pumaDB

Built-in safety rails and review capabilities constitute pumaDB's third feature group, implementing deliberate constraints to maintain memory quality and recoverability. The system enforces account limits of 20 tables, 1,000 rows per table, and 25 MB total storage per account, along with rate limits of 30 writes and 60 reads per minute per key. Every update and delete operation automatically archives prior row content, keeping the last 10 versions for 30 days with single-call restoration capability. Natural edit functionality allows agents to process plain-language requests like 'remember that I prefer short PR summaries' into filtered updates without creating duplicate rows, while viewer links provide access to larger result sets through short-lived URLs. These features collectively ensure that agent memory remains manageable, auditable, and recoverable even as it accumulates over time.

The overall workflow methodology centers on providing two parallel access patterns through a unified memory surface. Server-side applications can use REST endpoints with bearer API keys for operations like GET /v1/{table}, POST /v1/{table}, and DELETE /v1/{table} from trusted backend environments, serverless functions, or CLI tools. Simultaneously, AI agents access the same row operations through MCP tool calls when connected to the hosted MCP server, with tools including add, query, batch, upsert, update_row, update_where, list_tables, count, delete, versions, restore, open_row, open_text_field, and the primary remember function. This dual-access architecture ensures that both automated agents and traditional applications can interact with the same memory store, facilitating hybrid workflows where human developers and AI agents collaborate on shared projects and maintain consistent context.

Concrete use cases demonstrate pumaDB's practical applications across various AI development scenarios. When building a coding assistant, developers can store project-specific conventions and repository facts that persist across sessions, eliminating the need to repeatedly explain codebase structure. Customer support chatbots can maintain user preference memory for communication style and past issue history, creating more personalized interactions. Research assistants benefit from storing investigation summaries and follow-up questions that continue over extended periods, while task management agents can track open threads and handoff notes for long-running work. In each scenario, the outcome is more consistent, context-aware AI behavior that remembers critical information without requiring users to manually transfer context between sessions or rebuild knowledge from scratch.

pumaDB targets AI application developers, agent workflow builders, and teams implementing persistent AI capabilities across platforms like ChatGPT, Claude, and Codex. The technology stack centers on the Model Context Protocol standard with REST API alternatives, specifically designed for server-side applications that maintain puma_live_* keys in backend or serverless environments while avoiding client-side exposure in React bundles, static sites, mobile apps, or browser code. The platform's approach balances accessibility through hosted MCP with security through proper server-side API key management, making durable agent memory achievable without database expertise while maintaining appropriate data protection boundaries. Ultimately, pumaDB transforms agent memory from a persistent challenge into a managed resource, enabling more capable and consistent AI applications.