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Realtime: make sure we use the initial model settings #1198

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223 changes: 113 additions & 110 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -17,125 +17,19 @@ The OpenAI Agents SDK is a lightweight yet powerful framework for building multi

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

- Option A: Using venv (traditional method)
- Option A: Using venv (traditional method)

```bash
python -m venv env
source env/bin/activate # On Windows: env\Scripts\activate
```

- Option B: Using uv (recommended)
- Option B: Using uv (recommended)

```bash
uv venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
Expand Down Expand Up @@ -263,6 +157,114 @@ The Agents SDK is designed to be highly flexible, allowing you to model a wide r

The Agents SDK automatically traces your agent runs, making it easy to track and debug the behavior of your agents. Tracing is extensible by design, supporting custom spans and a wide variety of external destinations, including [Logfire](https://logfire.pydantic.dev/docs/integrations/llms/openai/#openai-agents), [AgentOps](https://docs.agentops.ai/v1/integrations/agentssdk), [Braintrust](https://braintrust.dev/docs/guides/traces/integrations#openai-agents-sdk), [Scorecard](https://docs.scorecard.io/docs/documentation/features/tracing#openai-agents-sdk-integration), and [Keywords AI](https://docs.keywordsai.co/integration/development-frameworks/openai-agent). For more details about how to customize or disable tracing, see [Tracing](http://openai.github.io/openai-agents-python/tracing), which also includes a larger list of [external tracing processors](http://openai.github.io/openai-agents-python/tracing/#external-tracing-processors-list).

## 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")
)
```

## Development (only needed if you need to edit the SDK/examples)

0. Ensure you have [`uv`](https://docs.astral.sh/uv/) installed.
Expand All @@ -284,6 +286,7 @@ make check # run tests linter and typechecker
```

Or to run them individually:

```
make tests # run tests
make mypy # run typechecker
Expand Down
19 changes: 12 additions & 7 deletions src/agents/realtime/session.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,7 +121,8 @@ async def __aenter__(self) -> RealtimeSession:

model_config = self._model_config.copy()
model_config["initial_model_settings"] = await self._get_updated_model_settings_from_agent(
self._current_agent
starting_settings=self._model_config.get("initial_model_settings", None),
agent=self._current_agent,
)

# Connect to the model
Expand Down Expand Up @@ -330,7 +331,8 @@ async def _handle_tool_call(self, event: RealtimeModelToolCallEvent) -> None:

# Get updated model settings from new agent
updated_settings = await self._get_updated_model_settings_from_agent(
self._current_agent
starting_settings=None,
agent=self._current_agent,
)

# Send handoff event
Expand Down Expand Up @@ -509,9 +511,16 @@ async def _cleanup(self) -> None:

async def _get_updated_model_settings_from_agent(
self,
starting_settings: RealtimeSessionModelSettings | None,
agent: RealtimeAgent,
) -> RealtimeSessionModelSettings:
updated_settings: RealtimeSessionModelSettings = {}
# Start with run config model settings as base
run_config_settings = self._run_config.get("model_settings", {})
updated_settings: RealtimeSessionModelSettings = run_config_settings.copy()
# Apply starting settings (from model config) next
if starting_settings:
updated_settings.update(starting_settings)

instructions, tools, handoffs = await asyncio.gather(
agent.get_system_prompt(self._context_wrapper),
agent.get_all_tools(self._context_wrapper),
Expand All @@ -521,10 +530,6 @@ async def _get_updated_model_settings_from_agent(
updated_settings["tools"] = tools or []
updated_settings["handoffs"] = handoffs or []

# Override with initial settings
initial_settings = self._model_config.get("initial_model_settings", {})
updated_settings.update(initial_settings)

disable_tracing = self._run_config.get("tracing_disabled", False)
if disable_tracing:
updated_settings["tracing"] = None
Expand Down
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