OpenAI Agents SDK (Python)

from agents import (
    set_default_openai_client,
    set_default_openai_api,
    Agent, Runner
)
from openai import AsyncOpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders
import os

# Set up Portkey as the global client
portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers=createHeaders(
        provider="@YOUR_OPENAI_PROVIDER"
    )
)

# Register as the SDK-wide default
set_default_openai_client(portkey, use_for_tracing=False)
set_default_openai_api("chat_completions")  # Responses API → Chat

# Create agent with any supported model
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    model="gpt-4o"  # Using Anthropic Claude through Portkey
)

# Run the agent
result = Runner.run_sync(agent, "Tell me about quantum computing.")
print(result.final_output)

from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from openai import AsyncOpenAI
from agents import set_default_openai_client

# Create a config with fallbacks, It's recommended that you create the Config in Portkey App rather than hard-code the config JSON directly
config = {
  "strategy": {
    "mode": "fallback"
  },
  "targets": [
    {
      "provider": "openai",
      "override_params": {"model": "gpt-4o"}
    },
    {
      "provider": "anthropic",
      "override_params": {"model": "claude-3-opus-20240229"}
    }
  ]
}

# Configure Portkey client with fallback config
portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers=createHeaders(config=config)
)
set_default_openai_client(portkey)

from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from openai import AsyncOpenAI
from agents import set_default_openai_client

# Create a config with input and output guardrails, It's recommended you create Config in Portkey App and pass the config ID in the client
config = {
    "provider":"@openai-xxx",
    "input_guardrails": ["guardrails-id-xxx", "guardrails-id-yyy"],
    "output_guardrails": ["guardrails-id-xxx"]
}

# Configure OpenAI client with guardrails
portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers=createHeaders(
        config=config,
        provider="@YOUR_OPENAI_PROVIDER"
    )
)
set_default_openai_client(portkey)

from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from openai import AsyncOpenAI
from agents import set_default_openai_client

# Configure client with user tracking
portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers=createHeaders(
        provider="@YOUR_LLM_PROVIDER",
        metadata={
            "_user": "user_123", # Special _user field for user analytics
            "user_name": "John Doe",
            "user_tier": "premium",
            "user_company": "Acme Corp"
        }
    )
)
set_default_openai_client(portkey)

# Configure Portkey with different LLM providers
from portkey_ai import createHeaders, PORTKEY_GATEWAY_URL
from openai import AsyncOpenAI
from agents import set_default_openai_client

# Using OpenAI
openai_config = {
    "provider": "@openai-prod",
    "override_params": {"model": "gpt-4o"}
}

# Using Anthropic
anthropic_config = {
    "provider": "@anthropic-prod",
    "override_params": {"model": "claude-3-5-sonnet-latest"}
}

# Choose which config to use
active_config = openai_config  # or anthropic_config

# Configure OpenAI client with chosen provider
portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers=createHeaders(config=active_config)
)
set_default_openai_client(portkey)

# Create and run agent - no changes needed in agent code
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant.",
    # The model specified here will be used as a reference but the actual model
    # is determined by the active_config
    model="gpt-4o"
)

result = Runner.run_sync(agent, "Tell me about quantum computing.")
print(result.final_output)