Gemini

Configure Google Gemini as an LLM provider in agentgateway.

Before you begin

Set up an agentgateway proxy.

Set up access to Gemini

  1. 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>

  2. 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

  3. Create an resource to configure an LLM provider that references the AI API key secret.

    kubectl apply -f- <<EOF
apiVersion: agentgateway.dev/v1alpha1
kind: 
metadata:
  name: google
  namespace: kgateway-system
spec:
  ai:
    provider:
      gemini:
        model: gemini-2.5-flash-lite
  policies:
    auth:
      secretRef:
        name: google-secret
EOF

    Review the following table to understand this configuration. For more information, see the API reference.

    Setting Description
    ai.provider.gemini Define the Gemini provider.
    gemini.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.
    policies.auth The authentication token to use to authenticate to the LLM provider. The example refers to the secret that you created in the previous step.

  4. Create an HTTPRoute resource that routes incoming traffic to the . The following example sets up a route on the /openai path to the that you previously created. The URLRewrite filter rewrites the path from /openai to the path of the API in the LLM provider that you want to use, /v1/chat/completions.

    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: agentgateway.dev
      kind: 
EOF

  1. 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

  • Want to use other endpoints than chat completions, such as embeddings or models? Check out the multiple endpoints guide.
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