Supermemory

What is Supermemory?

Supermemory builds infrastructure for memory and continuous learning in AI agents. It is a layer separate from model providers, so any AI system can learn and improve regardless of which model or interface it uses.

Key capabilities of Supermemory

• learner-1 model: Extracts context from users, tasks and tenants into a vector-graph database
• Context injection: Returns relevant learned information into agent context in real time
• Time-aware memory: Builds interconnected knowledge that grows with use
• Multi-model support: Works across ChatGPT, Claude, Cursor, Hermes and MCP integrations
• Flexible deployment: Cloud API, self-hosted in a VPC or data center, local, or air-gapped
• Compliance: SOC 2 certified, HIPAA compliant and GDPR aligned

How Supermemory works

Your app or agent sends content and conversations to Supermemory. The learner-1 model extracts context about users, tasks and tenants and stores it in a vector-graph database, and at query time Supermemory returns the relevant tokens to inject into the agent's context. The vendor reports 187ms server search time and over 1 trillion tokens processed monthly.

Who uses Supermemory?

Supermemory is used by developers building agents and AI apps that need persistent memory, and the vendor says it serves 100k+ organizations. Enterprises can use custom contracts and data processing agreements.

Supermemory pricing

Per published pricing coverage, the Free plan is $0 with 1M tokens processed and 10K search queries, Pro is $19 per month with 3M tokens and 100K queries, Max is $100 per month, and Scale is $399 per month with 80M tokens and 20M queries. Enterprise is custom. Overage is $0.01 per 1,000 tokens and $0.10 per 1,000 queries on paid plans.

Supermemory alternatives

Alternatives include Cognee and Graphlit for graph-based memory and data pipelines, Julep for agent workflows, and Hopsworks and ZenML for ML infrastructure. Supermemory focuses on a model-agnostic memory layer.