Pattern 5: Standalone Memory (Batch)

You own the entire conversation. Sonzai never sees it in real time. When the conversation ends — call wraps, support case closes, journaling session finishes — you POST the transcript once and Sonzai's extractor turns it into facts, mood updates, personality drift, habit detection, and proactive-outreach signal. Best for tutoring, fitness, CRM, voice calls, journaling, and any flow where Sonzai in the hot path is undesirable or impossible.

When to use this

• Latency budget can't tolerate a per-turn /turn round-trip.
• The transcript already exists (recorded calls, Gong/Zoom exports, journal entries).
• You want bulk ingest after the fact — replay logs, migrate users, benchmark agent quality.

When to switch

• You want fresh per-turn context — Pattern 4: Standalone Realtime.
• You're happy ceding the LLM call too — Pattern 1: Managed Runtime.

Architecture

┌─────────────┐     ┌──────────────────┐     ┌──────────────┐
│  Your App   │     │   Sonzai API     │     │   Your LLM   │
└──────┬──────┘     └────────┬─────────┘     └──────┬───────┘
     │                     │                       │
     │  GET /context       │                       │
     │────────────────────>│ (optional pre-session │
     │  <── user profile ──│  personalization)     │
     │                     │                       │
     │  ══ Your conversation (Sonzai not involved) ═════════│
     │                     │                       │              │
     │  Chat ──────────────┼──────────────────────>│             │
     │  <── reply ─────────┼───────────────────────│             │
     │  [N turns, your loop, your tools]            │             │
     │                     │                       │             │
     │  ════════════════════════════════════════════════════════│
     │                     │                       │
     │  /process or sessions.end({ messages })     │
     │────────────────────>│── extract facts,      │
     │  (full transcript)  │   personality, mood,  │
     │                     │   habits, interests   │
     │  <── extractions ───│   (Sonzai LLM)        │
     │                     │                       │
     │  Use insights       │                       │
     │  (push notif,       │                       │
     │   dashboard,        │                       │
     │   exercises, …)     │                       │
     └─────────────────────┴───────────────────────┘

End-to-end snippet

The simplest path is /process: one call, auto-creates the session, returns the generated session_id for correlation. Use the explicit sessions.start → end({ messages }) lifecycle when you need session-scoped tools, durations, or async polling.

import { Sonzai } from "@sonzai-labs/agents";

const sonzai = new Sonzai({ apiKey: process.env.SONZAI_API_KEY! });

async function ingestTranscript(
agentId: string,
userId: string,
transcript: { role: "user" | "assistant" | "tool"; content: string; tool_calls?: any[] }[],
) {
// One call. Auto-creates a session. Tool messages allowed.
const result = await sonzai.agents.process(agentId, {
  userId,
  messages: transcript,
  provider: "gemini",                          // optional override
  model:    "gemini-3.1-flash-lite-preview",   // optional override
});

// result.session_id is the auto-created session id.
// Pull extractions from the read endpoints when ready:
const memory = await sonzai.agents.memory.list(agentId, { userId });
const mood   = await sonzai.agents.getMood(agentId, { userId });

return { sessionId: result.session_id, memory, mood };
}

Where to next