About pumaDB
pumaDB is a hosted memory layer for AI agents that launched this week. It stores context such as notes, facts, preferences, transcripts, and task state, accessible to agents through MCP or a server-side API. No database setup is required.
Review
AI agents regularly lose context between sessions, tools, and chats. Common fixes include manually copying notes into documents or configuring databases, vector stores, and RAG stacks. pumaDB functions as a shared memory layer for agents, accessible through MCP or a server-side API without database setup.
Key Features
- MCP server integration for connecting agents from Claude, ChatGPT, Codex, and similar tools
- Server-side API as an alternative access method for reading and writing memory
- Hosted memory layer with key-based storage - no database or vector store setup needed
- Shared memory that multiple agents and tools can access, so context persists across sessions
- Storage for transcripts, research notes, preferences, project context, task state, and decisions
Pricing and Value
At launch, pumaDB is free. Long-term pricing has not yet been defined by the maker.
Pros
- Removes the need to set up a database or vector store for agent memory
- Works across multiple AI tools through MCP, reducing the need to copy context manually
- Straightforward key/value model that integrates into agent workflows without a learning curve
- Hosted solution means no infrastructure maintenance for developers
- Free during the launch period
Cons
- Not suited for teams that need production-grade databases with querying, indexing, or vector search capabilities
- Memory inspection and cleanup tooling - such as viewing who wrote an entry or expiring stale data - hasn't been detailed in the current release
- As a newly launched tool, it lacks an established track record and community-contributed integrations
pumaDB fits developers and small teams experimenting with AI agents who need agents to retain context between sessions. It's less appropriate for production workloads requiring advanced querying or high reliability guarantees. The MCP-first approach means agents connect to existing AI tools without workflow changes.
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