$ loading_
Build a project knowledge graph for code search, traversal, and Q&A.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "kg-memory-mcp" yet — see the docs or source repo.
Using the current project knowledge graph, analyze the upstream callers, downstream dependencies, and related config files for payment_service.py, and list them hierarchically.
A structured list of the module’s call chain, dependency nodes, and related configuration.
Retrieve ADR documents related to cache layer design and explain how they affect the implementation and interface constraints of cache_manager.py.
A summary of relevant ADRs with clear links between design decisions and code implementation.
What is the configuration loading flow in this project? Start from the entry file and explain the Python modules, environment variables, and config files involved.
A knowledge-graph-based flow explanation including key files, node relationships, and brief notes.
Turn unstructured text into a searchable knowledge graph memory system.
Build and query persistent knowledge graphs so coding agents remember across sessions.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Build a semantic graph from project files for search, knowledge, and task management.
Give AI coding agents graph memory across sessions for knowledge retrieval.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.