SeekDB is an AI-native search database that unifies vector, text, structured, and semi-structured data into a single engine designed for agent storage and retrieval.
The tool addresses the fragmentation problem of managing multiple specialized databases for different data types in AI agent systems. It combines vector search with full-text search and scalar filtering into unified SQL queries, allowing agents to perform hybrid searches across all data types simultaneously. The database implements copy-on-write sandbox functionality, enabling agents to fork and merge isolated data states for safe exploration without affecting the main state store. It supports both embedded and server deployment modes and maintains MySQL protocol compatibility for broad ecosystem integration.
SeekDB suits teams building AI agents that need persistent, queryable state management without operational overhead of multiple databases. It integrates with LangChain, LlamaIndex, and Dify, making it accessible to developers already using these frameworks. The embedded mode eliminates separate database infrastructure, while the server mode provides traditional client-server deployment. The tool distinguishes itself through its sandbox fork-merge capability, which is particularly valuable for agent exploration workflows where rollback and branching are essential.
The project shows consistent development activity with regular commits and active issue engagement. The codebase is maintained in C++ for performance-critical operations while providing Python bindings for accessibility. Documentation is comprehensive and actively maintained, with multiple language versions available. The project maintains an active community presence through Discord and social channels, indicating ongoing support and user engagement.
