Conductor is a macOS AI coding app built on top of Claude Code. It manages workspaces (git worktrees), sessions, and parallel development environments. Conductor configures providers through environment variables in its Settings → Providers panel. Add Portkey to get:

1600+ LLMs through one interface — switch providers by updating environment variables
Observability — track costs, tokens, and latency for every request
Reliability — automatic fallbacks, retries, and caching
Governance — budget limits, usage tracking, and team access controls

For enterprise deployments across teams, see Enterprise Governance.

1. Setup

Add Provider

Go to Model Catalog → Add Provider.

Configure Credentials

Select provider (OpenAI, Anthropic, etc.), enter API key, and create a slug like anthropic-prod.

Get Portkey API Key

Go to API Keys and generate your Portkey API key.

2. Configure Portkey in Conductor

Conductor sets provider configuration through environment variables in its Settings → Providers panel. Since Conductor uses Claude Code under the hood, it accepts the same ANTHROPIC_* environment variables. Set the following variables in Settings → Providers under Claude Code:

ANTHROPIC_BASE_URL=https://api.portkey.ai
ANTHROPIC_AUTH_TOKEN=YOUR_PORTKEY_API_KEY
ANTHROPIC_CUSTOM_HEADERS=x-portkey-api-key: YOUR_PORTKEY_API_KEY
ANTHROPIC_CUSTOM_HEADERS=x-portkey-provider: @anthropic-prod
ANTHROPIC_API_KEY=

Choose your provider

The configuration pattern is the same for all providers — only the provider slug and model names change:

ANTHROPIC_BASE_URL=https://api.portkey.ai
ANTHROPIC_AUTH_TOKEN=YOUR_PORTKEY_API_KEY
ANTHROPIC_CUSTOM_HEADERS=x-portkey-api-key: YOUR_PORTKEY_API_KEY
ANTHROPIC_CUSTOM_HEADERS=x-portkey-provider: @anthropic-prod
ANTHROPIC_DEFAULT_SONNET_MODEL=claude-sonnet-4-20250514
ANTHROPIC_DEFAULT_OPUS_MODEL=claude-opus-4-20250514
ANTHROPIC_DEFAULT_HAIKU_MODEL=claude-haiku-4-20250514
ANTHROPIC_API_KEY=

Model names are required. Each provider uses different model IDs. Without the correct model settings, requests will fail.

Monitor usage in the Portkey Dashboard.

3. Using Conductor with 1600+ models

With Portkey, you can route Conductor requests to any of 1600+ models. Change the x-portkey-provider header to your desired provider slug from Model Catalog.

Universal provider (any model)

Use Portkey’s unified gateway to route to any supported model:

ANTHROPIC_BASE_URL=https://api.portkey.ai
ANTHROPIC_AUTH_TOKEN=YOUR_PORTKEY_API_KEY
ANTHROPIC_CUSTOM_HEADERS=x-portkey-api-key: YOUR_PORTKEY_API_KEY
ANTHROPIC_CUSTOM_HEADERS=x-portkey-provider: @your-provider
ANTHROPIC_MODEL=$YOUR_MODEL
ANTHROPIC_API_KEY=

Swap @your-provider to any provider slug (e.g., @openai-prod, @xai-prod, @moonshot-prod) and set ANTHROPIC_MODEL to your target model ID.

Why use Portkey with Conductor?

Cross-provider fallbacks

Never lose a coding session due to provider outages. Configure automatic failover:

{
  "strategy": { "mode": "fallback" },
  "targets": [
    { "provider": "@anthropic-prod" },
    { "provider": "@bedrock-prod" },
    { "provider": "@vertex-prod" }
  ]
}

Budget controls for agentic coding

Conductor sessions can run expensive agentic loops. Set hard limits:

Cost limits: Maximum spend per day/week/month
Token limits: Maximum tokens consumed
Rate limits: Requests per minute/hour

Full session observability

Track every request in your coding session:

Request/response logs with full context
Token usage and cost breakdowns
Latency metrics
Trace IDs for grouping related requests

Caching

Reduce costs and latency for repeated queries (common in iterative coding):

{
  "cache": { "mode": "simple" }
}

Common configuration

Forward headers (required)

Some Claude Code features need the anthropic-beta header forwarded. Add this to your Portkey Config:

{
  "provider": "@your-provider-slug",
  "forward_headers": ["anthropic-beta"]
}

Troubleshooting

Symptom	Cause	Fix
`API Error: 500 fetch failed`	Wrong base URL	Use `https://api.portkey.ai` (no `/v1`)
Lightning symbol (⚡) in logs	Passthrough request	Check provider slug and API key
Requests not appearing	Authentication issue	Ensure `ANTHROPIC_API_KEY` is set to empty string

3. Set Up Enterprise Governance

Why Enterprise Governance?

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 needs. Enterprise Implementation Guide

Step 1: Implement Budget Controls & Rate Limits

Model Catalog enables you to have granular control over LLM access at the team/department level. This helps you:

Set up budget limits
Prevent unexpected usage spikes using Rate limits
Track departmental spending

Setting Up Department-Specific Controls:

Navigate to Model Catalog in Portkey dashboard
Create new Provider for each engineering team with budget limits and rate limits
Configure department-specific limits

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. You can simply manage AI models in your org by provisioning model at the top integration level.

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.

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

Step 4: Deploy & Monitor

After distributing API keys to your engineering teams, your enterprise-ready setup is ready to go. Each developer 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 engineering team
Model usage patterns for AI agent tasks
Request volumes
Error rates and debugging logs

Enterprise Features Now Available

You now have:

Departmental budget controls
Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Portkey Features

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

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

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 provider slug 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 departments and teams.

Custom Metata

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 Project’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

Guardrails

Implement real-time protection for your LLM 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 AI Provider limits after creation?

Update AI Provider limits at any time from Model Catalog: 1. Open the provider you want to modify. 2. Update the budget or rate limits. 3. Save your changes.

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

Yes! Add multiple AI Providers to Model Catalog (one for each provider) and attach them to a single config. This config can then be connected to your API key, allowing you to use multiple providers through a single API key.

How do I track costs for different teams?

Portkey provides several ways to track team costs:

Create separate AI Providers for each team
Use metadata tags in your configs
Set up team-specific API keys
Monitor usage in the analytics dashboard

What happens if a team exceeds their budget limit?

When a team reaches their budget limit:

Further requests will be blocked
Team admins receive notifications
Usage statistics remain available in dashboard
Limits can be adjusted if needed

Next Steps

Join our Community

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

Ecosystem

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Tracing Providers

MCP Clients

MCP Servers

Documentation Index

​1. Setup

​2. Configure Portkey in Conductor

​Choose your provider

​3. Using Conductor with 1600+ models

​Universal provider (any model)

​Why use Portkey with Conductor?

​Cross-provider fallbacks

​Budget controls for agentic coding

​Full session observability

​Caching

​Common configuration

​Forward headers (required)

​Troubleshooting

​3. Set Up Enterprise Governance

​Step 1: Implement Budget Controls & Rate Limits

​Setting Up Department-Specific Controls:

​Step 2: Define Model Access Rules

​Example Configuration:

​Step 3: Implement Access Controls

​Step 4: Deploy & Monitor

​Enterprise Features Now Available

​Portkey Features

​1. Comprehensive Metrics

​2. Advanced Logs

​3. Unified Access to 1600+ LLMs

​4. Advanced Metadata Tracking

Custom Metata

​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. Setup

2. Configure Portkey in Conductor

Choose your provider

3. Using Conductor with 1600+ models

Universal provider (any model)

Why use Portkey with Conductor?

Cross-provider fallbacks

Budget controls for agentic coding

Full session observability

Caching

Common configuration

Forward headers (required)

Troubleshooting

3. Set Up Enterprise Governance

Step 1: Implement Budget Controls & Rate Limits

Setting Up Department-Specific Controls:

Step 2: Define Model Access Rules

Example Configuration:

Step 3: Implement Access Controls

Step 4: Deploy & Monitor

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