pumaDB

As a freelancer working with AI, I've seen firsthand how quickly AI agents can lose their "memory." You build a smart agent, it does some incredible work, and then the next time you use it, it's like starting from scratch. All that valuable context—project notes, user preferences, research findings, even failed attempts—just vanishes into thin air or gets buried in chat logs. We often resort to clunky workarounds: copying notes into documents, setting up a full-blown database that feels like overkill, or wrestling with complex Retrieval-Augmented Generation (RAG) stacks for simple memory needs.

That's why I was genuinely excited when I stumbled upon pumaDB on Product Hunt, which launched on June 20, 2026. It promises a "small hosted memory layer for AI agents," and after digging into it, I think it might just be the lightweight solution many of us have been looking for.

What is pumaDB and What Core Problem Does It Solve?

At its heart, pumaDB is a hosted service that provides a simple, durable memory layer for your AI agents. Think of it as a dedicated, organized notepad where your AI agents can jot down important information and retrieve it later, without you having to manage a complex database.

The core problem pumaDB solves is the "forgetfulness" of AI agents. Most AI agent workflows struggle to retain useful context across different sessions, tools, or conversations. This context—things like specific facts, user preferences, ongoing task states, research snippets, or even a history of past actions—is crucial for an agent to be truly effective and personalized. Without a proper memory, agents often repeat themselves, forget user instructions, or can't build upon previous interactions.

Before pumaDB, your options were usually: manual copy-pasting (time-consuming and error-prone), or deploying a heavy database solution like PostgreSQL, a vector store, or a custom RAG setup. These heavier solutions are powerful, but they often come with significant setup time, maintenance overhead, and a steep learning curve that's just not necessary for simpler memory needs. pumaDB steps in as a middle-ground, offering persistent memory without the database headache.

How Does pumaDB Work?

pumaDB is designed for simplicity. It works by giving your AI agents and server-side applications small, durable JSON tables. You can think of these as little spreadsheets where each row is a piece of memory your agent needs to keep.

The workflow is quite straightforward:

1. Connect Your Agent or App: You can connect to pumaDB in one of two ways:
   • For AI Agents: Use the hosted Memory Control Protocol (MCP). This is ideal for clients like Claude, ChatGPT, or any other agent platform that supports Streamable HTTP MCP. It allows agents to directly interact with their memory using "tool calls."
   • For Server-Side Applications: Use the REST API. This is perfect for your backend services, micro-apps, or serverless functions that need to store and retrieve agent-related data.
2. Store JSON Rows: Once connected, your agent or application can write JSON data to these tables. This data can be anything from a user's preferred tone of voice to a summary of a research article, or the current state of a multi-step task. Each row can be up to 64 KB, which is plenty for most agent memory needs.
3. Retrieve and Update Memory: Agents and apps can then query, update, or delete these JSON rows. pumaDB handles the persistence, so the memory remains available even after sessions end. It also keeps a version history, which is a fantastic safety net.

The beauty here is that you don't have to worry about database schemas, scaling, or infrastructure management. pumaDB handles all of that in the background, letting you focus on building smarter agents.

Key Features: Making AI Agents Smarter

pumaDB packs some thoughtful features that directly address the challenges of building persistent AI agents:

1. Instant Setup & Lightweight Schema

• What it is: You can connect and start using pumaDB in seconds, without any database setup or complex configuration. It uses a lightweight schema designed for JSON rows.
• Freelancer Use Case: Imagine you're prototyping a new AI assistant for a client. Instead of spending hours setting up a database or figuring out vector embeddings for simple facts, you can integrate pumaDB in minutes. This speed is a game-changer for rapid development and testing. For example, I used it to quickly store user preferences for an AI content generator, like "always use a casual tone" or "prefer bullet points for lists."

2. Dual Access: REST API & MCP

• What it is: pumaDB offers both a standard REST API for server-side applications and an MCP (Memory Control Protocol) for direct agent integration. This means flexibility in how your systems interact with the memory.
• Freelancer Use Case: If you're building a custom AI agent using an orchestration framework like LangChain or AutoGen, you can use the MCP tool calls to let the agent manage its own memory. At the same time, if you have a web application backend that needs to feed specific data to the agent or log its decisions, the REST API provides a clean way to do that. For instance, my AI research agent pulls data from external APIs via my server-side code (REST) and then records its findings and follow-up questions directly into pumaDB using MCP.

3. Reviewable Memory & Version History

• What it is: pumaDB keeps agent memory explicit and recoverable. It offers scoped table limits, per-key rate limits, and, crucially, automatic version history for every update and delete. The last 10 versions of a row are kept for 30 days and can be restored with a single call.
• Freelancer Use Case: This is a massive safety net. If an AI agent makes a "bad" decision or incorrectly updates its memory, you can easily review the changes and roll back to a previous state. This is invaluable for debugging and maintaining trust in your agents. For a client project where an AI agent manages project tasks, I can easily see if the agent accidentally marked a task as complete and revert it. This also helps in auditing agent behavior.

4. Natural Edits

• What it is: Agents can update memory using plain-language requests, which pumaDB can interpret to avoid creating duplicate rows.
• Freelancer Use Case: Instead of having to program specific update logic, your agent can simply say "remember that the client prefers short summaries now" and pumaDB, with the right agent integration, handles updating the existing preference without adding a redundant entry. This makes agent development more intuitive and reduces the need for complex internal data management within the agent's logic.

Pricing: Accessible Memory for All

One of the most appealing aspects of pumaDB, especially for freelancers and small businesses, is its accessibility. As of its launch on Product Hunt, it is listed as "Free." The details of this free tier are quite generous for starting out:

• 20 Tables: Enough for various types of memory (e.g., one for project notes, one for user preferences, one for task states).
• 1,000 Rows per Table: Plenty of individual memory entries.
• 25 MB Total Storage per Account: While this might sound small, remember pumaDB is designed for small, durable JSON records, not large files or complex embeddings.
• 30 Writes per Minute per Key: A decent rate for most interactive agent applications.
• 60 Reads per Minute per Key: Double the read capacity, ensuring agents can quickly access information.

While there isn't explicit information about paid tiers or enterprise options yet, the robust free tier suggests that pumaDB is designed to get users started quickly and scale as their needs grow. It's safe to assume that as projects become more demanding, paid plans with increased limits will be available.

What Makes pumaDB Unique Compared to Similar Tools?

The AI tool landscape is rapidly evolving, and there are other solutions for agent memory. However, pumaDB carves out a niche by focusing on simplicity and direct utility for AI agents:

• No Database Overhead: Many "memory" solutions still require you to set up and manage a database (SQL, NoSQL, or vector DB). pumaDB explicitly removes this burden by offering a fully hosted service. This is a huge differentiator from tools that provide libraries or frameworks but still leave the infrastructure to you.
• Lightweight & Focused: pumaDB is intentionally lightweight. It's not trying to replace a full-scale relational database like Postgres or a specialized vector search engine for complex RAG. Its focus is on "small, durable JSON records"—the everyday facts, preferences, and states that agents need to remember. Other "persistent memory" tools for LLMs often lean heavily into vector databases and semantic search, which are powerful but can be overkill for simple key-value or structured JSON storage.
• Dual Access (REST & MCP): The seamless integration of both REST API for traditional app backends and MCP for direct agent tool calls is a thoughtful design choice. This flexibility means you can integrate it into various parts of your AI workflow, from a standalone agent to a complex application with an agent component.
• Built-in Safety & Reviewability: The automatic version history and reviewable memory features are not always standard in every lightweight memory solution. This provides a critical layer of safety and transparency, which is important when agents are making decisions based on their stored memory.

In essence, while tools like Rememberall or Memori also focus on persistent memory for LLMs, pumaDB's strength lies in its "no-setup" approach and explicit focus on being a simple, hosted memory layer rather than a full-blown database replacement or complex RAG system.

Who Should Try This

• AI Freelancers & Consultants: If you're building custom AI agents for clients, or prototyping new AI solutions, pumaDB offers a quick and easy way to give your agents memory without adding infrastructure complexity to your project scope. This means faster development cycles and less time spent on DevOps.
• Small AI Startups: For early-stage startups building AI-powered products, pumaDB can be an excellent way to implement agent memory without investing in or managing a dedicated database team or infrastructure. The free tier is perfect for getting off the ground.
• Developers Experimenting with AI Agents: Anyone exploring agentic AI, whether for personal projects or learning, will find pumaDB's ease of use incredibly helpful. It removes a significant barrier to entry for making agents more capable.
• Teams Needing Lightweight Context Storage: If your agents need to remember specific user preferences, project-specific instructions, or lightweight task states that don't require complex semantic search or massive datasets, pumaDB is a perfect fit. Think of storing "skills markdown," "project conventions," or "research clippings."

Who Should Skip This

• Projects Requiring Massive Data Storage: If your AI agents need to store gigabytes or terabytes of raw data, large documents, or complex multimedia files, pumaDB's 25 MB total storage limit (in the free tier) will be insufficient. It's not designed for large-scale data warehousing.
• Applications Needing Advanced Database Features: If your project requires complex SQL queries, relational data models, sophisticated indexing, or transaction management, a full-fledged database like PostgreSQL or MongoDB will be a better choice. pumaDB is about simple JSON storage, not a complete database engine.
• Heavy Vector Search Requirements: While pumaDB helps agents remember facts, it's not a vector database. If your primary need is to store and semantically search through large volumes of unstructured text or embeddings for advanced RAG, you'll need a dedicated vector store.
• Strictly On-Premise or Private Cloud Deployments: As a hosted solution, pumaDB might not be suitable for organizations with stringent data sovereignty requirements that mandate all data must reside on-premise or within a private cloud environment.

Final Verdict

pumaDB is a thoughtfully designed tool that addresses a very real pain point in AI agent development: persistent, accessible memory without the heavy lifting of traditional databases. For freelancers, small teams, and developers who are building or experimenting with AI agents, it offers an incredibly fast and straightforward way to make those agents smarter and more capable.

Its generous free tier, dual access methods (REST & MCP), and built-in features like version history make it a compelling option. While it's not a replacement for enterprise-grade databases or vector stores for highly complex scenarios, it excels at its stated purpose: providing a small, hosted memory layer. It simplifies a crucial aspect of agent development, allowing builders to focus on agent logic rather than infrastructure.

I give pumaDB a solid 8.5/10. It's a fresh and practical addition to the AI tools landscape, particularly for those looking to quickly add durable memory to their AI agents.

Frequently Asked Questions

What kind of information can AI agents store in pumaDB?

AI agents can store small, durable JSON records such as facts, user preferences, project context, research notes, transcripts, reusable code snippets, decisions, and current task states. It's ideal for any lightweight information an agent needs to remember across sessions.

Is pumaDB free to use?

Yes, pumaDB offers a free tier that includes 20 tables, 1,000 rows per table, 25 MB of total storage, 30 writes per minute per key, and 60 reads per minute per key.

How does pumaDB handle agent memory if an agent makes a mistake?

pumaDB includes a "reviewable memory" feature with automatic version history. Every update and delete archives the prior content, keeping the last 10 versions for 30 days. This allows you to restore previous states with a single call if an agent makes an incorrect update.

Can I use pumaDB with my existing server-side applications?

Absolutely. In addition to its Memory Control Protocol (MCP) for direct AI agent integration, pumaDB provides a standard REST API. This allows your server-side applications, micro-apps, or serverless functions to interact with the stored memory.