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"""Minimal realtime session implementation for voice agents."""
from __future__ import annotations
from ..run_context import TContext
from .agent import RealtimeAgent
from .config import (
RealtimeRunConfig,
)
from .model import (
RealtimeModel,
RealtimeModelConfig,
)
from .openai_realtime import OpenAIRealtimeWebSocketModel
from .session import RealtimeSession
class RealtimeRunner:
"""A `RealtimeRunner` is the equivalent of `Runner` for realtime agents. It automatically
handles multiple turns by maintaining a persistent connection with the underlying model
layer.
The session manages the local history copy, executes tools, runs guardrails and facilitates
handoffs between agents.
Since this code runs on your server, it uses WebSockets by default. You can optionally create
your own custom model layer by implementing the `RealtimeModel` interface.
"""
def __init__(
self,
starting_agent: RealtimeAgent,
*,
model: RealtimeModel | None = None,
config: RealtimeRunConfig | None = None,
) -> None:
"""Initialize the realtime runner.
Args:
starting_agent: The agent to start the session with.
context: The context to use for the session.
model: The model to use. If not provided, will use a default OpenAI realtime model.
config: Override parameters to use for the entire run.
"""
self._starting_agent = starting_agent
self._config = config
self._model = model or OpenAIRealtimeWebSocketModel()
async def run(
self, *, context: TContext | None = None, model_config: RealtimeModelConfig | None = None
) -> RealtimeSession:
"""Start and returns a realtime session.
Returns:
RealtimeSession: A session object that allows bidirectional communication with the
realtime model.
Example:
```python
runner = RealtimeRunner(agent)
async with await runner.run() as session:
await session.send_message("Hello")
async for event in session:
print(event)
```
"""
# Create and return the connection
session = RealtimeSession(
model=self._model,
agent=self._starting_agent,
context=context,
model_config=model_config,
run_config=self._config,
)
return session