pumaDB is a specialized hosted memory layer designed specifically for AI agents, serving as a durable storage solution that eliminates the need for traditional database management. It caters to developers and AI application builders who require persistent memory for agents across sessions, ensuring continuity and context retention. The core value of pumaDB lies in its ability to provide a lightweight, scalable memory schema that agents can rely on for storing and retrieving structured data seamlessly. By offering both MCP and REST API interfaces, it integrates smoothly into existing workflows, making it an essential tool for enhancing agent capabilities with reliable memory functions. This product addresses the critical need for agents to maintain state and learn from past interactions, which is fundamental for advanced AI applications.
AI agents often suffer from amnesia across sessions, forgetting user preferences, project context, or task states, which leads to repetitive interactions and inefficient workflows. This problem is particularly acute in conversational AI, coding assistants, and automated systems where continuity is key to productivity. pumaDB solves this by providing a dedicated memory layer that agents can write to and read from, ensuring that important information persists. Without such a solution, developers must build custom database integrations, which are time-consuming and error-prone. The pain point of agent forgetfulness undermines user trust and limits the sophistication of AI applications, making pumaDB's focused approach highly valuable for creating more intelligent and responsive agents.
The hosted MCP feature allows agents like ChatGPT, Claude, and Codex to connect via a Streamable HTTP endpoint, enabling OAuth-ready authentication and dynamic client registration. This works by providing a standardized MCP server URL that agents can add as a custom connector, facilitating secure communication without manual key management. The usefulness stems from simplifying the integration process, as developers can quickly enable memory capabilities without configuring servers or handling OAuth flows themselves. By supporting multiple AI platforms, this feature ensures broad compatibility and reduces the overhead of maintaining separate memory solutions for different agents, making it a versatile choice for multi-agent environments.
The server-side API offers REST endpoints for backend applications, serverless functions, and CLIs using bearer API keys, with explicit warnings against client-side use. This feature operates through HTTP routes like GET /v1/{table} for queries and POST /v1/{table} for writes, allowing precise control over memory operations from trusted server environments. Its utility lies in providing a programmatic interface for scenarios where MCP is not suitable, such as batch processing or server-initiated memory updates. By enforcing server-side usage, pumaDB enhances security and prevents exposure of sensitive keys, ensuring that memory access is restricted to authorized systems only.
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pumaDB supports various memory types through a lightweight JSON schema, including skills markdown, project conventions, user preferences, research clippings, task state, and typed safe memory. Each type serves a specific purpose, such as storing reusable instructions or archiving investigation notes, with inert safety metadata to prevent unintended executions. This capability allows agents to organize memory logically, improving retrieval accuracy and context awareness. The remember MCP tool consolidates these types for efficient writes, while older helpers remain compatible for backward compatibility, ensuring flexibility in how memory is structured and accessed across different agent versions.
The product works by exposing memory operations through two primary interfaces: MCP for agent-facing tools and REST for server-side calls, both interacting with the same underlying row-based storage. Agents initiate writes via tools like remember or query, which store or retrieve JSON rows in scoped tables, with automatic versioning and rate limiting applied. The workflow involves agents sending memory requests during interactions, pumaDB processing them with safety checks, and returning results inline or via viewer links for large data. This approach ensures that memory is managed consistently, whether accessed in real-time by agents or asynchronously by backend systems, promoting reliability and ease of use.
In concrete use cases, an AI coding assistant like Codex can store project conventions such as repository facts and branch rules, allowing it to adhere to team standards across multiple coding sessions. This outcome reduces setup time and errors by providing consistent context. Similarly, a conversational agent like Claude can remember user preferences for communication style, enabling personalized responses without repetitive instructions. For research tasks, agents can save clippings and summaries from investigations, facilitating continued analysis over time. These scenarios demonstrate how pumaDB enhances agent effectiveness by preserving critical information, leading to more efficient and context-aware AI interactions.
pumaDB targets developers building AI agents, including those working with ChatGPT, Claude, Codex, and OpenClaw, as well as server-side applications in backend or serverless environments. It is platform-agnostic, supporting any client with MCP or REST capabilities, and uses a simple tech stack centered on HTTP APIs and JSON storage. Pricing is not detailed, but account limits include 20 tables, 1,000 rows per table, and 25 MB storage. The takeaway is that pumaDB provides a hassle-free memory solution that empowers agents to operate with continuity, making AI applications smarter and more reliable without infrastructure overhead.
Developers building AI agents with platforms such as ChatGPT, Claude, Codex, or OpenClaw; server-side application developers using backends, Workers, serverless functions, or CLIs; teams requiring durable memory for agent state persistence without database projects.
Updated 2026-06-21