> ## Documentation Index > Fetch the complete documentation index at: https://portkey-docs-mintlify-add-terraform-provider-docs-72855.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # 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 Open [OpenAI Agent Builder](https://platform.openai.com/playground/agent-builder) and create your agent workflow using the visual canvas. Add nodes for agents, tools, and control flow logic. Once your workflow is ready, click **Code** in the top navigation and select **Agents SDK** to get the Python implementation of your workflow. ```bash theme={null} pip install -U openai-agents openai ``` Replace the default OpenAI client initialization in your exported code with Portkey: ```python theme={null} # 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. ```python theme={null} # 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) ``` 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. 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. Analytics with metadata filters Add custom metadata to your OpenAI agent calls to enable powerful filtering and segmentation: ```python theme={null} 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: ```python theme={null} 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. 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. Prevent your agents from hanging. Set timeouts to ensure you get responses (or can fail gracefully) within your required timeframes. 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. Keep running even if your primary provider fails. Automatically switch to backup providers to maintain availability. 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 Interface 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. The Prompt Render API retrieves your prompt templates with all parameters configured: ```python theme={null} 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) ``` 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: ```python theme={null} # 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", } ) ``` 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: ```python theme={null} 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", } ) ``` 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** ```python theme={null} 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 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. ```python theme={null} 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) 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: ```python theme={null} 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. ```python theme={null} # 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 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. 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](https://app.portkey.ai/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. 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](https://app.portkey.ai/configs) in Portkey dashboard 2. Create new config with: ```json theme={null} { "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. Now create Portkey API key access point and attach the config you created in Step 2: 1. Go to [API Keys](https://app.portkey.ai/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. 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: ```python theme={null} 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 Virtual Keys enable granular control over LLM access at the team/department level. This helps you: * Set up [budget limits](/product/ai-gateway/virtual-keys/budget-limits) * Prevent unexpected usage spikes using Rate limits * Track departmental spending #### Setting Up Department-Specific Controls: 1. Navigate to [Virtual Keys](https://app.portkey.ai/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 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: ```json theme={null} { "strategy": { "mode": "single" }, "targets": [ { "provider":"@YOUR_OPENAI_PROVIDER", "override_params": { "model": "gpt-4o" } } ] } ``` Create your config on the [Configs page](https://app.portkey.ai/configs) 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 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](https://app.portkey.ai/) * [API Key Management API](/api-reference/admin-api/control-plane/api-keys/create-api-key) Example using Python SDK: ```python theme={null} 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](/api-reference/admin-api/control-plane/api-keys/create-api-key). ### 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 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. 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. 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. 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. 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. 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

Official OpenAI Agents SDK documentation

Example implementations for various use cases

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