Memory Plugin

The memory plugin uses a Postgres database with the pgvector extension to store and retrieve long-term memories across conversations. Junior recalls relevant memories before each user turn, exposes explicit memory tools (remember, list, search, remove), and passively extracts memories from completed public-channel and local sessions.

New apps created with junior init include createMemoryPlugin() in plugins.ts by default.

Prerequisites

Provision a Postgres database with pgvector support before running migrations. The memory plugin migration creates the vector extension and stores 1536-dimensional embeddings. Most managed Postgres providers — Neon, Supabase, Railway, and AWS RDS/Aurora PostgreSQL with pgvector enabled — support this out of the box.

Install

Install the plugin package alongside @sentry/junior:

pnpm add @sentry/junior @sentry/junior-memory

Runtime setup

The memory plugin requires a factory function call to register its tools and session hooks. Add createMemoryPlugin() to the plugin set exported from plugins.ts:

plugins.ts

import { defineJuniorPlugins } from "@sentry/junior";
import { createMemoryPlugin } from "@sentry/junior-memory";

export const plugins = defineJuniorPlugins([createMemoryPlugin()]);

Do not register @sentry/junior-memory as a bare package-name string. The memory plugin uses defineJuniorPlugin with runtime hooks for tool registration and session processing; a bare string skips those hooks and the plugin will not activate its runtime behavior.

Pass modelId to override the model used for memory classification and consolidation:

plugins.ts

import { defineJuniorPlugins } from "@sentry/junior";
import { createMemoryPlugin } from "@sentry/junior-memory";

export const plugins = defineJuniorPlugins([
  createMemoryPlugin({ modelId: "anthropic/claude-sonnet-4-5" }),
]);

Configure environment variables

Variable	Required	Purpose
DATABASE_URL	Yes	Postgres connection string for memory storage.
JUNIOR_DATABASE_DRIVER	No	SQL client driver: neon (default) or postgres. Set postgres for non-Neon deployments.
AI_EMBEDDING_MODEL	No	Embedding model for vector search. Defaults to openai/text-embedding-3-small (1536 dims).
AI_MEMORY_MODEL	No	Model for memory classification and consolidation. Defaults to the app’s structured model.
MEMORY_RECALL_MAX_VECTOR_DISTANCE	No	Maximum cosine distance for vector recall candidates. Values 0–1; lower = stricter. Values above 1 are clamped to 1. Default 0.45 suits text-embedding-3-small. Tune when changing AI_EMBEDDING_MODEL.

AI_EMBEDDING_MODEL must produce 1536-dimensional vectors. Changing this value after memories exist requires flushing the junior_memory_embeddings table and re-running to regenerate vectors with the new model.

For non-Neon managed Postgres (Railway, Supabase, AWS RDS, or self-hosted), set JUNIOR_DATABASE_DRIVER=postgres. Local URLs (localhost, 127.0.0.1) automatically use the postgres driver.

Run migrations

After setting DATABASE_URL, run the upgrade command to apply the memory plugin schema:

pnpm junior upgrade

On a fresh database, this creates the vector extension, the junior_memory_memories table, and the junior_memory_embeddings table with a vector(1536) column.

Verify

Confirm memory storage and recall work end to end. In a Slack conversation where Junior has actor context, ask Junior to store an explicit memory:

Remember that I prefer concise bullet-point summaries

Then verify recall by listing memories directly:

what memories do you have about me?

Junior should list the stored preference. To confirm cross-conversation recall, start a new conversation as the same actor and ask:

What do you remember about my preferences?

Junior should recall the preference without prompting.

Public Slack channel memories are workspace-visible. A durable fact remembered in a public channel or public-channel thread can be recalled from another public channel in the same Slack workspace. Private Slack and local conversation memories remain scoped to their original conversation.

Failure modes

• Plugin not active after registration: @sentry/junior-memory was registered as a bare string instead of createMemoryPlugin(). Switch to the factory call and redeploy.
• Migration error — extension “vector” does not exist: the Postgres database does not have pgvector available. Use a provider that supports pgvector or install it manually with CREATE EXTENSION vector.
• DATABASE_URL is required: no database URL is configured. Set it in the deployment environment.
• Connection errors on non-Neon Postgres: set JUNIOR_DATABASE_DRIVER=postgres for Railway, Supabase, AWS RDS, or self-hosted Postgres.
• Embedding dimension mismatch: AI_EMBEDDING_MODEL was changed after memories were stored with a different model. Flush the junior_memory_embeddings table and re-run migrations to regenerate vectors with the new model.
• Memories not recalled: the database migration has not run yet. Run pnpm junior upgrade against the production database.

Next step

Read Config & Env Reference for the full list of database and model environment variables.