Welcome to Anona Memory
Managed, multi-tenant memory layer for AI agents - a developer API that lets any LLM app remember, recall, and reflect on user context across sessions.What is Anona Memory?
Large language models are stateless. Every conversation starts from scratch unless the application explicitly retrieves and injects context. Building a reliable, scalable memory layer requires operating vector databases, building retrieval pipelines, managing embedding models, and handling multi-tenancy - substantial infrastructure work that is not core to most AI products. Anona Memory abstracts this entirely. With a single API call, you can:- Retain - Store memories with full semantic indexing
- Recall - Query memories semantically to find the most relevant context
- Reflect - Synthesize learned models and insights from accumulated knowledge
Who Uses Anona Memory?
- AI product developers building user-facing agents that need to remember context across sessions
- Platform teams deploying internal AI assistants that must accumulate organizational knowledge
- ML engineers who want a production-grade memory store without operating vector databases directly
Key Features
✅ Semantic search - Find relevant memories by meaning, not keywords✅ Multi-tenant isolation - Each organization’s data lives in its own secure schema
✅ Managed embeddings - We handle all embedding generation and vector indexing
✅ Usage metering - Automatic quota enforcement and billing integration
✅ Simple REST API - No infrastructure to operate, no vector database to manage
How It Works
Your app talks to one REST API. Behind that single endpoint, Anona handles authentication, tenant isolation, embedding generation, semantic indexing, and caching automatically - you never touch or configure any of it.Next Steps
Quick Start
Get Anona Memory running in 5 minutes
Concepts
Learn key terminology and ideas
API Reference
Full REST API documentation
Python SDK
Client library reference