Memory System

Automate-E agents have a three-layer memory system backed by PostgreSQL, with an in-memory fallback for development.

Architecture

graph TB
    subgraph "Memory Store"
        C[Conversations<br/>Per-thread message history]
        F[Facts<br/>Per-user learned preferences]
        P[Patterns<br/>Per-entity confidence scores]
    end

    AG[Agent Loop] --> C
    AG --> F
    AG --> P
    C --> PG[(PostgreSQL)]
    F --> PG
    P --> PG

Conversations

Message history stored per Discord thread. Used to build the conversation context sent to Claude.

Column	Type	Description
`thread_id`	string	Discord thread or channel ID
`role`	string	`user` or `assistant`
`content`	text	Message content
`timestamp`	timestamp	When the message was sent
`tokens`	integer	Token count for this message

Conversations are loaded into the Claude prompt as message history. Older messages are trimmed to stay within the context window.

Retention

Controlled by memory.conversationRetention in character.json. Default: 30d. Messages older than the retention period are deleted on a periodic cleanup cycle.

Facts

Per-user facts learned during conversations. These persist across threads and are injected into the system prompt when the user sends a message.

Column	Type	Description
`user_id`	string	Discord user ID
`fact`	text	A learned fact about the user
`source_thread`	string	Thread where the fact was learned
`created_at`	timestamp	When the fact was stored

Example facts:

"Prefers Norwegian language"
"Is the CEO of Invotek AS"
"Always wants receipts categorized under konto 6540 for software"

Retention

Controlled by memory.patternRetention. Default: indefinite.

Patterns

Entity-level patterns used for confidence scoring and auto-approval. In the accounting domain, these track merchant recognition.

Column	Type	Description
`entity_key`	string	Entity identifier (e.g., merchant name)
`pattern_type`	string	Pattern category (e.g., `merchant_category`)
`data`	jsonb	Pattern data (account, VAT rate, confidence)
`occurrences`	integer	How many times this pattern has been seen
`last_seen`	timestamp	Last occurrence

Patterns are updated after each successful action. As occurrences increases, the confidence score rises, eventually enabling auto-approval.

In-Memory Fallback

When DATABASE_URL is not set, the memory store uses JavaScript Map objects:

conversations: Map<threadId, Message[]>
facts: Map<userId, string[]>
patterns: Map<entityKey, Pattern>

This works for development and testing but data is lost on restart.

Warning

In-memory mode is not suitable for production. Conversations and patterns will be lost when the pod restarts or is rescheduled.

Postgres Setup

The ai-accountant namespace runs PostgreSQL as a StatefulSet:

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: postgres
  namespace: ai-accountant
spec:
  replicas: 1
  template:
    spec:
      containers:
        - name: postgres
          image: postgres:16
          env:
            - name: POSTGRES_DB
              value: ai_accountant

The agent connects via DATABASE_URL and auto-creates tables on first run.