryStar for the project's architecture notes, core module relationships, and recent key design decisions, then summarize them in bullet points.
A concise summary of the architecture, module dependencies, and key design decisions.
I’m about to change the user authentication flow. Use MemoryStar to find related files, functions, classes, and recent changes, and tell me where to start reviewing.
A list of relevant files and symbols, change history clues, and recommended code areas to review first.
Based on MemoryStar's progress tracking, knowledge graph, and git history, tell me the current status of this feature, what remains unfinished, and the recommended next steps.
The current progress, open tasks, linked context, and actionable next-step recommendations.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Give AI coding agents long-term memory, search, and organization across sessions.
Give AI coding assistants persistent memory and full-text knowledge retrieval.
Give AI coding assistants persistent, conflict-aware memory across projects and sessions.
Build and query persistent knowledge graphs so coding agents remember across sessions.
Build a project knowledge graph for code search, traversal, and Q&A.