OpenAI Agents SDK + MemorySync

Overview

Install openai-agents-memorysync and pass session=MemorySyncSession(...) to Runner.run: conversation history persists server-side byte-for-byte, survives restarts and deploys, follows multi-agent handoffs, and user/assistant text lands in long-term memory automatically. memory_instructions injects recalled context per run, and five structured tools give agents explicit memory. No other memory vendor implements the SDK’s Session protocol.

Setup status and requirements

Supported version

openai-agents-memorysync 1.0.0 (PyPI)

Last setup review

2026-08-23

Permissions

A MemorySync API key with read and write scopes; pop_item/clear_session need delete permission.

Limits

Python 3.10+. Install openai-agents alongside (any current 0.x — the package deliberately pins no SDK version). Python only — TypeScript agents use the Vercel AI SDK or Mastra integrations.

Capabilities

• The first drop-in Session protocol implementation from any memory vendor
• History shared across multi-agent handoffs automatically
• Retry-safe transcript writes: total and partial batch failures converge
• Automatic long-term memory from conversation text
• pop_item/clear_session scoped and loud — never silent data loss
• memory_instructions: per-run recall injection with per-request identity
• Five never-raise agent tools with a read-only mode

Quick Start

from agents import Agent, Runner
from openai_agents_memorysync import MemorySyncSession

agent = Agent(name="Assistant", instructions="You are a helpful assistant.")
session = MemorySyncSession("thread-42", user_id="customer-7")

result = await Runner.run(agent, "Book my usual trip.", session=session)

Use Cases

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