Memory

Reactive Agents provides a four-tier memory architecture inspired by cognitive science.

Memory Types

Working Memory

Short-term, capacity-limited (default 7 items). Automatically evicts based on FIFO or importance policy.

// Items are automatically managed during agent execution.
// Working memory holds the current conversation context,
// recent tool results, and active reasoning state.

Semantic Memory

Long-term factual knowledge stored in SQLite with FTS5 full-text search.

// Semantic entries have importance scores, access counts,
// and support Zettelkasten-style linking between concepts.

Episodic Memory

Event log of agent actions and experiences. Supports session snapshots for conversation continuity.

Procedural Memory

Stored workflows and learned procedures with success rate tracking. Agents improve their strategies over time.

Memory Tiers

Tier	Storage	Search	Use Case
1	bun:sqlite WAL	FTS5 full-text	Most applications
2	bun:sqlite WAL + sqlite-vec	FTS5 + KNN vector	Semantic similarity

Tier 1 (Default)

const agent = await ReactiveAgents.create()
  .withMemory("1")  // FTS5 search, no embeddings needed
  .build();

Tier 2 (Vector Search)

Requires an embedding provider:

EMBEDDING_PROVIDER=openai
EMBEDDING_MODEL=text-embedding-3-small

const agent = await ReactiveAgents.create()
  .withMemory("2")  // FTS5 + KNN vector search
  .build();

Memory Bootstrap

At the start of each task, the memory layer bootstraps context:

1. Loads recent semantic entries for the agent
2. Retrieves the last session snapshot
3. Generates a markdown projection of relevant knowledge
4. Injects this into the agent’s system prompt

This gives agents continuity across conversations without explicit context management.