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会话

Agents SDK 提供内置的会话内存,可在多个智能体运行之间自动维护对话历史,无需在回合之间手动处理.to_input_list()

会话为特定会话存储对话历史,使智能体无需显式的手动内存管理即可保持上下文。这对于构建聊天应用或多轮对话尤为有用,你可以让智能体记住之前的交互。

快速开始

fromagentsimportAgent,Runner,SQLiteSession# Create agentagent=Agent(name="Assistant",instructions="Reply very concisely.",)# Create a session instance with a session IDsession=SQLiteSession("conversation_123")# First turnresult=awaitRunner.run(agent,"What city is the Golden Gate Bridge in?",session=session)print(result.final_output)# "San Francisco"# Second turn - agent automatically remembers previous contextresult=awaitRunner.run(agent,"What state is it in?",session=session)print(result.final_output)# "California"# Also works with synchronous runnerresult=Runner.run_sync(agent,"What's the population?",session=session)print(result.final_output)# "Approximately 39 million"

工作原理

当启用会话内存时:

  1. 每次运行前:运行器会自动检索该会话的对话历史,并将其预置到输入项之前。
  2. 每次运行后:在运行期间生成的所有新条目(用户输入、助手响应、工具调用等)都会自动存储到会话中。
  3. 上下文保留:使用相同会话的后续运行将包含完整对话历史,使智能体能够保持上下文。

这消除了在运行之间手动调用.to_input_list() 并管理对话状态的需要。

内存操作

基础操作

会话支持多种用于管理对话历史的操作:

fromagentsimportSQLiteSessionsession=SQLiteSession("user_123","conversations.db")# Get all items in a sessionitems=awaitsession.get_items()# Add new items to a sessionnew_items=[{"role":"user","content":"Hello"},{"role":"assistant","content":"Hi there!"}]awaitsession.add_items(new_items)# Remove and return the most recent itemlast_item=awaitsession.pop_item()print(last_item)# {"role": "assistant", "content": "Hi there!"}# Clear all items from a sessionawaitsession.clear_session()

使用 pop_item 进行更正

当你想要撤销或修改对话中的最后一个条目时,pop_item 方法特别有用:

fromagentsimportAgent,Runner,SQLiteSessionagent=Agent(name="Assistant")session=SQLiteSession("correction_example")# Initial conversationresult=awaitRunner.run(agent,"What's 2 + 2?",session=session)print(f"Agent:{result.final_output}")# User wants to correct their questionassistant_item=awaitsession.pop_item()# Remove agent's responseuser_item=awaitsession.pop_item()# Remove user's question# Ask a corrected questionresult=awaitRunner.run(agent,"What's 2 + 3?",session=session)print(f"Agent:{result.final_output}")

内存选项

无内存(默认)

# Default behavior - no session memoryresult=awaitRunner.run(agent,"Hello")

OpenAI Conversations API 内存

使用OpenAI Conversations API 来持久化conversation state,无需管理你自己的数据库。当你已经依赖由 OpenAI 托管的基础设施来存储对话历史时,这将很有帮助。

fromagentsimportOpenAIConversationsSessionsession=OpenAIConversationsSession()# Optionally resume a previous conversation by passing a conversation ID# session = OpenAIConversationsSession(conversation_id="conv_123")result=awaitRunner.run(agent,"Hello",session=session,)

SQLite 内存

fromagentsimportSQLiteSession# In-memory database (lost when process ends)session=SQLiteSession("user_123")# Persistent file-based databasesession=SQLiteSession("user_123","conversations.db")# Use the sessionresult=awaitRunner.run(agent,"Hello",session=session)

多会话

fromagentsimportAgent,Runner,SQLiteSessionagent=Agent(name="Assistant")# Different sessions maintain separate conversation historiessession_1=SQLiteSession("user_123","conversations.db")session_2=SQLiteSession("user_456","conversations.db")result1=awaitRunner.run(agent,"Hello",session=session_1)result2=awaitRunner.run(agent,"Hello",session=session_2)

由 SQLAlchemy 驱动的会话

对于更高级的用例,你可以使用由 SQLAlchemy 驱动的会话后端。这样就可以使用任何 SQLAlchemy 支持的数据库(PostgreSQL、MySQL、SQLite 等)来进行会话存储。

示例 1:使用from_url 搭配内存型 SQLite

这是最简单的入门方式,适合开发和测试。

importasynciofromagentsimportAgent,Runnerfromagents.extensions.memory.sqlalchemy_sessionimportSQLAlchemySessionasyncdefmain():agent=Agent("Assistant")session=SQLAlchemySession.from_url("user-123",url="sqlite+aiosqlite:///:memory:",create_tables=True,# Auto-create tables for the demo)result=awaitRunner.run(agent,"Hello",session=session)if__name__=="__main__":asyncio.run(main())

示例 2:使用现有的 SQLAlchemy 引擎

在生产应用中,你很可能已经拥有一个 SQLAlchemy 的AsyncEngine 实例。你可以将其直接传递给会话。

importasynciofromagentsimportAgent,Runnerfromagents.extensions.memory.sqlalchemy_sessionimportSQLAlchemySessionfromsqlalchemy.ext.asyncioimportcreate_async_engineasyncdefmain():# In your application, you would use your existing engineengine=create_async_engine("sqlite+aiosqlite:///conversations.db")agent=Agent("Assistant")session=SQLAlchemySession("user-456",engine=engine,create_tables=True,# Auto-create tables for the demo)result=awaitRunner.run(agent,"Hello",session=session)print(result.final_output)awaitengine.dispose()if__name__=="__main__":asyncio.run(main())

加密会话

对于需要对静态对话数据进行加密的应用,你可以使用EncryptedSession 来包装任意会话后端,实现透明加密和基于 TTL 的自动过期。这需要encrypt 可选依赖:pip install openai-agents[encrypt]

EncryptedSession 使用基于每个会话的密钥派生(HKDF)的 Fernet 加密,并支持旧消息的自动过期。当条目超过 TTL 时,它们在检索期间会被静默跳过。

示例:为 SQLAlchemy 会话数据加密

importasynciofromagentsimportAgent,Runnerfromagents.extensions.memoryimportEncryptedSession,SQLAlchemySessionasyncdefmain():# Create underlying session (works with any SessionABC implementation)underlying_session=SQLAlchemySession.from_url(session_id="user-123",url="postgresql+asyncpg://app:secret@db.example.com/agents",create_tables=True,)# Wrap with encryption and TTL-based expirationsession=EncryptedSession(session_id="user-123",underlying_session=underlying_session,encryption_key="your-encryption-key",# Use a secure key from your secrets managementttl=600,# 10 minutes - items older than this are silently skipped)agent=Agent("Assistant")result=awaitRunner.run(agent,"Hello",session=session)print(result.final_output)if__name__=="__main__":asyncio.run(main())

关键特性:

  • 透明加密:在存储前自动加密所有会话条目,并在检索时解密
  • 按会话派生密钥:使用会话 ID 作为盐的 HKDF 来派生唯一加密密钥
  • 基于 TTL 的过期:根据可配置的生存时间(默认:10 分钟)自动使旧消息过期
  • 灵活的密钥输入:接受 Fernet 密钥或原始字符串作为加密密钥
  • 可包装任意会话:适用于 SQLite、SQLAlchemy 或自定义会话实现

重要的安全注意事项

  • 安全存储你的加密密钥(如环境变量、密钥管理服务)
  • 过期令牌根据应用服务的系统时钟被拒绝——请确保所有服务均通过 NTP 同步时间,以避免因时钟漂移导致的误拒
  • 底层会话仍存储加密数据,因此你依然可以掌控你的数据库基础设施

自定义内存实现

你可以通过创建遵循Session 协议的类来实现你自己的会话内存:

fromagents.memory.sessionimportSessionABCfromagents.itemsimportTResponseInputItemfromtypingimportListclassMyCustomSession(SessionABC):"""Custom session implementation following the Session protocol."""def__init__(self,session_id:str):self.session_id=session_id# Your initialization hereasyncdefget_items(self,limit:int|None=None)->List[TResponseInputItem]:"""Retrieve conversation history for this session."""# Your implementation herepassasyncdefadd_items(self,items:List[TResponseInputItem])->None:"""Store new items for this session."""# Your implementation herepassasyncdefpop_item(self)->TResponseInputItem|None:"""Remove and return the most recent item from this session."""# Your implementation herepassasyncdefclear_session(self)->None:"""Clear all items for this session."""# Your implementation herepass# Use your custom sessionagent=Agent(name="Assistant")result=awaitRunner.run(agent,"Hello",session=MyCustomSession("my_session"))

会话管理

会话 ID 命名

使用有意义的会话 ID 来帮助组织对话:

  • 基于用户:"user_12345"
  • 基于线程:"thread_abc123"
  • 基于上下文:"support_ticket_456"

内存持久化

  • 临时会话使用内存型 SQLite(SQLiteSession("session_id")
  • 持久化会话使用基于文件的 SQLite(SQLiteSession("session_id", "path/to/db.sqlite")
  • 生产系统且已有数据库时,使用由 SQLAlchemy 驱动的会话(SQLAlchemySession("session_id", engine=engine, create_tables=True)),支持 SQLAlchemy 支持的数据库
  • 当你希望将历史存储在 OpenAI Conversations API 中时,使用 OpenAI 托管的存储(OpenAIConversationsSession()
  • 使用加密会话(EncryptedSession(session_id, underlying_session, encryption_key))为任意会话提供透明加密与基于 TTL 的过期
  • 针对其他生产系统(Redis、Django 等)考虑实现自定义会话后端,以满足更高级的用例

会话管理

# Clear a session when conversation should start freshawaitsession.clear_session()# Different agents can share the same sessionsupport_agent=Agent(name="Support")billing_agent=Agent(name="Billing")session=SQLiteSession("user_123")# Both agents will see the same conversation historyresult1=awaitRunner.run(support_agent,"Help me with my account",session=session)result2=awaitRunner.run(billing_agent,"What are my charges?",session=session)

完整示例

以下是展示会话内存实际效果的完整示例:

importasynciofromagentsimportAgent,Runner,SQLiteSessionasyncdefmain():# Create an agentagent=Agent(name="Assistant",instructions="Reply very concisely.",)# Create a session instance that will persist across runssession=SQLiteSession("conversation_123","conversation_history.db")print("=== Sessions Example ===")print("The agent will remember previous messages automatically.\n")# First turnprint("First turn:")print("User: What city is the Golden Gate Bridge in?")result=awaitRunner.run(agent,"What city is the Golden Gate Bridge in?",session=session)print(f"Assistant:{result.final_output}")print()# Second turn - the agent will remember the previous conversationprint("Second turn:")print("User: What state is it in?")result=awaitRunner.run(agent,"What state is it in?",session=session)print(f"Assistant:{result.final_output}")print()# Third turn - continuing the conversationprint("Third turn:")print("User: What's the population of that state?")result=awaitRunner.run(agent,"What's the population of that state?",session=session)print(f"Assistant:{result.final_output}")print()print("=== Conversation Complete ===")print("Notice how the agent remembered the context from previous turns!")print("Sessions automatically handles conversation history.")if__name__=="__main__":asyncio.run(main())

API 参考

详细的 API 文档请参阅:


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