Azure AI Foundry

The azure_foundry provider connects goose to Azure AI Foundry deployments. It supports two endpoint types:

For project endpoints, goose discovers deployments with GET /deployments. Deployment names can be customized; goose uses the returned modelPublisher to select the protocol and modelName to resolve model metadata such as the context window.

Resource endpoints do not expose project deployment discovery. goose routes recognizable model or deployment names by family: gpt-5* and supported o-series models use Responses, claude-* uses Anthropic Messages, and other names use Chat Completions. Use a project endpoint when aliases do not identify their underlying model family.

Configuration

Run goose configure, select Configure Providers, and choose Azure AI Foundry. You can also set the variables before starting goose:

export AZURE_FOUNDRY_ENDPOINT="https://my-resource.services.ai.azure.com/api/projects/my-project"
export AZURE_FOUNDRY_API_KEY="<key>"
goose session

For a MaaS endpoint:

export AZURE_FOUNDRY_ENDPOINT="https://my-deployment.eastus.models.ai.azure.com"
export AZURE_FOUNDRY_API_KEY="<key>"
export AZURE_FOUNDRY_MODEL="<model-bound-to-this-endpoint>"
goose session

MaaS endpoints expose a single deployed model. AZURE_FOUNDRY_MODEL is required for these endpoints.

Authentication

Authentication is selected in this order:

1. AZURE_FOUNDRY_AD_TOKEN
2. AZURE_FOUNDRY_API_KEY
3. Azure CLI credentials

When neither token nor key is configured, sign in with Azure CLI before starting goose:

az login

Project and resource endpoints request a token for https://ai.azure.com. MaaS endpoints request a token for https://ml.azure.com.

Protocol routing

For a project endpoint, goose routes each deployment using metadata returned by Azure:

• publisher OpenAI with a Responses-compatible model (gpt-5* and the supported o-series) → /openai/v1/responses
• publisher Anthropic → /anthropic/v1/messages
• older OpenAI models and all other publishers → /openai/v1/chat/completions

If deployment discovery is temporarily unavailable, recognizable Responses-compatible OpenAI and claude-* names use their native surfaces. Other names use Chat Completions.

Resource endpoints use the same inference surfaces without deployment discovery: recognizable model-family names select the native protocol. This allows a custom agent's model: declaration to select deployments such as gpt-5.6-sol without rewriting the name through canonical model resolution.

MaaS endpoints always use /v1/chat/completions and the model configured by AZURE_FOUNDRY_MODEL.

Model metadata and pricing

The deployments API provides the deployment name and underlying modelName, modelVersion, and modelPublisher. goose uses the underlying model name to look up a context window in its bundled model catalog. An explicit GOOSE_CONTEXT_LIMIT or session override still takes precedence.

Azure pricing depends on region, SKU, offer, deployment type, and contract. The deployments API does not provide a reliable per-token price, so this provider does not attach a price to discovered deployments.

Troubleshooting

401 or 403

• Ensure the key belongs to the configured endpoint.
• For Entra authentication, run az login again and verify that your identity has access to the Foundry project.
• Do not use a project endpoint key with a MaaS endpoint, or the reverse.

No deployments are listed

• Confirm that the endpoint includes /api/projects/<project>.
• Confirm that the project contains model deployments.
• If your project uses a non-default deployment API version, set AZURE_FOUNDRY_API_VERSION.

Wrong protocol for a custom deployment name

Refresh the provider model list so goose can retrieve modelPublisher. Without deployment metadata, routing can only use recognizable model-name prefixes.