Magneteco

Persistent Memory Infrastructure for AI Agents

Magneteco is a domain-aware memory service that gives AI agents the ability to remember, learn, and build knowledge across conversations and events. Unlike simple RAG implementations, Magneteco treats memory as infrastructure—with knowledge graphs, conflict resolution, temporal decay, and domain-specific ontologies.

Why Magneteco?

Most "memory" solutions for AI agents are just vector databases with retrieval. They fail in production because:

• Embeddings find similarity, not truth — "I love my job" and "I hate my job" embed similarly
• No temporal awareness — Old information drowns current context
• No relationship tracking — Can't answer "What companies has this user worked with?"
• No conflict resolution — Contradictory facts coexist without resolution
• One-size-fits-all — No understanding of domain-specific entities and relationships

Magneteco solves these problems with:

• Hybrid storage: Vector search + Knowledge graph
• Domain ontologies: Teach the system what matters for your app
• Event ingestion: Memory from conversations AND system events
• Temporal decay: Recent information weighted appropriately
• Conflict resolution: New facts supersede old, with audit trail
• Multi-tenant: One service, multiple apps, isolated data

Quick Links

How It Works

Magneteco is a fully-hosted service. You install our SDK, get an API key, and start using memory in your AI agents. We handle all the infrastructure—databases, vector search, knowledge graphs, and async processing.

What you do:

• Install @magneteco/client
• Get an API key from us
• Call memorize() and retrieve()

What we run:

• PostgreSQL + pgvector for facts and embeddings
• Neo4j for knowledge graphs
• Async processing pipelines
• LLM extraction and summarization