Getting Started
Introduction
What is HyperMemory and who is it for
Introduction
HyperMemory is a long-term memory service for AI agents, built on hypergraph technology and exposed as an MCP server.
What makes HyperMemory different
- Not a vector database — Memories have explicit relationships, not just embeddings
- Not a chat history store — A structured knowledge graph your agent navigates
- Hypergraph-native — Multi-way relationships that capture complex contexts
Who is HyperMemory for
- Developers building AI agents that need to remember across sessions
- Teams running multi-agent systems with shared knowledge needs
- Anyone using MCP-compatible tools (Claude, OpenClaw, CrewAI, OpenAI)
Key capabilities
Store memories
Save nodes with metadata and relationships
Recall by language
Query memories using natural language
Traverse relationships
Navigate multi-way connections (hyperedges)
Share knowledge
Export/import subgraphs between agents
How it works
HyperMemory runs as a remote MCP server at api.hypermemory.io. Your agent connects over HTTP/SSE, authenticates with an API key, and gets access to memory tools.
Memories are stored as nodes in a hypergraph. Relationships between memories are stored as edges and hyperedges. Your agent reads and writes to this graph using natural MCP tool calls.
Your Agent ─────► HyperMemory MCP Server ─────► Hypergraph Database
│ │
│ memory_store() │ Nodes
│ memory_recall() │ Edges
│ memory_find_related() │ Hyperedges
│ │
Available memory tools
| Tool | Description |
|---|---|
memory_store | Store a new memory node |
memory_recall | Query memories by natural language |
memory_find_related | Find nodes related to a given node |
memory_get_relationships | Get all edges/hyperedges for a node |
memory_update | Update an existing node |
memory_forget | Delete a node |
memory_export_subgraph | Export a portion of the graph |
memory_load_link | Import a subgraph |
Ready to start?
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