---
search:
  exclude: true
---
# 会话

Agents SDK 提供内置的会话记忆，可在多个智能体运行之间自动维护对话历史，无需在多轮之间手动处理 `.to_input_list()`。

会话会为特定会话存储对话历史，使智能体无需显式的手动记忆管理即可保持上下文。这对于构建聊天应用或多轮对话尤其有用，因为你希望智能体记住之前的交互。

## 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

启用会话记忆后：

1. **每次运行之前**：runner 会自动检索该会话的对话历史，并将其前置到输入项中。
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）会自动存储到会话中。
3. **上下文保留**：后续使用同一会话的每次运行都会包含完整的对话历史，使智能体能够保持上下文。

这消除了手动调用 `.to_input_list()` 并在多次运行之间管理对话状态的需求。

## 记忆操作

### 基础操作

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 进行纠正

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 针对不同用例提供了多种会话实现：

### OpenAI Conversations API 会话

通过 `OpenAIConversationsSession` 使用 [OpenAI 的 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"
```

### OpenAI Responses 压缩会话

使用 `OpenAIResponsesCompactionSession` 通过 Responses API（`responses.compact`）压缩会话历史。它会包装一个底层会话，并可根据 `should_trigger_compaction` 在每轮后自动压缩。

#### 典型用法（自动压缩）

```python
from agents import Agent, Runner, SQLiteSession
from agents.memory import OpenAIResponsesCompactionSession

underlying = SQLiteSession("conversation_123")
session = OpenAIResponsesCompactionSession(
    session_id="conversation_123",
    underlying_session=underlying,
)

agent = Agent(name="Assistant")
result = await Runner.run(agent, "Hello", session=session)
print(result.final_output)
```

默认情况下，一旦达到候选阈值，压缩会在每轮之后运行。

#### 自动压缩可能阻塞流式传输

压缩会清空并重写会话历史，因此 SDK 会等待压缩完成后才认为该次运行结束。在流式模式下，如果压缩较重，这意味着在最后一个输出 token 之后，`run.stream_events()` 可能还会保持打开几秒钟。

如果你希望低延迟流式传输或更快的轮次切换，请禁用自动压缩，并在多轮之间（或空闲时）自行调用 `run_compaction()`。你可以根据自己的标准决定何时强制压缩。

```python
from agents import Agent, Runner, SQLiteSession
from agents.memory import OpenAIResponsesCompactionSession

underlying = SQLiteSession("conversation_123")
session = OpenAIResponsesCompactionSession(
    session_id="conversation_123",
    underlying_session=underlying,
    # Disable triggering the auto compaction
    should_trigger_compaction=lambda _: False,
)

agent = Agent(name="Assistant")
result = await Runner.run(agent, "Hello", session=session)

# Decide when to compact (e.g., on idle, every N turns, or size thresholds).
await session.run_compaction({"force": True})
```

### SQLite 会话

默认的轻量级会话实现，使用 SQLite：

```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
)
```

### 异步 SQLite 会话

当你希望使用由 `aiosqlite` 支持的 SQLite 持久化时，请使用 `AsyncSQLiteSession`。

```bash
pip install aiosqlite
```

```python
from agents import Agent, Runner
from agents.extensions.memory import AsyncSQLiteSession

agent = Agent(name="Assistant")
session = AsyncSQLiteSession("user_123", db_path="conversations.db")
result = await Runner.run(agent, "Hello", session=session)
```

### Redis 会话

使用 `RedisSession` 在多个 worker 或服务之间共享会话记忆。

```bash
pip install openai-agents[redis]
```

```python
from agents import Agent, Runner
from agents.extensions.memory import RedisSession

agent = Agent(name="Assistant")
session = RedisSession.from_url(
    "user_123",
    url="redis://localhost:6379/0",
)
result = await Runner.run(agent, "Hello", session=session)
```

### SQLAlchemy 会话

面向生产的会话实现，可使用任何 SQLAlchemy 支持的数据库：

```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)
```

详见 [SQLAlchemy Sessions](sqlalchemy_session.md) 文档。



### 高级 SQLite 会话

增强版 SQLite 会话，支持对话分支、用量分析以及结构化查询：

```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
```

详见 [Advanced SQLite Sessions](advanced_sqlite_session.md) 文档。

### 加密会话

适用于任何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)
```

详见 [Encrypted Sessions](encrypted_session.md) 文档。

### 其他会话类型

还有一些内置选项。请参考 `examples/memory/` 以及 `extensions/memory/` 下的源代码。

## 会话管理

### 会话 ID 命名

使用有意义的会话 ID，帮助你组织对话：

-   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 SQLite（`SQLiteSession("session_id")`）用于临时对话
-   使用文件 SQLite（`SQLiteSession("session_id", "path/to/db.sqlite")`）用于持久化对话
-   当你需要基于 `aiosqlite` 的实现时，使用异步 SQLite（`AsyncSQLiteSession("session_id", db_path="...")`）
-   使用 Redis 支撑的会话（`RedisSession.from_url("session_id", url="redis://...")`）实现共享、低延迟的会话记忆
-   使用由 SQLAlchemy 驱动的会话（`SQLAlchemySession("session_id", engine=engine, create_tables=True)`）用于生产系统，并复用 SQLAlchemy 支持的现有数据库
-   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+ 数据库后端，并内置可观测性、追踪和数据隔离
-   当你倾向于将历史存储在 OpenAI Conversations API 中时，使用由OpenAI托管的存储（`OpenAIConversationsSession()`）
-   使用加密会话（`EncryptedSession(session_id, underlying_session, encryption_key)`）以透明加密与基于 TTL 的过期能力封装任意会话
-   对于更高级的用例，可考虑为其他生产系统（例如 Django）实现自定义会话后端

### 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

社区开发了额外的会话实现：

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

如果你构建了一个会话实现，欢迎提交文档 PR，将其添加到这里！

## API 参考

如需详细的 API 文档，请参阅：

-   [`Session`][agents.memory.session.Session] - 协议接口
-   [`OpenAIConversationsSession`][agents.memory.OpenAIConversationsSession] - OpenAI Conversations API 实现
-   [`OpenAIResponsesCompactionSession`][agents.memory.openai_responses_compaction_session.OpenAIResponsesCompactionSession] - Responses API 压缩封装
-   [`SQLiteSession`][agents.memory.sqlite_session.SQLiteSession] - 基础 SQLite 实现
-   [`AsyncSQLiteSession`][agents.extensions.memory.async_sqlite_session.AsyncSQLiteSession] - 基于 `aiosqlite` 的异步 SQLite 实现
-   [`RedisSession`][agents.extensions.memory.redis_session.RedisSession] - 基于 Redis 的会话实现
-   [`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] - 带分支与分析能力的增强 SQLite
-   [`EncryptedSession`][agents.extensions.memory.encrypt_session.EncryptedSession] - 适用于任意会话的加密封装器