RAG Memory
RAG Memory MCP Server provides AI assistants with a knowledge graph-enhanced retrieval system that combines vector search with graph-based relationships. Developed by ttommyth, it offers tools for document management (storing, chunking, embedding), knowledge graph operations (creating entities and relationships), and hybrid search capabilities that leverage both semantic similarity and structural connections. The implementation uses SQLite with vector extensions for efficient storage and retrieval, making it particularly valuable for applications requiring persistent memory, contextual information retrieval, and the ability to build complex knowledge representations that evolve over time.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/ttommyth/rag-memory-mcp
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve RAG Memory from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.