Choosing Databases for LLM-Powered AI Agents
TL;DR. The article compares database architectures like Postgres, JSONB, DuckDB, SQLite, and MongoDB for AI agents that ingest and query user-defined structured data. - Configurable AI agents require flexible schemas, multi-tenancy, and reliable LLM-generated SQL compatibility. - Traditional relational databases struggle with user-defined data structures, favoring semi-structured approaches. - Databases must handle CSV ingestion and support efficient, simple SQL generation for LLMs.
- Configurable AI agents need databases that support flexible schemas defined by end-users at runtime.
- Key requirements include strong multi-tenancy, easy ingestion of tabular data (e.g., CSVs), and robust compatibility with LLM-generated SQL.
- Traditional relational databases with fixed schemas are challenging for this use case, making semi-structured solutions like JSONB attractive.
- The article evaluates Postgres, JSONB, DuckDB, SQLite, and MongoDB for their suitability in these agentic systems.
Sources
- Choosing the Right Database for AI Agents: LLM Generated SQL — predictabledialogs.com