@kuralle-agents/postgres-store

Postgres-backed session store, memory service, and vector store for Kuralle.

Install

npm install @kuralle-agents/postgres-store pg

Peers: @kuralle-agents/core @kuralle-agents/rag pg@^8.

What it does

Three backend implementations — sessions, long-term memory, and pgvector similarity search — backed by a single Postgres connection pool.

Key exports:

• PostgresSessionStore — SessionStore implementation for durable session persistence.
• PostgresMemoryService — MemoryService implementation for cross-session long-term memory.
• PostgresPersistentMemoryStore — PersistentMemoryStore for durable USER/MEMORY markdown blocks.
• PgVectorStore — VectorStoreCore implementation using pgvector for similarity search.

Session store

import { Pool } from 'pg';
import { createRuntime } from '@kuralle-agents/core';
import { PostgresSessionStore } from '@kuralle-agents/postgres-store';

const pool = new Pool({ connectionString: process.env.DATABASE_URL });
const sessionStore = new PostgresSessionStore({ client: pool });

const runtime = createRuntime({
  agents: [agent],
  defaultAgentId: 'support',
  sessionStore,
});

Store options

• tableName (default: 'kuralle_sessions') — table to store sessions.
• autoMigrate (default: true) — create the table on first use.

Long-term memory

import { PostgresMemoryService } from '@kuralle-agents/postgres-store';

const memoryService = new PostgresMemoryService({ client: pool });

const runtime = createRuntime({
  agents: [agent],
  defaultAgentId: 'support',
  memoryService,
  preloadMemory: true,
  memoryIngestion: 'onEnd',
});

Working memory blocks

import { PostgresPersistentMemoryStore } from '@kuralle-agents/postgres-store';

const workingMemoryStore = new PostgresPersistentMemoryStore({ client: pool });

const runtime = createRuntime({
  agents: [agent],
  defaultAgentId: 'support',
  defaultWorkingMemoryStore: workingMemoryStore,
});

On Cloudflare Workers, connect the pool through Hyperdrive rather than a direct TCP connection.

Vector store (pgvector)

Requires the pgvector extension in your Postgres instance.

import { PgVectorStore } from '@kuralle-agents/postgres-store';
import { AiSdkEmbedder, VectorRetriever } from '@kuralle-agents/rag';
import { openai } from '@ai-sdk/openai';

const vectorStore = new PgVectorStore({ client: pool, tableName: 'kuralle_vectors' });
const embedder = new AiSdkEmbedder({ model: openai.embedding('text-embedding-3-small') });
const retriever = new VectorRetriever({ store: vectorStore, embedder, indexName: 'docs', topK: 5 });

Related

• @kuralle-agents/core — SessionStore interface and runtime.
• @kuralle-agents/rag — VectorStoreCore interface.
• @kuralle-agents/redis-store — Redis alternative.