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.

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.

Create an Integration

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

Find your preferred provider (e.g., OpenAI, Anthropic, etc.)
Click Connect on the provider card
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
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.

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

Copy the Provider Slug

Once your Integration is created:

Go to Model Catalog → Models tab
Find and click on you your model button (if your model is not visible, you need to edit your integration from the last step)
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

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).

Go to Configs in Portkey dashboard

Create new config with:

{
    "override_params": {
      "model": "@YOUR_SLUG-FROM-LAST_STEP" // example: @openai-test/gpt-4o-mini
    }
}

Save and note the Config ID & Name for the next step

Configure Portkey API Key

Finally, create a Portkey API key:

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

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

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

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.

Launch Goose

Open Goose either through:

The desktop application
The CLI by running goose in your terminal

Configure LLM Provider

Navigate to Goose’s LLM provider settings
Select OpenAI as your provider

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.

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 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

Step 1: Implement Budget Controls & Rate Limits

Step 2: Define Model Access Rules

Step 4: Set Routing Configuration

Portkey allows you to control your routing logic very simply with it’s Configs feature. Portkey Configs provide this control layer with things like:

Data Protection: Implement guardrails for sensitive code and data
Reliability Controls: Add fallbacks, load-balance, retry and smart conditional routing logic
Caching: Implement Simple and Semantic Caching. and more…

Example Configuration:

Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:

{
	"strategy": {
		"mode": "load-balance"
	},
	"targets": [
		{
			"override_params": {
				"model": "@YOUR_OPENAI_PROVIDER_SLUG/gpt-model"
			}
		},
		{
			"override_params": {
				"model": "@YOUR_ANTHROPIC_PROVIDER/claude-sonnet-model"
			}
		}
	]
}

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

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

Step 4: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

Track usage per developer/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:

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

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

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

Step 5: Deploy & Monitor

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

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.

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.

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

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.

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

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.

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.

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.

FAQs

How do I update my Virtual Key limits after creation?

Can I use multiple LLM providers with the same API key?

How do I track costs for different engineering teams?

What happens if a team exceeds their budget limit?

Next Steps

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

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On this page

1. Setting up Portkey
2. Integrate Portkey with Goose
3. Set Up Enterprise Governance for Goose
Portkey Features
1. Comprehensive Metrics
2. Advanced Logs
3. Unified Access to 1600+ LLMs
4. Advanced Metadata Tracking
5. Enterprise Access Management
6. Reliability Features
7. Advanced Guardrails
FAQs
Next Steps

Ecosystem

LLM Integrations

Cloud Platforms

Guardrails

Plugins

Vector Databases

Agents

AI Apps

Libraries

Tracing Providers

​1. Setting up Portkey

​2. Integrate Portkey with Goose

​3. Set Up Enterprise Governance for Goose

​Enterprise Features Now Available

​Portkey Features

​1. Comprehensive Metrics

​2. Advanced Logs

​3. Unified Access to 1600+ LLMs

​4. Advanced Metadata Tracking

Custom Metadata

​5. Enterprise Access Management

Budget Controls

Single Sign-On (SSO)

Organization Management

Access Rules & Audit Logs

​6. Reliability Features

Fallbacks

Conditional Routing

Load Balancing

Caching

Smart Retries

Budget Limits

​7. Advanced Guardrails

Guardrails

​FAQs

​Next Steps

1. Setting up Portkey

2. Integrate Portkey with Goose

3. Set Up Enterprise Governance for Goose

Enterprise Features Now Available

Portkey Features

1. Comprehensive Metrics

2. Advanced Logs

3. Unified Access to 1600+ LLMs

4. Advanced Metadata Tracking

5. Enterprise Access Management

6. Reliability Features

7. Advanced Guardrails

FAQs

Next Steps