Long-term memory for any LLM

Give your AI amemory that lasts.

iMemory is a context layer for LLM applications. Store, organize, and retrieve the right facts at the right time — across sessions, projects, and models.

Start free See how it works

No credit card required · Works with OpenAI, Anthropic, Gemini

Plays nicely with the models you already use

OpenAIAnthropicGeminiOpen source

The problem

LLMs forget. Context windows fill up. Your app pays the price.

Every prompt is a fresh start. You pad the context with history, hit token limits, watch quality drop, and burn money on tokens the model doesn't need to see.

Bloated prompts

Stuffing chat history into every request wastes tokens and slows responses.

Lost knowledge

Users repeat themselves across sessions. The model never learns who they are.

Fragmented context

Notes, docs, and decisions live in five different tools — none of which the LLM can see.

Features

A complete context layer for your LLM stack

Everything you need to capture, retrieve, and govern the knowledge that powers your AI features.

Persistent memory

Store facts, preferences, and history. Retrieved automatically on every call.

Semantic retrieval

Hybrid vector + keyword search surfaces the most relevant context, not all of it.

Scoped namespaces

Organize memory by user, project, or agent. No bleed between tenants.

Tool-ready

Drop-in MCP server and SDKs for TypeScript, Python, and HTTP.

Private by default

Row-level security, encryption at rest, and full audit trail.

Sub-100ms reads

Edge-deployed retrieval keeps your agent loop snappy.

How it works

Three calls. Smarter agents.

Write

Push facts, messages, or documents into iMemory with a single SDK call. We chunk, embed, and index automatically.

Retrieve

Query by user, topic, or natural language. We return the smallest set of tokens your model needs to answer well.

Inject

Splice retrieved context into your prompt or use our middleware. Works with chat, tools, and agent loops.

agent.ts

import { iMemory } from "imemory";

const memory = new iMemory({ namespace: "user_42" });

// Recall what matters
const context = await memory.recall("project deadline preferences");

// Inject into your LLM call
const reply = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [
    { role: "system", content: context },
    { role: "user", content: userMessage },
  ],
});

// Remember what's new
await memory.remember(reply.choices[0].message.content);

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