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Give AI agents semantic memory with hybrid search and knowledge graph support.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.AdelElo13/neuromcp" MCP server from askskill: Run: claude mcp add 'io-github-adelelo13-neuromcp' -- npx -y neuromcp
Developers building persistent AI agents can use it to store and retrieve historical knowledge, reducing context loss. It fits agents that need to remember information across sessions.
Research or product teams can use hybrid search to find relevant content in stored memories and improve use of past material in answers. It suits tasks needing both semantic retrieval and structured knowledge.
When information includes relationships between objects rather than just text snippets, its knowledge graph capability can organize those connections. This helps agents perform relational queries and knowledge consolidation.
It is a semantic memory tool for AI agents with hybrid search, knowledge graph, and consolidation capabilities. It is suitable for agent systems that need long-term memory and retrieval support.
Based on the description, it offers more than semantic memory alone by emphasizing hybrid search, a knowledge graph, and consolidation. In other words, it is positioned as a broader knowledge layer for agents, not just a single retrieval method.
The provided material does not include installation steps, runtime details, or key requirements. Please see the source repository for specifics.
Let AI agents store, search, and recall memories that improve over time.
Give AI agents persistent semantic memory with search, decay, and deduplication.
Build a self-evolving memory graph for coding agents with semantic search.
Give AI assistants local-first graph memory with semantic search tools.
Provide persistent, explainable, MCP-native memory for AI agents.
Give AI agents local persistent memory with hybrid search and decay management.