Memory System

PRX provides a flexible memory system that allows agents to persist and recall context across conversations. The memory system supports 5 storage backends, each optimized for different deployment scenarios.

Overview

The memory system serves three primary functions:

• Recall -- retrieve relevant past interactions and facts before each LLM call
• Store -- persist important information extracted from conversations
• Compact -- summarize and compress old memories to fit within context limits

Storage Backends

Backend	Persistence	Search	Best For
Markdown	File-based	Full-text grep	Single-user CLI, version-controlled memory
SQLite	Local database	FTS5 full-text	Local deployments, small teams
PostgreSQL	Remote database	pg_trgm + FTS	Multi-user server deployments
Embeddings	Vector store	Semantic similarity	RAG-style retrieval, large knowledge bases
In-memory	None (session only)	Linear scan	Ephemeral sessions, testing

Configuration

Select and configure the memory backend in config.toml:

[memory]
backend = "sqlite"  # "markdown" | "sqlite" | "postgres" | "embeddings" | "memory"
max_recall_items = 20
recall_relevance_threshold = 0.3

[memory.sqlite]
path = "~/.local/share/openprx/memory.db"

[memory.postgres]
url = "postgresql://user:pass@localhost/prx"

[memory.embeddings]
provider = "ollama"
model = "nomic-embed-text"
dimension = 768

Memory Lifecycle

1. Extraction -- after each conversation turn, the system extracts key facts
2. Deduplication -- new facts are compared against existing memories
3. Storage -- unique facts are persisted to the configured backend
4. Recall -- before each LLM call, relevant memories are retrieved
5. Hygiene -- periodic maintenance compacts and prunes stale entries

Related Pages

• Markdown Backend
• SQLite Backend
• PostgreSQL Backend
• Embeddings Backend
• Memory Hygiene