Add Sessions for Automatic Conversation History Management

README.md

@@ -12,10 +12,119 @@ The OpenAI Agents SDK is a lightweight yet powerful framework for building multi
 1. [**Agents**](https://openai.github.io/openai-agents-python/agents): LLMs configured with instructions, tools, guardrails, and handoffs
 2. [**Handoffs**](https://openai.github.io/openai-agents-python/handoffs/): A specialized tool call used by the Agents SDK for transferring control between agents
 3. [**Guardrails**](https://openai.github.io/openai-agents-python/guardrails/): Configurable safety checks for input and output validation
-4. [**Tracing**](https://openai.github.io/openai-agents-python/tracing/): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows
+4. [**Sessions**](#sessions): Automatic conversation history management across agent runs
+5. [**Tracing**](https://openai.github.io/openai-agents-python/tracing/): Built-in tracking of agent runs, allowing you to view, debug and optimize your workflows
 
 Explore the [examples](examples) directory to see the SDK in action, and read our [documentation](https://openai.github.io/openai-agents-python/) for more details.
 
+## Sessions
+
+The Agents SDK provides built-in session memory to automatically maintain conversation history across multiple agent runs, eliminating the need to manually handle `.to_input_list()` between turns.
+
+### Quick start
+
+```python
+from agents import Agent, Runner, SQLiteSession
+
+# Create agent
+agent = Agent(
+ name="Assistant",
+ instructions="Reply very concisely.",
+)
+
+# Create a session instance
+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"
+```
+
+### Session options
+
+- **No memory** (default): No session memory when session parameter is omitted
+- **`session: Session = DatabaseSession(...)`**: Use a Session instance to manage conversation history
+
+```python
+from agents import Agent, Runner, SQLiteSession
+
+# Custom SQLite database file
+session = SQLiteSession("user_123", "conversations.db")
+agent = Agent(name="Assistant")
+
+# Different session IDs maintain separate conversation histories
+result1 = await Runner.run(
+ agent,
+ "Hello",
+ session=session
+)
+result2 = await Runner.run(
+ agent,
+ "Hello",
+ session=SQLiteSession("user_456", "conversations.db")
+)
+```
+
+### Custom session implementations
+
+You can implement your own session memory by creating a class that follows the `Session` protocol:
+
+```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 the session
+ pass
+
+ async def add_items(self, items: List[dict]) -> None:
+ # Store new items for the session
+ pass
+
+ async def pop_item(self) -> dict | None:
+ # Remove and return the most recent item from the session
+ pass
+
+ async def clear_session(self) -> None:
+ # Clear all items for the session
+ pass
+
+# Use your custom session
+agent = Agent(name="Assistant")
+result = await Runner.run(
+ agent,
+ "Hello",
+ session=MyCustomSession("my_session")
+)
+```
+
 ## Get started
 
 1. Set up your Python environment

docs/index.md

@@ -5,6 +5,7 @@ The [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) enables
 - **Agents**, which are LLMs equipped with instructions and tools
 - **Handoffs**, which allow agents to delegate to other agents for specific tasks
 - **Guardrails**, which enable the inputs to agents to be validated
+- **Sessions**, which automatically maintains conversation history across agent runs
 
 In combination with Python, these primitives are powerful enough to express complex relationships between tools and agents, and allow you to build real-world applications without a steep learning curve. In addition, the SDK comes with built-in **tracing** that lets you visualize and debug your agentic flows, as well as evaluate them and even fine-tune models for your application.
 
@@ -21,6 +22,7 @@ Here are the main features of the SDK:
 - Python-first: Use built-in language features to orchestrate and chain agents, rather than needing to learn new abstractions.
 - Handoffs: A powerful feature to coordinate and delegate between multiple agents.
 - Guardrails: Run input validations and checks in parallel to your agents, breaking early if the checks fail.
+- Sessions: Automatic conversation history management across agent runs, eliminating manual state handling.
 - Function tools: Turn any Python function into a tool, with automatic schema generation and Pydantic-powered validation.
 - Tracing: Built-in tracing that lets you visualize, debug and monitor your workflows, as well as use the OpenAI suite of evaluation, fine-tuning and distillation tools.
 

docs/ref/memory.md

@@ -0,0 +1,8 @@
+# Memory
+
+::: agents.memory
+
+ options:
+ members:
+ - Session
+ - SQLiteSession

docs/running_agents.md

@@ -65,7 +65,9 @@ Calling any of the run methods can result in one or more agents running (and hen
 
 At the end of the agent run, you can choose what to show to the user. For example, you might show the user every new item generated by the agents, or just the final output. Either way, the user might then ask a followup question, in which case you can call the run method again.
 
-You can use the base [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] method to get the inputs for the next turn.
+### Manual conversation management
+
+You can manually manage conversation history using the [`RunResultBase.to_input_list()`][agents.result.RunResultBase.to_input_list] method to get the inputs for the next turn:
 
 ```python
 async def main():
@@ -84,6 +86,39 @@ async def main():
  # California
 ```
 
+### Automatic conversation management with Sessions
+
+For a simpler approach, you can use [Sessions](sessions.md) to automatically handle conversation history without manually calling `.to_input_list()`:
+
+```python
+from agents import Agent, Runner, SQLiteSession
+
+async def main():
+ agent = Agent(name="Assistant", instructions="Reply very concisely.")
+
+ # Create session instance
+ session = SQLiteSession("conversation_123")
+
+ with trace(workflow_name="Conversation", group_id=thread_id):
+ # 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
+```
+
+Sessions automatically:
+
+- Retrieves conversation history before each run
+- Stores new messages after each run
+- Maintains separate conversations for different session IDs
+
+See the [Sessions documentation](sessions.md) for more details.
+
 ## Exceptions
 
 The SDK raises exceptions in certain cases. The full list is in [`agents.exceptions`][]. As an overview: