The best memory & context (managed) for AI agents

Managed persistent memory service for agents built on Durable Objects + Vectorize, handling extraction, storage, search, and summarization. Best for agent builders.

Cloud-native context layer that ingests documents, audio, video, and web data into semantic memory retrievable by agents via one API. Best for agent builders.

Open-source SDK from LangChain that gives LangGraph agents long-term memory (extraction, search, prompt optimization) in your own store. Best for agent builders.

Fully-managed API for stateful agents that manage their own memory like an OS (context = RAM, archival = disk) and self-improve over time. Best for agent builders.

Universal memory layer that adds persistent, personalized long-term memory to AI agents and apps in a few lines of code. Best for agent builders.

User-profile-based long-term memory that batches conversations into structured profiles to personalize LLM apps cheaply and fast. Best for agent builders.

Managed knowledge/context layer on Pinecone that ingests files and serves grounded chat + context retrieval for production AI apps. Best for agent builders.

Redis-based dual-tier memory (working + long-term semantic) server and managed context engine for agents, with LangCache semantic caching. Best for agent builders.

Hosted universal memory API that stores, indexes, and retrieves long-term context, RAG, and user profiles for AI apps with one API. Best for agent builders.

Google Cloud's managed long-term memory for agents (Agent Engine) that uses Gemini to extract facts and preferences scoped per user. Best for agent builders.

Temporal knowledge-graph memory for agents that tracks what was true and when, engineered from chat history and business data. Best for agent builders.

Open-source AI memory platform that builds a knowledge-graph-plus-vector memory for agents via a self-improving cognify pipeline. Best for agent builders.