Roo is an AI coding assistant that integrates directly into your VS Code environment, providing autonomous coding capabilities. While Roo offers powerful AI assistance for development tasks, Portkey adds essential enterprise controls for production deployments:

  • Unified AI Gateway - Single interface for 1600+ LLMs with API key management
  • 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 Roo setup
  • Security Guardrails - PII detection, content filtering, and compliance controls

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

If you are an enterprise looking to standardize Roo usage across your development teams, check out this section.

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1. Setting up Portkey

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

1

Create an Integration

Navigate to the Integrations section on Portkey’s Sidebar. This is where you’ll connect your LLM providers.

  1. Find your preferred provider (e.g., OpenAI, Anthropic, etc.)
  2. Click Connect on the provider card
  3. In the “Create New Integration” window:
    • Enter a Name for reference
    • Enter a Slug for the integration
    • Enter your API Key and other provider specific details for the provider
  4. Click Next Step

In your next step you’ll see workspace provisioning options. You can select the default “Shared Team Workspace” if this is your first time OR chose your current one.

2

Configure Models

On the model provisioning page:

  • Leave all models selected (or customize)
  • Toggle Automatically enable new models if desired

Click Create Integration to complete the integration

3

Copy the Provider Slug

Once your Integration is created:

  1. Go to Model Catalog → Models tab
  2. Find and click on you your model button (if your model is not visible, you need to edit your integration from the last step)
  3. Copy the slug (e.g., @openai-dev/gpt-4o)

We recommend clicking the Run Test Request button on this step to verify your integration. If you see the error: You do not have enough permissions to execute this request, you’ll need to create a User API Key for this step to work properly.

You can create one here. You should be able to see simple chat request output on this step.

This is your unique identifier - you’ll need it for the next step. This slug is basically @your-provider-slug/your-model-name

4

Create Default Config

Portkey’s config is a JSON object used to define routing rules for requests to your gateway. You can create these configs in the Portkey app and reference them in requests via the config ID. For this setup, we’ll create a simple config using your provider (OpenAI) and model (gpt-4o).

  1. Go to Configs in Portkey dashboard
  2. Create new config with: 
    {
    "override_params": {
      "model": "@YOUR_SLUG-FROM-LAST_STEP" // example: @openai-test/gpt-4o-mini
    }
}
  3. Save and note the Config ID & Name for the next step
5

Configure Portkey API Key

Finally, create a Portkey API key:

  1. Go to API Keys in Portkey
  2. Create new API key
  3. Select the config that you create from previous step
  4. Generate and save your API key

Save your API key securely - you’ll need it for Roo Code integration.

🎉 Voila, Setup complete! You now have everything needed to integrate Portkey with your application.

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2. Integrate Portkey with Roo

Now that you have your Portkey components set up, let’s connect them to Roo. Since Portkey provides OpenAI API compatibility, integration is straightforward and requires just a few configuration steps in your VS Code settings.

You need your Portkey API Key from Step 1 before going further.

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Opening Roo Settings

  1. Open VS Code with Roo installed
  2. Click on the Roo icon in your github activity bar
  3. Click on the settings gear icon ⚙️ in the Roo tab

This method uses the default config you created in Portkey, making it easier to manage model settings centrally.

  1. In the Roo settings, navigate to Providers
  2. Configure the following settings:
    • API Provider: Select OpenAI Compatible
    • Base URL: https://api.portkey.ai/v1
    • OpenAI API Key: Your Portkey API key from the setup
    • Model: dummy (since the model is defined in your Portkey config)

Using a default config with override_params is recommended as it allows you to manage all model settings centrally in Portkey, reducing maintenance overhead.

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Method 2: Using Custom Headers

If you prefer more direct control or need to use multiple providers dynamically, you can pass Portkey headers directly:

  1. Configure the basic settings as in Method 1:

    • API Provider: OpenAI Compatible
    • Base URL: https://api.portkey.ai/v1
    • API Key: Your Portkey API key
    • Model ID: Your desired model (e.g., gpt-4o, claude-3-opus-20240229)

  2. Add custom headers by clicking the + button in the Custom Headers section:

    x-portkey-provider: YOUR_PROVIDER

    Optional headers:

    x-portkey-provider: google  // Only if not using virtual key
x-portkey-config: YOUR_CONFIG_ID  // For additional config

Custom headers give you flexibility but require updating headers in Roo whenever you want to change providers or models.

You can now use Roo with all of Portkey’s enterprise features enabled. Monitor your requests and usage in the Portkey Dashboard.

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3. Set Up Enterprise Governance for Roo Code

Why Enterprise Governance? If you are using Roo Code inside your orgnaization, you need to consider several governance aspects:

  • Cost Management: Controlling and tracking AI spending across teams
  • Access Control: Managing team access and workspaces
  • Usage Analytics: Understanding how AI is being used across the organization
  • Security & Compliance: Maintaining enterprise security standards
  • Reliability: Ensuring consistent service across all users
  • Model Management: Managing what models are being used in your setup

Portkey adds a comprehensive governance layer to address these enterprise

Enterprise Implementation Guide

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Enterprise Features Now Available

Roo now has:

  • Per-developer budget controls
  • Model access governance
  • Usage tracking & attribution
  • Code security guardrails
  • Reliability features for development workflows

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Portkey Features

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

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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. Filter these metrics by developer, team, or project using custom metadata.

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2. Advanced Logs

Portkey’s logging dashboard provides detailed logs for every request made by Roo. These logs include:

  • Complete request and response tracking
  • Code context and generation metrics
  • Developer attribution
  • Cost breakdown per coding session

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3. Unified Access to 250+ LLMs

Easily switch between 250+ LLMs for different coding tasks. Use GPT-4 for complex architecture decisions, Claude for detailed code reviews, or specialized models for specific languages - all through a single interface.

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4. Advanced Metadata Tracking

Track coding patterns and productivity metrics with custom metadata:

  • Language and framework usage
  • Code generation vs completion tasks
  • Time-of-day productivity patterns
  • Project-specific metrics

Custom Metadata

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5. Enterprise Access Management

Budget Controls

Set and manage spending limits per developer or team. Prevent budget overruns with automatic cutoffs.

Single Sign-On (SSO)

Enterprise-grade SSO integration for seamless developer onboarding and offboarding.

Organization Management

Hierarchical structure with teams, projects, and role-based access control for development organizations.

Access Rules & Audit Logs

Comprehensive audit logging for security compliance and code generation tracking.

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6. Reliability Features

Fallbacks

Automatically switch between models if one fails, ensuring uninterrupted coding.

Conditional Routing

Route requests based on code complexity or language requirements.

Load Balancing

Distribute requests across multiple API keys or providers.

Caching

Cache common code patterns to reduce costs and improve response times.

Smart Retries

Automatic retry handling for failed requests with exponential backoff.

Budget Limits

Enforce spending limits to control development costs.

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7. Advanced Guardrails

Protect your codebase and enhance security with real-time checks on AI interactions:

  • Prevent exposure of API keys and secrets
  • Block generation of malicious code patterns
  • Enforce coding standards and best practices
  • Custom security rules for your organization
  • License compliance checks

Guardrails

Implement real-time protection for your development environment with automatic detection and filtering of sensitive code, credentials, and security vulnerabilities.

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FAQs

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Next Steps

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For enterprise support and custom features for your development teams, contact our enterprise team.