Goose is a local, extensible, open source AI agent that automates engineering tasks. While Goose delivers powerful autonomous coding capabilities, Portkey adds essential enterprise controls for production deployments:

  • Unified AI Gateway - Single interface for 1600+ LLMs with API key management. (not just OpenAI & Anthropic)
  • 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 Goose setup
  • Security Guardrails - PII detection, content filtering, and compliance controls

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

If you are an enterprise looking to use Goose in your organisation, check out this section.

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1. Setting up Portkey

Portkey allows you to use 1600+ LLMs with your Goose 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: 
    {
    "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 Goose integration.

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2. Integrate Portkey with Goose

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

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

1

Launch Goose

Open Goose either through:

  • The desktop application
  • The CLI by running goose in your terminal
2

Configure LLM Provider

  1. Navigate to Goose’s LLM provider settings
  2. Select OpenAI as your provider
3

Configure Portkey Integration

In the OpenAI provider settings, configure:

  • Base URL: https://api.portkey.ai/v1
  • API Key: Your Portkey API key from the setup

That’s it! Goose will now route all LLM requests through Portkey.

You can monitor your requests and usage in the Portkey Dashboard.

Make sure your virtual key has sufficient budget and rate limits for your expected usage. Also use the complete model name given by the provider.

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3. Set Up Enterprise Governance for Goose

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

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

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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Enterprise Features Now Available

Goose now has:

  • Team-level budget controls
  • Model access governance
  • Usage tracking & attribution
  • Security guardrails for code generation
  • Reliability features for uninterrupted development

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Portkey Features

Now that you have enterprise-grade Goose setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI agent 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 by Goose. These logs include:

  • Complete request and response tracking for debugging
  • Metadata tags for filtering by team or project
  • Cost attribution per task
  • Complete conversation history with the AI agent

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3. Unified Access to 1600+ LLMs

You can easily switch between 1600+ 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 engineering teams and projects.

Custom Metadata

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5. Enterprise Access Management

Budget Controls

Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

Single Sign-On (SSO)

Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

Organization Management

Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.

Access Rules & Audit Logs

Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

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6. Reliability Features

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

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7. Advanced Guardrails

Protect your codebase and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:

  • Prevent sensitive code or API key leaks
  • Enforce compliance with coding standards
  • PII detection and masking in generated code
  • Content filtering for inappropriate code generation
  • Custom security rules for your organization
  • Compliance checks for internal coding policies

Guardrails

Implement real-time protection for your AI agent interactions with automatic detection and filtering of sensitive content, PII, and custom security rules. Enable comprehensive data protection while maintaining compliance with organizational policies.

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FAQs

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Next Steps

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For enterprise support and custom features, contact our enterprise team.