Your AI agents are only as powerful as the data they can access. Query memory turns documents, websites, and files into instantly queryable knowledge for AI agents. Upload files or connect web sources to create a knowledge base in seconds. Query Memory handles parsing, chunking, embeddings, and retrieval so you don’t have to build complex RAG pipelines. Build agents, attach knowledge, and query everything through API or built-in chat.
Hey 👋
I’m Hritvik, the maker of Query Memory.
While building AI agents, I kept running into the same problem: giving agents reliable access to knowledge is harder than building the agent itself. Parsing documents, chunking data, creating embeddings, and managing retrieval pipelines quickly turns into weeks of engineering work.
So I built Query Memory — a platform that turns documents, websites, and files into queryable knowledge your AI agents can use instantly, all through a single API
With Query Memory you can:
• Upload docs or connect websites
• Create a knowledge base in seconds
• Attach it directly to your AI agents
• Query everything via one simple API or built-in chat
It handles parsing, chunking, embeddings, and retrieval behind the scenes so you can focus on building the agent itself—not the infrastructure.
Would love to hear:
👉 What tools are you currently using for RAG / agent memory?
👉 What’s the hardest part of giving agents reliable knowledge?
Happy to answer questions and get feedback from the community!
Building RAG pipelines from scratch is one of those things that sounds straightforward until you're deep in chunking strategies and embedding models. Abstracting all of that into one API is the right call ,it lets builders focus on what the agent actually does, not on the plumbing. Congrats on the launch, excited to see where this goes! 🚀
This solves a real pain point. Building RAG pipelines from scratch every time you want an agent to access documents is such a time sink. Having parsing, chunking, and embeddings handled behind a single API is exactly what most developers need. How does it handle version updates when a document changes, does it re index automatically or do you need to trigger it manually?
hritvik Gupta