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pumaDB
A simple hosted memory layer for AI agents.
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.
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.
Category: AI & Automation
Team Size: 1
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As of 2026
AI & Automation
Revenue: 0-$100k ARR
A simple hosted memory layer for AI agents.
Overview
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.
Funding status: Bootstrapped
Revenue source: Subscriptions
Customer type: B2B
Pricing: Freemium
Tech stack: TypeScript, Node.js, REST API, MCP
Platform: Web
Integrations: Claude, ChatGPT, MCP, REST API
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.
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.
Growth strategies
- Product Hunt directory launch targeting AI developer communities
- Open API and MCP- first distribution strategy for rapid integration
- Word- of-mouth adoption among developers building multi-session LLM workflows
- Content sharing around efficient AI memory and context management practices
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.