At its core, Memary provides a modular memory management system tailored for large language model agents. By abstracting memory interactions through a common API, it supports multiple storage backends, including in-memory dictionaries, Redis for distributed caching, and vector stores like Pinecone or FAISS for semantic search. Users define schema-based memories (episodic, semantic, or long-term) and leverage embedding models to populate vector stores automatically. Retrieval functions allow contextually relevant memory recall during conversations, enhancing agent responses with past interactions or domain-specific data. Designed for extensibility, Memary can integrate custom memory backends and embedding functions, making it ideal for developing robust, stateful AI applications such as virtual assistants, customer service bots, and research tools requiring persistent knowledge over time.

Backmesh is a thoroughly tested Backend as a Service (BaaS) that offers an LLM API Gatekeeper, allowing your app to securely call LLM APIs. Using JWT authentication, configurable rate limits, and API resource access control, Backmesh ensures that only authorized users have access while preventing API abuse. Additionally, it provides LLM user analytics without extra packages, enabling identification of usage patterns, cost reduction, and improvements in user satisfaction.