Supermemory is a memory and context API for AI agents that helps apps store, retrieve, and use long-term user or task context.

Yes. Supermemory has a free plan with about $5 per month of usage built in for builders, prototypes, and side projects.

Who should use Supermemory?

Supermemory is best for developers building AI agents, copilots, personal assistants, RAG systems, and apps that need persistent memory.

Does Supermemory support MCP?

Yes. Supermemory includes MCP support, plugins, connectors, RAG, memory graph features, and developer APIs.

How does Supermemory pricing work?

Supermemory uses subscription tiers with included usage and pay-as-you-go limits. Pro, Max, and Scale add more usage and support.

What are Supermemory alternatives?

Alternatives include Mem0, LangGraph memory patterns, vector databases, custom RAG systems, and other agent memory APIs.

Supermemory

Name: Supermemory
Brand: Supermemory

Memory API for AI agents, RAG, plugins, MCP, connectors, and user context.

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What is Supermemory?

TL;DR

Supermemory is a memory API and context infrastructure platform for AI agents and applications. It helps developers add persistent recall, user context, retrieval, connectors, MCP, plugins, RAG, and memory graphs so AI products can remember information across sessions.

Add and retrieve memory for AI agents through a developer API
Use connectors, MCP, plugins, RAG, memory graph, and personal app workflows
Build with TypeScript and Python SDKs and low-latency recall infrastructure

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About

Supermemory is a memory API and context infrastructure platform for AI agents, helping developers add persistent recall, user context, memory retrieval, connectors, MCP, plugins, RAG, and memory graph workflows. It is built for teams that want long-term context without rebuilding memory infrastructure from scratch. For FutureStack users, Supermemory is valuable because agent memory is becoming a core product layer for copilots, assistants, second-brain apps, and personalized AI products. Developers can use it when an AI system needs to remember user preferences, prior documents, connected sources, or long-running task context. The platform supports developer APIs, TypeScript and Python SDKs, connectors, MCP, plugins, managed or self-hosted deployment, dedicated infrastructure, air-gapped options, compliance controls, RAG, memory graph workflows, personal apps, and enterprise deployment options. Supermemory can save engineering time compared with building memory infrastructure manually, but teams still need thoughtful privacy, permissions, deletion, retrieval quality, and user-control design. It is strongest when memory is a product feature, not just a temporary chat history.

Use Cases

Persistent Agent Memory

Give AI agents durable memory for user preferences, prior interactions, documents, and long-running tasks across sessions.

Personal AI Apps

Build copilots and personal assistants that can retrieve relevant user context instead of relying only on short chat histories.

Memory Infrastructure

Use APIs, SDKs, connectors, and retrieval infrastructure instead of building a complete persistent-memory layer from scratch.

Enterprise AI Context

Support governed AI applications with connected data, retrieval, memory workflows, and deployment options suited to larger teams.

Key Features

Add and retrieve memory for AI agents through a developer API
Use connectors, MCP, plugins, RAG, memory graph, and personal app workflows
Build with TypeScript and Python SDKs and low-latency recall infrastructure
Deploy with managed, self-hosted, dedicated, or air-gapped options

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