HyperMemory

Classical RAG retrieves documents based on semantic similarity, but similarity is not memory. It finds content that looks related, not content that is related. There is no structure, no relationships, no understanding of how pieces of knowledge connect. HyperMemory is a new generation of persistent memory layer. Built on hypergraph technology, it stores memories as nodes with explicit multi-way relationships, enabling agents to traverse knowledge the way humans do: through association, context, and meaning.

• Hypergraph-native, Multi-way relationships that capture complex contexts. Unlike traditional graphs with pairwise edges, hyperedges can connect any number of nodes simultaneously, representing meetings with multiple participants, projects with multiple stakeholders, or concepts that only make sense together.
• Natural language recall, Query memories using plain English. Ask your agent to remember what you discussed about a project last month, and it retrieves the relevant context without you needing to know where it was stored.
• MCP-compatible, Works with Claude, Cursor, OpenAI agents, CrewAI, and any MCP-compatible tool. Connect via HTTP/SSE to api.hypermemory.io and start storing memories immediately.
• Knowledge sharing, Export and import subgraphs between agents. Share relevant portions of one agent's knowledge with another, enabling team-based AI workflows with shared context.

HyperMemory runs as a remote MCP server. Your agent connects over HTTP/SSE, authenticates with an API key, and gets access to memory tools: memory_store, memory_recall, memory_find_related, memory_update, memory_forget, and more. Memories are stored as nodes in a hypergraph with explicit relationships, not just embeddings. When your agent needs to recall something, it traverses the graph through meaning, not similarity scores.

• Personal AI assistants that remember your projects, preferences, and people across sessions
• Multi-agent systems where agents need shared knowledge and context
• Development workflows where your coding agent remembers your codebase architecture
• Research assistants that build structured knowledge graphs from your reading

Developers building AI agents that need to remember across sessions, teams running multi-agent systems with shared knowledge needs, and anyone using MCP-compatible tools.