LANGMEM? HYBRIDMEM? LOCALMEM? CRAG? HRAG?
The Universal Memory API for AI apps. Store, recall, personalize — in milliseconds. Stop building RAG pipelines. Start shipping.
“Oh, I want an open source provider...”
Bro, you're sending all your stuff to embedding models and text models ANYWAYS.
Unless you're running everything locally (you're not), your data is already going to OpenAI, Anthropic, or Cohere. What exactly are you “protecting”?
Supermemory has 13,000+ stars on GitHub. Check out what they've built.
“But the OSS ones are better...”
They're not. And most aren't even really open source.
Half of them have “open source” clients that talk to proprietary backends. Others require you to sign up for their cloud to get basic features. Some changed their licenses after getting popular.
Supermemory has 13k+ stars and an active community. They ship fast, iterate in public, and actually care about developer experience.
“But I need fine-grained control over my RAG pipeline...”
No. You. Don't.
You need your app to remember things. That's it. You don't need to manually tune chunk sizes, experiment with 47 different embedding models, or build a custom reranking system.
Supermemory handles chunking, embedding, and retrieval so you can focus on building your actual product. You know, the thing users pay for?
“But my use case is special...”
It's not.
You need to store information, search it semantically, and recall it when relevant. Maybe you need to scope it by user, or by conversation, or by workspace.
That's what everyone needs. You're not building something that requires a custom vector database implementation. You're building an app with memory. Use the memory API.
“But I already have a RAG pipeline...”
Is it good? Is it fast? Are you constantly tuning it?
How many hours have you spent debugging retrieval quality? How many times have you “just quickly” adjusted the chunk overlap? How often do you wonder if there's a better embedding model?
Replace it. Seriously. Your time is worth more than maintaining glue code.
“What about LangChain memory / Mem0 / LlamaIndex / ...?”
More abstractions on top of abstractions.
LangChain memory is a wrapper around a wrapper around your actual storage. Mem0? Another layer. LlamaIndex? Great for prototypes, painful in production.
Supermemory is just an API. POST /memories to store. GET /search to retrieve. Done. No 500-line config files. No dependency hell.
“I'll just build it myself...”
Please don't.
I've seen this movie. You'll spend 2 weeks building a “simple” vector store integration. Then another week on chunking. Then you'll realize you need metadata filtering. Then hybrid search. Then reranking.
Three months later, you'll have a “memory system” that's slower, buggier, and harder to maintain than just calling an API.
Ship your product. Not your infrastructure.
Throw any content at it. Text, documents, conversations. It handles chunking and embedding.
Semantic search that actually works. Find what's relevant, not just what matches keywords.
Millisecond latency. Your users won't wait. Neither should your app.
13,000+ GitHub Stars. Built by developers who get it.
Simple API. Great docs. Actually works. Ship faster.