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Query Memory
One API for all documents your AI agents need
73 followers
One API for all documents your AI agents need
73 followers
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.
Suprbox
Humalike
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?
Suprbox
@mcarmonas Thank you for your comment. To address your questions, the synchronization process depends on the data source you are referring to. If you are connecting documents through live integrations such as Databricks, Postgres, or similar platforms, these connections are automatically synchronized. However, for certain cases, you will need to manually upload the documents you wish to use in your AI application.
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! 🚀
Potentially cool feature guys) Small question: Is the API mobile-friendly? Any iOS/Android developers already building on top of Query Memory?
AutonomyAI
Congrats on the launch!