> ## Documentation Index > Fetch the complete documentation index at: https://docs.remem.online/llms.txt > Use this file to discover all available pages before exploring further. # Remem: Persistent Memory API for AI Agents – Docs > Remem gives your AI agents persistent user memory across every session. Store, retrieve, and rank memories with hybrid scoring in minutes. Remem is a memory API built for AI agents — it gives your product the ability to remember what users say, prefer, and do across every session. With a single `remember()` call your agent stores what it learns, and a single `context()` call retrieves the most useful memories at the start of each conversation. These docs cover everything from getting your first API key to integrating Remem into a production LangGraph agent. Store your first memory and retrieve it in under 5 minutes Get your API key and make your first authenticated request Full endpoint docs for every Remem memory operation Add persistent memory to any LangGraph agent in minutes ## Why Remem Most vector stores return the most *similar* memory — Remem returns the most *useful* one. Hybrid scoring weights every result by 70% semantic relevance, 20% recency, and 10% importance, so stale or trivial memories never crowd out what actually matters. Built-in duplicate detection keeps your memory store clean automatically, and the `score_detail` field on every result makes the ranking fully transparent — no black box. ## How It Works Sign up at [dev.remem.online](https://dev.remem.online) — your key arrives in your inbox within 60 seconds. It starts with `rm_live_`. ```bash theme={null} pip install remem-py ``` ```python theme={null} client.remember( "User prefers concise bullet points", user_id="user_123", agent_id="support_bot", importance=0.8, ) ``` ```python theme={null} context = client.context(user_id="user_123", agent_id="support_bot") # Inject context.memories into your system prompt ``` ## Explore the Docs Episodic, semantic, and summary — when to use each How Remem ranks memories by relevance, recency, and importance Handle errors gracefully so your agent never crashes in production