adk-eval-guide

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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).

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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

FileContents
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

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

FailureWhat 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 toolsFix tool descriptions, agent instructions, or tool_config
Agent calls extra toolsUse 
IN_ORDER
/
ANY_ORDER
match type, add strict stop instructions, or switch to 
rubric_based_tool_use_quality_v1

Choosing the Right Criteria

GoalRecommended 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 conversationsUser 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

bash
# 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:
bash
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 (
test_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

json
{
  "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
)

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.
json
{
  "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:
python
# 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:
python
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:
json
// WRONG - initializes feedback_history as a string, breaks .append()
"state": { "feedback_history": "" }

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

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

SymptomCauseFix
Missing 
tool_uses
in intermediate turns		Trajectory expects match per invocationAdd expected tool calls to all turns
Agent mentions data not in tool outputHallucinationTighten agent instructions; add 
hallucinations_v1
metric
"Session not found" errorApp name mismatchEnsure App 
name
matches directory name
Score fluctuates between runsNon-deterministic modelSet 
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 correctExtra tools calledSwitch 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 using WebFetch:
  • 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)