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There are multiple ways to control the AI model of your agents running on xpander. Centrally Managed xpander key - xpander-provided API keys with the model provider and model ID you select in the Agent Workbench (recommended). Custom - Bring Your Own Model - Use your own API keys stored securely in xpander’s vault, manageable through the workbench. Local Override - Directly specify API keys in your code for local development and testing.
When you configure the model provider and model name in the Agent Workbench UI, xpander automatically provides the appropriate native client class to your agent at runtime, handling all the provider-specific setup and configuration for you.
from xpander_sdk import Backend
from agno.agent import Agent

# Uses xpander's provided API keys
backend = Backend()
agno_agent = Agent(**backend.get_args())
agno_agent.print_response(message="What's your role?")

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How to Use the Vault for Custom Keys

xpander provides a secure vault to store and manage your custom LLM API keys. This allows you to bring your own keys without exposing them in your code, providing a secure and flexible way to manage credentials.

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Storing Keys in the Vault

  1. Navigate to the Workbench: Go to the General tab and open the LLM settings section.
  2. Add Custom Credentials: Click the Access credentials dropdown -> select add LLM key. You’ll be prompted to provide a name, description, Model provider name, and the API key value.
  3. Save and Deploy: Once saved, the key is stored securely in the vault and linked to your agent. Deploy your agent for the changes to take effect.
All keys stored in the vault are encrypted and securely managed by xpander. You can update or delete them at any time from the workbench by clicking Manage LLM keys.

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How it Works

When your agent runs on xpander’s infrastructure, the Backend module automatically detects whether you’ve configured custom credentials. If so, it securely retrieves the key from the vault and injects it into the LLM client at runtime. This process is completely transparent to your code, allowing you to use backend.get_args() without any changes. For local development, you can still use the override parameter to test with different keys without affecting your production configuration. This hybrid approach gives you the best of both worlds: secure key management in production and flexibility during development.

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Deployment Modes

The custom credential system works seamlessly across different deployment modes:

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xpander hosted

  • Custom keys are securely retrieved from the vault
  • No additional configuration needed in your code
  • Full xpander backend capabilities (storage, memory, tools)

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Local Development

  • Environment variables take precedence over vault credentials
  • Use .env files for local API keys
  • Vault keys are used as fallback if env vars are not set

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Supported Providers

Currently supported LLM providers for custom credentials: OpenAI & Anthropic
Make sure your API keys have the necessary permissions for the models you want to use. Some providers require specific scopes or billing setups.

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Examples by Provider

from xpander_sdk import Backend
from agno.agent import Agent, OpenAIChat

backend = Backend()

# Option 1: Use vault-stored key (configured in workbench)
agno_agent = Agent(**backend.get_args())

# Option 2: Override for local testing
agno_agent = Agent(**backend.get_args(override={
    'model': OpenAIChat(id='gpt-4o', api_key='sk-...')
}))

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Key Differences

Centrally Managed:
  • No API key management in your code
  • Model configuration handled automatically by xpander
  • Simpler setup with just backend.get_args()
  • Secure - API keys managed centrally by xpander
  • Recommended for production environments
Custom (Vault-Stored):
  • Your own API keys stored securely in xpander’s vault
  • Managed through the workbench interface
  • Code remains clean with backend.get_args()
  • Full control over your LLM provider billing
  • Encrypted storage and secure key management
Local Override:
  • Direct API key specification in code
  • Manual model configuration with override parameter
  • Best for development, testing, and experimentation
  • Immediate flexibility without workbench changes
  • Not recommended for production use
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