THE MEMORY
LAYER
Persistent long-term memory for autonomous agents. A decentralized, relational layer with sub-10ms graph retrieval and sovereign identity.
Designed for Next-Gen Workloads
From personal knowledge assistants to multi-agent swarms, memro.co handles the state so you can focus on the reasoning.
Personalized Research Assistant
Agents that link disparate research papers, meeting notes, and internal docs into a cohesive, searchable knowledge graph. No more hallucinating facts from out-of-session data.
Autonomous Agent Swarms
Enable collaborative intelligence. Shared relational memory allows multiple agents to update a central knowledge hub, resolving dependencies in parallel.
[GRAPH-RAG PERFORMANCE]
Query: "Analyze relationships between Q1 projects and H2 goals"
Pure Vector Search:
- Latency: 42ms
- Context Precision: 68%
- Relational Depth: 1 (Flat)
Memro Hybrid Engine:
- Latency: 8.4ms
- Context Precision: 94%
- Relational Depth: 5 (Nested)
[VERDICT] 11.2x improvement in relational accuracy.
Relation-Aware Embeddings
We've pioneered hierarchy preservation in latent space. Our engine doesn't just store vectors; it preserves the structural relationships between memory fragments.
- check_circle Hybrid Qdrant + Neo4j Indexing
- check_circle Dynamic Subject-Object Linking
- check_circle Infinite Recursive Spreading Activation
Transparent SaaS Infrastructure
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Invisible Authentication
Identity resolution via Bearer tokens on SSE headers. No manual initialization required for AI agents.
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Hybrid Graph-Vector Engine
Memro combines Postgres metadata, Qdrant embeddings, and Neo4j relations for absolute context precision.
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Multi-Tenant Isolation
Enterprise-grade data segmentation with sub-millisecond latency for concurrent agent sessions.
[INIT] Cluster nodes active...
// Recall sequence waiting...