OpenAI Agent Builder (Python)

Use Portkey with OpenAI Agent Builder to add observability, reliability, and production features to your visual agent workflows

Introduction

OpenAI Agent Builder is a visual canvas for creating multi-step agent workflows. With its drag-and-drop interface, you can design complex agent workflows, test them with live data, and export production-ready code.

Portkey seamlessly integrates with Agent Builder’s exported code, adding enterprise features like:

• Complete observability of your agent workflows
• Cost tracking across all agent executions
• Reliability features like fallbacks and retries
• Access to 1600+ LLMs through the same interface
• Guardrails for safe and compliant agent behavior

Quick Start

1. Design Your Workflow in Agent Builder

   Open OpenAI Agent Builder and create your agent workflow using the visual canvas. Add nodes for agents, tools, and control flow logic.

   
2. Export Your Code

   Once your workflow is ready, click Code in the top navigation and select Agents SDK to get the Python implementation of your workflow.

   
3. Install Required Packages

   pip install -U openai-agents openai
   
4. Integrate Portkey

   Replace the default OpenAI client initialization in your exported code with Portkey:

   # Original Agent Builder code (Python)
   from agents import Agent, Runner
   
   # Add OpenAI SDK and default client setter
   from agents import set_default_openai_client, set_default_openai_api
   from openai import AsyncOpenAI
   import os, json
   
   PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"
   
   # Configure Portkey client via OpenAI SDK
   portkey = AsyncOpenAI(
       base_url=PORTKEY_GATEWAY_URL,
       api_key=os.environ["PORTKEY_API_KEY"],
       default_headers={
           "x-portkey-provider": "@your-openai-provider-slug",
       }
   )
   
   # Set as default client for all agents
   set_default_openai_client(portkey)
   set_default_openai_api("chat_completions")  # Responses API → Chat
   
   # Your Agent Builder workflow code continues as exported...
   # ...
   

   In your model field of your OpenAI Agents SDK code, enter the Portkey Model Slug from Model Catalog. Example: @openai-provider-slug/gpt-5, @anthropic-provider/claude-sonnet-latest.

Production Features

1. Enhanced Observability

Portkey provides comprehensive observability for your OpenAI Agents, helping you understand exactly what’s happening during each execution.

Traces

# Add tracing to your OpenAI Agents
from openai import AsyncOpenAI
from agents import set_default_openai_client
import os

portkey = AsyncOpenAI(
    base_url="https://api.portkey.ai/v1",
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers={
        "x-portkey-trace-id": "unique_execution_trace_id",  # Add unique trace ID
        "x-portkey-provider": "@your-openai-provider-slug",
    }
)
set_default_openai_client(portkey)

Logs

Portkey logs every interaction with LLMs, including:

• Complete request and response payloads
• Latency and token usage metrics
• Cost calculations
• Tool calls and function executions

All logs can be filtered by metadata, trace IDs, models, and more, making it easy to debug specific agent runs.

Metrics & Dashboards

Portkey provides built-in dashboards that help you:

• Track cost and token usage across all agent runs
• Analyze performance metrics like latency and success rates
• Identify bottlenecks in your agent workflows
• Compare different agent configurations and LLMs

You can filter and segment all metrics by custom metadata to analyze specific agent types, user groups, or use cases.

Metadata Filtering

Add custom metadata to your OpenAI agent calls to enable powerful filtering and segmentation:

from openai import AsyncOpenAI
from agents import set_default_openai_client
import os, json

portkey = AsyncOpenAI(
    base_url="https://api.portkey.ai/v1",
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers={
        "x-portkey-metadata": json.dumps({"agent_type": "research_agent"}),
        "x-portkey-provider": "@your-openai-provider-slug",
    }
)
set_default_openai_client(portkey)

This metadata can be used to filter logs, traces, and metrics on the Portkey dashboard, allowing you to analyze specific agent runs, users, or environments.

2. Reliability - Keep Your Agents Running Smoothly

When running agents in production, things can go wrong - API rate limits, network issues, or provider outages. Portkey’s reliability features ensure your agents keep running smoothly even when problems occur.

It’s this simple to enable fallback in your OpenAI Agents:

from openai import AsyncOpenAI
from agents import set_default_openai_client
import os, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

# Create a config with fallbacks. It's recommended that you create the Config
# in the 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={
        # You can also pass a Config ID instead of the full JSON
        "x-portkey-config": json.dumps(config),
    }
)
set_default_openai_client(portkey)

This configuration will automatically try Claude if the GPT-4o request fails, ensuring your agent can continue operating.

Automatic Retries

Handles temporary failures automatically. If an LLM call fails, Portkey will retry the same request for the specified number of times - perfect for rate limits or network blips.

Request Timeouts

Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes.

Conditional Routing

Send different requests to different providers. Route complex reasoning to GPT-4, creative tasks to Claude, and quick responses to Gemini based on your needs.

Fallbacks

Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability.

Load Balancing

Spread requests across multiple API keys or providers. Great for high-volume agent operations and staying within rate limits.

3. Prompting in OpenAI Agents

Portkey’s Prompt Engineering Studio helps you create, manage, and optimize the prompts used in your OpenAI Agents. Instead of hardcoding prompts or instructions, use Portkey’s prompt rendering API to dynamically fetch and apply your versioned prompts.

Prompt Playground

Prompt Playground is a place to compare, test and deploy perfect prompts for your AI application. It’s where you experiment with different models, test variables, compare outputs, and refine your prompt engineering strategy before deploying to production. It allows you to:

1. Iteratively develop prompts before using them in your agents
2. Test prompts with different variables and models
3. Compare outputs between different prompt versions
4. Collaborate with team members on prompt development

This visual environment makes it easier to craft effective prompts for each step in your OpenAI Agents agent’s workflow.

Using Prompt Templates

The Prompt Render API retrieves your prompt templates with all parameters configured:

from openai import AsyncOpenAI
from agents import Agent, Runner, set_default_openai_client
import os

# Initialize Portkey client for prompt management
# Using Portkey SDK here is recommended; otherwise call the REST API.
from portkey_ai import Portkey  # keep SDK only for prompt render

portkey_client = Portkey(api_key=os.environ["PORTKEY_API_KEY"])

# Retrieve prompt using the render API
prompt_data = portkey_client.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "user_input": "Tell me about artificial intelligence",
    }
)

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

# Configure OpenAI client with Portkey
openai_client = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
)
set_default_openai_client(openai_client)

# Use the rendered prompt in your OpenAI Agent
agent = Agent(
    name="Assistant",
    instructions=prompt_data.data["messages"][0]["content"],  # Use the rendered prompt as instructions
    model="gpt-4o",
)

result = Runner.run_sync(agent, "Tell me about artificial intelligence")
print(result.final_output)

Prompt Versioning

You can:

• Create multiple versions of the same prompt
• Compare performance between versions
• Roll back to previous versions if needed
• Specify which version to use in your code:

# Use a specific prompt version
prompt_data = portkey_client.prompts.render(
    prompt_id="YOUR_PROMPT_ID@version_number",
    variables={
        "user_input": "Tell me about quantum computing",
    }
)

Mustache Templating for variables

Portkey prompts use Mustache-style templating for easy variable substitution:

You are an AI assistant helping with {{task_type}}.

User question: {{user_input}}

Please respond in a {{tone}} tone and include {{required_elements}}.

When rendering, simply pass the variables:

prompt_data = portkey_client.prompts.render(
    prompt_id="YOUR_PROMPT_ID",
    variables={
        "task_type": "research",
        "user_input": "Tell me about quantum computing",
        "tone": "professional",
        "required_elements": "recent academic references",
    }
)

Prompt Engineering Studio

Learn more about Portkey’s prompt management features

4. Guardrails for Safe Agents

Guardrails ensure your OpenAI Agents operate safely and respond appropriately in all situations.

Why Use Guardrails?

OpenAI Agents can experience various failure modes:

• Generating harmful or inappropriate content
• Leaking sensitive information like PII
• Hallucinating incorrect information
• Generating outputs in incorrect formats

Portkey’s guardrails protect against these issues by validating both inputs and outputs.

Implementing Guardrails

from openai import AsyncOpenAI
from agents import set_default_openai_client
import os, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

# 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 = {
    "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={
        "x-portkey-config": json.dumps(config),
        "x-portkey-provider": "@openai-xxx",
    }
)
set_default_openai_client(portkey)

Portkey’s guardrails can:

• Detect and redact PII in both inputs and outputs
• Filter harmful or inappropriate content
• Validate response formats against schemas
• Check for hallucinations against ground truth
• Apply custom business logic and rules

Learn More About Guardrails

Explore Portkey’s guardrail features to enhance agent safety

5. User Tracking with Metadata

Track individual users through your OpenAI Agents using Portkey’s metadata system.

What is Metadata in Portkey?

Metadata allows you to associate custom data with each request, enabling filtering, segmentation, and analytics. The special _user field is specifically designed for user tracking.

from openai import AsyncOpenAI
from agents import set_default_openai_client
import os, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

# Configure client with user tracking
portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers={
        "x-portkey-metadata": json.dumps({
            "_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)

Filter Analytics by User

With metadata in place, you can filter analytics by user and analyze performance metrics on a per-user basis:

This enables:

• Per-user cost tracking and budgeting
• Personalized user analytics
• Team or organization-level metrics
• Environment-specific monitoring (staging vs. production)

Learn More About Metadata

Explore how to use custom metadata to enhance your analytics

6. Caching for Efficient Agents

Implement caching to make your OpenAI Agents agents more efficient and cost-effective:

Simple Caching

import os
from openai import AsyncOpenAI
from agents import set_default_openai_client

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

portkey = AsyncOpenAI(
    base_url=PORTKEY_GATEWAY_URL,
    api_key=os.environ["PORTKEY_API_KEY"],
    default_headers={
        "x-portkey-config": "your-config-id",  # or JSON with {"cache": {"mode": "simple"}}
    }
)
set_default_openai_client(portkey)

Simple caching performs exact matches on input prompts, caching identical requests to avoid redundant model executions.

7. Model Interoperability

With Portkey, you can easily switch between different LLMs in your OpenAI Agents without changing your core agent logic.

# Configure Portkey with different LLM providers via headers
from openai import AsyncOpenAI
from agents import set_default_openai_client, Agent, Runner
import os, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

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

# Using Anthropic
anthropic_config = {
    "provider": "anthropic",
    "api_key": "YOUR_ANTHROPIC_API_KEY",
    "override_params": {"model": "claude-3-opus-20240229"},
}

# 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={
        "x-portkey-config": json.dumps(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)

Portkey provides access to over 200 LLMs through a unified interface, including:

• OpenAI (GPT-4o, GPT-4 Turbo, etc.)
• Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, etc.)
• Mistral AI (Mistral Large, Mistral Medium, etc.)
• Google Vertex AI (Gemini 1.5 Pro, etc.)
• Cohere (Command, Command-R, etc.)
• AWS Bedrock (Claude, Titan, etc.)
• Local/Private Models

Supported Providers

See the full list of LLM providers supported by Portkey

Set Up Enterprise Governance for OpenAI Agents

Why Enterprise Governance? If you are using OpenAI Agents inside your organization, you need to consider several governance aspects:

• Cost Management: Controlling and tracking AI spending across teams
• Access Control: Managing which teams can use specific models
• Usage Analytics: Understanding how AI is being used across the organization
• Security & Compliance: Maintaining enterprise security standards
• Reliability: Ensuring consistent service across all users

Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step.

Enterprise Implementation Guide

Portkey allows you to use 1600+ LLMs with your OpenAI Agents setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

1. Integrate your LLM

   Virtual Keys are Portkey’s secure way to manage your LLM provider API keys. Think of them like disposable credit cards for your LLM API keys, providing essential controls like:

   • Budget limits for API usage
   • Rate limiting capabilities
   • Secure API key storage

   To create a virtual key: Go to Virtual Keys in the Portkey App. Save and copy the virtual key ID

   

   Save your virtual key ID - you’ll need it for the next step.

2. Create Default Config

   Configs in Portkey are JSON objects that define how your requests are routed. They help with implementing features like advanced routing, fallbacks, and retries.

   We need to create a default config to route our requests to the virtual key created in Step 1.

   To create your config:

   1. Go to Configs in Portkey dashboard
   2. Create new config with:

   {
       "provider":"@YOUR_PROVIDER_FROM_STEP1",
       "override_params": {
         "model": "gpt-4o" // Your preferred model name
       }
   }
   

   3. Save and note the Config name for the next step

   

   This basic config connects to your virtual key. You can add more advanced Portkey features later.

3. Configure Portkey API Key

   Now create Portkey API key access point and attach the config you created in Step 2:

   1. Go to API Keys in Portkey and Create new API key
   2. Select your config from Step 2
   3. Generate and save your API key

   

   Save your API key securely - you’ll need it for OpenAI Agents integration.

4. Once you have created your API Key after attaching default config, you can directly pass the API key + base URL in the OpenAI client. Here’s how:

   from openai import AsyncOpenAI
   import os
   
   PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"
   
   client = AsyncOpenAI(
       api_key=os.environ["PORTKEY_API_KEY"],  # Your Portkey API Key from Step 3
       base_url=PORTKEY_GATEWAY_URL,
   )
   
   # your rest of the code remains same
   

Step 1: Implement Budget Controls & Rate Limits

Step 1: Implement Budget Controls & Rate Limits

Virtual Keys enable granular control over LLM access at the team/department level. This helps you:

• Set up budget limits
• Prevent unexpected usage spikes using Rate limits
• Track departmental spending

Setting Up Department-Specific Controls:

1. Navigate to Virtual Keys in Portkey dashboard
2. Create new Virtual Key for each department with budget limits and rate limits
3. Configure department-specific limits

Step 2: Define Model Access Rules

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. Portkey Configs provide this control layer with features like:

Access Control Features:

• Model Restrictions: Limit access to specific models
• Data Protection: Implement guardrails for sensitive data
• Reliability Controls: Add fallbacks and retry logic

Example Configuration:

Here’s a basic configuration to route requests to OpenAI, specifically using GPT-4o:

{
	"strategy": {
		"mode": "single"
	},
	"targets": [
		{
			"provider":"@YOUR_OPENAI_PROVIDER",
			"override_params": {
				"model": "gpt-4o"
			}
		}
	]
}

Create your config on the Configs page in your Portkey dashboard. You’ll need the config ID for connecting to OpenAI Agents’s setup.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 3: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

• Track usage per user/team with the help of metadata
• Apply appropriate configs to route requests
• Collect relevant metadata to filter logs
• Enforce access permissions

Create API keys through:

• Portkey App
• API Key Management API

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="engineering-team",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "production",
            "department": "engineering",
        },
    },
    scopes=["logs.view", "configs.read"],
)

For detailed key management instructions, see our API Keys documentation.

Step 4: Deploy & Monitor

Step 4: Deploy & Monitor

After distributing API keys to your team members, your enterprise-ready OpenAI Agents setup is ready to go. Each team member can now use their designated API keys with appropriate access levels and budget controls. Apply your governance setup using the integration steps from earlier sections Monitor usage in Portkey dashboard:

• Cost tracking by department
• Model usage patterns
• Request volumes
• Error rates

Enterprise Features Now Available

OpenAI Agents now has:

• Departmental budget controls
• Model access governance
• Usage tracking & attribution
• Security guardrails
• Reliability features

Frequently Asked Questions

How does Portkey enhance OpenAI Agents?

Portkey adds production-readiness to OpenAI Agents through comprehensive observability (traces, logs, metrics), reliability features (fallbacks, retries, caching), and access to 1600+ LLMs through a unified interface. This makes it easier to debug, optimize, and scale your agent applications.

Can I use Portkey with existing OpenAI Agents?

Yes! Portkey integrates seamlessly with existing OpenAI Agents. You only need to replace your client initialization code with the Portkey-enabled version. The rest of your agent code remains unchanged.

Does Portkey work with all OpenAI Agents features?

Portkey supports all OpenAI Agents SDK features, including tool use, memory, planning, and more. It adds observability and reliability without limiting any of the SDK’s functionality.

How does Portkey handle streaming in OpenAI Agents?

Portkey fully supports streaming responses in OpenAI Agents. You can enable streaming by using the appropriate methods in the OpenAI Agents SDK, and Portkey will properly track and log the streaming interactions.

How do I filter logs and traces for specific agent runs?

Portkey allows you to add custom metadata to your agent runs, which you can then use for filtering. Add fields like agent_name, agent_type, or session_id to easily find and analyze specific agent executions.

Can I use my own API keys with Portkey?

Yes! Portkey uses your own API keys for the various LLM providers. It securely stores them, allowing you to easily manage and rotate keys without changing your code.

Resources

OpenAI Agents Docs

Official OpenAI Agents SDK documentation

Agent Examples

Example implementations for various use cases

Book a Demo

Get personalized guidance on implementing this integration