VMware Tanzu Platform

VMware Tanzu Platform provides enterprise-managed LLM access through AI Services. goose connects to VMware Tanzu Platform as an OpenAI-compatible provider, supporting both single-model and multi-model service plans with streaming enabled by default.

Prerequisites

A VMware Tanzu Platform (TAS) foundation with GenAI tile installed and configured
Access to a CF org/space where the genai service is available in the marketplace
The CF CLI (cf) installed and authenticated (cf login)
goose v1.28.0 or later

Step 1: Check Available Plans

First, verify the genai service is available in your marketplace and review the available plans:

cf marketplace -e genai

You will see output similar to:

broker: genai-service
   plan                                description                                            free or paid
   tanzu-Qwen3-Coder-30B-A3B-vllm-v1  Access to: Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8.    free
   tanzu-gpt-oss-120b-vllm-v1          Access to: openai/gpt-oss-120b.                        free
   tanzu-all-models                    Access to: Qwen3.5-122B, Qwen3-Coder-30B, gpt-oss...   free

Each plan corresponds to a different model or set of models. Single-model plans give access to one model. Multi-model plans (e.g., tanzu-all-models) give access to multiple models behind a single endpoint.

Step 2: Create a Service Instance

Option A: Single-Model Plan

Create a service instance using a single-model plan:

cf create-service genai tanzu-Qwen3-Coder-30B-A3B-vllm-v1 my-qwen-coder --wait

Option B: Multi-Model Plan

Create a service instance using the multi-model plan:

cf create-service genai tanzu-all-models my-all-models --wait

Verify the instance was created:

cf services

Step 3: Create a Service Key

Create a service key to generate API credentials:

cf create-service-key my-qwen-coder my-goose-key --wait

Then retrieve the credentials:

cf service-key my-qwen-coder my-goose-key

Single-Model Plan Output

For a single-model plan, the output includes model metadata at the top level:

{
  "credentials": {
    "api_base": "https://genai-proxy.sys.example.com/tanzu-my-model-abc1234/openai",
    "api_key": "eyJhbGciOi...",
    "endpoint": {
      "api_base": "https://genai-proxy.sys.example.com/tanzu-my-model-abc1234",
      "api_key": "eyJhbGciOi...",
      "config_url": "https://genai-proxy.sys.example.com/tanzu-my-model-abc1234/config/v1/endpoint",
      "name": "tanzu-my-model-abc1234"
    },
    "model_capabilities": ["chat", "tools"],
    "model_name": "Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8",
    "wire_format": "openai"
  }
}

Multi-Model Plan Output

For a multi-model plan, the output only contains the endpoint object:

{
  "credentials": {
    "endpoint": {
      "api_base": "https://genai-proxy.sys.example.com/tanzu-all-models-abc1234",
      "api_key": "eyJhbGciOi...",
      "config_url": "https://genai-proxy.sys.example.com/tanzu-all-models-abc1234/config/v1/endpoint",
      "name": "tanzu-all-models-abc1234"
    }
  }
}

Step 4: Identify Your Endpoint and API Key

From the service key output, you need two values from the credentials.endpoint object:

Value	JSON Path	Example
Endpoint URL	`credentials.endpoint.api_base`	`https://genai-proxy.sys.example.com/tanzu-my-model-abc1234`
API Key	`credentials.endpoint.api_key`	`eyJhbGciOi...` (JWT token)

Use credentials.endpoint.api_base, not credentials.api_base

Single-model plans include a top-level credentials.api_base field that has an /openai suffix. Do not use this value. Always use credentials.endpoint.api_base (without /openai), because goose automatically appends the correct path.

Using the wrong value would produce a double-path URL like .../openai/openai/v1/chat/completions.

Step 5: Configure goose

goose Desktop
goose CLI

Open goose Desktop
Click the sidebar button, then Settings > Models > Configure providers
Find VMware Tanzu Platform in the provider list and click Configure
Enter your values:
- TANZU_AI_ENDPOINT: Paste the credentials.endpoint.api_base URL
- TANZU_AI_API_KEY: Paste the credentials.endpoint.api_key JWT token
Click Submit
Select a model from the dynamically fetched list

Option 1: Using `goose configure`

goose configure

Select Configure Providers
Choose VMware Tanzu Platform from the list
Enter your TANZU_AI_ENDPOINT when prompted
Enter your TANZU_AI_API_KEY when prompted
Select a model from the fetched list

Option 2: Using environment variables

Set the following environment variables before launching goose:

export TANZU_AI_ENDPOINT="https://genai-proxy.sys.example.com/tanzu-my-model-abc1234"
export TANZU_AI_API_KEY="eyJhbGciOi..."

Then start goose:

goose session

tip

Add these exports to your shell profile (~/.bashrc, ~/.zshrc, etc.) to persist them across sessions.

Step 6: Select a Model

goose dynamically fetches available models from your Tanzu endpoint. After configuring the provider:

Single-model plan: The one available model will be listed (e.g., Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8)
Multi-model plan: All models on the plan will be listed, and you can switch between them

To change models later, use Settings > Models > Switch models in Desktop, or run goose configure in the CLI.

note

Embedding-only models (e.g., nomic-ai/nomic-embed-text-v2-moe) will appear in the model list but cannot be used as a chat model.

Troubleshooting

"Could not contact provider" / 401 Unauthorized on models endpoint

This means the API key is not being sent correctly. Common causes:

Environment variables not set: If using goose Desktop, env vars from your shell may not be inherited. Use the Settings UI to configure the provider instead.
Wrong api_base: Make sure you used credentials.endpoint.api_base (without /openai), not credentials.api_base.
Expired API key: Tanzu API keys are JWT tokens that may expire. Generate a new service key with cf create-service-key.

Verify your endpoint manually

You can test connectivity with curl:

# Test model discovery
curl -H "Authorization: Bearer $TANZU_AI_API_KEY" \
  "$TANZU_AI_ENDPOINT/openai/v1/models"

# Test chat completions
curl -X POST "$TANZU_AI_ENDPOINT/openai/v1/chat/completions" \
  -H "Authorization: Bearer $TANZU_AI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"YOUR_MODEL_NAME","messages":[{"role":"user","content":"hello"}]}'

Streaming

Streaming is enabled by default. If your endpoint does not support streaming, you can disable it by unchecking the Streaming checkbox in the provider configuration UI, or by setting the TANZU_AI_STREAMING environment variable to false.

Model not found

If the model you selected returns an error, verify available models on your plan:

curl -H "Authorization: Bearer $TANZU_AI_API_KEY" \
  "$TANZU_AI_ENDPOINT/openai/v1/models"

Ensure the model name matches exactly (including the prefix, e.g., Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8).

Cleaning up

To remove a service instance and its keys:

cf delete-service-key my-qwen-coder my-goose-key -f
cf delete-service my-qwen-coder -f

Prerequisites​

Step 1: Check Available Plans​

Step 2: Create a Service Instance​

Option A: Single-Model Plan​

Option B: Multi-Model Plan​

Step 3: Create a Service Key​

Single-Model Plan Output​

Multi-Model Plan Output​

Step 4: Identify Your Endpoint and API Key​

Step 5: Configure goose​

Option 1: Using goose configure​

Option 2: Using environment variables​

Step 6: Select a Model​

Troubleshooting​

"Could not contact provider" / 401 Unauthorized on models endpoint​

Verify your endpoint manually​

Streaming​

Model not found​

Cleaning up​