How is memory and user context stored and retrieved?

Storage — PostgreSQL. Personal memories live in the personalMemories table in PostgreSQL, accessed through Drizzle ORM (server/src/db/schema/memory). The memory store (server/src/memory/store.ts:16-80) provides CRUD operations: list (up to 500, newest-first), create, formMemory (Bot-observed facts awaiting human review), update, remove. Deduplication uses a SHA-256 hash of lowercased content (deleted memories stay forgotten).

Memory types. Personal memories are free-text records with optional sourceApp and sourceLink. Bot-formed memories (formMemory) are observed facts the person can confirm or dismiss. A MemorySource records where imported data came from (a connected app). Memory import from connected apps is handled by normalizeConnectorRecords (server/src/memory/ingestion.ts:16-80) which parses JSON responses from external services (searching items, files, or results keys), normalizes records to text, and caps at 50 records or 256KB.

Injection into prompts. The PersonalMemoryMiddleware (server/src/memory/tools.ts:29-70) is an AG-UI Middleware that injects the person's memories as both a system message and a context entry on every agent run. The system message id is "openbot:personal-memory" — the middleware also deduplicates stale ones. Memory content is loaded asynchronously and injected before the run begins.

Learning (skills). ``CopilotKit Intelligence's Learning system (server/src/learning/runtime.ts:1-35) provides learned skills — reusable capabilities published from observed patterns. The runtime exposes copilotkit_load_skill and copilotkit_read_skill_file tools to agents, backed by CopilotKit Intelligence. A RemoteLearnedSkillsMiddleware injects learned skill context into agent runs. Learning targets are per-agent or default, configured through admin settings (server/src/learning/settings.ts).

Storage layers. Rakazo has three memory systems:

1. Conversation history — stored as Message rows in PostgreSQL (packages/db/src/messages.ts), scoped to a thread. Each message has blocks (text, tool calls, images) and a sequential seq. To manage context windows, history is compacted asynchronously via packages/adapters/src/history-compaction.ts: messages beyond a sliding window of 50 are removed and replaced with an LLM-generated summary stored in thread.historyCompactionSummary. Compaction is triggered when the uncompacted window exceeds the batch size threshold (shouldEnqueueCompaction, line 20). The summary is injected as a user-role message in subsequent runs (line 6047-6054 of executor.ts).

2. Markdown memory store (packages/memory/src/index.ts — MarkdownMemoryStore): a user-and-bot-scoped key-value store for structured facts. Implements the MemoryStore interface from @rakazo/adapter-kit. Documents are stored in memoryDocument and memoryRevision tables. Supports read (list docs by scope/path), commit (upsert with optimistic concurrency via revision numbers), and search (simple substring match on content/path). The agent calls remember/recall_memory tools to write/read from this store. It uses Prisma transactions with Serializable isolation (line 117).

3. Semantic (vector) memory — optional providers accessed via SpaceMemoryProviderResolver (packages/adapters/src/memory-provider-factory.ts). Supports Serenity (a self-hostable vector memory service) and Supermemory (external SaaS). These providers expose recall(), save(), and forget() operations for semantic similarity search. The memory scope (isolated vs shared, resolved in packages/db/src/memory-config.ts) controls whether a bot sees only its own memories or the whole space's.

Injection into prompts. At run start, the executor calls semanticMemory.recall() (line 3459 of executor.ts) with the user's prompt as query, getting up to 5 relevant memories. The recalled text is formatted and injected into the prompt's historicalContext array (line 6056-6060). Similarly, compacted history summaries are injected as a synthetic user message. All memory content passes through redactSecrets() before injection.