Supermemory

Open source alternative to Mem0, Pinecone, Weaviate Cloud and Qdrant Cloud

31.3K stars
MIT
Active this week
Last commit 1 day ago

Last verified Oct 21, 2026

Supermemory is an open source memory API for AI applications that stores, indexes, and retrieves user-specific context so LLMs can deliver personalized responses across sessions. Apache-2.0 licensed.

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Read the full breakdownWho it is for, how it works, trade-offs and the FAQ

who it's for

Who Supermemory is for#

Developers adding memory to AI products

Supermemory can shorten the path from raw embeddings to user-facing memory behavior.

Skip if:

Skip if all your context already lives in one database with simple keyword search.

Power users keeping agent context across tools

Browser and agent integrations help users carry context between coding and research sessions.

Skip if:

Skip if you require a fully local-only memory stack and cannot use hosted components.

the problem

The problem it solves#

AI agents become less useful when they forget work between sessions or scatter context across chats, documents, browser tabs, and project tools. Developers often bolt a vector database onto an app, but retrieval quality, ingestion UX, and workspace boundaries still require product work.

Teams building coding agents, personal assistants, or knowledge workflows need memory that can collect context from real user activity and retrieve it when the agent needs it. The hard part is not only storage; it is making the memory operational in daily tools.

how Supermemory solves it

How it solves it#

AI memory API

Supermemory provides a developer-facing memory layer for storing and retrieving context for AI apps and agents.

Browser and app capture workflows

The product includes user-facing ways to save web and workspace context, not just backend vector-store primitives.

Agent ecosystem integrations

Supermemory positions itself around coding agents, MCP, and AI workspace context where memory has to follow the agent workflow.

strengths · trade-offs

Strengths and trade-offs#

Strengths

Purpose-built for agent memorySupermemory addresses the retrieval and UX layer around memory instead of only exposing a raw vector database.
Useful for both developers and power usersThe project spans APIs and user capture surfaces, making it relevant to app builders and people managing personal AI context.

Trade-offs

-Hosted service may differ from the repositoryTeams that need full self-hosting should verify which Supermemory components are available in the public repository and which depend on the managed service.
-Memory quality depends on ingestion disciplineNo memory tool helps if users save noisy context or agents retrieve broad results without ranking and workspace boundaries.

versus alternatives

Supermemory vs alternatives#

Supermemory vs a raw vector database

Supermemory and vector databases both support retrieval, but they sit at different layers. A vector database stores embeddings; Supermemory aims to provide memory workflows around capture, context, and agent use.

Criteria	Supermemory	Raw vector database
Scope	Memory product and APIs	Storage and similarity search
User capture	Yes, via product surfaces	Usually custom built
Best fit	AI apps and agents needing memory UX	Teams building their own retrieval layer

Supermemory is stronger when a team wants agent memory behavior without designing every ingestion and context surface from scratch. A raw vector database is better when the team needs maximum control and already has the application layer planned.

tech stack · detected from GitHub

What it's built on#

Languages: JavaScriptPythonTypeScript
Frameworks: Next.jsReactRemixTailwind CSS
Databases: PostgreSQL

frequently asked

FAQ#

What is Supermemory?

Supermemory is an AI memory product and open source project for storing and retrieving context for apps, agents, and user workflows.

Is Supermemory fully open source?

Supermemory has an MIT-licensed public repository, but teams should verify which hosted-service components are available for self-hosting before adopting it as infrastructure.

Can Supermemory replace a vector database?

Supermemory can replace a hand-rolled memory layer for many AI app workflows, but it is not just a database. It adds product and retrieval behavior around context storage.

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Who Supermemory is for#