pumaDB
pumaDB is a lightweight, database-free memory API layer purpose-built for AI agents. It enables agents to persistently store and retrieve context, state, and memory without the complexity or overhead of a full database system. By providing a simple HTTP endpoint or embeddable library, pumaDB allows developers to focus on building intelligent behavior rather than managing infrastructure. Data is stored using efficient file‐based or in‐memory structures, making it ideal for prototypes, small to medium workloads, and rapid experimentation. The core mechanism revolves around a flexible key‑value store with support for JSON documents and vector embeddings. This dual‑store design means agents can perform both precise lookups and semantic similarity searches over past interactions. For example, an agent can recall exact user settings by ID or retrieve the most relevant past conversations based on meaning. The API is stateless and RESTful, with low‑latency access that doesn’t slow down agent reasoning loops. Key features include automatic persistence, optional encryption for sensitive data, built‑in embedding generation via popular models, and seamless compatibility with common AI agent frameworks like LangChain, AutoGPT, and others. It can be deployed in seconds via pip as an in‑process library, or as a standalone Docker container for shared access across multiple agents. The project is free and open‑source, encouraging community contributions and transparency. pumaDB lowers the barrier to adding long‑term memory to AI agents. Its minimalist design eliminates configuration burdens while still offering essential features like time‑to‑live (TTL) for entries, batch operations, and a simple dashboard for inspecting stored memories. As a result, teams can move from idea to a working agent with persistent context in a matter of minutes.
- Category
- AI Agent
- Pricing
- Free
Problem solved
AI agent developers struggle to give their agents persistent memory without spinning up and maintaining a full database. Setting up a traditional database introduces operational overhead, schema design, and scaling concerns that slow down prototyping and deployment. pumaDB solves this by offering a ready‑to‑use, database‑free memory API that stores state in lightweight files or in memory, making it trivial to add recall, context retention, and long‑term memory to any agent with just a few lines of code.
Use cases
- Building a customer support chatbot that remembers user history and past issues across sessions.
- Developing a task‑planning AI agent that persists intermediate reasoning steps and decisions for later review or resumption.
- Creating an AI persona with long‑term memory of user preferences, conversation style, and personal details.
- Storing embeddings of past interactions to semantically retrieve relevant context for more accurate and contextual responses.
- Rapid prototyping of AI agents without the friction of setting up and managing a database.
- Adding session state management to a multi‑turn LLM application, enabling continuation from where the user left off.
Audience
- Developers
- ML Engineers
- Data Scientists
Pros
- Extremely easy to set up and integrate
- No external database required, reducing operational complexity
- Lightweight and fast, with minimal performance overhead
- Open‑source and free to use under a permissive license
- Works seamlessly with popular AI agent frameworks
Cons
- Limited scalability for very large or high‑throughput deployments
- Lacks advanced querying capabilities (e.g., complex joins or aggregations)
- Community and ecosystem are still growing, so third‑party resources may be scarce
Official website: https://pumadb.ai/
Canonical LambdaBase page: https://www.lambdabase.com/tools/pumadb