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# セッション

Agents SDK には組み込みのセッションメモリがあり、複数回のエージェント実行をまたいで会話履歴を自動的に維持します。そのため、ターン間で `.to_input_list()` を手動で扱う必要がありません。

Sessions は特定のセッションの会話履歴を保存し、明示的なメモリ管理を行わなくてもエージェントがコンテキストを維持できるようにします。これは、エージェントに過去のやり取りを覚えさせたいチャットアプリケーションやマルチターンの会話を構築する際に特に便利です。

## クイックスタート

```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"
```

## 仕組み

セッションメモリを有効にすると、次の処理が行われます。

1. **各実行前**: Runner は自動的にそのセッションの会話履歴を取得し、入力アイテムの先頭に追加します。  
2. **各実行後**: 実行中に生成された新しいアイテム（ユーザー入力、アシスタントの応答、ツール呼び出しなど）がすべて自動的にセッションに保存されます。  
3. **コンテキストの保持**: 同じセッションでの後続の実行では、完全な会話履歴が含まれるため、エージェントがコンテキストを維持できます。  

これにより、`.to_input_list()` を手動で呼び出したり、実行間で会話状態を管理したりする必要がなくなります。

## メモリ操作

### 基本操作

Sessions では、会話履歴を管理するために次の操作が利用できます。

```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()
```

### pop_item を使った修正

`pop_item` メソッドは、会話の最後のアイテムを取り消したり変更したりしたい場合に特に役立ちます。

```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}")
```

## メモリオプション

### メモリなし（デフォルト）

```python
# Default behavior - no session memory
result = await Runner.run(agent, "Hello")
```

### SQLite メモリ

```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
)
```

### 複数セッション

```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,
    "Hello",
    session=session_1
)
result2 = await Runner.run(
    agent,
    "Hello",
    session=session_2
)
```

## カスタムメモリ実装

[`Session`][agents.memory.session.Session] プロトコルに従うクラスを作成することで、独自のセッションメモリを実装できます。

````python
from agents.memory import Session
from typing import List

class MyCustomSession:
    """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[dict]:
        """Retrieve conversation history for this session."""
        # Your implementation here
        pass

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

    async def pop_item(self) -> dict | 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")
)

## Session management

### Session ID naming

Use meaningful session IDs that help you organize conversations:

-   User-based: `"user_12345"`
-   Thread-based: `"thread_abc123"`
-   Context-based: `"support_ticket_456"`

### Memory persistence

-   Use in-memory SQLite (`SQLiteSession("session_id")`) for temporary conversations
-   Use file-based SQLite (`SQLiteSession("session_id", "path/to/db.sqlite")`) for persistent conversations
-   Consider implementing custom session backends for production systems (Redis, PostgreSQL, etc.)

### Session management

```python
# Clear a session when conversation should start fresh
await session.clear_session()

# 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
)
````

## Complete example

Here's a complete example showing session memory in action:

```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())