Databaset
Databaset is a persistent memory API for AI applications that stores user context semantically and delivers relevant memories to large language models (LLMs) on every call. It replaces the need to build and maintain a separate vector database, embedding pipeline, or retrieval layer. Developers can store any text—conversations, preferences, documents, or events—and instantly recall the most semantically similar memories for a given user and query. The service targets sub-50ms recall latency and supports hybrid vector plus keyword search to ensure accuracy. Databaset integrates with all major AI models and frameworks, including OpenAI, Anthropic Claude, Google Gemini, LangChain, and Vercel AI SDK. Official Node.js and Python SDKs allow memory operations with just a few lines of code. A built-in dashboard provides API key management, usage monitoring, and billing. The platform is production-ready with per-organization rate limits, monthly quotas, and a free tier offering 3,000 API calls in the first month.
- Category
- Apis
- Pricing
- Free
Problem solved
Adding long-term, context-aware memory to AI agents typically requires setting up and tuning vector databases, embedding services, and retrieval logic. Databaset eliminates this complexity by providing a ready-to-use API that stores and retrieves user memories based on meaning, not keywords, so developers can focus on building AI features without infrastructure overhead.
Use cases
- Storing user preferences and past interactions for personalized AI responses
- Injecting recalled user context into LLM prompts to improve relevance
- Building AI copilots that remember user tool choices, settings, and history
- Adding memory to chatbots and voice assistants for multi-turn consistency
- Enabling AI agents to recall user-specific facts across sessions
- Integrating with LangChain or Vercel AI SDK as a drop-in memory layer
Audience
- Developers
- ML Engineers
- Product Managers
- Startups
Pros
- Simple SDK: store and recall memories in a few lines of code
- No vector database or embedding pipeline to manage
- Sub-50ms recall latency target
- Works with any LLM and major AI frameworks
- Free tier to start with 3,000 API calls per month
Cons
- Limited free quota may constrain testing for high-volume applications
- Requires internet connectivity; no self-hosted option
- Potential vendor lock-in with proprietary memory layer
- Pricing at scale may become expensive
- Metadata handling may be limited compared to custom solutions
Official website: https://databaset.com/
Canonical LambdaBase page: https://www.lambdabase.com/tools/databaset