pumaDB: A Small Hosted Memory Layer for AI Agents
pumaDB is a lightweight hosted memory layer for AI agents, offering shared memory via MCP or server-side API to store project context, research notes, transcripts, and more, without managing databases or vector stores.
pumaDB: a small hosted memory layer for AI agents | Product Hunt
pumaDB
Launching today
a small hosted memory layer for AI agents
14 followers
a small hosted memory layer for AI agents
14 followers
Visit website
AI Infrastructure Tools
•
AI Databases
Most AI agent workflows lose useful context between sessions, tools, and chats. The usual fixes are either too manual, like copying notes into docs, or too heavy, like setting up a database, vector store, or custom RAG stack. pumaDB gives agents a simple shared place to save and reuse notes, facts, preferences, project context, transcripts, task state, and other useful memory. No database setup, vector DB, or infrastructure to manage
Overview
Reviews
Alternatives
Team
Awards
More
Free
Launch tags:Developer Tools•Artificial Intelligence•Database
Launch Team
Subscribe
Promoted
Maker
📌
I built pumaDB because I kept running into the same problem with AI agents: they do useful work, then the useful context disappears into chat history, local files, Notion, GitHub, or some custom setup.
I wanted something simpler.
pumaDB gives agents a shared memory they can read and write through MCP or a server-side API. You can use it for things like project context, research notes, transcripts, reusable snippets, preferences, decisions, task state, and things already tried.
It is intentionally lightweight. It is not trying to replace Postgres, vector search, or your production database. It is for the smaller but very common problem of giving agents a reliable place to remember useful context across sessions and tools.
A simple example: I moved transcripts from my last 23 videos into pumaDB. Now I can ask Claude, ChatGPT, Codex, or Conductor to summarize, repurpose, or search that same content without copying it between tools.
Would love feedback from anyone building with agents:
- What do you currently use for agent memory?
- Do you prefer MCP, API, or both?
- What would you want agents to remember automatically?
- What would make you trust a shared memory layer?
Report
1d ago