feat: Add OpenAI Agents SDK integration service
- Create new OpenAIAgentService that integrates OpenAI Agents SDK with Pipecat - Support for agent loops, handoffs, guardrails, and session management - Add streaming and non-streaming response modes - Include comprehensive tool integration and error handling - Add optional dependency for openai-agents package - Create foundational examples showing basic usage and agent handoffs - Add comprehensive tests with mocked dependencies - Include detailed documentation and README Key features: - Real-time streaming responses compatible with Pipecat pipelines - Agent handoffs for specialized task delegation - Tool calling with automatic schema generation - Input/output guardrails for safety and validation - Session context management for conversation continuity - Built-in tracing and monitoring integration Examples: - 45-openai-agent-basic.py: Basic agent with weather and trivia tools - 46-openai-agent-handoffs.py: Multi-agent system with specialist handoffs
This commit is contained in:
James Hush
161
examples/foundational/45-openai-agent-basic.py
Normal file
161
examples/foundational/45-openai-agent-basic.py
Normal file
@@ -0,0 +1,161 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""
|
||||
Basic OpenAI Agent service example.
|
||||
|
||||
This example demonstrates how to use the OpenAI Agents SDK within a Pipecat
|
||||
pipeline to create an interactive agent with tool calling capabilities.
|
||||
|
||||
Requirements:
|
||||
- OpenAI API key
|
||||
- OpenAI Agents SDK: pip install openai-agents
|
||||
"""
|
||||
|
||||
import os
|
||||
import random
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import EndFrame, TextFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai_agent.agent_service import OpenAIAgentService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# Transport configuration
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
"webrtc": lambda: TransportParams(audio_out_enabled=True),
|
||||
}
|
||||
|
||||
|
||||
def get_weather_tool():
|
||||
"""Example tool function for weather information."""
|
||||
|
||||
def get_weather(location: str) -> str:
|
||||
"""Get the current weather for a location.
|
||||
|
||||
Args:
|
||||
location: The city or location to get weather for.
|
||||
|
||||
Returns:
|
||||
A weather description string.
|
||||
"""
|
||||
# Simulate weather data
|
||||
conditions = ["sunny", "cloudy", "rainy", "snowy", "windy"]
|
||||
temp = random.randint(-10, 35)
|
||||
condition = random.choice(conditions)
|
||||
|
||||
return f"The weather in {location} is {condition} with a temperature of {temp}°C."
|
||||
|
||||
return get_weather
|
||||
|
||||
|
||||
def get_random_fact_tool():
|
||||
"""Example tool function for random facts."""
|
||||
|
||||
def get_random_fact() -> str:
|
||||
"""Get a random interesting fact.
|
||||
|
||||
Returns:
|
||||
A random fact string.
|
||||
"""
|
||||
facts = [
|
||||
"Honey never spoils. Archaeologists have found edible honey in ancient Egyptian tombs.",
|
||||
"A group of flamingos is called a 'flamboyance'.",
|
||||
"Octopuses have three hearts and blue blood.",
|
||||
"The Great Wall of China isn't visible from space with the naked eye.",
|
||||
"Bananas are berries, but strawberries aren't.",
|
||||
]
|
||||
return random.choice(facts)
|
||||
|
||||
return get_random_fact
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting OpenAI Agent bot")
|
||||
|
||||
# Set up TTS for voice output
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# Create tools for the agent
|
||||
tools = [
|
||||
get_weather_tool(),
|
||||
get_random_fact_tool(),
|
||||
]
|
||||
|
||||
# Initialize the OpenAI Agent service
|
||||
agent_service = OpenAIAgentService(
|
||||
name="Assistant",
|
||||
instructions="""You are a helpful assistant with access to weather information and random facts.
|
||||
You can:
|
||||
- Check weather for any location using the get_weather tool
|
||||
- Share interesting facts using the get_random_fact tool
|
||||
- Have natural conversations
|
||||
|
||||
Be friendly, informative, and engaging in your responses.""",
|
||||
tools=tools,
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
streaming=True,
|
||||
)
|
||||
|
||||
# Create the processing pipeline
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
agent_service,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Send an initial greeting when client connects
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected, sending greeting")
|
||||
await task.queue_frames(
|
||||
[
|
||||
TextFrame(
|
||||
"Hello! I'm an AI assistant powered by the OpenAI Agents SDK. "
|
||||
"I can help you with weather information, share interesting facts, "
|
||||
"or just have a conversation. What would you like to know?"
|
||||
),
|
||||
EndFrame(),
|
||||
]
|
||||
)
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
254
examples/foundational/46-openai-agent-handoffs.py
Normal file
254
examples/foundational/46-openai-agent-handoffs.py
Normal file
@@ -0,0 +1,254 @@
|
||||
#
|
||||
# Copyright (c) 2024–2025, Daily
|
||||
#
|
||||
# SPDX-License-Identifier: BSD 2-Clause License
|
||||
#
|
||||
|
||||
"""
|
||||
Advanced OpenAI Agent service example with handoffs.
|
||||
|
||||
This example demonstrates how to use multiple agents with handoffs in the
|
||||
OpenAI Agents SDK within a Pipecat pipeline, showcasing agent orchestration
|
||||
and specialization.
|
||||
|
||||
Requirements:
|
||||
- OpenAI API key
|
||||
- OpenAI Agents SDK: pip install openai-agents
|
||||
"""
|
||||
|
||||
import os
|
||||
import random
|
||||
from typing import Any, Dict
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from loguru import logger
|
||||
|
||||
from pipecat.frames.frames import EndFrame, TextFrame
|
||||
from pipecat.pipeline.pipeline import Pipeline
|
||||
from pipecat.pipeline.runner import PipelineRunner
|
||||
from pipecat.pipeline.task import PipelineTask
|
||||
from pipecat.runner.types import RunnerArguments
|
||||
from pipecat.runner.utils import create_transport
|
||||
from pipecat.services.cartesia.tts import CartesiaTTSService
|
||||
from pipecat.services.openai_agent.agent_service import OpenAIAgentService
|
||||
from pipecat.transports.base_transport import BaseTransport, TransportParams
|
||||
from pipecat.transports.daily.transport import DailyParams
|
||||
from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
|
||||
|
||||
load_dotenv(override=True)
|
||||
|
||||
# Transport configuration
|
||||
transport_params = {
|
||||
"daily": lambda: DailyParams(audio_out_enabled=True),
|
||||
"twilio": lambda: FastAPIWebsocketParams(audio_out_enabled=True),
|
||||
"webrtc": lambda: TransportParams(audio_out_enabled=True),
|
||||
}
|
||||
|
||||
|
||||
def create_weather_tools():
|
||||
"""Create weather-related tools."""
|
||||
|
||||
def get_weather(location: str) -> str:
|
||||
"""Get current weather for a location."""
|
||||
conditions = ["sunny", "cloudy", "rainy", "snowy", "windy"]
|
||||
temp = random.randint(-10, 35)
|
||||
condition = random.choice(conditions)
|
||||
return f"The weather in {location} is {condition} with a temperature of {temp}°C."
|
||||
|
||||
def get_forecast(location: str, days: int = 3) -> str:
|
||||
"""Get weather forecast for multiple days."""
|
||||
forecast = []
|
||||
for i in range(days):
|
||||
conditions = ["sunny", "cloudy", "rainy", "snowy"]
|
||||
temp = random.randint(-5, 30)
|
||||
condition = random.choice(conditions)
|
||||
day = "today" if i == 0 else f"in {i} day{'s' if i > 1 else ''}"
|
||||
forecast.append(f"{day.capitalize()}: {condition}, {temp}°C")
|
||||
return f"Weather forecast for {location}:\n" + "\n".join(forecast)
|
||||
|
||||
return [get_weather, get_forecast]
|
||||
|
||||
|
||||
def create_trivia_tools():
|
||||
"""Create trivia and fact tools."""
|
||||
|
||||
def get_random_fact() -> str:
|
||||
"""Get a random interesting fact."""
|
||||
facts = [
|
||||
"Honey never spoils. Archaeologists have found edible honey in ancient Egyptian tombs.",
|
||||
"A group of flamingos is called a 'flamboyance'.",
|
||||
"Octopuses have three hearts and blue blood.",
|
||||
"The Great Wall of China isn't visible from space with the naked eye.",
|
||||
"Bananas are berries, but strawberries aren't.",
|
||||
"Wombat poop is cube-shaped.",
|
||||
"A shrimp's heart is in its head.",
|
||||
"It's impossible to hum while holding your nose.",
|
||||
]
|
||||
return random.choice(facts)
|
||||
|
||||
def get_science_fact() -> str:
|
||||
"""Get a random science fact."""
|
||||
facts = [
|
||||
"The speed of light in a vacuum is approximately 299,792,458 meters per second.",
|
||||
"DNA stands for Deoxyribonucleic Acid.",
|
||||
"The human brain uses about 20% of the body's total energy.",
|
||||
"There are more possible games of chess than atoms in the observable universe.",
|
||||
"A single bolt of lightning contains enough energy to toast 100,000 slices of bread.",
|
||||
]
|
||||
return random.choice(facts)
|
||||
|
||||
return [get_random_fact, get_science_fact]
|
||||
|
||||
|
||||
def create_math_tools():
|
||||
"""Create math calculation tools."""
|
||||
|
||||
def calculate(expression: str) -> str:
|
||||
"""Safely calculate a mathematical expression."""
|
||||
try:
|
||||
# Only allow basic math operations for safety
|
||||
allowed_chars = set("0123456789+-*/.() ")
|
||||
if not all(c in allowed_chars for c in expression):
|
||||
return "Sorry, I can only calculate basic math expressions with +, -, *, /, and parentheses."
|
||||
|
||||
result = eval(expression)
|
||||
return f"{expression} = {result}"
|
||||
except Exception as e:
|
||||
return f"Error calculating '{expression}': {str(e)}"
|
||||
|
||||
def generate_math_problem() -> str:
|
||||
"""Generate a random math problem."""
|
||||
operations = ["+", "-", "*"]
|
||||
a = random.randint(1, 20)
|
||||
b = random.randint(1, 20)
|
||||
op = random.choice(operations)
|
||||
|
||||
if op == "+":
|
||||
answer = a + b
|
||||
elif op == "-":
|
||||
answer = a - b
|
||||
else: # multiplication
|
||||
answer = a * b
|
||||
|
||||
return f"Here's a math problem for you: {a} {op} {b} = ?"
|
||||
|
||||
return [calculate, generate_math_problem]
|
||||
|
||||
|
||||
async def create_specialist_agents():
|
||||
"""Create specialized agents for different domains."""
|
||||
|
||||
# Weather specialist agent
|
||||
weather_agent = OpenAIAgentService(
|
||||
name="Weather Specialist",
|
||||
instructions="""You are a weather specialist. You provide detailed weather information,
|
||||
forecasts, and weather-related advice. Use your tools to get accurate weather data.
|
||||
Be informative and helpful about weather conditions and what they might mean for
|
||||
outdoor activities.""",
|
||||
tools=create_weather_tools(),
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
streaming=True,
|
||||
)
|
||||
|
||||
# Trivia specialist agent
|
||||
trivia_agent = OpenAIAgentService(
|
||||
name="Trivia Master",
|
||||
instructions="""You are a trivia and facts specialist. You love sharing interesting
|
||||
facts, trivia, and educational content. Use your tools to provide fascinating
|
||||
information and engage users with fun facts. Make learning enjoyable!""",
|
||||
tools=create_trivia_tools(),
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
streaming=True,
|
||||
)
|
||||
|
||||
# Math specialist agent
|
||||
math_agent = OpenAIAgentService(
|
||||
name="Math Helper",
|
||||
instructions="""You are a mathematics specialist. You help with calculations,
|
||||
math problems, and mathematical concepts. Use your tools to solve problems
|
||||
and generate practice questions. Make math accessible and fun!""",
|
||||
tools=create_math_tools(),
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
streaming=True,
|
||||
)
|
||||
|
||||
return weather_agent, trivia_agent, math_agent
|
||||
|
||||
|
||||
async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
|
||||
logger.info("Starting OpenAI Agent bot with handoffs")
|
||||
|
||||
# Set up TTS for voice output
|
||||
tts = CartesiaTTSService(
|
||||
api_key=os.getenv("CARTESIA_API_KEY", ""),
|
||||
voice_id="71a7ad14-091c-4e8e-a314-022ece01c121", # British Reading Lady
|
||||
)
|
||||
|
||||
# Create specialist agents
|
||||
weather_agent, trivia_agent, math_agent = await create_specialist_agents()
|
||||
|
||||
# Create the main triage agent that can hand off to specialists
|
||||
triage_agent = OpenAIAgentService(
|
||||
name="Assistant Coordinator",
|
||||
instructions="""You are a helpful assistant coordinator. Your role is to understand
|
||||
what the user needs and direct them to the right specialist:
|
||||
|
||||
- For weather questions, forecasts, or outdoor activity planning -> Weather Specialist
|
||||
- For interesting facts, trivia, or educational content -> Trivia Master
|
||||
- For calculations, math problems, or mathematical help -> Math Helper
|
||||
|
||||
If the request doesn't clearly fit a specialist, you can handle general conversation
|
||||
yourself. Always be friendly and explain when you're connecting them to a specialist.""",
|
||||
handoffs=[weather_agent.agent, trivia_agent.agent, math_agent.agent],
|
||||
api_key=os.getenv("OPENAI_API_KEY"),
|
||||
streaming=True,
|
||||
)
|
||||
|
||||
# Create the processing pipeline (using just the triage agent)
|
||||
# Note: In a real implementation, you might want to handle handoffs
|
||||
# by switching the active agent in the pipeline dynamically
|
||||
pipeline = Pipeline(
|
||||
[
|
||||
triage_agent,
|
||||
tts,
|
||||
transport.output(),
|
||||
]
|
||||
)
|
||||
|
||||
task = PipelineTask(
|
||||
pipeline,
|
||||
idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
|
||||
)
|
||||
|
||||
# Send an initial greeting when client connects
|
||||
@transport.event_handler("on_client_connected")
|
||||
async def on_client_connected(transport, client):
|
||||
logger.info("Client connected, sending greeting")
|
||||
await task.queue_frames(
|
||||
[
|
||||
TextFrame(
|
||||
"Hello! I'm your AI assistant coordinator. I work with a team of specialists "
|
||||
"who can help you with different topics:\n\n"
|
||||
"🌤️ Weather Specialist - for weather information and forecasts\n"
|
||||
"🧠 Trivia Master - for interesting facts and trivia\n"
|
||||
"🔢 Math Helper - for calculations and math problems\n\n"
|
||||
"What would you like help with today?"
|
||||
),
|
||||
EndFrame(),
|
||||
]
|
||||
)
|
||||
|
||||
runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
|
||||
await runner.run(task)
|
||||
|
||||
|
||||
async def bot(runner_args: RunnerArguments):
|
||||
"""Main bot entry point compatible with Pipecat Cloud."""
|
||||
transport = await create_transport(runner_args, transport_params)
|
||||
await run_bot(transport, runner_args)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from pipecat.runner.run import main
|
||||
|
||||
main()
|
||||
Reference in New Issue
Block a user