utun2/.opencode/context/development/ai/mastra-ai/concepts/storage.md

Concept: Mastra Data Storage

Purpose: Persistence layer for cases, documents, assessments, and observability.

Last Updated: 2026-01-09

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Core Idea

Mastra uses a dual-storage approach: a local SQLite database (via Drizzle ORM) for business entities and a built-in LibSQLStore for Mastra-specific execution data (traces, spans).

Key Points

• Business Entities: Managed in src/db/schema.ts. Includes cases, documents, assessments, and outputs.
• Mastra Store: LibSQLStore handles mastra_traces, mastra_ai_spans, and mastra_scorers.
• V3 Extensions: Specific tables for timeline_events, evidence_gaps, sub_claims, and vulnerability_flags.
• Observability: prompt_execution_traces provides detailed cost and token tracking per AI call.
• File Storage: Large blobs (PDFs, JSON outputs) are stored in ./tmp/ with paths referenced in the DB.

Quick Example

// Business Schema (Drizzle)
export const cases = sqliteTable('cases', {
  id: text('id').primaryKey(),
  status: text('status').default('new'),
});

// Mastra Store Config
storage: new LibSQLStore({
  url: process.env.MASTRA_DB_PATH || 'file:./mastra.db',
}),

Reference: src/db/schema.ts, src/mastra/index.ts Related:

• concepts/core.md
• lookup/mastra-config.md