See your memories from all angles
Memory infrastructure your agents can rely on
Seven capabilities that turn scattered context into something you can record, inspect, share, and trust—across every tool in your stack.
Record and recall every agent interaction—four ways
HyperMemory keeps a durable history of what your agents store and read, so important context is not trapped in a single chat thread. When it is time to remember, the same underlying memories can be worked as a graph, semantic search, domain-scoped collections, or a timeline—whichever lens matches the question.
Hypergraph hyperedges let one relationship include many nodes at once—so multi-party facts stay together instead of being split into fragile chains of pairwise links.
The four views
Graph
Navigate nodes and hyperedges—see how people, projects, and facts connect instead of scanning flat lists.
Semantics
Recall by meaning. Agents find the right memory even when the wording in the question does not match what was stored.
Domains
Scope memory by topic or workspace—product, research, customer, or anything you define—so agents stay in the right world.
Timeline
See what was true when. Compare how understanding changed and pin the answer to a point in time.
One memory layer for every agentic platform
HyperMemory is MCP-first. Point Claude, OpenClaw, CrewAI, OpenAI Agents, n8n—or your own client—at the same graphs. Facts learned in one tool show up in the next, with no vendor-locked scratchpads.
Works with
Safe, reliable, and always yours
We do not use customer memories to train public models, and we do not sell them. Strict access controls and encryption in transit round out a posture teams can explain to security and legal.
GDPR-aligned
Built for European privacy expectations. DPAs available when memory holds customer or regulated data.
Download your data
Export what you stored whenever you need it. Your memories are portable—not a roach motel.
Encrypted at rest
Data at rest is encrypted so a lost disk or misrouted backup is not a headline event.
Look into your memories, not only query them
Beyond one-shot recall APIs, you get multiple ways to inspect what is stored: skim structure, browse by theme, compare over time, and verify what an agent would see before it reaches production. Transparency beats treating memory as an opaque vector bucket.
- Align humans and agents on the same underlying truth.
- Catch stale or conflicting memories before they influence customer-facing runs.
Upload files. They become first-class memories.
Drop in documents and media and fold them into your graph. Files sit alongside live agent learnings so onboarding does not reset to zero every session—and agents can cite structured memory instead of re-ingesting raw files on every turn.
Supported file types
- DOC
- XLS
- PPT
- MD
- TXT
- RTF
- CSV
- PNG
- JPG
Multilingual, natively
Store and retrieve in the languages your team and customers actually use. Mixed-language projects, translated source material, and locale-specific nuance can live in one memory model without bolting on separate per-language silos.
Multiple graphs: knowledge bases, isolation, and sharing
Run separate memory graphs for products, clients, or environments. Use one graph like a curated knowledge base, keep production and playground memories apart, and grant read-only slices to specific agents or collaborators so they only see what you intend.
- Dedicated graphs per product line, tenant, or team.
- Read-only scopes for audits, partners, or narrow agent roles.
- Share structured context without handing over your entire memory estate.