AI Agent Memory Optimisation Software

A fully managed memory layer for AI agents and applications that automatically extracts, stores, and retrieves persistent user and agent context across sessions, eliminating the need to manage vector stores, rerankers, or retrieval infrastructure.

Letta Agent is a self-improving AI agent whose memory, identity, and capabilities evolve with experience; it provides a stateful agent runtime that persists context across sessions, supports cross-device portability, and enables coding assistants, personal assistants, and AI coworkers embedded in applications.

Redis Agent Memory is a managed memory layer that gives AI agents intelligent short-term working memory per session and persistent long-term context across conversations, with automatic summarization, semantic retrieval, and configurable extraction policies.

An open-source AI agent memory platform that provides a memory-native API (remember, recall, forget, improve) built on knowledge graphs, vector search, and relational storage, enabling agents to retain and retrieve context across sessions with self-improving retrieval.

Engram is Weaviate's fully managed memory and context service for AI agents, providing persistent, structured memory across sessions via asynchronous pipelines that extract, deduplicate, reconcile, and retrieve memories using vector and hybrid search built on the Weaviate vector database.

Goodmem is an agentic AI memory infrastructure that stores, retrieves, and re-ranks memories using semantic vectors, enabling AI agents to maintain persistent context across sessions while cutting 10–40% of token spend by retrieving only relevant context rather than stuffing full conversation histories into prompts.