Single Agent Loop

One agent, persistent wisdom across sessions.

The Simplest Pattern

The single agent loop is the foundation of every LocusGraph workflow: one agent, one graph. The agent stores what it learns and retrieves what it needs. Each session builds on the last.

Session Start: Retrieve Context

Begin every session by pulling recent context from the graph. A broad query surfaces the most relevant prior knowledge.

During Session: Store Events

As the agent observes, decides, and acts, store each meaningful event. Tag events with descriptive contexts so they can be retrieved later.

Session End: Summarize

Close the session by storing a summary event. This captures the session's key outcomes in a single retrievable node.

Full Example

const client = new LocusGraphClient({ graphId: 'my-project' });
 
// Session start — retrieve recent context
const context = await client.retrieveMemories({
  query: 'recent work and decisions',
  limit: 10,
});
 
// During work — store what you learn
await client.storeEvent({
  graph_id: 'my-project',
  event_kind: 'fact',
  source: 'agent',
  context_id: 'skill:error_handling',
  payload: { topic: 'retry_pattern', value: 'Use exponential backoff for network errors' },
});
 
// Session end — summarize
await client.storeEvent({
  graph_id: 'my-project',
  event_kind: 'observation',
  source: 'agent',
  context_id: 'session:2025_03_19',
  payload: { topic: 'session_summary', value: 'Refactored error handling to use exponential backoff' },
});

What Gets Stored

Focus on events that compound over time:

• Facts — durable knowledge the agent discovers (e.g., "this API requires auth headers").
• Observations — patterns noticed during work (e.g., "tests fail when DB migrations are pending").
• Decisions — choices made and why (e.g., "chose REST over gRPC for simplicity").
• Summaries — session-level recaps that anchor future retrieval.

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