OpenAI Codex CLI is a lightweight coding agent that runs directly in your terminal, letting you interact with AI to analyze, modify, and execute code in your projects. While Codex delivers powerful code generation and execution capabilities, Portkey adds essential enterprise controls for production deployments:

• Unified AI Gateway - Single interface for 250+ LLMs with API key management (beyond Codex’s default model options)
• Centralized AI observability: Real-time usage tracking for 40+ key metrics and logs for every request
• Governance - Real-time spend tracking, set budget limits and RBAC in your Codex setup
• Security Guardrails - PII detection, content filtering, and compliance controls

This guide will walk you through integrating Portkey with OpenAI Codex CLI and setting up essential enterprise features including usage tracking, access controls, and budget management.

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1. Setting up Portkey

Portkey allows you to use 250+ LLMs with your Codex CLI setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

1

Create Virtual Key

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:
   Copy

   Ask AI

   {
       "virtual_key": "YOUR_VIRTUAL_KEY_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 Codex CLI integration.

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2. Integrate Portkey with OpenAI Codex CLI

Now that you have your Portkey components set up, let’s connect them to OpenAI Codex CLI. The integration leverages Codex’s provider configuration options to route all requests through Portkey’s AI Gateway.

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Using Portkey Configuration File

OpenAI Codex CLI supports a configuration file where you can specify your AI provider settings. Let’s set up this configuration to use Portkey:

1. Create or edit the Codex configuration file at ~/.codex/config.json:

{
  "provider": "portkey",
  "model": "gpt-4o",
  "providers": {
    "portkey": {
      "name": "Portkey",
      "baseURL": "https://api.portkey.ai/v1",
      "envKey": "PORTKEY_API_KEY"
    }
  }
}

2. Set your Portkey API key as an environment variable:

export PORTKEY_API_KEY="your-portkey-api-key-here"

Note: This command sets the key only for your current terminal session. You can add the export line to your shell’s configuration file (e.g., ~/.zshrc) for persistence, or place it in a .env file at the root of your project.

3. Test your integration:

codex "explain this repository to me"

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3. Set Up Enterprise Governance for Codex

Why Enterprise Governance? If you are using Codex CLI 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

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Portkey Features

Now that you have enterprise-grade Codex CLI setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

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1. Comprehensive Metrics

Using Portkey you can track 40+ key metrics including cost, token usage, response time, and performance across all your LLM providers in real time. You can also filter these metrics based on custom metadata that you can set in your configs. Learn more about metadata here.

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2. Advanced Logs

Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:

• Complete request and response tracking
• Metadata tags for filtering
• Cost attribution and much more…

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3. Unified Access to 250+ LLMs

You can easily switch between 250+ LLMs. Call various LLMs such as Anthropic, Gemini, Mistral, Azure OpenAI, Google Vertex AI, AWS Bedrock, and many more by simply changing the virtual_key in your default config object.

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4. Advanced Metadata Tracking

Using Portkey, you can add custom metadata to your LLM requests for detailed tracking and analytics. Use metadata tags to filter logs, track usage, and attribute costs across departments and teams.

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5. Enterprise Access Management

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6. Reliability Features

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7. Advanced Guardrails

Protect your Codex CLI’s data and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:

• Prevent sensitive data leaks
• Enforce compliance with organizational policies
• PII detection and masking
• Content filtering
• Custom security rules
• Data compliance checks

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FAQs

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Next Steps

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