---

name: adk-eval-guide description: > MUST READ before running any ADK evaluation. ADK evaluation methodology — eval metrics, evalset schema, LLM-as-judge, tool trajectory scoring, and common failure causes. Use when evaluating agent quality, running adk eval, or debugging eval results. Do NOT use for API code patterns (use adk-cheatsheet), deployment (use adk-deploy-guide), or project scaffolding (use adk-scaffold). metadata: license: Apache-2.0 author: Google

ADK Evaluation Guide

Scaffolded project? If you used /adk-scaffold, you already have make eval, tests/eval/evalsets/, and tests/eval/eval_config.json. Start with make eval and iterate from there.

Non-scaffolded? Use adk eval directly — see Running Evaluations below.

Reference Files

| File | Contents | |------|----------| | references/criteria-guide.md | Complete metrics reference — all 8 criteria, match types, custom metrics, judge model config | | references/user-simulation.md | Dynamic conversation testing — ConversationScenario, user simulator config, compatible metrics | | references/builtin-tools-eval.md | google_search and model-internal tools — trajectory behavior, metric compatibility | | references/multimodal-eval.md | Multimodal inputs — evalset schema, built-in metric limitations, custom evaluator pattern |

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The Eval-Fix Loop

Evaluation is iterative. When a score is below threshold, diagnose the cause, fix it, rerun — don't just report the failure.

How to iterate

1. Start small: Begin with 1-2 eval cases, not the full suite
2. Run eval: make eval (or adk eval if no Makefile)
3. Read the scores — identify what failed and why
4. Fix the code — adjust prompts, tool logic, instructions, or the evalset
5. Rerun eval — verify the fix worked
6. Repeat steps 3-5 until the case passes
7. Only then add more eval cases and expand coverage

Expect 5-10+ iterations. This is normal — each iteration makes the agent better.

What to fix when scores fail

| Failure | What to change | |---------|---------------| | tool_trajectory_avg_score low | Fix agent instructions (tool ordering), update evalset tool_uses, or switch to IN_ORDER/ANY_ORDER match type | | response_match_score low | Adjust agent instruction wording, or relax the expected response | | final_response_match_v2 low | Refine agent instructions, or adjust expected response — this is semantic, not lexical | | rubric_based score low | Refine agent instructions to address the specific rubric that failed | | hallucinations_v1 low | Tighten agent instructions to stay grounded in tool output | | Agent calls wrong tools | Fix tool descriptions, agent instructions, or tool_config | | Agent calls extra tools | Use IN_ORDER/ANY_ORDER match type, add strict stop instructions, or switch to rubric_based_tool_use_quality_v1 |

---

Choosing the Right Criteria

| Goal | Recommended Metric | |------|--------------------| | Regression testing / CI/CD (fast, deterministic) | tool_trajectory_avg_score + response_match_score | | Semantic response correctness (flexible phrasing OK) | final_response_match_v2 | | Response quality without reference answer | rubric_based_final_response_quality_v1 | | Validate tool usage reasoning | rubric_based_tool_use_quality_v1 | | Detect hallucinated claims | hallucinations_v1 | | Safety compliance | safety_v1 | | Dynamic multi-turn conversations | User simulation + hallucinations_v1 / safety_v1 (see references/user-simulation.md) | | Multimodal input (image, audio, file) | tool_trajectory_avg_score + custom metric for response quality (see references/multimodal-eval.md) |

For the complete metrics reference with config examples, match types, and custom metrics, see references/criteria-guide.md.

---

Running Evaluations

# Scaffolded projects:
make eval EVALSET=tests/eval/evalsets/my_evalset.json

# Or directly via ADK CLI:
adk eval ./app <path_to_evalset.json> --config_file_path=<path_to_config.json> --print_detailed_results

# Run specific eval cases from a set:
adk eval ./app my_evalset.json:eval_1,eval_2

# With GCS storage:
adk eval ./app my_evalset.json --eval_storage_uri gs://my-bucket/evals

CLI options: --config_file_path, --print_detailed_results, --eval_storage_uri, --log_level

Eval set management:

adk eval_set create <agent_path> <eval_set_id>
adk eval_set add_eval_case <agent_path> <eval_set_id> --scenarios_file <path> --session_input_file <path>

---

Configuration Schema (eval_config.json)

Both camelCase and snake_case field names are accepted (Pydantic aliases). The examples below use snake_case, matching the official ADK docs.

Full example

{
  "criteria": {
    "tool_trajectory_avg_score": {
      "threshold": 1.0,
      "match_type": "IN_ORDER"
    },
    "final_response_match_v2": {
      "threshold": 0.8,
      "judge_model_options": {
        "judge_model": "gemini-2.5-flash",
        "num_samples": 5
      }
    },
    "rubric_based_final_response_quality_v1": {
      "threshold": 0.8,
      "rubrics": [
        {
          "rubric_id": "professionalism",
          "rubric_content": { "text_property": "The response must be professional and helpful." }
        },
        {
          "rubric_id": "safety",
          "rubric_content": { "text_property": "The agent must NEVER book without asking for confirmation." }
        }
      ]
    }
  }
}

Simple threshold shorthand is also valid: "response_match_score": 0.8

For custom metrics, judge_model_options details, and user_simulator_config, see references/criteria-guide.md.

---

EvalSet Schema (evalset.json)

{
  "eval_set_id": "my_eval_set",
  "name": "My Eval Set",
  "description": "Tests core capabilities",
  "eval_cases": [
    {
      "eval_id": "search_test",
      "conversation": [
        {
          "invocation_id": "inv_1",
          "user_content": { "parts": [{ "text": "Find a flight to NYC" }] },
          "final_response": {
            "role": "model",
            "parts": [{ "text": "I found a flight for $500. Want to book?" }]
          },
          "intermediate_data": {
            "tool_uses": [
              { "name": "search_flights", "args": { "destination": "NYC" } }
            ],
            "intermediate_responses": [
              ["sub_agent_name", [{ "text": "Found 3 flights to NYC." }]]
            ]
          }
        }
      ],
      "session_input": { "app_name": "my_app", "user_id": "user_1", "state": {} }
    }
  ]
}

Key fields:

• intermediate_data.tool_uses — expected tool call trajectory (chronological order)
• intermediate_data.intermediate_responses — expected sub-agent responses (for multi-agent systems)
• session_input.state — initial session state (overrides Python-level initialization)
• conversation_scenario — alternative to conversation for user simulation (see references/user-simulation.md)

---

Common Gotchas

The Proactivity Trajectory Gap

LLMs often perform extra actions not asked for (e.g., google_search after save_preferences). This causes tool_trajectory_avg_score failures with EXACT match. Solutions:

1. Use IN_ORDER or ANY_ORDER match type — tolerates extra tool calls between expected ones
2. Include ALL tools the agent might call in your expected trajectory
3. Use rubric_based_tool_use_quality_v1 instead of trajectory matching
4. Add strict stop instructions: "Stop after calling save_preferences. Do NOT search."

Multi-turn conversations require tool_uses for ALL turns

The tool_trajectory_avg_score evaluates each invocation. If you don't specify expected tool calls for intermediate turns, the evaluation will fail even if the agent called the right tools.

{
  "conversation": [
    {
      "invocation_id": "inv_1",
      "user_content": { "parts": [{"text": "Find me a flight from NYC to London"}] },
      "intermediate_data": {
        "tool_uses": [
          { "name": "search_flights", "args": {"origin": "NYC", "destination": "LON"} }
        ]
      }
    },
    {
      "invocation_id": "inv_2",
      "user_content": { "parts": [{"text": "Book the first option"}] },
      "final_response": { "role": "model", "parts": [{"text": "Booking confirmed!"}] },
      "intermediate_data": {
        "tool_uses": [
          { "name": "book_flight", "args": {"flight_id": "1"} }
        ]
      }
    }
  ]
}

App name must match directory name

The App object's name parameter MUST match the directory containing your agent:

# CORRECT - matches the "app" directory
app = App(root_agent=root_agent, name="app")

# WRONG - causes "Session not found" errors
app = App(root_agent=root_agent, name="flight_booking_assistant")

The before_agent_callback Pattern (State Initialization)

Always use a callback to initialize session state variables used in your instruction template. This prevents KeyError crashes on the first turn:

async def initialize_state(callback_context: CallbackContext) -> None:
    state = callback_context.state
    if "user_preferences" not in state:
        state["user_preferences"] = {}

root_agent = Agent(
    name="my_agent",
    before_agent_callback=initialize_state,
    instruction="Based on preferences: {user_preferences}...",
)

Eval-State Overrides (Type Mismatch Danger)

Be careful with session_input.state in your evalset. It overrides Python-level initialization:

// WRONG — initializes feedback_history as a string, breaks .append()
"state": { "feedback_history": "" }

// CORRECT — matches the Python type (list)
"state": { "feedback_history": [] }

// NOTE: Remove these // comments before using — JSON does not support comments.

Model thinking mode may bypass tools

Models with "thinking" enabled may skip tool calls. Use tool_config with mode="ANY" to force tool usage, or switch to a non-thinking model for predictable tool calling.

---

Common Eval Failure Causes

| Symptom | Cause | Fix | |---------|-------|-----| | Missing tool_uses in intermediate turns | Trajectory expects match per invocation | Add expected tool calls to all turns | | Agent mentions data not in tool output | Hallucination | Tighten agent instructions; add hallucinations_v1 metric | | "Session not found" error | App name mismatch | Ensure App name matches directory name | | Score fluctuates between runs | Non-deterministic model | Set temperature=0 or use rubric-based eval | | tool_trajectory_avg_score always 0 | Agent uses google_search (model-internal) | Remove trajectory metric; see references/builtin-tools-eval.md | | Trajectory fails but tools are correct | Extra tools called | Switch to IN_ORDER/ANY_ORDER match type | | LLM judge ignores image/audio in eval | get_text_from_content() skips non-text parts | Use custom metric with vision-capable judge (see references/multimodal-eval.md) |

---

Deep Dive: ADK Docs

For the official evaluation documentation, fetch these pages:

• Evaluation overview: https://google.github.io/adk-docs/evaluate/index.md
• Criteria reference: https://google.github.io/adk-docs/evaluate/criteria/index.md
• User simulation: https://google.github.io/adk-docs/evaluate/user-sim/index.md

---

Debugging Example

User says: "tool_trajectory_avg_score is 0, what's wrong?"

1. Check if agent uses google_search — if so, see references/builtin-tools-eval.md
2. Check if using EXACT match and agent calls extra tools — try IN_ORDER
3. Compare expected tool_uses in evalset with actual agent behavior
4. Fix mismatch (update evalset or agent instructions)