/google-agents-cli-eval

Multimodal Evaluation

Two distinct cases are covered here:

1. Evaluate generated image / video quality against a text prompt.
2. Evaluate an agent that consumes multimodal input and produces text (e.g., the agent describes an image and we want to verify the description).

Both cases use a custom LLMMetric with a vision-capable judge model. The built-in adaptive metrics only inspect text parts, so they can't reason about media content directly — a custom metric is required for true multimodal grading.

Multimodal field-model note. agents-cli eval generate populates {response} by extracting the text parts of the agent's final event. If your agent returns non-text parts (e.g., inline_data images, file_data URIs), those parts are not copied into {response} automatically. To grade with the full multimodal Content, either hand-author the eval case with a responses[0].response Content containing the media parts, or post-process the generated trace file to copy the media parts into responses.

File paths below reference the Python scaffolded layout (tests/eval/). Adjust for your project structure if you're scaffolding a Go project (eval/) or not using google-agents-cli-scaffold.

Dataset shape for multimodal parts

Multimodal content lives inside parts as either inline_data (base64-encoded bytes with a mime type) or file_data (GCS URI reference). Use whichever fits — file_data is preferred for anything larger than a few KB.

{ "inline_data": { "mime_type": "image/png", "data": "<base64>" } }

{ "file_data": { "mime_type": "image/jpeg", "file_uri": "gs://my-bucket/photos/test.jpg" } }

Case 1: Evaluate generated image / video against a text prompt

The eval case has the user prompt as text and the model response as a Content with a media file_data (or inline_data) part.

{
  "eval_cases": [
    {
      "eval_case_id": "coffee_image",
      "prompt": {
        "role": "user",
        "parts": [{"text": "steaming cup of coffee and a croissant on a table"}]
      },
      "responses": [
        {
          "response": {
            "role": "model",
            "parts": [
              {"file_data": {"mime_type": "image/png", "file_uri": "gs://cloud-samples-data/generative-ai/evaluation/images/coffee.png"}}
            ]
          }
        }
      ]
    }
  ]
}

For video, swap mime_type to video/mp4 (or appropriate) and point at a video URI.

Custom metric (eval_config.yaml)

custom_metrics:
  - name: image_prompt_alignment
    prompt_template: |
      You are evaluating whether the generated image (in {response}) matches
      the user's text prompt. Consider object presence, attributes, actions,
      composition, and style.

      Prompt: {prompt}
      Image: {response}

      Return JSON: {"score": <0.0-1.0>, "explanation": "<reason>"}
    judge_model: gemini-3.8-flash
    judge_model_sampling_count: 3

Run with agents-cli eval grade --config tests/eval/eval_config.yaml. For video evaluation, use the same pattern with a video-capable judge model and rubric criteria (motion consistency, temporal coherence, scene transitions).

Case 2: Agent consumes multimodal input, produces text

The user input contains an image / audio / file; the agent produces a text response. To verify the text against the original media (e.g., "did the agent correctly describe this image?"), use a custom LLMMetric with a vision-capable judge.

Dataset shape

The multimodal input lives in the prompt field for single-turn, or inside the user-authored event in agent_data for multi-turn:

{
  "eval_cases": [
    {
      "eval_case_id": "describe_chart",
      "prompt": {
        "role": "user",
        "parts": [
          {"text": "Describe this image"},
          {"inline_data": {"mime_type": "image/png", "data": "<base64>"}}
        ]
      },
      "responses": [
        {
          "response": {
            "role": "model",
            "parts": [{"text": "The image shows a bar chart..."}]
          }
        }
      ]
    }
  ]
}

Custom metric (eval_config.yaml)

custom_metrics:
  - name: multimodal_response_quality
    prompt_template: |
      You are evaluating whether the agent's text response accurately reflects
      the user's multimodal input. Inspect the user input parts (which may
      include images, audio, or files) and the agent response, then return JSON:
      {"score": <0.0-1.0>, "explanation": "<reason>"}.

      User input: {prompt}
      Agent response: {response}
    judge_model: gemini-3.8-flash
    judge_model_sampling_count: 3

Run with agents-cli eval run --config tests/eval/eval_config.yaml.

Notes

• Built-in adaptive metrics (final_response_quality, etc.) skip media parts. They extract only .text parts when constructing the judge prompt. Use a custom LLMMetric for true multimodal grading.
• Choose a vision-capable judge_model. gemini-3.8-flash handles images and video; verify capability before relying on it.
• Sampling count (judge_model_sampling_count) of 3–5 reduces variance for multimodal judges, which can be noisier than text-only.

For the full custom-metric field reference, see references/metrics-guide.md. For dataset schema and the inline_data / file_data part types, see references/dataset_schema.md.