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
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?
About pumaDB on Product Hunt
“a small hosted memory layer for AI agents”
pumaDB launched on Product Hunt on June 20th, 2026 and earned 159 upvotes and 9 comments, placing #5 on the daily leaderboard. 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
On the analytics side, pumaDB competes within Developer Tools, Artificial Intelligence and Database — topics that collectively have 988.2k followers on Product Hunt. The dashboard above tracks how pumaDB performed against the three products that launched closest to it on the same day.
Who hunted pumaDB?
pumaDB was hunted by Stuart Sim. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of pumaDB including community comment highlights and product details, visit the product overview.
Stuart Sim