$ loading_
Enable offline graph-based memory and precise fact retrieval for edge-device agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Seahorse RAG MCP" yet — see the docs or source repo.
Use Seahorse RAG MCP to design a local memory setup for my edge-device agent: ingest meeting notes, manuals, and troubleshooting logs into graph-backed retrieval, then explain how to link facts by topic and query history offline.
A local memory architecture with data ingestion, fact-linking strategy, and an offline retrieval workflow.
I have multiple scattered project documents. Using Seahorse RAG MCP, design retrieval logic so an agent can connect related facts across passages and return high-precision answers with source traces first.
A retrieval strategy describing graph construction, cross-document fact linking, and improved answer accuracy and traceability.
Explain how to deploy Seahorse RAG MCP on a resource-constrained local device, supporting offline operation, persistent memory, and a query interface for multiple agents.
An edge-device deployment plan covering runtime requirements, storage approach, and multi-agent access recommendations.
Store and search AI conversation history for cross-session recall of reasoning.
Give AI assistants local-first graph memory with semantic search tools.
Provide encrypted persistent memory and cross-platform knowledge graph context for AI tools.
Give AI assistants persistent memory, adaptive recall, and graph-based knowledge retrieval.
Turn unstructured documents into a searchable knowledge base for AI agents.
Give AI expiring memory and semantic search for documents and context.