Gemini

Configure Google Gemini as an LLM provider in agentgateway.

Before you begin

Set up access to Gemini

Save your Gemini API key as an environment variable. To retrieve your API key, log in to the Google AI Studio and select API Keys.
```
export GOOGLE_KEY=<your-api-key>
```

Create a secret to authenticate to Google.

kubectl apply -f - <<EOF
apiVersion: v1
kind: Secret
metadata:
  name: google-secret
  namespace: kgateway-system
type: Opaque
stringData:
  Authorization: $GOOGLE_KEY
EOF

Create a resource to define the Gemini destination.

kubectl apply -f- <<EOF
apiVersion: gateway.kgateway.dev/v1alpha1
kind: 
metadata:
  labels:
    app: agentgateway
  name: google
  namespace: kgateway-system
spec:
  ai:
    llm:
     gemini:
          apiVersion: v1beta
          authToken:
            kind: SecretRef
            secretRef:
              name: google-secret
          model: gemini-2.5-flash-lite
  type: AI
EOF

Review the following table to understand this configuration.

Setting	Description
`gemini`	The Gemini AI provider.
`apiVersion`	The API version of Gemini that is compatible with the model that you plan to use. In this example, you must use `v1beta` because the `gemini-2.5-flash-lite` model is not compatible with the `v1` API version. For more information, see the Google AI docs.
`authToken`	The authentication token to use to authenticate to the LLM provider. The example refers to the secret that you created in the previous step.
`model`	The model to use to generate responses. In this example, you use the `gemini-2.5-flash-lite` model. For more models, see the Google AI docs.

Create an HTTPRoute resource to route requests to the Gemini . Note that kgateway automatically rewrites the endpoint that you set up (such as /gemini) to the appropriate chat completion endpoint of the LLM provider for you, based on the LLM provider that you set up in the resource.

kubectl apply -f- <<EOF
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: google
  namespace: kgateway-system
spec:
  parentRefs:
    - name: agentgateway
  rules:
  - matches:
    - path:
        type: PathPrefix
        value: /gemini
    backendRefs:
    - name: google
      group: gateway.kgateway.dev
      kind: 
EOF

Send a request to the LLM provider API. Verify that the request succeeds and that you get back a response from the chat completion API.

curl -vik "$INGRESS_GW_ADDRESS/gemini" -H content-type:application/json  -d '{
  "model": "",
  "messages": [
   {"role": "user", "content": "Explain how AI works in simple terms."}
 ]
}'

curl -vik "localhost:8080/gemini" -H content-type:application/json  -d '{
  "model": "",
  "messages": [
   {"role": "user", "content": "Explain how AI works in simple terms."}
 ]
}'

Example output:

{"id":"aGLEaMjbLp6p_uMPopeAoAc",
"choices":
  [{"index":0,"message":{
      "content":"Imagine teaching a dog a trick.  You show it what to do, reward it when it's right, and correct it when it's wrong.  Eventually, the dog learns.\n\nAI is similar.  We \"teach\" computers by showing them lots of examples.  For example, to recognize cats in pictures, we show it thousands of pictures of cats, labeling each one \"cat.\"  The AI learns patterns in these pictures – things like pointy ears, whiskers, and furry bodies – and eventually, it can identify a cat in a new picture it's never seen before.\n\nThis learning process uses math and algorithms (like a secret code of instructions) to find patterns and make predictions.  Some AI is more like a dog learning tricks (learning from examples), and some is more like following a very detailed recipe (following pre-programmed rules).\n\nSo, in short: AI is about teaching computers to learn from data and make decisions or predictions, just like we teach dogs tricks.\n",
      "role":"assistant"
      },
   "finish_reason":"stop"
   }],
 "created":1757700714,
 "model":"gemini-1.5-flash-latest",
 "object":"chat.completion",
 "usage":{
     "prompt_tokens":8,
     "completion_tokens":205,
     "total_tokens":213
     }
}

Next steps

Explore other guides for LLM consumption, such as function calling, model failover, and prompt guards.

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