---
search:
  exclude: true
---
# 会话

Agents SDK 提供了内置的会话记忆，用于在多个智能体运行之间自动维护对话历史，无需在轮次之间手动处理 `.to_input_list()`。

会话为特定 session 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需要多轮对话且希望智能体记住先前交互的场景尤其有用。

## 快速开始

```python
from agents import Agent, Runner, SQLiteSession

# Create agent
agent = Agent(
    name="Assistant",
    instructions="Reply very concisely.",
)

# Create a session instance with a session ID
session = SQLiteSession("conversation_123")

# First turn
result = await Runner.run(
    agent,
    "What city is the Golden Gate Bridge in?",
    session=session
)
print(result.final_output)  # "San Francisco"

# Second turn - agent automatically remembers previous context
result = await Runner.run(
    agent,
    "What state is it in?",
    session=session
)
print(result.final_output)  # "California"

# Also works with synchronous runner
result = Runner.run_sync(
    agent,
    "What's the population?",
    session=session
)
print(result.final_output)  # "Approximately 39 million"
```

## 工作原理

当启用会话记忆时：

1. **每次运行之前**：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前面。
2. **每次运行之后**：运行期间生成的所有新项（用户输入、助手回复、工具调用等）都会自动存储到会话中。
3. **上下文保留**：同一会话的后续运行将包含完整的对话历史，使智能体能够保持上下文。

这消除了在轮次之间手动调用 `.to_input_list()` 并管理对话状态的需求。

## 记忆操作

### 基本操作

会话支持多种用于管理对话历史的操作：

```python
from agents import SQLiteSession

session = SQLiteSession("user_123", "conversations.db")

# Get all items in a session
items = await session.get_items()

# Add new items to a session
new_items = [
    {"role": "user", "content": "Hello"},
    {"role": "assistant", "content": "Hi there!"}
]
await session.add_items(new_items)

# Remove and return the most recent item
last_item = await session.pop_item()
print(last_item)  # {"role": "assistant", "content": "Hi there!"}

# Clear all items from a session
await session.clear_session()
```

### 使用 pop_item 进行更正

当你想撤销或修改对话中的最后一项时，`pop_item` 方法尤其有用：

```python
from agents import Agent, Runner, SQLiteSession

agent = Agent(name="Assistant")
session = SQLiteSession("correction_example")

# Initial conversation
result = await Runner.run(
    agent,
    "What's 2 + 2?",
    session=session
)
print(f"Agent: {result.final_output}")

# User wants to correct their question
assistant_item = await session.pop_item()  # Remove agent's response
user_item = await session.pop_item()  # Remove user's question

# Ask a corrected question
result = await Runner.run(
    agent,
    "What's 2 + 3?",
    session=session
)
print(f"Agent: {result.final_output}")
```

## 会话类型

SDK 为不同用例提供了多种会话实现：

### OpenAI Conversations API 会话

通过 `OpenAIConversationsSession` 使用 [OpenAI's Conversations API](https://platform.openai.com/docs/api-reference/conversations)。

```python
from agents import Agent, Runner, OpenAIConversationsSession

# Create agent
agent = Agent(
    name="Assistant",
    instructions="Reply very concisely.",
)

# Create a new conversation
session = OpenAIConversationsSession()

# Optionally resume a previous conversation by passing a conversation ID
# session = OpenAIConversationsSession(conversation_id="conv_123")

# Start conversation
result = await Runner.run(
    agent,
    "What city is the Golden Gate Bridge in?",
    session=session
)
print(result.final_output)  # "San Francisco"

# Continue the conversation
result = await Runner.run(
    agent,
    "What state is it in?",
    session=session
)
print(result.final_output)  # "California"
```

### SQLite 会话

默认的轻量级 SQLite 会话实现：

```python
from agents import SQLiteSession

# In-memory database (lost when process ends)
session = SQLiteSession("user_123")

# Persistent file-based database
session = SQLiteSession("user_123", "conversations.db")

# Use the session
result = await Runner.run(
    agent,
    "Hello",
    session=session
)
```

### SQLAlchemy 会话

使用任何 SQLAlchemy 支持的数据库的生产就绪会话：

```python
from agents.extensions.memory import SQLAlchemySession

# Using database URL
session = SQLAlchemySession.from_url(
    "user_123",
    url="postgresql+asyncpg://user:pass@localhost/db",
    create_tables=True
)

# Using existing engine
from sqlalchemy.ext.asyncio import create_async_engine
engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db")
session = SQLAlchemySession("user_123", engine=engine, create_tables=True)
```

参见 [SQLAlchemy Sessions](sqlalchemy_session.md) 获取详细文档。



### 高级 SQLite 会话

增强的 SQLite 会话，包含对话分支、使用分析和结构化查询：

```python
from agents.extensions.memory import AdvancedSQLiteSession

# Create with advanced features
session = AdvancedSQLiteSession(
    session_id="user_123",
    db_path="conversations.db",
    create_tables=True
)

# Automatic usage tracking
result = await Runner.run(agent, "Hello", session=session)
await session.store_run_usage(result)  # Track token usage

# Conversation branching
await session.create_branch_from_turn(2)  # Branch from turn 2
```

参见 [Advanced SQLite Sessions](advanced_sqlite_session.md) 获取详细文档。

### 加密会话

对任意会话实现的透明加密封装：

```python
from agents.extensions.memory import EncryptedSession, SQLAlchemySession

# Create underlying session
underlying_session = SQLAlchemySession.from_url(
    "user_123",
    url="sqlite+aiosqlite:///conversations.db",
    create_tables=True
)

# Wrap with encryption and TTL
session = EncryptedSession(
    session_id="user_123",
    underlying_session=underlying_session,
    encryption_key="your-secret-key",
    ttl=600  # 10 minutes
)

result = await Runner.run(agent, "Hello", session=session)
```

参见 [Encrypted Sessions](encrypted_session.md) 获取详细文档。

### 其他会话类型

还有更多内置选项。请参考 `examples/memory/` 以及 `extensions/memory/` 下的源码。

## 会话管理

### 会话 ID 命名

使用有助于组织对话的有意义的会话 ID：

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

### 记忆持久化

- 使用内存型 SQLite（`SQLiteSession("session_id")`）用于临时对话
- 使用文件型 SQLite（`SQLiteSession("session_id", "path/to/db.sqlite")`）用于持久对话
- 使用由 SQLAlchemy 驱动的会话（`SQLAlchemySession("session_id", engine=engine, create_tables=True)`）用于由 SQLAlchemy 支持的现有数据库的生产系统
- 使用 Dapr 状态存储会话（`DaprSession.from_address("session_id", state_store_name="statestore", dapr_address="localhost:50001")`）用于生产级云原生部署，支持
30+ 种数据库后端，内置遥测、追踪和数据隔离
- 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)`）为任意会话添加透明加密与基于 TTL 的过期
- 为其他生产系统（Redis、Django 等）实现自定义会话后端以满足更高级的用例

### 多个会话

```python
from agents import Agent, Runner, SQLiteSession

agent = Agent(name="Assistant")

# Different sessions maintain separate conversation histories
session_1 = SQLiteSession("user_123", "conversations.db")
session_2 = SQLiteSession("user_456", "conversations.db")

result1 = await Runner.run(
    agent,
    "Help me with my account",
    session=session_1
)
result2 = await Runner.run(
    agent,
    "What are my charges?",
    session=session_2
)
```

### 会话共享

```python
# Different agents can share the same session
support_agent = Agent(name="Support")
billing_agent = Agent(name="Billing")
session = SQLiteSession("user_123")

# Both agents will see the same conversation history
result1 = await Runner.run(
    support_agent,
    "Help me with my account",
    session=session
)
result2 = await Runner.run(
    billing_agent,
    "What are my charges?",
    session=session
)
```

## 完整示例

下面是一个展示会话记忆实际效果的完整示例：

```python
import asyncio
from agents import Agent, Runner, SQLiteSession


async def main():
    # Create an agent
    agent = Agent(
        name="Assistant",
        instructions="Reply very concisely.",
    )

    # Create a session instance that will persist across runs
    session = SQLiteSession("conversation_123", "conversation_history.db")

    print("=== Sessions Example ===")
    print("The agent will remember previous messages automatically.\n")

    # First turn
    print("First turn:")
    print("User: What city is the Golden Gate Bridge in?")
    result = await Runner.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 conversation
    print("Second turn:")
    print("User: What state is it in?")
    result = await Runner.run(
        agent,
        "What state is it in?",
        session=session
    )
    print(f"Assistant: {result.final_output}")
    print()

    # Third turn - continuing the conversation
    print("Third turn:")
    print("User: What's the population of that state?")
    result = await Runner.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())
```

## 自定义会话实现

你可以通过创建一个遵循 [`Session`][agents.memory.session.Session] 协议的类来实现你自己的会话记忆：

```python
from agents.memory.session import SessionABC
from agents.items import TResponseInputItem
from typing import List

class MyCustomSession(SessionABC):
    """Custom session implementation following the Session protocol."""

    def __init__(self, session_id: str):
        self.session_id = session_id
        # Your initialization here

    async def get_items(self, limit: int | None = None) -> List[TResponseInputItem]:
        """Retrieve conversation history for this session."""
        # Your implementation here
        pass

    async def add_items(self, items: List[TResponseInputItem]) -> None:
        """Store new items for this session."""
        # Your implementation here
        pass

    async def pop_item(self) -> TResponseInputItem | None:
        """Remove and return the most recent item from this session."""
        # Your implementation here
        pass

    async def clear_session(self) -> None:
        """Clear all items for this session."""
        # Your implementation here
        pass

# Use your custom session
agent = Agent(name="Assistant")
result = await Runner.run(
    agent,
    "Hello",
    session=MyCustomSession("my_session")
)
```

## 社区会话实现

社区已开发了其他会话实现：

| Package | Description |
|---------|-------------|
| [openai-django-sessions](https://pypi.org/project/openai-django-sessions/) | 基于 Django ORM 的会话，适用于任意 Django 支持的数据库（PostgreSQL、MySQL、SQLite 等） |

如果你已经构建了会话实现，欢迎提交文档 PR 将其添加到这里！

## API 参考

有关详细的 API 文档，请参阅：

- [`Session`][agents.memory.session.Session] - 协议接口
- [`OpenAIConversationsSession`][agents.memory.OpenAIConversationsSession] - OpenAI Conversations API 实现
- [`SQLiteSession`][agents.memory.sqlite_session.SQLiteSession] - 基本的 SQLite 实现
- [`SQLAlchemySession`][agents.extensions.memory.sqlalchemy_session.SQLAlchemySession] - 由 SQLAlchemy 驱动的实现
- [`DaprSession`][agents.extensions.memory.dapr_session.DaprSession] - Dapr 状态存储实现
- [`AdvancedSQLiteSession`][agents.extensions.memory.advanced_sqlite_session.AdvancedSQLiteSession] - 具备分支和分析能力的增强型 SQLite
- [`EncryptedSession`][agents.extensions.memory.encrypt_session.EncryptedSession] - 适用于任意会话的加密封装