Provides a hosted memory layer allowing AI agents to read and write persistent context.
Stores project notes, transcripts, preferences, and task states across multiple chat sessions.
Integrates seamlessly using Model Context Protocol (MCP) or direct server-side API connections.
Eliminates the friction of manual copy-pasting or heavy vector database infrastructure setups.
Why it works
Offers an explicit and structured key/notes memory model avoiding complex vector storage overhead.
Integrates instantly via Model Context Protocol (MCP) directly into chat interfaces like Claude and ChatGPT.
Keeps context alive across independent sessions to prevent cold-start workflow repetitions.
Maintains minimal setup friction with zero local infrastructure or heavy database deployment required.
pumaDB is a lightweight hosted memory layer designed to give AI agents a reliable space to store and reuse context across sessions. It solves the issue of agents losing valuable research notes, preferences, transcripts, and task state within chat histories. Users can easily connect the platform via a server-side API or Model Context Protocol (MCP) without configuring complex vector databases or heavy RAG stacks. It provides an efficient way to maintain cross-tool continuity.
Founded year:2024
Team size:1
Popularity:175+ followers
HQ:United States
Status:Active
Funding status:Bootstrapped
Customer type:B2B
Pricing:Freemium
Tech stack:TypeScript, Node.js, REST API, MCP
Platform:Web
Integrations:Claude, ChatGPT, MCP, REST API
What it does
Provides a hosted memory layer allowing AI agents to read and write persistent context.
Stores project notes, transcripts, preferences, and task states across multiple chat sessions.
Integrates seamlessly using Model Context Protocol (MCP) or direct server-side API connections.
Eliminates the friction of manual copy-pasting or heavy vector database infrastructure setups.
Who it's for
AI Developers
Software Engineers
Indie Makers
AI Power Users
Why it works
Offers an explicit and structured key/notes memory model avoiding complex vector storage overhead.
Integrates instantly via Model Context Protocol (MCP) directly into chat interfaces like Claude and ChatGPT.
Keeps context alive across independent sessions to prevent cold-start workflow repetitions.
Maintains minimal setup friction with zero local infrastructure or heavy database deployment required.
Founder story
pumaDB was created to solve the persistent problem of losing useful context during iterative AI agent workflows. Recognizing that information frequently disappears into scattered chat logs and local files, the platform was built to offer a lightweight, plug-and-play memory layer for developers and intelligent agents.
Alternatives
Comparison overview
pumaDB provides an explicit, structured key/notes memory layer via MCP for AI agents, whereas traditional vector databases and RAG stacks require extensive schema design and data embeddings.
Unlike workspace search tools like Cortex, pumaDB focuses specifically on cross-session agent memory persistence.